[med-svn] [r-bioc-mergeomics] 02/05: Imported Upstream version 1.2.0
Dylan Aïssi
bob.dybian-guest at moszumanska.debian.org
Fri Nov 18 22:13:03 UTC 2016
This is an automated email from the git hooks/post-receive script.
bob.dybian-guest pushed a commit to branch master
in repository r-bioc-mergeomics.
commit d1f60474c436c09cb15082db5fc23bed5e0fca67
Author: Dylan Aïssi <bob.dybian at gmail.com>
Date: Fri Nov 18 21:51:35 2016 +0100
Imported Upstream version 1.2.0
---
DESCRIPTION | 20 +
NAMESPACE | 1 +
R/cle.LS.R | 3898 +
R/msea.kda.onestep.R | 69 +
build/vignette.rds | Bin 0 -> 233 bytes
data/job_kda_analyze.RData | Bin 0 -> 403488 bytes
inst/NEWS | 2 +
inst/doc/Mergeomics.R | 274 +
inst/doc/Mergeomics.Rnw | 642 +
inst/doc/Mergeomics.pdf | Bin 0 -> 265347 bytes
inst/extdata/Significant_pathways.info.txt | 17 +
inst/extdata/Significant_pathways.txt | 2351 +
inst/extdata/coexpr.info.txt | 21 +
.../genes.hdlc_040kb_ld70.human_eliminated.txt | 132706 +++++++++++++++
.../marker.hdlc_040kb_ld70.human_eliminated.txt | 76867 +++++++++
inst/extdata/mergedModules.info.txt | 16 +
inst/extdata/mergedModules.txt | 2320 +
inst/extdata/modules.mousecoexpr.liver.human.txt | 2907 +
inst/extdata/msea2kda.nodes.txt | 14879 ++
inst/extdata/network.mouseliver.mouse.txt | 156853 ++++++++++++++++++
inst/extdata/symbols.txt | 20956 +++
inst/unitTests/test_kda.analyze.R | 38 +
inst/unitTests/test_ssea.analyze.R | 29 +
man/MSEA.KDA.onestep.Rd | 94 +
man/Mergeomics-package.Rd | 70 +
man/job.kda.Rd | 19 +
man/kda.analyze.Rd | 97 +
man/kda.analyze.exec.Rd | 102 +
man/kda.analyze.simulate.Rd | 138 +
man/kda.analyze.test.Rd | 127 +
man/kda.configure.Rd | 98 +
man/kda.finish.Rd | 78 +
man/kda.finish.estimate.Rd | 54 +
man/kda.finish.save.Rd | 61 +
man/kda.finish.summarize.Rd | 65 +
man/kda.finish.trim.Rd | 58 +
man/kda.prepare.Rd | 105 +
man/kda.prepare.overlap.Rd | 86 +
man/kda.prepare.screen.Rd | 94 +
man/kda.start.Rd | 96 +
man/kda.start.edges.Rd | 68 +
man/kda.start.identify.Rd | 54 +
man/kda.start.modules.Rd | 70 +
man/kda2cytoscape.Rd | 101 +
man/kda2cytoscape.colorize.Rd | 58 +
man/kda2cytoscape.colormap.Rd | 37 +
man/kda2cytoscape.drivers.Rd | 75 +
man/kda2cytoscape.edges.Rd | 79 +
man/kda2cytoscape.exec.Rd | 103 +
man/kda2cytoscape.identify.Rd | 59 +
man/kda2himmeli.Rd | 84 +
man/kda2himmeli.colorize.Rd | 59 +
man/kda2himmeli.colormap.Rd | 37 +
man/kda2himmeli.drivers.Rd | 77 +
man/kda2himmeli.edges.Rd | 81 +
man/kda2himmeli.exec.Rd | 115 +
man/kda2himmeli.identify.Rd | 59 +
man/ssea.analyze.Rd | 113 +
man/ssea.analyze.observe.Rd | 101 +
man/ssea.analyze.randgenes.Rd | 118 +
man/ssea.analyze.randloci.Rd | 119 +
man/ssea.analyze.simulate.Rd | 113 +
man/ssea.analyze.statistic.Rd | 45 +
man/ssea.control.Rd | 97 +
man/ssea.finish.Rd | 112 +
man/ssea.finish.details.Rd | 102 +
man/ssea.finish.fdr.Rd | 102 +
man/ssea.finish.genes.Rd | 102 +
man/ssea.meta.Rd | 98 +
man/ssea.prepare.Rd | 118 +
man/ssea.prepare.counts.Rd | 107 +
man/ssea.prepare.structure.Rd | 105 +
man/ssea.start.Rd | 110 +
man/ssea.start.configure.Rd | 108 +
man/ssea.start.identify.Rd | 51 +
man/ssea.start.relabel.Rd | 114 +
man/ssea2kda.Rd | 133 +
man/ssea2kda.analyze.Rd | 133 +
man/ssea2kda.import.Rd | 93 +
man/tool.aggregate.Rd | 50 +
man/tool.cluster.Rd | 59 +
man/tool.cluster.static.Rd | 42 +
man/tool.coalesce.Rd | 64 +
man/tool.coalesce.exec.Rd | 55 +
man/tool.coalesce.find.Rd | 58 +
man/tool.coalesce.merge.Rd | 62 +
man/tool.fdr.Rd | 36 +
man/tool.fdr.bh.Rd | 32 +
man/tool.fdr.empirical.Rd | 33 +
man/tool.graph.Rd | 75 +
man/tool.graph.degree.Rd | 94 +
man/tool.graph.list.Rd | 81 +
man/tool.metap.Rd | 43 +
man/tool.normalize.Rd | 49 +
man/tool.normalize.quality.Rd | 41 +
man/tool.overlap.Rd | 46 +
man/tool.read.Rd | 35 +
man/tool.save.Rd | 34 +
man/tool.subgraph.Rd | 51 +
man/tool.subgraph.find.Rd | 53 +
man/tool.subgraph.search.Rd | 59 +
man/tool.subgraph.stats.Rd | 73 +
man/tool.translate.Rd | 37 +
man/tool.unify.Rd | 29 +
tests/runTests.R | 1 +
vignettes/Mergeomics.Rnw | 642 +
vignettes/top5KDs.png | Bin 0 -> 148067 bytes
107 files changed, 421752 insertions(+)
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..5ca0d32
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,20 @@
+Package: Mergeomics
+Type: Package
+Title: Integrative network analysis of omics data
+Version: 1.2.0
+Date: 2016-01-04
+Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang,
+ Xia Yang
+Maintainer: Zeyneb Kurt <zeyneb at ucla.edu>
+Description: The Mergeomics pipeline serves as a flexible framework for
+ integrating multidimensional omics-disease associations, functional
+ genomics, canonical pathways and gene-gene interaction networks to
+ generate mechanistic hypotheses. It includes two main parts,
+ 1) Marker set enrichment analysis (MSEA);
+ 2) Weighted Key Driver Analysis (wKDA).
+biocViews: Software
+Suggests: RUnit, BiocGenerics
+License: GPL (>= 2)
+Depends: R (>= 3.0.1)
+Packaged: 2016-10-18 00:16:52 UTC; biocbuild
+NeedsCompilation: no
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..40ea8e2
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1 @@
+exportPattern("^[[:alpha:]]+")
diff --git a/R/cle.LS.R b/R/cle.LS.R
new file mode 100644
index 0000000..c1dde9f
--- /dev/null
+++ b/R/cle.LS.R
@@ -0,0 +1,3898 @@
+#
+# Generate input files for Himmeli. The network visualization
+# is a streamlined depiction of the module enrichment in hub
+# neighborhoods. For instance, only links that connect a key
+# driver to another node are depicted.
+#
+# Input:
+# job KDA data list as returned by kda.finish()
+#
+# Optional input:
+# modules array of module names to be visualized
+# ndrivers maximum number of drivers per module
+#
+# Written by Ville-Petteri Makinen 2013
+#
+kda2himmeli <- function(job, modules=NULL, ndrivers=5) {
+ # Import node values.
+ cat("\nImporting node data...\n")
+ valdata <- tool.read(job$nodfile)
+
+ # Set node sizes.
+ z <- as.double(valdata$VALUE)
+ z <- (z/quantile(z, 0.95) + rank(z)/length(z))
+ valdata$SIZE <- pmin(4.0, z)
+
+ # Select subset of genes.
+ valdata <- kda2himmeli.identify(valdata, "NODE", job$graph$nodes)
+ print(summary(valdata))
+
+ # Select top scoring modules.
+ cat("\nForwarding KDA results to Himmeli...\n")
+ if(is.null(modules) & (is.null(job$ssearesults) == FALSE)) {
+ tmp <- job$ssearesults
+ tmp <- tmp[order(tmp$P),]
+ modules <- tmp$modules
+ if(length(modules) > 8) modules <- modules[1:8]
+ }
+
+ # Convert module names to indices.
+ if(is.null(modules) == FALSE) {
+ modules <- match(modules, job$modules)
+ modules <- modules[which(modules > 0)]
+ if(length(modules) < 1)
+ stop("Unknown module names.")
+ }
+
+ # Select top key drivers from each module.
+ drivers <- kda2himmeli.drivers(job$results, modules, ndrivers)
+ mods <- unique(drivers$MODULE)
+ palette <- kda2himmeli.colormap(length(mods))
+
+ # Process each module separately.
+ edgdata <- data.frame()
+ noddata <- data.frame()
+ for(i in 1:length(mods)) {
+ rows <- which(drivers$MODULE == mods[i])
+ tmp <- kda2himmeli.exec(job, valdata, drivers[rows,], mods, palette)
+ edgdata <- rbind(edgdata, tmp$edat)
+ noddata <- rbind(noddata, tmp$vdat)
+ }
+
+ # Create work folder.
+ dpath <- file.path(job$folder, "himmeli")
+ if(file.exists(dpath) == FALSE)
+ dir.create(path=dpath, recursive=TRUE)
+
+ # Save data files.
+ edgfile <- file.path(job$folder, "kda2himmeli.edges.txt")
+ nodfile <- file.path(job$folder, "kda2himmeli.nodes.txt")
+ tool.save(frame=edgdata, file=edgfile)
+ tool.save(frame=noddata, file=nodfile)
+
+ # Configuration data.
+ gname <- file.path(dpath, job$label)
+ instr <- paste("GraphName", gname, sep="\t")
+ instr <- c(instr, paste("EdgeFile", edgfile, sep="\t"))
+ instr <- c(instr, paste("EdgeTailVariable", "TAIL", sep="\t"))
+ instr <- c(instr, paste("EdgeHeadVariable", "HEAD", sep="\t"))
+ instr <- c(instr, paste("EdgeWeightVariable", "WEIGHT", sep="\t"))
+ instr <- c(instr, paste("EdgeColorVariable", "COLOR", sep="\t"))
+ instr <- c(instr, paste("VertexFile", nodfile, sep="\t"))
+ instr <- c(instr, paste("VertexNameVariable", "NODE", sep="\t"))
+ instr <- c(instr, paste("VertexColorVariable", "COLOR", sep="\t"))
+ instr <- c(instr, paste("VertexLabelVariable", "LABEL", sep="\t"))
+ instr <- c(instr, paste("VertexShapeVariable", "SHAPE", sep="\t"))
+ instr <- c(instr, paste("VertexSectorVariable", "SECTOR", sep="\t"))
+ instr <- c(instr, paste("VertexSizeVariable", "SIZE", sep="\t"))
+ instr <- c(instr, paste("DistanceUnit", "1.1", sep="\t"))
+ instr <- c(instr, paste("ChassisMode", "on", "2.0", sep="\t"))
+
+ # Color info.
+ modnames <- job$modules[mods]
+ for(j in 1:ncol(palette)) {
+ c <- palette[,j]
+ value <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
+ value <- paste("VertexColorInfo", modnames[j], value, sep="\t")
+ instr <- c(instr, value)
+ }
+
+ # Save configuration data.
+ fname <- file.path(job$folder, "kda2himmeli.config.txt")
+ write.table(x=instr, sep="\t", file=fname, na="",
+ row.names=FALSE, quote=FALSE)
+ cat("\rSaved ", length(instr), " rows in '", fname, "'.\n", sep="")
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+kda2himmeli.exec <- function(job, valdata, drivers, modpool, palette) {
+ # Create star topology.
+ edgdata <- data.frame()
+ for(i in unique(drivers$NODE)) {
+ tmp <- kda2himmeli.edges(job$graph, i, job$depth, job$direction)
+ edgdata <- rbind(edgdata, tmp)
+ }
+
+ # Select affected nodes.
+ tmp <- c(edgdata$TAIL, edgdata$HEAD)
+ pos <- match(valdata$NODE, tmp)
+ valdata <- valdata[which(pos > 0),]
+
+ # Assign node shapes.
+ valdata$SHAPE <- "circle"
+ pos <- match(valdata$NODE, drivers$NODE)
+ valdata[which(pos > 0),"SHAPE"] <- "star"
+
+ # Trace module memberships.
+ noddata <- kda2himmeli.colorize(valdata, job$moddata, modpool, palette)
+
+ # Trim edge dataset.
+ edgdata <- edgdata[which(edgdata$TAIL != edgdata$HEAD),]
+ edgdata <- unique(edgdata[,c("TAIL", "HEAD", "WEIGHT")])
+ edgdata$COLOR <- "808080"
+
+ # Restore original identities.
+ edgdata$TAIL <- job$graph$nodes[edgdata$TAIL]
+ edgdata$HEAD <- job$graph$nodes[edgdata$HEAD]
+ noddata$NODE <- job$graph$nodes[noddata$NODE]
+ noddata$LABEL <- noddata$NODE
+
+ # Make identities unique for the current module.
+ modtag <- job$modules[drivers[1,"MODULE"]]
+ for(i in 1:nrow(noddata))
+ noddata[i,"NODE"] <- paste(noddata[i,"NODE"], modtag, sep="@")
+ for(i in 1:nrow(edgdata)) {
+ edgdata[i,"TAIL"] <- paste(edgdata[i,"TAIL"], modtag, sep="@")
+ edgdata[i,"HEAD"] <- paste(edgdata[i,"HEAD"], modtag, sep="@")
+ }
+
+ # Return results.
+ res <- list(edat=edgdata, vdat=noddata)
+ return(res)
+}
+
+#----------------------------------------------------------------------------
+
+kda2himmeli.drivers <- function(data, modules, ndriv) {
+ nmods <- 8 # optimal number of colors
+
+ # Select modules.
+ if(is.null(modules) == FALSE) {
+ nmods <- length(unique(modules))
+ pos <- match(data$MODULE, modules)
+ data <- data[which(pos > 0),]
+ if(nrow(data) < 1) stop("No data on target modules.")
+ }
+
+ # Include only significant key drivers.
+ rows <- which(data$FDR < 0.05)
+ data <- data[rows,]
+ if(nrow(data) < 1) stop("No key drivers for target modules.")
+
+ # Separate modules.
+ st <- tool.aggregate(data$MODULE)
+ blocks <- st$blocks
+
+ # Collect top drivers.
+ peaks <- double()
+ scores <- data$P
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ peaks[k] <- min(scores[rows])
+ }
+
+ # Collect drivers from top modules.
+ ind <- integer()
+ mask <- order(peaks)
+ mask <- mask[1:min(length(mask),nmods)]
+ for(k in mask) {
+ rows <- blocks[[k]]
+ rows <- rows[order(scores[rows])]
+ rows <- rows[1:min(length(rows),ndriv)]
+ ind <- c(ind, rows)
+ }
+ return(data[ind,c("MODULE","NODE")])
+}
+
+#----------------------------------------------------------------------------
+
+kda2himmeli.edges <- function(graph, center, depth, direction) {
+ g <- tool.subgraph.search(graph, center, depth, direction)
+ if(direction >= 0) {
+ g$HEAD <- g$RANK
+ g$TAIL <- center
+ } else {
+ g$TAIL <- g$RANK
+ g$HEAD <- center
+ }
+ g$WEIGHT <- (g$STRENG)/(1.0 + g$LEVEL)
+ return(g)
+}
+
+#---------------------------------------------------------------------------
+kda2himmeli.colorize <- function(noddata, moddata, modpool, palette) {
+
+ # Collect module memberships.
+ pos <- match(moddata$NODE, noddata$NODE)
+ moddata <- moddata[which(pos > 0),c("MODULE","NODE")]
+ moddata <- unique(moddata)
+
+ # Merge duplicate rows.
+ st <- tool.aggregate(moddata$NODE)
+ blocks <- st$blocks
+ colors <- rep(NA, length(blocks))
+ sectors <- rep("", length(blocks))
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ mods <- moddata[rows,"MODULE"]
+ mods <- intersect(mods, modpool)
+ mods <- sort(unique(mods))
+ n <- length(mods)
+ if(n < 1) next
+ if(n < 2) {
+ c <- palette[,which(modpool == mods[1])]
+ colors[k] <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
+ next;
+ }
+ for(i in 1:n) {
+ c <- palette[,which(modpool == mods[i])]
+ c <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
+ if(i < 2) {
+ sectors[k] <- paste(sectors[k], "1:", c, sep="")
+ } else {
+ sectors[k] <- paste(sectors[k], ",1:", c, sep="")
+ }
+ }
+ }
+
+ # Combine results.
+ res <- data.frame(NODE=as.integer(st$labels), COLOR=colors,
+ SECTOR=sectors, stringsAsFactors=FALSE)
+ res <- merge(noddata, res, all.x=TRUE)
+
+ # Fill in missing values.
+ rows <- which(is.na(res$COLOR))
+ res[rows,"COLOR"] <- "909090"
+ return(res)
+}
+
+#----------------------------------------------------------------------------
+
+kda2himmeli.colormap <- function(ncolors) {
+ palette <- rainbow(n=(ncolors + 2))
+ palette <- (col2rgb(palette)/255)
+
+ # Dampen raw colors.
+ kappa <- pmax(0.25*palette[1,], 0.25*palette[2,], 0.4*palette[3,])
+ palette[1,] <- ((1.0 - kappa)*palette[1,] + kappa)
+ palette[2,] <- ((1.0 - kappa)*palette[2,] + kappa)
+ palette <- round(99*palette)
+
+ # Remove strongest greens (easy to confuse on screen).
+ while(ncol(palette) > ncolors) {
+ rb <- (palette[1,] + palette[3,])
+ palette <- palette[,order(rb)]
+ ind <- which.max(palette[,2])
+ mask <- setdiff(1:ncol(palette), ind)
+ palette <- palette[,mask]
+ }
+
+ # Sort by components.
+ x <- (10000*palette[1,] + 100*palette[2,] + palette[3,])
+ return(palette[,order(x)])
+}
+
+#----------------------------------------------------------------------------
+
+kda2himmeli.identify <- function(dat, varname, labels) {
+ if(nrow(dat) < 1) return(dat)
+
+ # Find matching identities.
+ pos <- match(dat[,varname], labels)
+ rows <- which(pos > 0)
+
+ # Select subset.
+ dat[,varname] <- pos
+ res <- dat[rows,]
+ return(res)
+}
+
+
+#
+# Generate input files for cytoscape. The network visualization
+# is a streamlined depiction of the module enrichment in hub
+# neighborhoods. Hence, only links that connect a key
+# driver to another node are depicted within a particular depth.
+#
+# Input:
+# job KDA data list as returned by kda.finish()
+#
+# Optional input:
+# modules array of module names to be visualized
+# ndrivers maximum number of drivers per module
+# depth search depth of subgraphs
+#
+# Written by Zeyneb Kurt 2015
+#
+kda2cytoscape <- function(job, node.list=NULL, modules=NULL, ndrivers=5,
+depth=1) {
+ # Select top scoring modules.
+ cat("\nForwarding KDA results to Cytoscape...\n")
+
+ if(is.null(modules) ) # if module names were not provided by user,
+ # take module names from kda results
+ modules <- job$modules[unique(job$results$MODULE)]
+
+ # Convert module names to indices.
+ if(!is.null(modules)) {
+ modules <- match(modules, job$modules)
+ modules <- modules[which(modules > 0)]
+ if(length(modules) < 1) stop("Unknown module names.")
+ }
+
+ # Select top key drivers from each module.
+ drivers <- kda2cytoscape.drivers(job$results, modules, ndrivers)
+ mods <- unique(drivers$MODULE)
+ modnames <- job$modules[mods]
+ modnames[which(mods == 0)] <- "NON.MODULE"
+ palette <- kda2cytoscape.colormap(length(mods))
+ palette[,which(mods == 0)] <- c(90,90,90)
+
+ if(!is.null(node.list)){
+ if (all(is.na(match(node.list, job$graph$nodes))) )
+ stop("These nodes are not in the provided graph")
+ else{
+ drivers <- c()
+ nds <- which(!is.na(match(job$graph$nodes, node.list)))
+ for(ii in 1:length(nds)){
+ if (length(which(job$results$NODE == nds[ii])) > 0 ){
+ mdls <- job$results$MODULE[which(job$results$NODE ==
+ nds[ii])]
+ drivers <- rbind(drivers, cbind(mdls, nds[ii]))
+ }
+ else
+ drivers <- rbind(drivers, cbind(0, nds[ii]))
+ }
+ }
+ }
+ drivers <- as.data.frame(drivers)
+ colnames(drivers) <- c("MODULE" , "NODE")
+
+ # Create work folder.
+ dpath <- file.path(job$folder, "cytoscape")
+ if(file.exists(dpath) == FALSE)
+ dir.create(path=dpath, recursive=TRUE)
+ # Save top KDAs into file
+ drivers$MODNAMES <- modnames[match(drivers$MODULE, mods)]
+ drivers$NODNAMES <- job$graph$nodes[drivers$NODE]
+
+ for(i in 1:nrow(drivers))
+ drivers$COLOR[i] <- sprintf("#%02d%02d%02d",
+ palette[1, match(drivers$MODULE[i], mods)],
+ palette[2, match(drivers$MODULE[i], mods)],
+ palette[3, match(drivers$MODULE[i], mods)])
+
+ kdafile <- file.path(dpath, "kda2cytoscape.top.kds.txt")
+ tool.save(frame=drivers, file=kdafile)
+
+ # Process each module separately.
+ edgdata <- data.frame()
+ noddata <- data.frame()
+ for(i in 1:length(mods)) {
+ rows <- which(drivers$MODULE == mods[i])
+ if(length(rows) > 0){
+ tmp <- kda2cytoscape.exec(job, drivers[rows,], mods, palette,
+ depth)
+ if (!is.null(tmp)){
+ edgdata <- rbind(edgdata, tmp$edat)
+ noddata <- rbind(noddata, tmp$vdat)
+ }
+ }
+ }
+
+ noddata[which(noddata[,5] == "") , 5] <- NA
+ keep.list <- c()
+ unq.nodes <- unique(noddata$NODE)
+ for ( i in 1:length(unq.nodes) ) {
+ rws <- which(noddata[,1] == unq.nodes[i])
+ if (any(is.na(noddata[rws, 5])) ) {
+ nn <- which(as.integer(noddata[rws, 3]) == 100)
+ if (length(nn) >0) keep.list <- c(keep.list, rws[nn[1]])
+ else keep.list <- c(keep.list, rws[1])
+ }
+ else {
+ keep.list <- c(keep.list, rws[1])
+ lenlen <- length(strsplit(noddata[rws[1], "SECTOR"], "1:")[[1]])
+ noddata[rws, "COLOR"] <-
+ strsplit(noddata[rws[1], "SECTOR"], "1:")[[1]][lenlen]
+ noddata[rws, "COLOR"] <- paste0("#", noddata[rws[1], "COLOR"])
+ }
+ }
+
+ noddata <- noddata[keep.list, ]
+ noddata$MODULE <- NULL
+
+ # Save data files.
+ edgfile <- file.path(dpath, "kda2cytoscape.edges.txt")
+ nodfile <- file.path(dpath, "kda2cytoscape.nodes.txt")
+ if (!is.null(edgdata)) tool.save(frame=edgdata, file=edgfile)
+ if (!is.null(noddata))
+ tool.save(frame=noddata[, c("NODE", "LABEL", "COLOR", "SIZE",
+ "SHAPE", "SECTOR", "URL", "LABELSIZE")], file=nodfile)
+
+ # Color info.
+ instr <- NULL
+ instr <- paste("MODULES", "COLOR", sep="\t")
+
+ for(j in 1:ncol(palette)) {
+ c <- palette[,j]
+ value <- sprintf("#%02d%02d%02d", c[1], c[2], c[3])
+ value <- paste(modnames[j], value, sep="\t")
+ instr <- c(instr, value)
+ }
+
+ fname <- file.path(dpath, "module.color.mapping.txt")
+ write.table(instr, sep="\t", file=fname, na="", row.names=FALSE,
+ quote=FALSE, col.names=FALSE)
+
+ cat("\rInformation is stored into relevant files.\n", sep="")
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+kda2cytoscape.exec <- function(job, drivers, modpool, palette, graph.depth=1){
+ # Create star topology.
+ edgdata <- data.frame()
+ for(j in unique(drivers$NODE)) {
+ tmp <- kda2cytoscape.edges(job$graph, j, graph.depth, job$direction)
+ edgdata <- rbind(edgdata, tmp)
+ }
+
+ # Select affected nodes.
+ tmp <- data.frame(unique(c(edgdata$TAIL, edgdata$HEAD)),
+ stringsAsFactors=FALSE)
+ names(tmp) <- "NODE"
+ # Assign node shapes.
+ shapes <- rep("Ellipse", length(tmp))
+ sizes <- rep(50, length(tmp))
+ tmp$SHAPE <- shapes
+ tmp$SIZE <- sizes
+
+ pos <- match(tmp$NODE, drivers$NODE)
+ tmp[which(pos > 0),"SHAPE"] <- "Diamond"
+ tmp[which(pos > 0),"SIZE"] <- 100
+ # Trace module memberships.
+ noddata <- kda2cytoscape.colorize(tmp, job$moddata, modpool, palette)
+
+ if(!is.null(noddata)){
+ # Trim edge dataset.
+ edgdata <- edgdata[which(edgdata$TAIL != edgdata$HEAD),]
+ edgdata <- unique(edgdata[,c("TAIL", "HEAD", "WEIGHT")])
+ edgdata$COLOR <- "#808080"
+
+ # Restore original identities.
+ edgdata$TAIL <- job$graph$nodes[edgdata$TAIL]
+ edgdata$HEAD <- job$graph$nodes[edgdata$HEAD]
+ modtag <- job$modules[drivers[1,"MODULE"]]
+ if(length(modtag) == 0) modtag <- "NON.MODULE"
+
+ noddata$NODE <- job$graph$nodes[as.numeric(noddata$NODE)]
+ noddata$LABEL <- noddata$NODE
+
+ # Make identities unique for the current module.
+
+ noddata[1:nrow(noddata), "MODULE"] <- modtag
+
+ edgdata[1:nrow(edgdata), "MODULE"] <- modtag
+
+ # Return results.
+ res <- list(edat=edgdata, vdat=noddata)
+ return(res)
+ }
+ else res=NULL
+ return(res)
+}
+
+#----------------------------------------------------------------------------
+
+kda2cytoscape.drivers <- function(data, modules, ndriv) {
+
+ nmods <- length(unique(data$MODULE))
+ # Select modules.
+ if(!is.null(modules)) {
+ nmods <- length(unique(modules))
+ pos <- match(data$MODULE, modules)
+ data <- data[which(pos > 0),]
+ if(nrow(data) < 1) stop("No data on target modules.")
+ }
+
+ # Include only significant key drivers.
+ rows <- which(data$FDR < 0.05)
+ data <- data[rows,]
+ if(nrow(data) < 1) stop("No key drivers for target modules.")
+
+ # Separate modules.
+ st <- tool.aggregate(data$MODULE)
+ blocks <- st$blocks
+
+ # Collect top drivers.
+ peaks <- double()
+ scores <- data$P
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ peaks[k] <- min(scores[rows])
+ }
+
+ # Collect drivers from modules.
+ ind <- integer()
+ mask <- order(peaks)
+ mask <- mask[1:min(length(mask),nmods)]
+ for(k in mask) {
+ rows <- blocks[[k]]
+ rows <- rows[order(scores[rows])]
+ rows <- rows[1:min(length(rows),ndriv)]
+ ind <- c(ind, rows)
+ }
+ return(data[ind,c("MODULE","NODE")])
+}
+
+#----------------------------------------------------------------------------
+
+kda2cytoscape.edges <- function(graph, center, depth, direction) {
+ global.HEAD <- c()
+ global.TAIL <- c()
+ centeri <- center
+ for(i in 1:depth){
+ new.cent <- c()
+ for(j in centeri){
+ gi <- tool.subgraph.search(graph, j, depth=1, direction)
+ if(direction >= 0) {
+ global.HEAD <- c(global.HEAD, gi$RANK)
+ global.TAIL <- c(global.TAIL, rep(j, length(gi$RANK)) )
+ } else {
+ global.TAIL <- c(global.TAIL, gi$RANK)
+ global.HEAD <- c(global.HEAD, rep(j, length(gi$RANK)) )
+ }
+ new.cent <- unique(c(new.cent, gi$RANK))
+ }
+ centeri <- new.cent[which(!(new.cent %in% centeri))]
+ }
+ g <- unique(data.frame(cbind(HEAD=global.HEAD, TAIL=global.TAIL,
+ WEIGHT=1)))
+
+ return(g)
+}
+
+#---------------------------------------------------------------------------
+
+kda2cytoscape.colorize <- function(noddata, moddata, modpool, palette) {
+
+ # Google chart service.
+ urlbase <- "http://chart.apis.google.com/chart?cht=p&chs=200x200"
+ urlbase <- paste(urlbase, "chf=bg,s,00000000", sep="&")
+
+ # Collect module memberships.
+ pos <- match(moddata$NODE, noddata$NODE)
+ moddata <- moddata[which(pos > 0),c("MODULE","NODE")]
+ moddata <- unique(moddata)
+
+ # moddata <- moddata[which(!is.na(match(moddata$MODULE, modpool))), ]
+
+ # Merge duplicate rows.
+ if(length(moddata$NODE) > 0){
+ st <- tool.aggregate(moddata$NODE)
+ blocks <- st$blocks
+ colors <- rep(NA, length(blocks))
+ sectors <- rep("", length(blocks))
+ urls <- rep("", length(blocks))
+
+ for(k in 1:length(blocks)) {
+ chd <- ""
+ chco <- ""
+ rows <- blocks[[k]]
+ mods <- moddata[rows,"MODULE"]
+ mods <- intersect(mods, modpool)
+ mods <- sort(unique(mods))
+ n <- length(mods)
+ if(n < 1) next
+
+ for(i in 1:n) {
+ c <- palette[,which(modpool == mods[i])]
+ c <- sprintf("%02d%02d%02d", c[1], c[2], c[3])
+ if(i < 2) {
+ chd <- "chd=t:1"
+ chco <- paste("chco=", c, sep="")
+ cc <- paste("#", c, sep="")
+ colors[k] <- cc
+ sectors[k] <- paste(sectors[k], "1:", c, sep="")
+ } else {
+ chd <- paste(chd, 1, sep=",")
+ chco <- paste(chco, c, sep="|")
+ sectors[k] <- paste(sectors[k], ",1:", c, sep="")
+ }
+ }
+ urls[k] <- paste(urlbase, chd, chco, sep="&")
+ }
+
+ label.size <- rep(12, length(blocks))
+ # keep 1st clr in colors, keep all sectors in sectors
+ res <- data.frame(NODE=as.integer(st$labels), COLOR=colors,
+ SECTOR=sectors, URL=urls, LABELSIZE=label.size,
+ stringsAsFactors=FALSE)
+ res <- merge(noddata, res, all.x=TRUE)
+
+ # Fill in missing values.
+ rows <- which(is.na(res$COLOR))
+ res[rows,"COLOR"] <- "#909090"
+ res[which(as.integer(res$SIZE) == 100), "LABELSIZE"] <- 20
+ return(res)
+ }
+ else res <- NULL
+ return (res)
+}
+
+#----------------------------------------------------------------------------
+
+kda2cytoscape.colormap <- function(ncolors) {
+ palette <- rainbow(n=(ncolors + 2))
+ palette <- (col2rgb(palette)/255)
+
+ # Dampen raw colors.
+ kappa <- pmax(0.25*palette[1,], 0.25*palette[2,], 0.4*palette[3,])
+ palette[1,] <- ((1.0 - kappa)*palette[1,] + kappa)
+ palette[2,] <- ((1.0 - kappa)*palette[2,] + kappa)
+ palette <- round(99*palette)
+
+ # Remove strongest greens (easy to confuse on screen).
+ if (ncolors == 1){
+ rb <- (palette[1,] + palette[3,])
+ palette <- palette[,order(rb)]
+ ind <- which.max(palette[,2])
+ mask <- setdiff(1:ncol(palette), ind)
+ palette <- palette[,mask]
+ }
+ else {
+ while(ncol(palette) > ncolors) {
+ rb <- (palette[1,] + palette[3,])
+ palette <- palette[,order(rb)]
+ ind <- which.max(palette[,2])
+ mask <- setdiff(1:ncol(palette), ind)
+ palette <- palette[,mask]
+ }
+ }
+ # Sort by components.
+ x <- (10000*palette[1,] + 100*palette[2,] + palette[3,])
+ return(palette[,order(x)])
+}
+
+#----------------------------------------------------------------------------
+
+kda2cytoscape.identify <- function(dat, varname, labels) {
+ if(nrow(dat) < 1) return(dat)
+
+ # Find matching identities.
+ pos <- match(dat[,varname], labels)
+ rows <- which(pos > 0)
+
+ # Select subset.
+ dat[,varname] <- pos
+ res <- dat[rows,]
+ return(res)
+}
+
+# Determine statistical significance of key driver genes.
+#
+# Input:
+# job$graph see tool.graph()
+# job$graph$hubs nodes considered hubs (indexed)
+# job$graph$hubnets lists of neighboring nodes (indexed)
+# job$graph$cohubsets lists of overlapping hubs (indexed)
+# job$module2nodes lists of node indices for each module
+#
+# Output:
+# job$results data frame of results (indexed)
+#
+# Written by Ville-Petteri Makinen 2013
+#
+kda.analyze <- function(job) {
+ set.seed(job$seed)
+
+ cat("\nAnalyzing network...\n")
+ nmods <- length(job$modules)
+
+ # Analyze modules.
+ res <- data.frame()
+ hubs <- job$graph$hubs
+ for(i in 1:nmods) {
+ members <- job$module2nodes[[i]]
+ p <- kda.analyze.exec(members, job$graph, job$nperm)
+ mask <- which(p >= 0.0)
+ if(length(mask) < 1) next
+
+ # Find top hit.
+ tmp <- data.frame(MODULE=i, NODE=hubs[mask], P=p[mask])
+ pmin <- min(tmp$P)
+ hit <- which(tmp$P == pmin)
+ hit <- tmp$NODE[hit[1]]
+
+ # Update results.
+ nmemb <- length(members)
+ name <- job$graph$nodes[hit]
+ kd <- sprintf("%s, n=%d, p=%.2e", name, nmemb, pmin)
+ cat(job$modules[i], ": ", kd, "\n", sep="")
+ res <- rbind(res, tmp)
+ }
+
+ # Estimate false discovery rates.
+ res$FDR <- p.adjust(res$P, method="fdr")
+ job$results <- res
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+kda.analyze.exec <- function(memb, graph, nsim) {
+ hubs <- graph$hubs
+ hubnets <- graph$hubnets
+ nhubs <- length(hubs)
+ nnodes <- length(graph$nodes)
+ nmemb <- length(memb)
+
+ # Observed enrichment scores.
+ obs <- rep(NA, nhubs)
+ for(k in 1:nhubs) {
+ g <- hubnets[[hubs[k]]]
+ obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)
+ }
+
+ # Estimate P-values.
+ pvals <- rep(NA, nhubs)
+ for(k in which(obs > 0)) {
+ g <- hubnets[[hubs[k]]]
+
+ # First pass.
+ x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
+
+ # Estimate preliminary P-value.
+ param <- tool.normalize(x)
+ z <- tool.normalize(obs[k], param)
+ p <- pnorm(z, lower.tail=FALSE)
+ if(p*nhubs > 2.0) next
+
+ # Estimate final P-value.
+ n <- min(nsim, abs(nsim - length(x))+1) # it was: (nsim - length(x))
+ y <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, n)
+ param <- tool.normalize(c(x, y))
+ z <- tool.normalize(obs[k], param)
+ p <- pnorm(z, lower.tail=FALSE)
+ p <- max(p, .Machine$double.xmin)
+ if(p*nhubs > 1.0) next
+
+ # Apply Bonferroni adjustment.
+ pvals[k] <- p*nhubs
+ }
+ return(pvals)
+}
+
+#----------------------------------------------------------------------------
+
+kda.analyze.simulate <- function(o, g, nmemb, nnodes, nsim) {
+ neigh <- as.integer(g$RANK)
+ w <- as.double(g$STRENG)
+
+ # Simulate null distribution.
+ nfalse <- 0
+ x <- rep(NA, nsim)
+ for(n in 1:nsim) {
+ if(nfalse > 10) break
+ memb <- sample.int(nnodes, nmemb)
+ x[n] <- kda.analyze.test(neigh, w, memb, nnodes)
+ if(is.na(x[n])) x[n] <- rnorm(1)
+ nfalse <- (nfalse + as.integer(x[n] >= o))
+ }
+
+ # Trim results.
+ x <- x[which(0*x == 0)]
+ return(x)
+}
+
+#----------------------------------------------------------------------------
+
+#kda.analyze.test <- function(ind, w, members, nnodes) {
+# shared <- which(match(ind, members) > 0)
+# obsmass <- sum(w[shared])
+# return(obsmass)
+#}
+
+#----------------------------------------------------------------------------
+
+kda.analyze.test <- function(neigh, w, members, nnodes) {
+
+ # Check if enough neighbors.
+ nneigh <- length(neigh)
+ if(nneigh < 3) return(0.0)
+
+ # Find member nodes.
+ nmemb <- length(members)
+ pos <- match(neigh, members)
+ shared <- which(pos > 0)
+
+ # Background edge mass.
+ totmass <- sum(w)
+
+ # Edge mass captured by members.
+ obsmass <- sum(w[shared])
+
+ # Effective number of shared nodes.
+ rho <- (obsmass/totmass)
+ nobserv <- rho*nneigh
+
+ # Expected number of shared nodes.
+ nexpect <- (nmemb/nnodes)*nneigh
+
+ # Calculate enrichment score.
+ if(nobserv < 1.0)
+ return(NA)
+ z <- (nobserv - nexpect)/(sqrt(nexpect) + 1.0)
+ return(z)
+}
+#
+# Set parameters for a weighted key driver analysis
+#
+# Input:
+# plan$label unique identifier for the analysis
+# plan$folder parent folder for results
+# plan$netfile path to network file
+# columns: TAIL HEAD WEIGHT
+# plan$modfile path to module file
+# columns: MODULE NODE
+#
+# Optional:
+# plan$inffile path to module info file
+# columns: MODULE DESCR
+# plan$nodfile path to node selection file
+# columns: NODE
+# plan$depth search depth for subgraph search
+# plan$direction use zero for undirected, negative for
+# downstream and positive for upstream
+# plan$maxoverlap maximum allowed overlap between two
+# key driver neighborhoods
+# plan$minsize minimum module size
+# plan$mindegree minimum node degree to qualify as a hub
+# plan$maxdegree maximum node degree to include
+# plan$edgefactor influence of node strengths:
+# 0.0 no influence, 1.0 full influence
+# plan$seed seed for random number generator
+#
+# Output:
+# job data structure for KDA
+#
+# Written by Ville-Petteri Makinen 2013
+#
+kda.configure <- function(plan) {
+ cat("\nKDA Version:12.7.2015\n")
+ cat("\nParameters:\n")
+ plan$stamp <- Sys.time()
+ if(is.null(plan$folder)) stop("No parent folder.")
+ if(is.null(plan$label)) stop("No job label.")
+ if(is.null(plan$netfile)) stop("No network file.")
+ if(is.null(plan$modfile)) stop("No module file.")
+
+ if(is.null(plan$depth)) plan$depth <- 1
+ plan$depth <- round(plan$depth)
+ if(plan$depth < 1) stop("Unusable search depth.")
+ cat(" Search depth: ", plan$depth, "\n", sep="")
+
+ if(is.null(plan$direction)) plan$direction <- 0
+ plan$direction <- round(plan$direction)
+ if(abs(plan$direction) > 1) stop("Unusable search direction.")
+ cat(" Search direction: ", plan$direction, "\n", sep="")
+
+ if(is.null(plan$maxoverlap)) plan$maxoverlap <- 0.33
+ if(plan$maxoverlap > 1) stop("Unusable overlap limit.")
+ if(plan$maxoverlap < 0) stop("Unusable overlap limit.")
+ cat(" Maximum overlap: ", plan$maxoverlap, "\n", sep="")
+
+ if(is.null(plan$minsize)) plan$minsize <- 20
+ if(plan$minsize < 1) stop("Unusable size limit.")
+ cat(" Minimum module size: ", plan$minsize, "\n", sep="")
+
+ if(is.null(plan$mindegree)) plan$mindegree <- "automatic"
+ if(is.null(plan$maxdegree)) plan$maxdegree <- "automatic"
+ cat(" Minimum degree: ", plan$mindegree, "\n", sep="")
+ cat(" Maximum degree: ", plan$maxdegree, "\n", sep="")
+
+ if(is.null(plan$edgefactor)) plan$edgefactor <- 0.5
+ if(plan$edgefactor > 1) stop("Unusable edge factor.")
+ if(plan$edgefactor < 0) stop("Unusable edge factor.")
+ cat(" Edge factor: ", plan$edgefactor, "\n", sep="")
+
+ if(is.null(plan$nperm)) plan$nperm <- 2000
+
+ if(is.null(plan$seed)) plan$seed <- 1
+ cat(" Random seed: ", plan$seed, "\n", sep="")
+ return(plan)
+}
+# Organize and save results.
+#
+# Input:
+# job$label unique identifier for the analysis
+# job$folder output folder for results
+#
+# Results are also saved in tab-delimited text files.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+kda.finish <- function(job) {
+ cat("\nFinishing results...\n")
+ if (nrow(job$results)==0){
+ cat("No Key Driver Found!!!!")
+ }else{
+
+ # Estimate additional measures.
+ res <- kda.finish.estimate(job)
+
+ # Save full results.
+ res <- kda.finish.save(res, job)
+
+ # Create a simpler file for viewing.
+ res <- kda.finish.trim(res, job)
+
+ # Create a summary file of top hits.
+ res <- kda.finish.summarize(res, job)
+ return(job)
+ }
+}
+
+#---------------------------------------------------------------------------
+
+kda.finish.estimate <- function(job) {
+ res <- job$results
+
+ # Collect module sizes.
+ sizes <- rep(0, nrow(res))
+ modnames <- res$MODULE
+ mod2nod <- job$module2nodes
+ for(i in 1:nrow(res)) {
+ key <- modnames[i]
+ sizes[i] <- length(mod2nod[[key]])
+ }
+ res$N.mod <- sizes
+
+ # Collect overlaps with hub neighborhoods.
+ nnodes <- length(job$graph$nodes)
+ hubnets <- job$graph$hubnets
+ sizes <- matrix(nrow=nrow(res), ncol=4)
+ nodenames <- res$NODE
+ for(i in 1:nrow(res)) {
+ key <- modnames[i]
+ node <- nodenames[i]
+ memb <- mod2nod[[key]]
+ g <- hubnets[[node]]
+ sizes[i,1] <- nrow(g)
+ sizes[i,2] <- length(intersect(g$RANK, memb))
+ sizes[i,3] <- nrow(g)*length(memb)/nnodes
+ sizes[i,4] <- sum(node == memb)
+ }
+
+ # Update data frame.
+ res$N.neigh <- sizes[,1]
+ res$N.obsrv <- sizes[,2]
+ res$N.expct <- sizes[,3]
+ res$MEMBER <- (sizes[,4] > 0)
+ res$FILL <- (res$N.obsrv)/(res$N.neigh + 1e-20)
+ res$FOLD <- (res$N.obsrv)/(res$N.expct + 1e-20)
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+kda.finish.save <- function(res, job) {
+
+ # Collect co-hubs.
+ mtx <- matrix(nrow=0, ncol=0)
+ nodes <- job$graph$nodes
+ masters <- unique(res$NODE)
+ cohubsets <- job$graph$cohubsets
+ for(key in masters) {
+ cohubs <- cohubsets[[key]]
+ cohubs <- unique(c(key, cohubs))
+ tmp <- matrix(key, nrow=length(cohubs), ncol=2)
+ tmp[,2] <- cohubs
+ if(ncol(mtx) < 2) mtx <- tmp
+ else mtx <- rbind(mtx, tmp)
+ }
+
+ # Convert indices to identities.
+ dat <- data.frame(HUB=mtx[,1], NODE=mtx[,2])
+ dat$NODE <- nodes[dat$NODE]
+ dat$HUB <- nodes[dat$HUB]
+
+ # Save co-hub information.
+ jdir <- file.path(job$folder, "kda")
+ fname <- paste(job$label, ".hubs.txt", sep="")
+ tool.save(frame=dat, file=fname, directory=jdir)
+
+ # Merge with module info.
+ if(nrow(job$modinfo) > 0) res <- merge(res, job$modinfo, all.x=TRUE)
+
+ # Convert indices to identities.
+ res$MODULE <- job$modules[res$MODULE]
+ res$NODE <- job$graph$nodes[res$NODE]
+
+ # Sort and save results.
+ res <- res[order(res$P),]
+ fname <- paste(job$label, ".results.txt", sep="")
+ tool.save(frame=res, file=fname, directory=jdir)
+ return(res)
+}
+
+#----------------------------------------------------------------
+
+kda.finish.trim <- function(res, job) {
+ # Select columns.
+ header <- c("MODULE", "NODE", "P", "FDR", "FOLD")
+ if(is.null(res$DESCR) == FALSE) header <- c(header, "DESCR")
+ res <- res[,header]
+
+ # Make numbers nicer to look at.
+ preals <- res$P
+ pvals <- rep("", nrow(res))
+ fdreals <- res$FDR
+ fdrates <- rep("", nrow(res))
+ ldreals <- res$FOLD
+ folds <- rep("", nrow(res))
+ for(i in 1:nrow(res)) {
+ pvals[i] <- sprintf("%.2e", preals[ i])
+ if(is.na(fdreals[i]))
+ fdrates[i] <- ""
+ else
+ fdrates[i] <- sprintf("%.4f", fdreals[i])
+ folds[i] <- sprintf("%.2f", ldreals[i])
+ }
+
+ # Update results.
+ res$P <- pvals
+ res$FDR <- fdrates
+ res$FOLD <- folds
+
+ # Rename columns for post-processing.
+ trimres <- res
+ header[[3]] <- paste("P.", job$label, sep="")
+ header[[4]] <- paste("FDR.", job$label, sep="")
+ names(trimres) <- header
+
+ # Save P-values.
+ jdir <- file.path(job$folder, "kda")
+ fname <- paste(job$label, ".pvalues.txt", sep="")
+ tool.save(frame=trimres, file=fname, directory=jdir)
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+kda.finish.summarize <- function(res, job) {
+
+ # Determine ranking scores.
+ nres <- nrow(res)
+ rA <- rank(as.double(res$P))
+ rB <- (nres - rank(as.double(res$FOLD)))
+ scores <- (rA*nrow(res) + rB)
+
+ # Determine blocks of modules.
+ struct <- tool.aggregate(res$MODULE)
+ blocks <- struct$blocks
+
+ # Find the top node for each block.
+ tops <- rep(0, length(blocks))
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ tmp <- scores[rows]
+ pos <- which(tmp == min(tmp))
+ tops[k] <- rows[pos]
+ }
+
+ # Select top drivers.
+ scores <- scores[tops]
+ res <- res[tops,]
+
+ # Save P-values.
+ res <- res[order(scores),]
+ jdir <- file.path(job$folder, "kda")
+ fname <- paste(job$label, ".tophits.txt", sep="")
+ tool.save(frame=res, file=fname, directory=jdir)
+ return(res)
+}
+#
+# Prepare graph topology for weighted key driver analysis.
+#
+# Input:
+# job$graph see tool.graph()
+# job$depth search depth for subgraph search
+# job$direction use zero for undirected, negative for
+# downstream and positive for upstream
+# job$maxoverlap maximum allowed overlap between two
+# key driver neighborhoods
+# job$mindegree minimum hub degree to include
+# job$edgefactor influence of node strengths:
+# 0.0 no influence, 1.0 full influence
+#
+# Output:
+# job$graph$hubs nodes considered hubs (indexed)
+# job$graph$hubnets lists of neighboring nodes (indexed)
+# job$graph$cohubsets lists of overlapping hubs (indexed)
+#
+# Written by Ville-Petteri Makinen 2013, Modified by Le Shu 2015
+#
+kda.prepare <- function(job) {
+
+ # Determine minimum hub degree.
+ nnodes <- length(job$graph$nodes)
+ #if(job$mindegree == "automatic") {
+ # dmin <- nnodes/median(job$modulesizes)
+ # job$mindegree <- round(0.1*dmin)
+ #}
+ if (job$mindegree == "automatic") {
+ dmin <- as.numeric(quantile(job$graph$stats$DEGREE,0.75))
+ job$mindegree <- dmin
+ cat("\nMinimum degree set to", dmin,"\n")
+ }
+ if (job$maxdegree == "automatic") {
+ dmax <- as.numeric(quantile(job$graph$stats$DEGREE,1))
+ job$maxdegree <- dmax
+ cat("\nMaximum degree set to", dmax,"\n")
+ }
+ # Collect neighbors.
+ cat("\nCollecting hubs...\n")
+ job$graph <- kda.prepare.screen(job$graph, job$depth, job$direction,
+ job$edgefactor, job$mindegree, job$maxdegree)
+ if(length(job$graph$hubs) < 1) stop("No usable hubs detected.")
+
+ # Collect overlapping co-hubs.
+ job$graph <- kda.prepare.overlap(job$graph, job$direction,
+ job$maxoverlap)
+
+ # Print report.
+ nhubs <- length(job$graph$hubs)
+ nmem <- (object.size(job$graph))*(0.5^20)
+ cat(sprintf("%d hubs (%.2f%%)\n", nhubs, 100*nhubs/nnodes))
+ cat("Graph: ", nmem, " Mb\n", sep="")
+ return(job)
+}
+
+#---------------------------------------------------------------------------
+
+kda.prepare.screen <- function(graph, depth, direction, efactor, dmin, dmax) {
+ stamp <- Sys.time()
+ hubnets <- list()
+ accepted <- integer()
+ nnodes <- length(graph$nodes)
+
+ # Determine strength cutoff.
+ stren <- rep(0, nnodes)
+ if(direction <= 0) stren <- (stren + graph$outstats$STRENG)
+ if(direction >= 0) stren <- (stren + graph$instats$STRENG)
+ slimit <- quantile(stren, 0.75)
+
+ degr <- graph$stats$DEGREE
+
+ # Select hubs.
+ accepted <- integer()
+ for(i in which(degr >= dmin & degr <= dmax)) {
+ g <- tool.subgraph.search(graph, i, depth, direction)
+
+ # Apply edge factor.
+ g$STRENG <- (g$STRENG)^efactor
+
+ # Use average strength for hub itself (by definition, the hub
+ # typically has huge strength within its neighborhood).
+ mask <- which(g$LEVEL < 1)
+ g[mask,"STRENG"] <- median(g$STRENG)
+
+ # Progress report.
+ delta <- as.double(Sys.time() - stamp)
+ if((delta >= 10.0) & (i > 1)) {
+ cat(sprintf("%d hubs (%d nodes)\n", length(accepted), i))
+ stamp <- Sys.time()
+ }
+
+ # Exclude extreme neighborhoods.
+ #if(nrow(g) > nnodes/3) next
+ #if(nrow(g) < dmin) next
+
+ # Store subnetwork.
+ hubnets[[i]] <- g[,c("RANK", "STRENG")]
+ accepted <- c(accepted, i)
+ }
+
+ # Return results.
+ graph$hubs <- accepted
+ graph$hubnets <- hubnets
+ return(graph)
+}
+
+#---------------------------------------------------------------------------
+
+kda.prepare.overlap <- function(graph, direction, rmax) {
+ hubs <- graph$hubs
+ nhubs <- length(hubs)
+ hubnets <- graph$hubnets
+ stamp <- Sys.time()
+
+ # Determine node strengths.
+ nnodes <- length(graph$nodes)
+ stren <- rep(0.0, nnodes)
+ if(direction <= 0) stren <- (stren + graph$outstats$STRENG)
+ if(direction >= 0) stren <- (stren + graph$instats$STRENG)
+
+ # Sort hubs according to strength.
+ mask <- order(stren[hubs], decreasing=TRUE)
+ hubs <- hubs[mask]
+
+ # Collect overlapping co-hubs.
+ cohubsets <- list()
+ for(i in 1:nhubs) {
+ key <- hubs[i]
+
+ # Progress report.
+ delta <- as.double(Sys.time() - stamp)
+ if((delta >= 10.0) & (i > 1)) {
+ cat(sprintf("%d/%d co-hub sets\n", i, nhubs))
+ stamp <- Sys.time()
+ }
+
+ # Neibhgorhood topology.
+ g <- hubnets[[key]]
+ neighbors <- g$RANK
+ locals <- intersect(neighbors, hubs)
+ locals <- setdiff(locals, key)
+ strenA <- g$STRENG
+
+ # Calculate overlaps.
+ cohubs <- integer()
+ overlaps <- double()
+ for(k in locals) {
+ w <- hubnets[[k]]
+ strenB <- w$STRENG
+ # Find overlapping nodes.
+ posA <- match(neighbors, w$RANK)
+ posB <- match(w$RANK, neighbors)
+ sharedA <- which(posA > 0)
+ sharedB <- which(posB > 0)
+ uniqA <- which(is.na(posA))
+ uniqB <- which(is.na(posB))
+
+ # Calculate strength sums.
+ wsharedA <- sum(strenA[sharedA])
+ wsharedB <- sum(strenB[sharedB])
+ wuniqA <- sum(strenA[uniqA])
+ wuniqB <- sum(strenB[uniqB])
+
+ # Average symmetric sum.
+ wshared <- 0.5*(wsharedA + wsharedB)
+
+ # Overlap ratio.
+ r <- wshared/(wuniqA + wuniqB + wshared)
+ if(r < rmax) next
+
+ # Update data structures.
+ cohubs <- c(cohubs, k)
+ }
+
+ # Store results.
+ mask <- order(stren[cohubs], decreasing=TRUE)
+ cohubsets[[key]] <- cohubs[mask]
+ }
+
+ # Return results.
+ graph$cohubsets <- cohubsets
+ return(graph)
+}
+#
+# Import data for weighted key driver analysis.
+#
+# Input:
+# job data structure for wKDA
+#
+# Output:
+# job$graph indexed topology, see tool.graph()
+# job$modules module identities
+# job$modinfo module descriptions (indexed)
+# job$moddata module data (indexed)
+# columns: MODULE NODE
+# job$module2nodes lists of node indices for each module
+# job$modulesizes module sizes
+#
+# Written by Ville-Petteri Makinen 2013, Modified by Le Shu 2015
+#
+kda.start <- function(job) {
+
+ # Import topology.
+ edgdata <- kda.start.edges(job)
+ moddata <- kda.start.modules(job, edgdata)
+
+ # Import module descriptions.
+ modinfo <- tool.read(job$inffile, c("MODULE", "DESCR"))
+ modinfo <- unique(modinfo)
+ if(nrow(modinfo) > 0) print(summary(modinfo))
+
+ # Create an indexed graph structure.
+ job$graph <- tool.graph(edgdata)
+ nmem <- (object.size(job$graph))*(0.5^20)
+ cat("Graph: ", nmem, " Mb\n", sep="")
+ remove(edgdata)
+ gc(FALSE)
+
+ # Convert identities to indices.
+ modules <- unique(moddata$MODULE)
+ modinfo <- kda.start.identify(modinfo, "MODULE", modules)
+ moddata <- kda.start.identify(moddata, "MODULE", modules)
+ moddata <- kda.start.identify(moddata, "NODE", job$graph$nodes)
+
+ # Collect module members.
+ st <- tool.aggregate(moddata$MODULE)
+ blocks <- st$blocks
+ members <- as.integer(moddata$NODE)
+ for(k in 1:length(blocks)) {
+ mask <- blocks[[k]]
+ blocks[[k]] <- members[mask]
+ }
+
+ # Finish results.
+ job$modules <- modules
+ job$modinfo <- modinfo
+ job$moddata <- moddata
+ job$module2nodes <- blocks
+ job$modulesizes <- st$lengths
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+kda.start.edges <- function(job) {
+
+ # Import edge data.
+ cat("\nImporting edges...\n")
+ varnames <- c("TAIL", "HEAD", "WEIGHT")
+ edgdata <- tool.read(file=job$netfile, vars=varnames)
+ edgdata$WEIGHT <- suppressWarnings(as.double(edgdata$WEIGHT))
+ edgdata <- edgdata[which(edgdata$WEIGHT > 0),]
+
+ # Collect node names.
+ nodes <- character()
+ if(job$direction >= 0) nodes <- c(nodes, edgdata$TAIL)
+ if(job$direction <= 0) nodes <- c(nodes, edgdata$HEAD)
+
+ # Select nodes.
+ if(is.null(job$nodfile) == FALSE) {
+ cat("Selecting nodes...\n")
+ symdata <- tool.read(file=job$nodfile, vars=c("NODE"))
+ nodes <- intersect(as.character(symdata), nodes)
+ }
+
+ # Calculate node degrees.
+ struct <- tool.aggregate(nodes)
+ degrees <- struct$lengths
+ nodes <- struct$labels
+
+ # Automatic maximum degree.
+ #if(job$maxdegree == "automatic") {
+ # dmax <- floor(0.05*length(nodes))
+ # mask <- which(degrees <= dmax)
+ # degrees <- degrees[mask]
+ # nodes <- nodes[mask]
+ # job$maxdegree <- dmax
+ #}
+
+ # Filter edges.
+ posT <- match(edgdata$TAIL, nodes)
+ posH <- match(edgdata$HEAD, nodes)
+ rows <- which(posT*posH > 0)
+ edgdata <- edgdata[rows,]
+
+ # Print report.
+ print(summary(edgdata))
+ return(edgdata)
+}
+
+#----------------------------------------------------------------------------
+
+kda.start.modules <- function(job, edgdata) {
+
+ # Import module data.
+ cat("\nImporting modules...\n")
+ moddata <- tool.read(file=job$modfile, vars=c("MODULE", "NODE"))
+ moddata <- unique(moddata)
+
+ # Collect node names.
+ nodes <- character()
+ if(job$direction >= 0) nodes <- c(nodes, edgdata$TAIL)
+ if(job$direction <= 0) nodes <- c(nodes, edgdata$HEAD)
+
+ # Filter module members.
+ pos <- match(moddata$NODE, nodes)
+ moddata <- moddata[which(pos > 0),]
+
+ # Remove small modules.
+ st <- tool.aggregate(moddata$MODULE)
+ mask <- which(st$lengths >= job$minsize)
+ mods <- as.character(st$labels[mask])
+ pos <- match(moddata$MODULE, mods)
+ moddata <- moddata[which(pos > 0),]
+
+ # Print report.
+ print(summary(moddata))
+ return(moddata)
+}
+
+#----------------------------------------------------------------------------
+
+kda.start.identify <- function(dat, varname, labels) {
+ if(nrow(dat) < 1) return(dat)
+
+ # Find matching identities.
+ pos <- match(dat[,varname], labels)
+ rows <- which(pos > 0)
+
+ # Select subset.
+ dat[,varname] <- pos
+ res <- dat[rows,]
+ return(res)
+}
+#
+# Generate inputs for weighted key driver analysis.
+#
+# Input:
+# job MSEA data list as returned by ssea.finish().
+#
+# Optional input:
+# symbols data.frame for translating gene symbols
+# columns: FROM TO
+# rmax maximum allowed overlap between gene sets
+#
+# Output:
+# plan$label unique identifier for the analysis
+# plan$parent parent folder for results
+# plan$modfile path to module file
+# columns: MODULE NODE
+# plan$inffile path to module info file
+# columns: MODULE DESCR
+# plan$nodfile path to node selection file
+# columns: NODE
+#
+# Written by Ville-Petteri Makinen 2013
+#
+ssea2kda <- function(job, symbols=NULL, rmax=NULL, min.module.count=NULL) {
+ cat("\nForwarding MSEA results to KDA...\n")
+ if(is.null(rmax)) rmax <- 0.33
+
+ # Collect genes and top markers from original files.
+ noddata <- ssea2kda.import(job$genfile, job$locfile)
+
+ # Select candidate modules.
+ res <- job$results
+ res <- res[order(res$P),]
+ rows <- which(res$FDR < 0.25)
+ if(!is.null(min.module.count))
+ if(length(rows) < min.module.count) rows <- (1:min.module.count)
+ if(length(rows) < nrow(res)) res <- res[rows,]
+
+ # Collect member genes.
+ moddata <- job$moddata
+ pos <- match(moddata$MODULE, res$MODULE)
+ moddata <- moddata[which(pos > 0),]
+
+ # Restore original identities.
+ modinfo <- job$modinfo
+ modinfo$MODULE <- job$modules[modinfo$MODULE]
+ moddata$MODULE <- job$modules[moddata$MODULE]
+ moddata$GENE <- job$genes[moddata$GENE]
+
+ # Merge and trim overlapping modules.
+ moddata$OVERLAP <- moddata$MODULE
+ moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+ rcutoff=rmax)
+ moddata$MODULE <- moddata$CLUSTER
+ moddata$GENE <- moddata$ITEM
+ moddata$OVERLAP <- moddata$GROUPS
+ moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+ moddata <- unique(moddata)
+
+ # Calculate enrichment scores for merged modules.
+ tmp <- unique(moddata[,c("MODULE","OVERLAP")])
+ res <- ssea2kda.analyze(job, moddata)
+ res <- merge(res, tmp)
+ res <- merge(res, modinfo, all.x=TRUE)
+
+ # Mark modules with overlaps.
+ for(i in which(moddata$MODULE != moddata$OVERLAP))
+ moddata[i,"MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+ for(i in which(res$MODULE != res$OVERLAP))
+ res[i,"MODULE"] <- paste(res[i,"MODULE"], "..", sep=",")
+
+ # Separate merged genes.
+ nodes <- character()
+ genenames <- job$genes
+ for(i in 1:length(genenames)) {
+ segm <- strsplit(genenames[i], ",", fixed=TRUE)
+ nodes <- c(nodes, segm[[1]])
+ }
+
+ # Expand rows with merged genes.
+ tmp <- data.frame(stringsAsFactors=FALSE)
+ for(i in 1:nrow(moddata)) {
+ segm <- strsplit(moddata[i,"GENE"], ",", fixed=TRUE)
+ segm <- segm[[1]]
+ if(length(segm) < 2) next
+ batch <- data.frame(MODULE=moddata[i,"MODULE"], GENE=segm,
+ stringsAsFactors=FALSE)
+ tmp <- rbind(tmp, batch)
+ moddata[i,] <- NA
+ }
+ moddata <- na.omit(moddata[,c("MODULE","GENE")])
+ moddata <- unique(rbind(moddata, tmp))
+
+ # Translate gene symbols.
+ moddata$NODE <- moddata$GENE
+ noddata$NODE <- noddata$GENE
+ if(is.null(symbols) == FALSE) {
+ moddata$NODE <- tool.translate(words=moddata$NODE, from=symbols$FROM,
+ to=symbols$TO)
+ noddata$NODE <- tool.translate(words=noddata$NODE, from=symbols$FROM,
+ to=symbols$TO)
+ moddata <- na.omit(moddata)
+ noddata <- na.omit(noddata)
+ }
+
+ # Save module info for KDA.
+ res <- res[order(res$P),]
+ names(res)[5] = "NMARKER"
+ inffile <- "msea2kda.info.txt"
+ tool.save(frame=res, file=inffile, directory=job$folder)
+
+ # Save modules for KDA.
+ modfile <- "msea2kda.modules.txt"
+ tool.save(frame=unique(moddata[,c("MODULE", "NODE", "GENE")]),
+ file=modfile, directory=job$folder)
+
+ # Save nodes for KDA.
+ nodfile <- "msea2kda.nodes.txt"
+ out_node = unique(noddata[,c("NODE", "GENE", "LOCUS", "VALUE")])
+ names(out_node)[3] = "MARKER"
+ tool.save(frame=out_node, file=nodfile, directory=job$folder)
+
+ # Return KDA plan template.
+ plan <- list()
+ plan$label <- job$label
+ plan$folder <- job$folder
+ plan$inffile <- file.path(job$folder, inffile)
+ plan$modfile <- file.path(job$folder, modfile)
+ plan$nodfile <- file.path(job$folder, nodfile)
+ plan$ssearesults <- res
+ return(plan)
+}
+
+#---------------------------------------------------------------------------
+
+ssea2kda.import <- function(genfile, locfile) {
+
+ # Import marker values.
+ cat("\nImporting marker values...\n")
+ locdata <- tool.read(locfile, c("LOCUS", "VALUE"))
+ locdata$VALUE <- as.double(locdata$VALUE)
+ rows <- which(0*(locdata$VALUE) == 0)
+ locdata <- unique(na.omit(locdata[rows,]))
+ locdata_ex <- locdata
+ names(locdata_ex) <- c("MARKER","VALUE")
+ print(summary(locdata_ex))
+
+ # Import mapping data.
+ cat("\nImporting mapping data...\n")
+ gendata <- tool.read(genfile, c("GENE", "LOCUS"))
+ gendata <- unique(na.omit(gendata))
+ gendata_ex <- gendata
+ names(gendata_ex) <- c("GENE","MARKER")
+ print(summary(gendata_ex))
+
+ # Merge datasets.
+ data <- merge(gendata, locdata)
+
+ # Find top marker.
+ mask <- integer()
+ st <- tool.aggregate(data$GENE)
+ blocks <- st$blocks
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ ind <- which.max(data[rows,"VALUE"])
+ mask <- c(mask, rows[ind])
+ }
+ return(data[mask,])
+}
+
+#---------------------------------------------------------------------------
+
+ssea2kda.analyze <- function(job, moddata) {
+
+ # Convert identities to indices.
+ moddata <- ssea.start.identify(moddata, "MODULE", job$modules)
+ moddata <- ssea.start.identify(moddata, "GENE", job$genes)
+
+ # Collect row indices for each module.
+ st <- tool.aggregate(moddata$MODULE)
+ keys <- as.integer(st$labels)
+ blocks <- st$blocks
+ nmods <- length(blocks)
+
+ # Prepare gene lists.
+ genlists <- list()
+ for(k in 1:nmods) genlists[[k]] <- integer()
+
+ # Collect gene sets.
+ modsizes <- rep(0, nmods)
+ genes <- as.integer(moddata$GENE)
+ for(k in 1:length(blocks)) {
+ key <- keys[k]
+ rows <- blocks[[k]]
+ members <- unique(genes[rows])
+ genlists[[key]] <- as.integer(members)
+ modsizes[[key]] <- length(members)
+ }
+
+ # Determine marker set sizes.
+ modlengths <- rep(0, nmods)
+ moddensities <- rep(0.0, nmods)
+ loclists <- job$database$gene2loci
+ for(k in 1:length(genlists)) {
+ locset <- integer()
+ for(i in genlists[[k]])
+ locset <- c(locset, loclists[[i]])
+ modlengths[[k]] <- length(unique(locset))
+ moddensities[[k]] <- modlengths[[k]]/length(locset)
+ }
+
+ # Update database.
+ job$database$modulesizes <- modsizes
+ job$database$modulelengths <- modlengths
+ job$database$moduledensities <- moddensities
+ job$database$module2genes <- genlists
+
+ # Run enrichment analysis.
+ job <- ssea.analyze(job)
+ res <- job$results
+
+ # Restore module identities.
+ res$NGENES <- modsizes[res$MODULE]
+ res$NLOCI <- modlengths[res$MODULE]
+ res$DENSITY <- moddensities[res$MODULE]
+ res$MODULE <- job$modules[res$MODULE]
+ return(res)
+}
+#
+# Marker set enrichment analysis with gene-level permutations.
+#
+# Input:
+# job$seed seed for random number generator
+# job$permtype random permutation algorithm
+# job$nperm maximum nubmer of permutations
+# job$database see ssea.prepare()
+#
+# Output:
+# job$results data frame:
+# MODULE module identity (indexed)
+# P enrichment P-values
+# FREQ enrichment P-values (raw frequencies)
+#
+# Written by Ville-Petteri Makinen 2013
+#
+ssea.analyze <- function(job) {
+ cat("\nEstimating enrichment...\n")
+ set.seed(job$seed)
+
+ # Observed enrichment scores.
+ db <- job$database
+ scores <- ssea.analyze.observe(db)
+ nmods <- length(scores)
+
+ # Simulated scores.
+ nperm <- job$nperm
+ nullsets <- ssea.analyze.simulate(db, scores, nperm, job$permtype)
+
+ # Estimate scores based on Gaussian distribution.
+ cat("\nNormalizing scores...\n")
+ znull <- double()
+ zscores <- NA*scores
+ for(i in which(0*scores == 0)) {
+ x <- nullsets[[i]]
+ x <- x[which(0*x == 0)]
+ param <- tool.normalize(x)
+ z <- tool.normalize(x, param)
+ zscores[i] <- tool.normalize(scores[i], param)
+ znull <- c(znull, z)
+ }
+
+ # Estimate hit frequencies.
+ freq <- NA*scores
+ nnull <- length(znull)
+ for(i in which(0*scores == 0))
+ freq[i] <- sum(zscores[i] <= znull)/nnull
+
+ # Estimate scores based on frequencies.
+ hscores <- NA*freq
+ rows <- which(freq > 5.0/nnull)
+ hscores[rows] <- qnorm(freq[rows], lower.tail=FALSE)
+
+ # Fill in scores for low frequencies.
+ if(length(rows) < length(freq)) {
+ omega <- which.max(zscores)
+ hscores[omega] <- zscores[omega]
+ rows <- c(rows, omega)
+ pt <- approx(x=zscores[rows], y=hscores[rows], xout=zscores)
+ hscores <- pt$y
+ }
+
+ # Estimate statistical significance.
+ z <- 0.5*(zscores + hscores)
+ pvalues <- pnorm(z, lower.tail=FALSE)
+
+ # Collect results.
+ res <- data.frame(MODULE=(1:nmods), stringsAsFactors=FALSE)
+ res$P <- pvalues
+ res$FREQ <- freq
+
+ # Remove missing scores.
+ targets <- which(0*scores == 0)
+ job$results <- res[targets,]
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.analyze.simulate <- function(db, observ, nperm, permtype) {
+
+ # Include only non-empty modules for simulation.
+ nmods <- length(db$modulesizes)
+ targets <- which(db$modulesizes > 0)
+ hits <- rep(NA, nmods)
+ hits[targets] <- 0
+
+ # Prepare data structures to hold null samples.
+ keys <- rep(0, nperm)
+ scores <- rep(NA, nperm)
+ scoresets <- list()
+ for(i in 1:nmods)
+ scoresets[[i]] <- double()
+
+ # Simulate random scores.
+ nelem <- 0
+ snull <- double()
+ stamp <- Sys.time()
+ for(k in 1:nperm) {
+ if(permtype == "gene") snull <- ssea.analyze.randgenes(db, targets)
+ if(permtype == "locus") snull <- ssea.analyze.randloci(db, targets)
+ if(length(snull) < 1) stop("Unknown permutation type.")
+
+ # Check data capacity.
+ ntarg <- length(targets)
+ if((nelem + ntarg) >= length(keys)) {
+ keys <- c(keys, rep(0, nelem))
+ scores <- c(scores, rep(NA, nelem))
+ }
+
+ # Collect scores.
+ for(i in 1:ntarg) {
+ nelem <- (nelem + 1)
+ t <- targets[i]
+ keys[nelem] <- t
+ scores[nelem] <- snull[i]
+ hits[t] <- (hits[t] + (snull[i] > observ[t]))
+ }
+
+ # Drop less significant modules.
+ hmax <- min(sqrt(nperm/k), 10)
+ targets <- which(hits < hmax)
+ if(length(targets) < 1) break
+
+ # Progress report.
+ delta <- as.double(Sys.time() - stamp)
+ if((delta < 10.0) & (k < nperm)) next
+ cat(sprintf("%d/%d cycles\n", k, nperm))
+ stamp <- Sys.time()
+ }
+
+ # Remove missing entries.
+ scores <- scores[1:nelem]
+ keys <- keys[1:nelem]
+
+ # Organize null scores into lists.
+ st <- tool.aggregate(keys)
+ labels <- as.integer(st$labels)
+ blocks <- st$blocks
+ for(i in 1:length(blocks)) {
+ key <- labels[i]
+ rows <- blocks[[i]]
+ scoresets[[key]] <- scores[rows]
+ }
+ return(scoresets)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.analyze.observe <- function(db) {
+ mod2gen <- db$module2genes
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+ nmods <- length(mod2gen)
+
+ # Test every module.
+ scores <- rep(NA, nmods)
+ for(k in 1:nmods) {
+ genes <- mod2gen[[k]]
+
+ # Collect markers.
+ loci <- integer()
+ for(i in genes)
+ loci <- c(loci, gene2loci[[i]])
+
+ # Determine data rows.
+ loci <- unique(loci)
+ rows <- locus2row[loci]
+ nloci <- length(rows)
+
+ # Calculate total counts.
+ e <- nloci*expected
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ scores[k] <- ssea.analyze.statistic(o, e)
+ }
+ return(scores)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.analyze.randgenes <- function(db, targets) {
+ mod2gen <- db$module2genes
+ modsizes <- db$modulesizes
+ modlengths <- db$modulelengths
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+
+ # Test target modules.
+ scores <- double()
+ nrows <- length(locus2row)
+ npool <- length(gene2loci)
+ for(k in targets) {
+ msize <- modsizes[[k]]
+ nloci <- modlengths[[k]]
+
+ # Collect pre-defined number of markers from random genes.
+ loci <- integer()
+ genes <- sample.int(npool, (msize + 10))
+ while(length(loci) < nloci) {
+ for(i in genes) {
+ tmp <- gene2loci[[i]]
+ loci <- c(loci, tmp)
+ }
+ loci <- unique(loci)
+ genes <- sample.int(npool, msize)
+ }
+
+ # Determine data rows.
+ loci <- loci[1:nloci]
+ rows <- locus2row[loci]
+
+ # Calculate total counts.
+ e <- nloci*expected
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ z <- ssea.analyze.statistic(o, e)
+ scores <- c(scores, z)
+ }
+ return(scores)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.analyze.randloci <- function(db, targets) {
+ modlengths <- db$modulelengths
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+
+ # Test target modules.
+ scores <- double()
+ nrows <- length(locus2row)
+ for(k in targets) {
+ nloci <- modlengths[[k]]
+
+ # Determine data rows.
+ loci <- sample.int(nrows, nloci)
+ rows <- locus2row[loci]
+
+ # Calculate total counts.
+ e <- nloci*expected
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment.
+ z <- ssea.analyze.statistic(o, e)
+ scores <- c(scores, z)
+ }
+
+ # Return results.
+ return(scores)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.analyze.statistic <- function(o, e) {
+ z <- (o - e)/(sqrt(e) + 1.0)
+ return(mean(z))
+}
+#
+# Add internal positive control modules.
+#
+# Input:
+# job$modules module identities as characters
+# job$genes gene identities as characters
+# job$moddata preprocessed module data (indexed identities)
+# job$database see ssea.prepare()
+#
+# Output:
+# job$modules augmented module names
+# job$moddata augmented module data
+# job$database augmented database
+#
+# Written by Ville-Petteri Makinen 2013
+#
+ssea.control <- function(job) {
+ cat("\nAdding positive controls...\n")
+ db <- job$database
+ gene2loci <- db$gene2loci
+ locus2row <- db$locus2row
+ observed <- db$observed
+ expected <- db$expected
+
+ # Find slots for control module.
+ modules <- job$modules
+ slotA <- which(modules == "_ctrlA")
+ slotB <- which(modules == "_ctrlB")
+ if(length(slotA) != 1) stop("No control slot A.")
+ if(length(slotB) != 1) stop("No control slot B.")
+
+ # Calculate gene scores.
+ ngens <- length(gene2loci)
+ scores <- rep(NA, ngens)
+ for(k in 1:ngens) {
+ loci <- gene2loci[[k]]
+ nloci <- length(loci)
+ if(nloci < 1) next
+
+ # Calculate total counts.
+ rows <- locus2row[loci]
+ e <- nloci*expected
+ o <- observed[rows,]
+ if(nloci > 1) o <- colSums(o)
+
+ # Estimate enrichment score.
+ scores[k] <- ssea.analyze.statistic(o, e)
+ }
+
+ # Select top genes.
+ sizes <- db$modulesizes
+ sizes <- sizes[which(sizes > 0)]
+ ntop <- floor(median(sizes))
+ genesA <- order(scores, decreasing=TRUE)
+ genesA <- genesA[1:ntop]
+
+ # Collect genes within modules.
+ members <- integer()
+ mod2gen <- db$module2genes
+ for(k in 1:length(mod2gen))
+ members <- c(members, mod2gen[[k]])
+ members <- unique(members)
+
+ # Select top genes among module members.
+ genesB <- order(scores[members], decreasing=TRUE)
+ genesB <- members[genesB[1:ntop]]
+
+ # Collect markers.
+ locsetA <- integer()
+ locsetB <- integer()
+ for(k in genesA)
+ locsetA <- c(locsetA, gene2loci[[k]])
+ for(k in genesB)
+ locsetB <- c(locsetB, gene2loci[[k]])
+ locsetA <- unique(locsetA)
+ locsetB <- unique(locsetB)
+
+ # Force matching number of markers.
+ modlen <- min(length(locsetA), length(locsetB))
+
+ # Create new modules.
+ db$genescores <- scores
+ db$modulesizes[[slotA]] <- ntop
+ db$modulesizes[[slotB]] <- ntop
+ db$modulelengths[[slotA]] <- modlen
+ db$modulelengths[[slotB]] <- modlen
+ db$moduledensities[[slotA]] <- length(locsetA)/sum(db$genesizes[genesA])
+ db$moduledensities[[slotB]] <- length(locsetB)/sum(db$genesizes[genesB])
+ db$module2genes[[slotA]] <- genesA
+ db$module2genes[[slotB]] <- genesB
+
+ # Update module data.
+ tmpA <- data.frame(MODULE=slotA, GENE=genesA)
+ tmpB <- data.frame(MODULE=slotB, GENE=genesB)
+ job$moddata <- rbind(job$moddata, tmpA, tmpB)
+
+ # Return results.
+ job$database <- db
+ remove(db)
+ gc(FALSE)
+ nmem <- (object.size(job))*(0.5^20)
+ cat("Job: ", nmem, " Mb\n", sep="")
+ return(job)
+}
+# Organize and save results.
+#
+# Input:
+# job$label unique identifier for the analysis
+# job$folder output folder for results
+# job$results data frame:
+# MODULE module identity (indexed)
+# P enrichment P-values
+# job$database see ssea.prepare()
+#
+# Output:
+# job$results updated columns in data frame:
+# NGENES number of distinct member genes
+# NMARKER number of distinct member markers
+# DENSITY ratio of distinct to non-distinct markers
+# FDR false discovery rates
+# job$generesults data frame:
+# GENE gene identity (indexed)
+# NMARKER gene size
+# SCORE unadjusted enrichment score
+# MARKER marker with maximum value
+# VALUE marker value
+#
+# Results are also saved in tab-delimited text files.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+ssea.finish <- function(job) {
+ cat("\nPostprocessing results...\n")
+ job <- ssea.finish.fdr(job)
+ job <- ssea.finish.genes(job)
+ job <- ssea.finish.details(job)
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.finish.genes <- function(job) {
+ db <- job$database
+ gen2loci <- db$gene2loci
+ loc2row <- db$locus2row
+ values <- job$locdata$VALUE
+
+ # Collect top loci within genes.
+ toploci <- integer()
+ topvals <- double()
+ ngenes <- length(gen2loci)
+ for(k in 1:ngenes) {
+ locset <- gen2loci[[k]]
+ locvals <- values[loc2row[locset]]
+ ind <- which.max(locvals)
+ toploci[k] <- locset[ind]
+ topvals[k] <- locvals[ind]
+ }
+
+ # Create data frame.
+ res <- data.frame(GENE=(1:ngenes))
+ res$SCORE <- db$genescores
+ res$NLOCI <- db$genesizes
+ res$LOCUS <- toploci
+ res$VALUE <- topvals
+ job$generesults <- res
+
+ # Restore original identities.
+ res$GENE <- job$genes[res$GENE]
+ res$LOCUS <- job$loci[res$LOCUS]
+
+ # Save results.
+ jdir <- file.path(job$folder, "msea")
+ fname <- paste(job$label, ".genes.txt", sep="")
+ names(res)[3:4] = c("NMARKER","MARKER")
+ tool.save(frame=res, file=fname, directory=jdir)
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.finish.fdr <- function(job) {
+ res <- job$results
+
+ # Add module statistics.
+ db <- job$database
+ res$NGENES <- db$modulesizes[res$MODULE]
+ res$NLOCI <- db$modulelengths[res$MODULE]
+ res$DENSITY <- db$moduledensities[res$MODULE]
+
+ # Estimate false discovery rates.
+ res$FDR <- tool.fdr(res$P)
+ job$results <- res
+
+ # Merge with module info.
+ res <- merge(res, job$modinfo, all.x=TRUE)
+
+ # Sort according to significance.
+ res <- res[order(res$P),]
+
+ # Restore module names.
+ res$MODULE <- job$modules[res$MODULE]
+
+ # Save full results.
+ jdir <- file.path(job$folder, "msea")
+ fname <- paste(job$label, ".results.txt", sep="")
+ names(res)[5] = "NMARKER"
+ tool.save(frame=res, file=fname, directory=jdir)
+
+ # Prepare results for post-processing.
+ header <- rep("MODULE", 4)
+ header[[2]] <- paste("P.", job$label, sep="")
+ header[[3]] <- paste("FDR.", job$label, sep="")
+ header[[4]] <- "DESCR"
+ res <- res[,c("MODULE", "P", "FDR", "DESCR")]
+ names(res) <- header
+
+ # Make numbers nicer to look at.
+ pvals <- character()
+ fdrates <- character()
+ for(i in 1:nrow(res)) {
+ pvals[i] <- sprintf("%.2e", res[i,2])
+ fdrates[i] <- sprintf("%.4f", res[i,3])
+ }
+ res[,2] <- pvals
+ res[,3] <- fdrates
+
+ # Save P-values.
+ fname <- paste(job$label, ".pvalues.txt", sep="")
+ tool.save(frame=res, file=fname, directory=jdir)
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.finish.details <- function(job) {
+
+ # Find signficant modules.
+ res <- job$results
+ mask <- which(res$FDR < 0.25)
+ if(length(mask) < 5) {
+ mask <- order(res$P)
+ mask <- mask[1:min(5,length(mask))]
+ }
+
+ # Collect gene members of top modules.
+ dtl <- data.frame()
+ mod2genes <- job$database$module2genes
+ for(k in res[mask,"MODULE"]) {
+ genset <- mod2genes[[k]]
+ tmp <- data.frame(MODULE=k, GENE=genset)
+ dtl <- rbind(dtl, tmp)
+ }
+
+ # Merge with gene results.
+ dtl <- merge(dtl, job$generesults, all.x=TRUE)
+
+ # Merge with module info.
+ if(nrow(job$modinfo) > 0)
+ dtl <- merge(dtl, job$modinfo, all.x=TRUE)
+ else
+ dtl$DESCR = ""
+
+ # Merge with module statistics.
+ dtl <- merge(res[,c("MODULE", "P", "FDR")], dtl, all.y=TRUE)
+
+ # Sort according to enrichment and marker value.
+ scores <- 1000*rank(-(dtl$P))
+ gscores <- tool.unify(dtl$VALUE)
+ rows <- order((scores + gscores), decreasing=TRUE)
+ dtl <- dtl[rows,]
+
+ # Restore names and sort columns.
+ dtl$MODULE <- job$modules[dtl$MODULE]
+ dtl$GENE <- job$genes[dtl$GENE]
+ dtl$LOCUS <- job$loci[dtl$LOCUS]
+ dtl <- dtl[,c("MODULE", "FDR", "GENE", "NLOCI", "LOCUS", "VALUE",
+ "DESCR")]
+
+ # Make numbers look nicer.
+ values <- character()
+ fdrates <- character()
+ for(i in 1:nrow(dtl)) {
+ values[i] <- sprintf("%.2f", dtl[i,"VALUE"])
+ fdrates[i] <- sprintf("%.2f%%", 100*dtl[i,"FDR"])
+ }
+ dtl$FDR <- fdrates
+ dtl$VALUE <- values
+
+ # Save contents.
+ jdir <- file.path(job$folder, "msea")
+ fname <- paste(job$label, ".details.txt", sep="")
+ names(dtl) = c("MODULE","FDR","GENE","NMARKER","MARKER","VALUE","DESCR")
+ tool.save(frame=dtl, file=fname, directory=jdir)
+ return(job)
+}
+#
+# Merge multiple MSEA results into meta MSEA.
+#
+# Input:
+# jobs MSEA list objects
+# label label for meta job
+# folder parent folder for meta job
+#
+# Output:
+# meta MSEA list object
+#
+# Written by Ville-Petteri Makinen 2013, Modified by Le Shu 2015
+#
+ssea.meta <- function(jobs, label, folder) {
+
+ # Create meta job.
+ #cat("\nMerging jobs...\n")
+ meta <- list()
+ meta$label <- label
+ meta$folder <- folder
+ meta$modfile <- "undefined"
+ meta$genfile <- "undefined"
+ meta$marfile <- "undefined"
+ meta <- ssea.start.configure(meta)
+
+ # Collect data.
+ meta$results <- data.frame()
+ meta$modinfo <- data.frame()
+ meta$moddata <- data.frame()
+ meta$gendata <- data.frame()
+ meta$locdata <- data.frame()
+ for(k in 1:length(jobs)) {
+ job <- jobs[[k]]
+ results <- job$results
+ modinfo <- job$modinfo
+ moddata <- job$moddata
+ gendata <- job$gendata
+ locdata <- job$locdata
+
+ # Restore original identities.
+ results$MODULE <- job$modules[results$MODULE]
+ modinfo$MODULE <- job$modules[modinfo$MODULE]
+ moddata$MODULE <- job$modules[moddata$MODULE]
+ moddata$GENE <- job$genes[moddata$GENE]
+ gendata$GENE <- job$genes[gendata$GENE]
+ gendata$LOCUS <- job$loci[gendata$LOCUS]
+ locdata$LOCUS <- job$loci[locdata$LOCUS]
+
+ # Update meta sets.
+ meta$results <- rbind(meta$results, results)
+ meta$modinfo <- rbind(meta$modinfo, modinfo)
+ meta$moddata <- rbind(meta$moddata, moddata)
+ meta$gendata <- rbind(meta$gendata, gendata)
+ meta$locdata <- rbind(meta$locdata, locdata)
+ }
+
+ # Remove duplicate rows (non-numeric values only).
+ meta$modinfo <- unique(meta$modinfo)
+ meta$moddata <- unique(meta$moddata)
+ meta$gendata <- unique(meta$gendata)
+
+ # Determine identities.
+ meta$modules <- unique(meta$moddata$MODULE)
+ meta$genes <- unique(meta$gendata$GENE)
+ meta$loci <- unique(meta$gendata$LOCUS)
+
+ # Convert identities to indices.
+ meta$results <- ssea.start.identify(meta$results, "MODULE", meta$modules)
+ meta$modinfo <- ssea.start.identify(meta$modinfo, "MODULE", meta$modules)
+ meta$moddata <- ssea.start.identify(meta$moddata, "MODULE", meta$modules)
+ meta$moddata <- ssea.start.identify(meta$moddata, "GENE", meta$genes)
+ meta$gendata <- ssea.start.identify(meta$gendata, "GENE", meta$genes)
+ meta$gendata <- ssea.start.identify(meta$gendata, "LOCUS", meta$loci)
+ meta$locdata <- ssea.start.identify(meta$locdata, "LOCUS", meta$loci)
+
+ # Convert marker values to z-scores.
+ values <- meta$locdata$VALUE
+ qvals <- tool.unify(values)
+ qvals <- pmax(qvals, .Machine$double.xmin)
+ qvals <- pmin(qvals, (1.0 - .Machine$double.eps))
+ zvals <- qnorm(qvals)
+
+ # Merge matching markers.
+ st <- tool.aggregate(meta$locdata$LOCUS)
+ blocks <- st$blocks
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ z <- sum(zvals[rows])
+ zvals[rows] <- z/sqrt(length(rows))
+ }
+
+ # Convert back to original data space.
+ meta$locdata$VALUE <- quantile(values, pnorm(zvals))
+ meta$locdata <- unique(meta$locdata)
+
+ # Construct hierarchical representation.
+ cat("\nPreparing data structures...\n")
+ ngens <- length(meta$genes)
+ nmods <- length(meta$modules)
+ meta$database <- ssea.prepare.structure(meta$moddata, meta$gendata,
+ nmods, ngens)
+
+ # Determine test cutoffs.
+ lengths <- meta$database$modulelengths
+ mu <- median(lengths[which(lengths > 0)])
+ meta$quantiles <- seq(0.5, (1.0 - 1.0/mu), length.out=10)
+
+ # Calculate hit counts.
+ nloci <- length(meta$loci)
+ hits <- ssea.prepare.counts(meta$locdata, nloci, meta$quantiles)
+ meta$database <- c(meta$database, hits)
+
+ # Check result values.
+ meta$results <- meta$results[,c("MODULE", "P")]
+ pvalues <- pmax(meta$results$P, .Machine$double.xmin)
+ pvalues <- pmin(pvalues, (1.0 - .Machine$double.eps))
+ meta$results$P <- pvalues
+
+ # Calculate meta P-values.
+ cat("\nPostprocessing meta results...\n")
+ st <- tool.aggregate(meta$results$MODULE)
+ blocks <- st$blocks
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ tmp <- meta$results[rows,]
+ z <- qnorm(tmp$P)
+ z <- sum(z)/sqrt(length(z))
+ meta$results[rows,"P"] <- NA
+ meta$results[rows[1],"P"] <- pnorm(z)
+ }
+
+ # Finish and save statistics.
+ meta$results <- na.omit(meta$results)
+ meta <- ssea.finish.fdr(meta)
+ #meta <- ssea.finish.details(meta)
+ return(meta)
+}
+#
+# Prepare an indexed database for Marker set enrichment analysis.
+#
+# Input:
+# job$modules module identities as characters
+# job$genes gene identities as characters
+# job$loci marker identities as characters
+# job$moddata preprocessed module data (indexed identities)
+# job$gendata preprocessed mapping data (indexed identities)
+# job$locdata preprocessed marker data (indexed identities)
+# job$mingenes minimum module size allowed
+# job$maxgenes maximum module size allowed
+# job$maxoverlap maximum module overlap allowed (use 1.0 to skip)
+#
+# Optional input:
+# job$quantiles quantile points for test statistic
+#
+# Output:
+# job$modules finalized module names
+# job$moddata finalized module data
+# job$gendata finalized mapping data
+# job$locdata finalized marker data
+# job$quantiles verified quantile points
+# job$database$modulesizes
+# gene counts for modules
+# job$database$modulelengths
+# distinct marker counts for modules
+# job$database$moduledensities
+# ratio between distinct and non-distinct markers
+# job$database$genesizes
+# marker count for each gene
+# job$database$module2genes
+# gene lists for each module
+# job$database$gene2loci
+# marker lists for each gene
+# job$database$locus2row
+# row indices in the marker data frame for each marker
+# job$database$observed
+# matrix of observed counts of values that exceed each
+# quantile point for each marker
+# job$database$expected
+# 1.0 - quantile points
+#
+# The database uses indexed identities for modules, genes and marker.
+# Output also includes all the other items from input list.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+ssea.prepare <- function(job) {
+ cat("\nPreparing data structures...\n")
+
+ # Remove extreme modules.
+ st <- tool.aggregate(job$moddata$MODULE)
+ mask <- which((st$lengths >= job$mingenes) & (st$lengths <= job$maxgenes))
+ pos <- match(job$moddata$MODULE, st$labels[mask])
+ job$moddata <- job$moddata[which(pos > 0),]
+
+ # Construct hierarchical representation.
+ ngens <- length(job$genes)
+ nmods <- length(job$modules)
+ db <- ssea.prepare.structure(job$moddata, job$gendata, nmods, ngens)
+
+ # Determine test cutoffs.
+ if(is.null(job$quantiles)) {
+ lengths <- db$modulelengths
+ mu <- median(lengths[which(lengths > 0)])
+ job$quantiles <- seq(0.5, (1.0 - 1.0/mu), length.out=10)
+ }
+
+ # Calculate hit counts.
+ nloci <- length(job$loci)
+ hits <- ssea.prepare.counts(job$locdata, nloci, job$quantiles)
+ db <- c(db, hits)
+
+ # Return results.
+ job$database <- db
+ remove(db)
+ gc(FALSE)
+ nmem <- (object.size(job))*(0.5^20)
+ cat("Job: ", nmem, " Mb\n", sep="")
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.prepare.structure <- function(moddata, gendata, nmods, ngens) {
+
+ # Prepare list structures.
+ genlists <- list()
+ loclists <- list()
+ for(k in 1:nmods) genlists[[k]] <- integer()
+ for(k in 1:ngens) loclists[[k]] <- integer()
+ modsizes <- rep(0, nmods)
+ modlengths <- rep(0, nmods)
+ moddensities <- rep(0.0, nmods)
+ gensizes <- rep(0, ngens)
+
+ # Collect row indices for each module.
+ st <- tool.aggregate(moddata$MODULE)
+ keys <- as.integer(st$labels)
+ blocks <- st$blocks
+
+ # Collect gene lists.
+ genes <- as.integer(moddata$GENE)
+ for(k in 1:length(blocks)) {
+ key <- keys[k]
+ rows <- blocks[[k]]
+ members <- unique(genes[rows])
+ genlists[[key]] <- as.integer(members)
+ modsizes[[key]] <- length(members)
+ }
+
+ # Collect row indices for each gene.
+ st <- tool.aggregate(gendata$GENE)
+ keys <- as.integer(st$labels)
+ blocks <- st$blocks
+
+ # Collect marker lists.
+ loci <- as.integer(gendata$LOCUS)
+ for(k in 1:length(blocks)) {
+ key <- keys[k]
+ rows <- blocks[[k]]
+ members <- unique(loci[rows])
+ loclists[[key]] <- as.integer(members)
+ gensizes[[key]] <- length(members)
+ }
+
+ # Count distinct markers in each module.
+ for(k in which(modsizes > 0)) {
+ locset <- integer()
+ genset <- genlists[[k]]
+ for(i in genset)
+ locset <- c(locset, loclists[[i]])
+ modlengths[[k]] <- length(unique(locset))
+ moddensities[[k]] <- modlengths[[k]]/length(locset)
+ }
+
+ # Check data integrity.
+ if(sum(gensizes == 0) > 0) stop("Incomplete locus data.")
+ if(length(gensizes) != ngens) stop("Inconsistent gene data.")
+ if(length(modsizes) != nmods) stop("Inconsistent module data.")
+
+ # Return results.
+ res <- list()
+ res$modulesizes <- modsizes
+ res$modulelengths <- modlengths
+ res$moduledensities <- moddensities
+ res$genesizes <- gensizes
+ res$module2genes <- genlists
+ res$gene2loci <- loclists
+ return(res)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.prepare.counts <- function(locdata, nloci, quantiles) {
+
+ # Make sure there are at least two points to prevent
+ # R automagic on matrices to mess things up.
+ if(length(quantiles) < 2) quantiles <- rep(quantiles, 2)
+
+ # Create mapping table.
+ nrows <- nrow(locdata)
+ locmap <- rep(0, nloci)
+ locmap[locdata$LOCUS] <- (1:nrows)
+
+ # Convert values to standardized range.
+ values <- tool.unify(locdata$VALUE)
+
+ # Create bit matrix of values above quantiles.
+ nquant <- length(quantiles)
+ bits <- matrix(data=FALSE, nrow=nrows, ncol=nquant)
+ for(i in 1:nrows)
+ bits[i,] <- (values[i] > quantiles)
+
+ # Return results.
+ res <- list()
+ res$locus2row <- locmap
+ res$observed <- bits
+ res$expected <- (1.0 - quantiles)
+ return(res)
+}
+#
+# Create a job for Marker set enrichment analysis
+#
+# Input:
+# plan$label unique identifier for the analysis
+# plan$folder output folder for results
+# plan$modfile path to module file
+# columns: MODULE GENE
+# plan$marfile path to marker file
+# columns: MARKER VALUE
+# plan$genfile path to gene file
+# columns: GENE MARKER
+#
+# Optional input:
+# plan$inffile path to module info file
+# columns: MODULE DESCR
+# plan$seed seed for random number generator
+# plan$permtype 'gene' for gene-level, 'marker' for marker-level
+# plan$nperm maximum number of random permutations
+# plan$mingenes minimum number of genes per module (after merging)
+# plan$maxgenes maximum number of genes per module
+# plan$quantiles cutoffs for test statistic
+# plan$maxoverlap maximum overlap allowed between genes
+#
+# Output:
+# job$modules module identities as characters
+# job$genes gene identities as characters
+# job$loci marker identities as characters
+# job$moddata preprocessed module data (indexed identities)
+# job$modinfo preprocessed module info (indexed identities)
+# job$gendata preprocessed mapping data (indexed identities)
+# job$locdata preprocessed marker data (indexed identities)
+# job$geneclusters genes with shared markers
+#
+# Output also includes the items from input list.
+#
+# Written by Ville-Petteri Makinen 2013, Modified by Le Shu 2015
+#
+ssea.start <- function(plan) {
+ cat("\nMSEA Version:01.04.2016\n")
+ # Check parameters.
+ job <- ssea.start.configure(plan)
+
+ # Create output folder.
+ dir.create(path=job$folder, recursive=FALSE, showWarnings=FALSE)
+ if(file.access(job$folder, 2) != 0)
+ stop("Cannot access '" + job$folder + "'.")
+
+ # Import gene sets.
+ cat("\nImporting modules...\n")
+ modinfo <- tool.read(plan$inffile, c("MODULE", "DESCR"))
+ moddata <- tool.read(plan$modfile, c("MODULE", "GENE"))
+ moddata <- unique(na.omit(moddata))
+ modinfo <- unique(na.omit(modinfo))
+ if(nrow(modinfo) > 0) print(summary(modinfo))
+ print(summary(moddata))
+
+ # Add slots for control modules.
+ modules <- unique(moddata$MODULE)
+ if((sum(modules == "_ctrlA") > 0) | (sum(modules == "_ctrlA") > 0))
+ stop("Module names '_ctrlA' and '_ctrlB' are reserved.")
+ modules <- c(modules, "_ctrlA", "_ctrlB")
+ tmp <- data.frame(MODULE=c("_ctrlA", "_ctrlB"),
+ DESCR=c("Top genes", "Top genes (module members)"))
+ modinfo <- rbind(modinfo, tmp)
+
+ # Import marker values.
+ cat("\nImporting marker values...\n")
+ locdata <- tool.read(job$locfile, c("LOCUS", "VALUE"))
+ locdata$VALUE <- as.double(locdata$VALUE)
+ rows <- which(0*(locdata$VALUE) == 0)
+ locdata <- unique(na.omit(locdata[rows,]))
+ locdata_ex <- locdata
+ names(locdata_ex) <- c("MARKER","VALUE")
+ print(summary(locdata_ex))
+
+ # Import mapping data.
+ cat("\nImporting mapping data...\n")
+ gendata <- tool.read(job$genfile, c("GENE", "LOCUS"))
+ gendata <- unique(na.omit(gendata))
+ gendata_ex <- gendata
+ names(gendata_ex) <- c("GENE","MARKER")
+ print(summary(gendata_ex))
+
+ # Remove genes with no marker values.
+ pos <- match(gendata$LOCUS, locdata$LOCUS)
+ gendata <- gendata[which(pos > 0),]
+
+ # Merge overlapping genes.
+ cat("\nMerging genes containing shared markers...\n")
+ gendata <- tool.coalesce(items=gendata$LOCUS, groups=gendata$GENE,
+ rcutoff=job$maxoverlap)
+ job$geneclusters <- gendata[,c("CLUSTER","GROUPS")]
+ job$geneclusters <- unique(job$geneclusters)
+
+ # Update gene symbols.
+ moddata <- ssea.start.relabel(moddata, gendata)
+ gendata <- unique(gendata[,c("GROUPS", "ITEM")])
+ names(gendata) <- c("GENE", "LOCUS")
+
+ # Collect identities.
+ job$modules <- modules
+ job$loci <- intersect(gendata$LOCUS, locdata$LOCUS)
+ pos <- match(gendata$LOCUS, job$loci)
+ job$genes <- gendata[which(pos > 0), "GENE"]
+ job$genes <- unique(job$genes)
+
+ # Exclude missing data and factorize identities.
+ job$modinfo <- ssea.start.identify(modinfo, "MODULE", job$modules)
+ job$moddata <- ssea.start.identify(moddata, "MODULE", job$modules)
+ job$moddata <- ssea.start.identify(job$moddata, "GENE", job$genes)
+ job$gendata <- ssea.start.identify(gendata, "GENE", job$genes)
+ job$gendata <- ssea.start.identify(job$gendata, "LOCUS", job$loci)
+ job$locdata <- ssea.start.identify(locdata, "LOCUS", job$loci)
+
+ # Show job size.
+ nmem <- (object.size(job))*(0.5^20)
+ cat("Job: ", nmem, " Mb\n", sep="")
+
+ # Clean-up.
+ remove(modinfo)
+ remove(moddata)
+ remove(gendata)
+ remove(locdata)
+ gc(FALSE)
+ return(job)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.start.configure <- function(plan) {
+ #bypass if running Meta-MSEA
+ if (!is.null(plan$folder) & !is.null(plan$label) &
+ plan$marfile == "undefined" & plan$genfile == "undefined" &
+ plan$modfile == "undefined"){
+ plan$permtype <- "gene"
+ plan$nperm <- 20000
+ plan$seed <- 1
+ plan$mingenes <- 10
+ plan$maxgenes <- 500
+ plan$maxoverlap <- 0.33
+ cat("\nRunning Meta-MSEA...\n")
+ }else{
+ cat("\nParameters:\n")
+ plan$stamp <- Sys.time()
+ if(is.null(plan$folder)) stop("No output folder.")
+ if(is.null(plan$label)) stop("No job label.")
+ if(is.null(plan$modfile)) stop("No module file.")
+ if(is.null(plan$genfile)) stop("No gene file.")
+ if(is.null(plan$marfile)) stop("No marker file.")
+
+
+ if(is.null(plan$permtype)) plan$permtype <- "gene"
+ cat(" Permutation type: ", plan$permtype, "\n", sep="")
+
+ if(is.null(plan$nperm)) plan$nperm <- 20000
+ cat(" Permutations: ", plan$nperm, "\n", sep="")
+
+ if(is.null(plan$seed)) plan$seed <- 1
+ cat(" Random seed: ", plan$seed, "\n", sep="")
+
+ if(is.null(plan$mingenes)) plan$mingenes <- 10
+ if(is.null(plan$maxgenes)) plan$maxgenes <- 500
+ cat(" Minimum gene count: ", plan$mingenes, "\n", sep="")
+ cat(" Maximum gene count: ", plan$maxgenes, "\n", sep="")
+
+ if(is.null(plan$maxoverlap)) plan$maxoverlap <- 0.33
+ #if(plan$permtype == "locus") plan$maxoverlap <- 1.0 # no effect
+ cat(" Maximum overlap between genes: ", plan$maxoverlap, "\n",
+ sep="")
+
+ if(is.null(plan$quantiles) == FALSE) {
+ cat(" Test quantiles:");
+ for(q in plan$quantiles)
+ cat(sprintf(" %.2f", 100*q), "%", sep="")
+ cat("\n")
+ }
+
+ #Resolve inconsistencies
+ if(plan$permtype == "marker") plan$permtype = "locus"
+ dir.create("tmp", showWarnings = FALSE)
+ tmploci=tool.read(plan$marfile,c("MARKER","VALUE"))
+ tmpgene=tool.read(plan$genfile,c("GENE","MARKER"))
+ names(tmploci)=c("LOCUS","VALUE")
+ names(tmpgene)=c("GENE","LOCUS")
+ write.table(tmploci,"tmp/loci.txt",quote = FALSE,row.names = FALSE,
+ sep = "\t")
+ write.table(tmpgene,"tmp/gene.txt",quote = FALSE,row.names = FALSE,
+ sep = "\t")
+ plan$locfile = "tmp/loci.txt"
+ plan$genfile = "tmp/gene.txt"
+ }
+
+ return(plan)
+}
+
+#----------------------------------------------------------------------------
+
+ssea.start.relabel <- function(dat, grp) {
+
+ # New gene group symbols.
+ oldgenes <- character()
+ newgenes <- character()
+ syms <- unique(grp[,c("CLUSTER","GROUPS")])
+ rows <- which(syms$CLUSTER != syms$GROUPS)
+ for(i in rows) {
+ g <- syms[i,"GROUPS"]
+ a <- strsplit(g, ",", fixed=TRUE)
+ a <- a[[1]]
+ b <- rep(g, length(a))
+ oldgenes <- c(oldgenes, a)
+ newgenes <- c(newgenes, b)
+ }
+
+ # Update dataset.
+ if(length(newgenes) < 1) return(dat)
+ pos <- match(dat$GENE, oldgenes)
+ rows <- which(pos > 0)
+ dat[rows,"GENE"] <- newgenes[pos[rows]]
+ return(unique(dat))
+}
+
+#----------------------------------------------------------------------------
+
+ssea.start.identify <- function(dat, varname, labels) {
+ if(nrow(dat) < 1) return(dat)
+
+ # Find matching identities.
+ pos <- match(dat[,varname], labels)
+ rows <- which(pos > 0)
+
+ # Select subset.
+ dat[,varname] <- pos
+ res <- dat[rows,]
+ return(res)
+}
+
+#
+# Sort an array and find the indices of blocks with the same values.
+# The second argument sets the minimum block size to be included.
+#
+# Output list:
+# res$labels shared values within blocks
+# res$lengths numbers of entries in blocks
+# res$blocks integer arrays of entry positions within blocks
+# res$ranks entry positions included in blocks
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.aggregate <- function(entries, limit=1) {
+ res <- list()
+ res$blocks <- list()
+
+ # Check input size.
+ nelem <- length(entries)
+ if(nelem < 1) stop("Unusable input.")
+
+ # Factorize entries.
+ entries <- as.factor(entries)
+
+ # Convert factors to integers.
+ elevels <- levels(entries)
+ entries <- as.integer(entries)
+
+ # Sort entries.
+ mask <- order(entries)
+
+ # Remove missing entries.
+ rows <- which(entries > 0)
+ mask <- intersect(mask, rows)
+ if(length(mask) < 1) stop("Unusable input.")
+
+ # Single entry.
+ if(length(mask) < 2) {
+ label <- na.omit(elevels)
+ res$labels <- label
+ res$lengths <- 1
+ res$blocks[[1]] <- 1
+ res$ranks <- 1
+ return(res)
+ }
+
+ # Starting point.
+ nstack <- 1
+ stack <- integer(length=nelem)
+ stack[nstack] <- mask[1]
+ prev <- entries[mask[1]]
+ mask <- mask[2:nelem]
+
+ # Find segments of identical entries.
+ buffer <- list()
+ subsets <- list()
+ for(i in mask) {
+ if(entries[i] != prev) {
+
+ # Clear stack.
+ if(nstack >= limit) {
+ ind <- (length(buffer) + 1)
+ buffer[[ind]] <- stack[1:nstack]
+ }
+ nstack <- 0
+
+ # Buffering for speed-up.
+ if(length(buffer) > 120) {
+ subsets <- c(subsets, buffer)
+ buffer <- list()
+ }
+ }
+
+ # Add item to stack.
+ nstack <- (nstack + 1)
+ stack[nstack] <- i
+ prev <- entries[i]
+ }
+
+ # Clear last item(s).
+ if(nstack >= limit) {
+ ind <- (length(buffer) + 1)
+ buffer[[ind]] <- stack[1:nstack]
+ }
+
+ # Clear buffer.
+ if(length(buffer) > 0)
+ subsets <- c(subsets, buffer)
+
+ # Check if any subsets.
+ nuniq <- length(subsets)
+ if(nuniq < 1) return(NULL)
+
+ # Determine additional attributes.
+ loci <- rep(NA, nelem)
+ sizes <- integer(nuniq)
+ identities <- integer(nuniq)
+ for(k in 1:nuniq) {
+ mask <- as.integer(subsets[[k]])
+ identities[k] <- entries[mask[1]]
+ sizes[k] <- length(mask)
+ loci[mask] <- mask
+ }
+
+ # Finish.
+ res$labels <- elevels[identities]
+ res$lengths <- sizes
+ res$blocks <- subsets
+ res$ranks <- na.omit(loci)
+ return(res)
+}
+#
+# Use hierarchical clustering to assign nodes into clusters.
+#
+# Input:
+# edges data.frame:
+# A item name
+# B item name
+# POSa item name rank
+# POSb item name rank
+# R overlap between A and B
+# cutoff maximum overlap not considered clustered
+#
+# Output:
+# res data frame
+# CLUSTER cluster rank
+# NODE item name
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.cluster <- function(edges, cutoff=NULL) {
+
+ # Default output.
+ a <- edges$A
+ b <- edges$B
+ labels <- unique(c(a, b))
+ res <- data.frame(CLUSTER=labels, stringsAsFactors=FALSE)
+ res$NODE <- labels
+
+ # Check if clustering is needed.
+ r <- as.double(edges$R)
+ posA <- as.integer(edges$POSa)
+ posB <- as.integer(edges$POSb)
+ ndim <- max(c(posA, posB))
+ if(sum(posA != posB) < 1) return(res)
+ if(max(r) <= 0.0) return(res)
+
+ # Allocate distance matrix.
+ mtx <- matrix(data=0.0, nrow=ndim, ncol=ndim)
+ labels <- rep(NA, ndim)
+
+ # Collect group labels.
+ for(k in 1:nrow(edges)) {
+ labels[posA[k]] <- a[k]
+ labels[posB[k]] <- b[k]
+ }
+
+ # Recreate matrix form.
+ for(k in 1:nrow(edges)) {
+ i <- posA[k]
+ j <- posB[k]
+ mtx[i,j] <- r[k]
+ mtx[j,i] <- r[k]
+ }
+
+ # Hierarchical clustering.
+ d <- as.dist(1 - mtx)
+ tree <- hclust(d)
+
+ # Height cutoff.
+ hlim <- max(tree$height)
+ if(is.null(cutoff) == FALSE) hlim <- (1.0 - cutoff)
+
+ # Find clusters.
+ clusters <- tool.cluster.static(tree, hlim)
+
+ # Enumerate clusters with singletons included.
+ mask <- which(clusters == 0)
+ clusters[mask] <- -mask
+ clusters <- as.factor(clusters)
+ clusters <- as.integer(clusters)
+
+ # Create supergroups.
+ res <- data.frame(CLUSTER=clusters, stringsAsFactors=FALSE)
+ res$NODE <- labels[1:length(clusters)]
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.cluster.static <- function(dendro, hlim) {
+ merged <- dendro$merge
+ heights <- dendro$height
+ ndim <- (length(heights) + 1)
+
+ # Assign clusters by static cut.
+ clusters <- rep(0, ndim)
+ for(i in which(heights <= hlim)) {
+ a <- as.integer(merged[i, 1])
+ b <- as.integer(merged[i, 2])
+
+ # Put previous cluster (if any) in A.
+ if(a < 0) {
+ tmp <- a
+ a <- b
+ b <- tmp
+ }
+
+ # De novo merging.
+ if(a < 0) {
+ clusters[-a] <- i
+ clusters[-b] <- i
+ next
+ }
+
+ # Merge with previous cluster.
+ if(b < 0) {
+ mask <- which(clusters == a)
+ mask <- c(mask, -b)
+ clusters[mask] <- i
+ next;
+ }
+
+ # Merge two clusters.
+ mask <- which((clusters == a) | (clusters == b))
+ clusters[mask] <- i
+ }
+ return(clusters)
+}
+# Calculate overlaps between groups of items.
+#
+# Input:
+# items array of item identities
+# groups array of group identities for items
+#
+# Optional input:
+# rcutoff maximum overlap not coalesced
+# ncore minimum number of items required for trimming
+#
+# Output:
+# res data frame:
+# CLUSTER cluster identities
+# ITEM item identities
+# GROUPS comma separated group identities
+#
+# Due to trimming, output may contain fewer distinct items.
+# Cluster identities are a subset of group identities.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.coalesce <- function(items, groups, rcutoff=0.0, ncore=NULL) {
+
+ # Check arguments.
+ if(length(items) != length(groups))
+ stop("Incompatible inputs.")
+
+ # Default output.
+ res <- data.frame(CLUSTER=groups, GROUPS=groups, ITEM=items,
+ stringsAsFactors=FALSE)
+ if(rcutoff >= 1.0) return(res)
+
+ # Check that group names are usable.
+ grlabels <- unique(groups)
+ if(is.character(grlabels)) {
+ for(s in grlabels) {
+ segm <- strsplit(s, ",", fixed=TRUE)
+ if(length(segm[[1]]) < 2) next
+ stop("Group labels must not contain ','.")
+ }
+ }
+
+ # Determine core item set size.
+ nitems <- length(items)
+ if(is.null(ncore)) {
+ ncore <- nitems/length(unique(groups))
+ ncore <- round(ncore)
+ }
+
+ # Convert to integers.
+ itemlev <- as.factor(items)
+ grouplev <- as.factor(groups)
+ members <- as.integer(itemlev)
+ modules <- as.integer(grouplev)
+ itemlev <- levels(itemlev)
+ grouplev <- levels(grouplev)
+
+ # Determine item freguencies.
+ freq <- table(members)
+ labels <- as.integer(names(freq))
+ freq <- as.integer(freq)
+
+ # Limit the number of comparisons.
+ kappa <- 0
+ while(TRUE) {
+ kappa <- (kappa + 1)
+ shared <- which(freq > kappa)
+ shared <- labels[shared]
+
+ # Determine groups with overlaps.
+ pos <- match(members, shared)
+ rows <- which(pos > 0)
+ mods <- unique(modules[rows])
+ if(length(mods) < 2000) break
+ }
+
+ # Show warning if overlaps too extensive.
+ if(kappa > 1) {
+ cat("WARNING! Limited overlap analysis due ")
+ cat("to large number of groups.\n")
+ }
+
+ # Determine subset with shared items.
+ pos <- match(modules, mods)
+ incl <- which(pos > 0)
+ excl <- setdiff((1:nitems), incl)
+
+ # Find and trim clusters.
+ res <- tool.coalesce.exec(members[incl], modules[incl], rcutoff, ncore)
+ res <- rbind(res, tool.coalesce.exec(members[excl], modules[excl], 1.0))
+
+ # Convert identities back to original.
+ res$ITEM <- itemlev[res$ITEM]
+ res$CLUSTER <- grouplev[res$CLUSTER]
+ groupdat <- rep("", nrow(res))
+ groupsets <- as.character(res$GROUPS)
+ for(i in 1:nrow(res)) {
+ gset <- strsplit(groupsets[i], ",", fixed=TRUE)
+ gset <- as.integer(gset[[1]])
+ groupdat[i] <- paste(grouplev[gset], collapse=",")
+ }
+ res$GROUPS <- groupdat
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.coalesce.exec <- function(items, groups, rcutoff, ncore) {
+ if(is.numeric(items) == FALSE) stop("Unusable input.")
+ if(is.numeric(groups) == FALSE) stop("Unusable input.")
+
+ # Default output.
+ res <- data.frame(CLUSTER=groups, GROUPS=groups, ITEM=items,
+ stringsAsFactors=FALSE)
+ if(rcutoff >= 1.0) return(res)
+
+ # Iterative merging and trimming.
+ res$COUNT <- 0.0
+ while(TRUE) {
+ clust <- tool.coalesce.find(res, rcutoff)
+ if(is.null(clust)) break
+ res <- tool.coalesce.merge(clust, ncore)
+ }
+
+ # Select columns.
+ res$COUNT <- NULL
+ res$CLUSTER <- 0
+ itemdat <- res$ITEM
+ clustdat <- res$CLUSTER
+ groupdat <- res$GROUPS
+
+ # Select representative label for clusters.
+ st <- tool.aggregate(res$GROUP)
+ blocks <- st$blocks
+ labels <- st$labels
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ locals <- itemdat[rows]
+ nodes <- strsplit(labels[[k]], ",", fixed=TRUE)
+ nodes <- as.numeric(nodes[[1]])
+ overlaps <- rep(0, length(nodes))
+ for(i in 1:length(nodes)) {
+ mask <- which(groups == nodes[i])
+ shared <- intersect(items[mask], locals)
+ overlaps[i] <- length(shared)
+ }
+ mask <- order(overlaps, decreasing=TRUE)
+ clustdat[rows] <- nodes[mask[1]]
+ groupdat[rows] <- paste(nodes[mask], collapse=",")
+ }
+
+ # Finish results.
+ res$CLUSTER <- clustdat
+ res$GROUPS <- groupdat
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.coalesce.find <- function(data, rmax) {
+
+ # Harmonize column names.
+ data <- data[,c("GROUPS", "ITEM", "COUNT")]
+ names(data) <- c("NODE", "ITEM", "COUNT")
+
+ # Find clusters.
+ edges <- tool.overlap(items=data$ITEM, groups=data$NODE)
+ clustdat <- tool.cluster(edges, cutoff=rmax)
+ nclust <- length(unique(clustdat$CLUSTER))
+ nnodes <- length(unique(clustdat$NODE))
+ if(nclust >= nnodes) return(NULL)
+
+ # Merge with original dataset.
+ res <- merge(clustdat, data)
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.coalesce.merge <- function(data, ncore) {
+
+ # Determine item clusters.
+ st <- tool.aggregate(data$CLUSTER)
+ blocks <- st$blocks
+
+ # Trim clusters.
+ res <- data.frame()
+ for(k in 1:length(blocks)) {
+ rows <- blocks[[k]]
+ batch <- data[rows,]
+
+ # Item hit counts.
+ st <- tool.aggregate(batch$ITEM)
+ counts <- as.integer(st$lengths)
+ labels <- as.integer(st$labels)
+ segm <- st$blocks
+
+ # Add hit counts from previous round.
+ for(j in 1:length(segm)) {
+ mask <- segm[[j]]
+ nj <- mean(batch[mask,"COUNT"])
+ counts[j] <- sqrt(counts[j] + nj)
+ }
+
+ # Remove rarest items.
+ levels <- sort(unique(counts))
+ for(kappa in levels) {
+ mask <- which(counts > kappa)
+ nmask <- length(mask)
+ if(nmask < ncore) break
+ counts <- counts[mask]
+ labels <- labels[mask]
+ }
+
+ # Collect groups.
+ nodeset <- unique(batch$NODE)
+ nodeset <- paste(nodeset, collapse=",")
+
+ # Update results.
+ tmp <- data.frame(GROUPS=nodeset, ITEM=labels, COUNT=counts,
+ stringsAsFactors=FALSE)
+ res <- rbind(res, tmp)
+ }
+ return(res)
+}
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.fdr <- function(p, f=NULL) {
+ if(length(p) < 5) return(NA*p)
+ if(is.null(f)) return(tool.fdr.bh(p))
+ return(tool.fdr.empirical(p, f))
+}
+
+#---------------------------------------------------------------------------
+
+tool.fdr.bh <- function(p) {
+ nelem <- length(p)
+
+ # Revert to z-scores.
+ pvals <- pmax(p, .Machine$double.xmin)
+ pvals <- pmin(pvals, (1 - .Machine$double.eps))
+ z <- qnorm(pvals)
+
+ # Benjamini Hochberg (1995) false discovery rate.
+ fdr <- p.adjust(pvals, method="fdr")
+ fdr <- pmax(fdr, .Machine$double.xmin)
+
+ # Sort data points.
+ mask <- order(z)
+ xcoord <- z[mask]
+ ycoord <- fdr[mask]
+
+ # Remove steps in FDR values.
+ prev <- 1
+ for(i in 2:nelem) {
+ if(ycoord[i] == ycoord[prev]) {
+ ycoord[i] = NA
+ next
+ }
+ window <- prev:(i-1)
+ xcoord[prev] <- mean(xcoord[window])
+ prev <- i
+ }
+
+ # Select distinct points.
+ rows <- which(0*ycoord == 0)
+ xcoord <- xcoord[rows]
+ ycoord <- ycoord[rows]
+
+ # Add sentinels.
+ xcoord <- c((min(z) - 1.0), xcoord, (max(z) + 1.0))
+ ycoord <- c(0.0, ycoord, 1.0)
+
+ # Interpolate missing points.
+ points <- approx(xcoord, ycoord, xout=z)
+ return(points$y)
+}
+
+#---------------------------------------------------------------------------
+
+tool.fdr.empirical <- function(p, f0) {
+
+ # Estimate raw false discovery rates.
+ f <- rank(p)/length(p)
+ fdr <- pmin(pmax(f0/f, p), 1)
+
+ # Pre-defined sections for sampling the FDR curve.
+ p <- pmin(p, (1.0 - .Machine$double.eps))
+ p <- pmax(p, .Machine$double.xmin)
+ z <- qnorm(p, lower.tail=TRUE)
+ alpha <- sort(unique(floor(z)))
+
+ # Estimate pivot points.
+ xcoord <- (min(z) - 1.0)
+ ycoord <- 0.0
+ for(a in alpha) {
+ o <- (a + 1.0)
+ elem <- which((z >= a) & (z < o))
+ xcoord <- c(xcoord, mean(z[elem]))
+ ycoord <- c(ycoord, mean(fdr[elem]))
+ }
+
+ # Interpolate back to the original resolution.
+ points <- approx(xcoord, ycoord, xout=z)
+ return(points$y)
+}
+#
+# Convert an edge dataset into indexed graph representation.
+# The input is a data frame with three columns TAIL, HEAD and WEIGHT
+# for the edge end-points.
+#
+# Return value:
+# res$nodes - N-element array of node names
+# res$tails - K-element array of node indices
+# res$heads - K-element array of node indices
+# res$weights - K-element array of edge weights
+# res$tail2edge - N-element list of adjacent edge indices
+# res$head2edge - N-element list of adjacent edge indices
+# res$outstats - N-row data frame of node statistics
+# res$instats - N-row data frame of node statistics
+# res$stats - N-row data frame of node statistics
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.graph <- function(edges) {
+ tails <- as.character(edges$TAIL)
+ heads <- as.character(edges$HEAD)
+ wdata <- as.double(edges$WEIGHT)
+
+ # Remove empty end-points and non-positive weights.
+ mask <- which((tails != "") & (heads != "") &
+ (tails != heads) & (wdata > 0))
+ tails <- tails[mask]
+ heads <- heads[mask]
+ wdata <- wdata[mask]
+ nedges <- length(mask)
+
+ # Create factorized representation.
+ labels <- as.character(c(tails, heads))
+ labels <- as.factor(labels)
+ labelsT <- as.integer(labels[1:nedges])
+ labelsH <- as.integer(labels[(nedges+1):(2*nedges)])
+
+ # Create edge lists.
+ nodnames <- levels(labels)
+ nnodes <- length(nodnames)
+ elistT <- tool.graph.list(labelsT, nnodes)
+ elistH <- tool.graph.list(labelsH, nnodes)
+
+ # Collect results.
+ res <- list()
+ res$nodes <- as.character(nodnames)
+ res$outstats <- tool.graph.degree(elistT, wdata)
+ res$instats <- tool.graph.degree(elistH, wdata)
+ res$stats <- (res$outstats + res$instats)
+ res$tail2edge <- elistT
+ res$head2edge <- elistH
+ res$tails <- as.integer(labelsT)
+ res$heads <- as.integer(labelsH)
+ res$weights <- wdata
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.graph.list <- function(entries, nnodes) {
+
+ # Allocate list.
+ groups <- list()
+ for(i in 1:nnodes)
+ groups[[i]] <- integer()
+
+ # Find entry groups.
+ st <- tool.aggregate(entries)
+ labels <- as.integer(st$labels)
+ blocks <- st$blocks
+
+ # Reorganize according to node indices.
+ nblocks <- length(blocks)
+ for(k in 1:nblocks) {
+ ind <- labels[[k]]
+ groups[[ind]] <- blocks[[k]]
+ }
+
+ # Return edge lists.
+ return(groups)
+}
+
+#---------------------------------------------------------------------------
+
+tool.graph.degree <- function(node2edge, weights) {
+ nnodes <- length(node2edge)
+ stren <- rep(0.0, nnodes)
+ degrees <- rep(0, nnodes)
+ for(i in 1:nnodes) {
+ rows <- node2edge[[i]]
+ degrees[i] <- length(rows)
+ if(degrees[i] < 1) next
+ stren[i] <- sum(weights[rows])
+ }
+ res <- data.frame(DEGREE=degrees, STRENG=stren)
+ return(res)
+}
+#
+#
+#
+tool.metap <- function(datasets, idcolumn, pcolumn, weights=NULL) {
+
+ # Collect identities.
+ id <- character()
+ nsets <- length(datasets)
+ for(i in 1:nsets) {
+ dat <- datasets[[i]]
+ id <- as.character(dat[,idcolumn])
+ }
+
+ # Remove duplicates.
+ id <- unique(id[which(id != "")])
+
+ # Check weights.
+ if(is.null(weights)) weights <- rep(1, nsets)
+ if(length(weights) != nsets) stop("Incompatible weights.")
+ if(sum(weights <= 0.0) > 0) stop("Non-positive weights.")
+ weights <- nsets*(weights/sum(weights))
+
+ # Calculate meta scores.
+ h <- rep(0, length(id))
+ z <- rep(0.0, length(id))
+ for(i in 1:nsets) {
+ dat <- datasets[[i]]
+ pos <- match(dat[,idcolumn], id)
+ rows <- which(pos > 0)
+ pos <- pos[rows]
+ p <- as.double(dat[rows,pcolumn])
+ p <- pmax(p, .Machine$double.xmin)
+ p <- pmin(p, (1 - .Machine$double.eps))
+ z[pos] <- (z[pos] + weights[i]*qnorm(p))
+ h[pos] <- (h[pos] + (weights[i])^2)
+ }
+
+ # Estimate meta P-values.
+ rows <- which(h > 0.0)
+ z[rows] <- z[rows]/sqrt(h[rows])
+ pmeta <- pnorm(z)
+
+ # Return results.
+ res <- data.frame(ID=id, P=pmeta)
+ names(res) <- c(idcolumn, pcolumn)
+ res <- res[order(res$P),]
+ return(res)
+}
+#
+# One-sided.
+#
+tool.normalize <- function(x, prm=NULL, inverse=FALSE) {
+
+ # Apply transform.
+ if(is.null(prm) == FALSE) {
+ if(inverse == TRUE) {
+ if(is.na(prm$mu)) {
+ y <- (x*(prm$scale) + prm$offset)
+ return(y)
+ }
+ z <- (x*(prm$sigma) + prm$mu)
+ z <- (exp(z) - 1.0)/(prm$scale)
+ z <- (z + prm$offset)
+ return(z)
+ } else {
+ z <- (x - prm$offset)*(prm$scale)
+ if(is.na(prm$mu)) return(z)
+ mask <- which(z < 0.0)
+ z[mask] <- z[mask]/(1.0 - z[mask])
+ z <- (log(z + 1.0) - prm$mu)/(prm$sigma)
+ }
+ return(z)
+ }
+
+ # Remove unusable values.
+ x <- x[which(0*x == 0)]
+
+ # Default parameters.
+ prm$offset <- mean(x)
+ prm$scale <- sd(x)
+ prm$mu <- NA
+ prm$sigma <- NA
+ prm$quality <- 0.0
+ if(length(x) < 10) return(prm)
+
+ # Disable warnings.
+ prev <- getOption("warn")
+ options(warn=-1)
+
+ # Ensure positivity and check size.
+ xmin <- min(x)
+ z <- (x[which(x > xmin)] - xmin)
+ if(length(z) < 10) return(prm)
+
+ # Scale by median.
+ zmed <- median(z)
+ z <- z/zmed
+
+ # Find the best log transform.
+ ctrl <- list(reltol=1e-3)
+ gamma <- optim(par=1.0, fn=tool.normalize.quality, gr=NULL, z,
+ lower=-9, upper=9, control=ctrl)
+
+ # Apply transform.
+ z <- log(exp(gamma$par)*z + 1.0)
+
+ # Evaluate fit quality.
+ mu <- mean(z)
+ sigma <- sd(z)
+ z <- (z - mu)/sigma
+ kappa <- ks.test(x=z, y="pnorm", exact=FALSE)
+
+ # Enable warnings.
+ options(warn=prev)
+
+ # Collect transformation parameters.
+ prm$offset <- xmin
+ prm$scale <- exp(gamma$par)/zmed
+ prm$quality <- kappa$p.value
+ prm$mu <- mu
+ prm$sigma <- sigma
+ return(prm)
+}
+
+#----------------------------------------------------------------------------
+
+tool.normalize.quality <- function(g, z) {
+ t <- log(exp(g)*z + 1.0)
+ t <- (t - mean(t))/(sd(t) + 1e-20)
+ if(length(t) < 1) return(0)
+ suppressWarnings(res <- ks.test(x=t, y="pnorm", exact=FALSE))
+ return(res$statistic)
+}
+#
+# Calculate overlaps between groups of items.
+#
+# Input:
+# items array of item identities
+# groups array of group identities for items
+#
+# Optional input:
+# nbackground total number of items
+#
+# Output:
+# res data frame:
+# A group name
+# B group name
+# POSa group name rank
+# POSb group name rank
+# Na group A size
+# Nb group B size
+# Nab shared items
+# R overlap ratio
+# F fold change to null expectation
+# P overlap P-value (Fisher's test)
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.overlap <- function(items, groups, nbackground=NULL) {
+
+ # Check arguments.
+ if(length(items) != length(groups))
+ stop("Incompatible inputs.")
+
+ # Remove duplicate entries.
+ data <- data.frame(ITEM=items, GROUP=groups, stringAsFactors=FALSE)
+ data <- unique(data)
+
+ # Convert to integers.
+ items <- as.integer(as.factor(data$ITEM))
+ groups <- as.factor(data$GROUP)
+ grouplevs <- levels(groups)
+ groups <- as.integer(groups)
+
+ # Determine block structure.
+ modules <- tool.aggregate(groups)
+ labels <- modules$labels
+ blocks <- modules$blocks
+
+ # Convert labels to original identities.
+ labels <- as.integer(labels)
+ labels <- grouplevs[labels]
+
+ # Default background size.
+ nitems <- length(unique(items))
+ if(is.null(nbackground)) nbackground <- nitems
+ if(nbackground < nitems)
+ stop("tool.overlap: Invalid background size.")
+
+ # Number of shared items for each pair of blocks.
+ row <- 1
+ stamp <- Sys.time()
+ nblocks <- length(blocks)
+ nrows <- (nblocks + 1)*nblocks/2
+ mtx <- matrix(nrow=nrows, ncol=5)
+ for(a in 1:nblocks) {
+ maskA <- blocks[[a]]
+ setA <- items[maskA]
+ nA <- length(maskA)
+ for(b in a:nblocks) {
+ maskB <- blocks[[b]]
+ setB <- items[maskB]
+ nB <- length(setB)
+ ind <- intersect(setA, setB)
+ nAB <- length(ind)
+ mtx[row,] <- c(a, b, nA, nB, nAB)
+ row <- (row + 1)
+
+ # Progress report.
+ if(as.double(Sys.time() - stamp) < 10.0) next
+ cat(sprintf("\r%d/%d ", row, nrows))
+ stamp <- Sys.time()
+ }
+ }
+ cat(sprintf("\r%d comparisons\n", nrows))
+
+ # Calculate fold enrichment.
+ numA <- mtx[,3]
+ numB <- mtx[,4]
+ numAB <- mtx[,5]
+ fAB <- (sqrt(numAB)*sqrt(nbackground)/sqrt(numA)/sqrt(numB))^2
+
+ # Estimate statistical significance.
+ pAB <- phyper(numAB, numB, (nbackground - numB), numA, lower.tail=FALSE)
+ pAB <- (pAB + .Machine$double.xmin)
+
+ # Fix diagonal values.
+ mask <- which(mtx[,1] == mtx[,2])
+ fAB[mask] <- 1.0
+ pAB[mask] <- 1.0
+
+ # Fix P-values for small fold changes.
+ mask <- which(fAB < 1.0)
+ pAB[mask] <- 1.0
+
+ # Collect results.
+ res <- data.frame(A=labels[mtx[,1]], B=labels[mtx[,2]],
+ POSa=as.integer(mtx[,1]), POSb=as.integer(mtx[,2]),
+ Na=numA, Nb=numB, Nab=numAB,
+ R=numAB/(numA + numB - numAB),
+ F=fAB, P=pAB, stringsAsFactors=FALSE)
+ return(res)
+}
+#
+# Read a data frame from a file. All lines with NAs are excluded.
+# The second argument selects a subset of columns.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.read <- function(file, vars=NULL) {
+ if(is.null(file)) return(data.frame())
+ if(file == "") return(data.frame())
+ dat <- read.delim(file=file, header=TRUE,
+ na.strings=c("NA", "NULL", "null", ""),
+ colClasses="character", comment.char="",
+ stringsAsFactors=FALSE)
+ if(is.null(vars) == FALSE) dat <- dat[,vars]
+ dat <- na.omit(dat)
+ return(dat)
+}
+#
+# Save a data frame in tab-delimited file. Compression
+# only works on UNIX-family systems with gzip.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.save <- function(frame, file, directory=NULL, verbose=TRUE,
+compression=FALSE) {
+ if(verbose) cat("\rWriting to file... ")
+
+ # Concatenate directory prefix.
+ fname <- file
+ if(is.null(directory) == FALSE) {
+ if(file.exists(directory) == FALSE)
+ dir.create(path=directory, recursive=TRUE)
+ fname <- file.path(directory, file)
+ }
+
+ # Write data to file.
+ write.table(x=frame, sep="\t", file=fname, na="",
+ row.names=FALSE, quote=FALSE)
+
+ # Compress file.
+ if(compression) {
+ if(verbose) cat("\rCompressing file... ")
+ system(paste("gzip -f \"", fname, "\"", sep=""))
+ fname <- paste(fname, ".gz", sep="")
+ }
+
+ # Print report.
+ n <- nrow(frame)
+ if(verbose) cat("\rSaved ", n, " rows in '", fname, "'.\n", sep="")
+ return(fname)
+}
+#
+# Determine the network neighbors for a set of nodes. The first
+# argument is the indexed graph structure from tool.graph().
+# The second argument is the list of seed node names and the third
+# indicates the maximum number of links to connect neighbors.
+# The fourth input sets the directionality: use a negative value
+# for dowstream, positive for upstream or zero for undirected.
+#
+# Output:
+# res$RANK - indices of neighboring nodes (including seeds)
+# res$LEVEL - num of edges away from seed
+# res$STRENG - sum of adjacent edge weights within neighborhood
+# res$DEGREE - number of adjacent edges within neighborhood
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.subgraph <- function(graph, seeds, depth=1, direction=0) {
+ depth <- as.integer(depth[[1]])
+ direction <- as.integer(direction[[1]])
+
+ # Convert seed names to indices.
+ nodes <- graph$nodes
+ ranks <- match(seeds, nodes)
+ ranks <- ranks[which(ranks > 0)]
+ ranks <- as.integer(ranks)
+
+ # Find neighbors.
+ res <- tool.subgraph.search(graph, ranks, depth[1], direction)
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.subgraph.search <- function(graph, seeds, depth, direction) {
+ tails <- graph$tails
+ heads <- graph$heads
+ weights <- graph$weights
+ tail2edge <- list()
+ head2edge <- list()
+
+ # Downstream and upstream searches.
+ if(direction <= 0) tail2edge <- graph$tail2edge
+ if(direction >= 0) head2edge <- graph$head2edge
+
+ # Collect neighboring nodes.
+ visited <- seeds
+ levels <- 0*seeds
+ for(i in 1:depth) {
+
+ # Find edges to adjacent nodes.
+ foundT <- tool.subgraph.find(seeds, tail2edge, heads, visited)
+ foundH <- tool.subgraph.find(seeds, head2edge, tails, visited)
+
+ # Expand neighborhood.
+ seeds <- unique(c(foundT, foundH))
+ visited <- c(visited, seeds)
+ levels <- c(levels, (0*seeds + i))
+ if(length(seeds) < 1) break
+ }
+
+ # Calculate node degrees and strengths.
+ res <- data.frame(RANK=visited, LEVEL=levels, DEGREE=0,
+ STRENG=0.0, stringsAsFactors=FALSE)
+ res <- tool.subgraph.stats(res, tail2edge, heads, weights)
+ res <- tool.subgraph.stats(res, head2edge, tails, weights)
+ return(res)
+}
+
+#---------------------------------------------------------------------------
+
+tool.subgraph.find <- function(seeds, edgemap, heads, visited) {
+ if(length(edgemap) < 1) return(integer())
+
+ # Collect all neighboring nodes.
+ nneigh <- length(seeds)
+ neighbors <- seeds
+ for(i in seeds) {
+
+ # Edges adjacent to the seed.
+ mask <- edgemap[[i]]
+ nmask <- length(mask)
+ if(nmask < 1) next
+
+ # Check capacity.
+ if((length(neighbors) - nneigh) < nmask)
+ neighbors <- c(neighbors, 0*neighbors, 0*mask)
+
+ # Store node indices.
+ neighbors[nneigh+(1:nmask)] <- heads[mask]
+ nneigh <- (nneigh + nmask)
+ }
+
+ # Remove nodes already visited.
+ neighbors <- unique(neighbors[1:nneigh])
+ neighbors <- setdiff(neighbors, visited)
+ return(neighbors)
+}
+
+#---------------------------------------------------------------------------
+
+tool.subgraph.stats <- function(frame, edgemap, heads, weights) {
+ if(length(edgemap) < 1) return(frame)
+ nmemb <- nrow(frame)
+ wcounts <- frame$DEGREE
+ wsums <- frame$STRENG
+ members <- frame$RANK
+ for(i in members) {
+ edges <- edgemap[[i]]
+ pos <- match(heads[edges], members)
+ edges <- edges[which(pos > 0)]
+ if(length(edges) < 1) next
+ rows <- match(heads[edges], members)
+ wcounts[rows] <- (wcounts[rows] + 1)
+ wsums[rows] <- (wsums[rows] + weights[edges])
+ }
+ frame$DEGREE <- wcounts
+ frame$STRENG <- wsums
+ return(frame)
+}
+#
+# Translate words.
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.translate <- function(words, from, to) {
+
+ # Check translation table.
+ if(length(from) != length(to)) stop("Incompatible inputs.")
+ rows <- which((is.na(from) == FALSE) & (is.na(to) == FALSE))
+ from <- as.character(from[rows])
+ to <- as.character(to[rows])
+
+ # Find words that can be translated.
+ pos <- match(words, from)
+ words[which(is.na(pos))] <- NA
+
+ # Translate words.
+ rows <- which(pos > 0)
+ pos <- pos[rows]
+ words[rows] <- to[pos]
+ return(words)
+}
+#
+# Convert a distribution to uniform ranks ]0,1[ with respect
+# to a background distribution (or self if no
+# background available).
+#
+# Written by Ville-Petteri Makinen 2013
+#
+tool.unify <- function(xtrait, xnull=NULL) {
+ if(is.null(xnull)) xnull <- xtrait
+ nt <- length(xtrait)
+ n0 <- length(xnull)
+
+ # Enforce distinct values.
+ xmin <- min(xnull)
+ sigma <- (max(xnull) - xmin)
+ amp <- 0.001*sd(xnull)
+ jitter <- seq(-amp/2, amp/2, length.out=n0)
+ xnull <- (xnull + jitter)
+ xnull <- (xnull - min(xnull))
+ xnull <- xnull/max(xnull)
+ xnull <- (xmin + xnull*sigma)
+
+ # Cumulative reference distribution.
+ f0 <- seq(0, 1, length.out=n0)
+ q0 <- quantile(xnull, f0)
+ q0 <- as.double(q0)
+
+ # Make sure null range contains trait values.
+ xmin <- min(xtrait)
+ xmax <- max(xtrait)
+ if(xmin < q0[1]) q0[1] <- xmin
+ if(xmax > q0[n0]) q0[n0] <- xmax
+
+ # Map observations on null cumulative axis.
+ out <- approx(x=q0, y=f0, xout=xtrait)
+ return(out$y)
+}
diff --git a/R/msea.kda.onestep.R b/R/msea.kda.onestep.R
new file mode 100644
index 0000000..17f9a2b
--- /dev/null
+++ b/R/msea.kda.onestep.R
@@ -0,0 +1,69 @@
+MSEA.KDA.onestep <- function(plan, apply.MSEA=TRUE, apply.KDA=FALSE,
+maxoverlap.genesets=0.33, symbol.transfer.needed=FALSE,
+sym.from=c("HUMAN", "MOUSE"), sym.to=c("HUMAN", "MOUSE")){
+
+ if((!apply.MSEA) & (!apply.KDA)){
+ stop("You need to set either apply.MSEA argument or apply.KDA argument
+ to TRUE.")
+ }
+
+ cat("\tIf you want to perform only MSEA, set apply.MSEA=T and
+ apply.KDA=F.\n")
+ cat("\tIf you want to perform only KDA, set apply.MSEA=F and
+ apply.KDA=T.\n")
+
+ if(apply.MSEA)
+ plan.check <- ssea.start.configure(plan)
+ if(apply.KDA)
+ plan.check <- kda.configure(plan)
+
+ ################ MSEA (Marker set enrichment analysis) ###
+ if(apply.MSEA){
+ plan <- ssea.start(plan)
+ plan <- ssea.prepare(plan)
+ plan <- ssea.control(plan)
+ plan <- ssea.analyze(plan)
+ plan <- ssea.finish(plan)
+ }
+
+ ## if you want to first run MSEA, then run KDA:
+ if((apply.MSEA) & (apply.KDA)){
+ netfile.backup <- plan$netfile
+
+ ## delete control groups if there are any:
+ indA <- which(plan$modules == "_ctrlA")
+ if (length(indA) > 0)
+ indA <- which(plan$results$MODULE == indA)
+ if (length(indA) > 0) plan$results <- plan$results[-indA,]
+ indB <- which(plan$modules == "_ctrlB")
+ if (length(indB) > 0)
+ indB <- which(plan$results$MODULE == indB)
+ if (length(indB) > 0) plan$results <- plan$results[-indB,]
+
+ ######### Create intermediary datasets for KDA ##########
+ if(symbol.transfer.needed){
+ syms <- tool.read(system.file("extdata", "symbols.txt",
+ package="Mergeomics"))
+ syms <- syms[,c(as.character(sym.from), as.character(sym.to))]
+ names(syms) <- c("FROM", "TO")
+ plan <- ssea2kda(plan, symbols=syms, rmax=maxoverlap.genesets)
+ }
+ else
+ plan <- ssea2kda(plan, rmax=maxoverlap.genesets)
+ plan$netfile <- netfile.backup
+ }
+
+ ## if you want to both directly run KDA or right after running the MSEA:
+ if (apply.KDA){
+ ####### wKDA (Weighted key driver analysis) ##########
+ plan <- kda.configure(plan)
+ plan <- kda.start(plan)
+ plan <- kda.prepare(plan)
+ plan <- kda.analyze(plan)
+ plan <- kda.finish(plan)
+ ###### Prepare network files for visualization #########
+ ## Creates the input files for Cytoscape (http://www.cytoscape.org/)
+ plan <- kda2cytoscape(plan)
+ }
+ return(plan)
+}
\ No newline at end of file
diff --git a/build/vignette.rds b/build/vignette.rds
new file mode 100644
index 0000000..3652ad0
Binary files /dev/null and b/build/vignette.rds differ
diff --git a/data/job_kda_analyze.RData b/data/job_kda_analyze.RData
new file mode 100644
index 0000000..f587595
Binary files /dev/null and b/data/job_kda_analyze.RData differ
diff --git a/inst/NEWS b/inst/NEWS
new file mode 100644
index 0000000..554d745
--- /dev/null
+++ b/inst/NEWS
@@ -0,0 +1,2 @@
+0.0-1
+ o first introduction as separate package
diff --git a/inst/doc/Mergeomics.R b/inst/doc/Mergeomics.R
new file mode 100644
index 0000000..09965dd
--- /dev/null
+++ b/inst/doc/Mergeomics.R
@@ -0,0 +1,274 @@
+### R code from vignette source 'Mergeomics.Rnw'
+
+###################################################
+### code chunk number 1: Mergeomics.Rnw:80-85 (eval = FALSE)
+###################################################
+## install.packages("Mergeomics.tar.gz", repos = NULL,
+## type="source")
+## ## or from bioconductor3.3 release, use following lines:
+## source("https://bioconductor.org/biocLite.R")
+## biocLite("Mergeomics")
+
+
+###################################################
+### code chunk number 2: Mergeomics.Rnw:197-233 (eval = FALSE)
+###################################################
+## ###########################################################
+## ####### One-step analysis for Mergeomics - 1st way ##
+## ###########################################################
+## ## Import library scripts.
+## library(Mergeomics)
+##
+## ## first, give the module info file, genefile, marker file, and network file
+## ## paths for the pipeline
+## plan <- list()
+## plan$label <- "hdlc"
+## plan$folder <- "Results"
+## plan$genfile <- system.file("extdata",
+## "genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## plan$marfile <- system.file("extdata",
+## "marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## plan$modfile <- system.file("extdata",
+## "modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+## plan$inffile <- system.file("extdata",
+## "coexpr.info.txt", package="Mergeomics")
+## plan$netfile <- system.file("extdata",
+## "network.mouseliver.mouse.txt", package="Mergeomics")
+##
+## ## second, define pipeline parameters (e.g. permutation #, seed for random #
+## ## generator, min and max module sizes, max overlapping ratio, etc.)
+## plan$permtype <- "gene" ## default setting is gene permutation
+## plan$nperm <- 100 ## default value is 20000
+## plan$mingenes <- 10 ## default value is 10
+## plan$maxgenes <- 500 ## default value is 500
+##
+## ## then, call the one-step pipeline function including both MSEA and KDA steps
+## plan <- MSEA.KDA.onestep(plan, apply.MSEA=TRUE, apply.KDA=TRUE,
+## maxoverlap.genesets=0.20, symbol.transfer.needed=TRUE,
+## sym.from=c("HUMAN", "MOUSE"), sym.to=c("HUMAN", "MOUSE"))
+## ## NOTE: default value of maxoverlap.genesets=0.33; but in all the examples of
+## ## this tutorial it is 0.2 (see job.msea$rmax<- 0.2 in the following example)
+## ## maxoverlap.genesets=0.33 (or job.msea$rmax<- 0.33) is recommended to use.
+
+
+###################################################
+### code chunk number 3: Mergeomics.Rnw:240-286
+###################################################
+###########################################################
+####### One-step analysis for Mergeomics - 2nd way ##
+###########################################################
+## Import library scripts.
+library(Mergeomics)
+################ MSEA (Marker set enrichment analysis) ###
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+######### Create intermediary datasets for KDA ###########
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+
+## default and recommended rmax=0.33.
+## min.module.count is the number of the pathways to be taken from the MSEA
+## results to merge. If it is not specified (NULL), all the pathways having
+## MSEA-FDR value less than 0.25 will be considered for merging if they are
+## overlapping with the given ratio rmax.
+job.kda <- ssea2kda(job.msea, rmax=0.2, symbols=syms, min.module.count=NULL)
+####### wKDA (Weighted key driver analysis) ##########
+job.kda$netfile <- system.file("extdata",
+"network.mouseliver.mouse.txt", package="Mergeomics")
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+###### Prepare network files for visualization #########
+## Creates the input files for Cytoscape (http://www.cytoscape.org/)
+job.kda <- kda2cytoscape(job.kda)
+
+
+###################################################
+### code chunk number 4: Mergeomics.Rnw:297-346 (eval = FALSE)
+###################################################
+## ###########################################################
+## ## Import Mergeomics library.
+## library("Mergeomics")
+## ## create an empty list for setting parameters
+## job.msea <- list()
+## ## Next, label your project
+## job.msea$label <- "HDLC"
+## ## The pathway size varies from 1 to a few thousands and will
+## ## introduce bias to the analysis. We set criteria for the
+## ## min. (mingenes) and max. (maxgenes) gene size for the pathways.
+## job.msea$maxgenes <- 500
+## job.msea$mingenes <- 10
+## ## The permutation setting and number of permutations. We recommend
+## ## using gene permutation due to its robust performance.
+## ## The alternative is locus permutation.
+## job.msea$permtype <- "gene"
+## job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+## ## set the output folder
+## job.msea$folder <- "./Results"
+## ## The parameter genfile defines the Marker-to-Gene mapping file
+## ## It contains two columns, GENE and MARKER, delimited by tab
+## job.msea$genfile <- system.file("extdata",
+## "genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## ## The parameter marfile defines the Disease association data file
+## ## It contains two columns, MARKER and VALUE, delimited by tab
+## ## Here, the marfile comes from the GWAS file after marker
+## ## dependency pruning, so the VALUE is the minus log10 transformed
+## job.msea$marfile <- system.file("extdata",
+## "marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## ## The modfile defines the pathway information, which could come
+## ## from knowledge-based databases (such as KEGG, and Reactome)
+## ## or data-driven data sets (such as co-expression modules).
+## ## It contains two columns, MOUDLE and GENE, delimited by tab
+## job.msea$modfile <- system.file("extdata",
+## "modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+## ## The inffile provides the basic descriptions for the pathways
+## ## It contains three columns, MODULE, SOURCE, and DESCR, which
+## ## provide information for pathway IDs corresponding to the
+## ## pathway names in modfile, the sources of the pathways, and
+## ## pathway annotations
+## job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+## package="Mergeomics")
+## ## Then, MSEA will run for ~30 minutes to ~2 hours
+## job.msea <- ssea.start(job.msea)
+## job.msea <- ssea.prepare(job.msea)
+## job.msea <- ssea.control(job.msea)
+## job.msea <- ssea.analyze(job.msea)
+## job.msea <- ssea.finish(job.msea)
+## ###########################################################
+
+
+###################################################
+### code chunk number 5: Mergeomics.Rnw:381-428 (eval = FALSE)
+###################################################
+## ###########################################################
+## job <-list()
+## job$folder <- c("module_merge")
+## ## The moddata and modinfo either come from the significant pathways found in
+## ## any previous MSEA run or these files can be manually curated by the user.
+## ## moddata is an obligatory input file; while modinfo is an optional input.
+## ## It is recommended to merge the overlapping pathways among significant
+## ## ones before applying KDA to proceed with the independent gene sets.
+## moddata <- tool.read(system.file("extdata", "Significant_pathways.txt",
+## package="Mergeomics"), c("MODULE","GENE"))
+## modinfo <- tool.read(system.file("extdata", "Significant_pathways.info.txt",
+## package="Mergeomics"),c("MODULE","SOURCE","DESCR"))
+##
+## syms <- tool.read(system.file("extdata", "symbols.txt",
+## package="Mergeomics"))
+## syms <- syms[,c("HUMAN", "MOUSE")]
+## names(syms) <- c("FROM", "TO")
+## moddata$GENE <- tool.translate(words=moddata$GENE, from=syms$FROM,
+## to=syms$TO)
+##
+## ## Merge and trim overlapping modules.
+## rmax <- 0.2
+## moddata$OVERLAP <- moddata$MODULE
+## moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+## rcutoff=rmax)
+## moddata$MODULE <- moddata$CLUSTER
+## moddata$GENE <- moddata$ITEM
+## moddata$OVERLAP <- moddata$GROUPS
+## moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+## moddata <- unique(moddata)
+## modinfo <- modinfo[which(!is.na(match(modinfo[,1], moddata[,1]))), ]
+##
+## ## Mark modules with overlaps.
+## for(i in which(moddata$MODULE != moddata$OVERLAP)){
+## modinfo[which(modinfo[,"MODULE"] == moddata[i,"MODULE"]),
+## "MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+## moddata[i,"MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+## }
+## ## Save merged module data and info for KDA.
+## modfile <- "mergedModules.txt"
+## modinfofile <- "mergedModules.info.txt"
+## moddata$NODE <- moddata$GENE
+## tool.save(frame=unique(moddata[,c("MODULE", "GENE", "OVERLAP", "NODE")]),
+## file=modfile, directory=job$folder)
+## tool.save(frame=unique(modinfo[,c("MODULE","SOURCE","DESCR")]),
+## file=modinfofile, directory=job$folder)
+## ###########################################################
+
+
+###################################################
+### code chunk number 6: Mergeomics.Rnw:443-453 (eval = FALSE)
+###################################################
+## ###########################################################
+## ## Assume there are three MSEA objects passed down by
+## ## ssea.finish()
+## # job.metamsea = list()
+## # job.metamsea$job1 = job.msea1
+## # job.metamsea$job2 = job.msea2
+## # job.metamsea$job3 = job.msea3
+## # job.metamsea = ssea.meta(job.metamsea,"meta_label",
+## # "meta_folder")
+## ###########################################################
+
+
+###################################################
+### code chunk number 7: Mergeomics.Rnw:464-497 (eval = FALSE)
+###################################################
+## ###########################################################
+## job.kda <- list()
+## job.kda$label<-"HDLC"
+## ## parent folder for results
+## job.kda$folder<-"./Results"
+## ## Input a network
+## ## columns: TAIL HEAD WEIGHT
+## job.kda$netfile <- system.file("extdata", "network.mouseliver.mouse.txt",
+## package="Mergeomics")
+## ## Gene sets derived from ModuleMerge, containing two columns,
+## ## MODULE, NODE, delimited by tab
+## job.kda$modfile<- system.file("extdata",
+## "mergedModules.txt", package="Mergeomics")
+## ## Annotation file for the gene sets
+## ## if module or pathway annotation is not available, skip this:
+## job.kda$inffile<-system.file("extdata",
+## "mergedModules.info.txt", package="Mergeomics")
+## ## "0" means we do not consider edge weights while 1 is
+## ## opposite.
+## job.kda$edgefactor<-0.5 ## default value
+## ## The searching depth for the KDA
+## job.kda$depth<-1 ## default value
+## ## "0" means we do not consider the directions of the
+## ## regulatory interactions
+## ## while 1 is opposite.
+## job.kda$direction<-0 ## default value
+## ## Let us run KDA!
+## job.kda <- kda.configure(job.kda)
+## job.kda <- kda.start(job.kda)
+## job.kda <- kda.prepare(job.kda)
+## job.kda <- kda.analyze(job.kda)
+## job.kda <- kda.finish(job.kda)
+## ###########################################################
+
+
+###################################################
+### code chunk number 8: Mergeomics.Rnw:546-552 (eval = FALSE)
+###################################################
+## ###########################################################
+## ## run following line after finishing the KDA process
+## ## i.e., after the kda.finish() function concluded
+## job.kda <- kda2cytoscape (job.kda, node.list=NULL,
+## modules=NULL, ndrivers=5, depth=1)
+## ###########################################################
+
+
diff --git a/inst/doc/Mergeomics.Rnw b/inst/doc/Mergeomics.Rnw
new file mode 100644
index 0000000..294180d
--- /dev/null
+++ b/inst/doc/Mergeomics.Rnw
@@ -0,0 +1,642 @@
+%\VignetteIndexEntry{Mergeomics}
+%\VignetteKeywords{Integrative network analysis of omics data}
+%\VignettePackage{Mergeomics}
+
+\documentclass[11pt]{article}
+
+\usepackage{graphicx}
+\usepackage{color}
+\usepackage{multirow}
+\usepackage{longtable}
+\usepackage{pdflscape}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{natbib}
+\usepackage{hyperref}
+
+\begin{document}
+\SweaveOpts{concordance=TRUE}
+\SweaveOpts{echo=FALSE}
+
+\setkeys{Gin}{width=0.99\textwidth}
+
+\title{\bf Mergeomics: Integrative Network Analysis of Omics Data}
+
+\maketitle
+
+\begin{center}
+\author{Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang,
+Xia Yang}\\
+
+Department of Integrative Biology and Physiology, University of California,
+Los Angeles\\
+South Australian Health and Medical Research Institute\\
+Department of Genetics and Genomics Sciences, Mount Sinai School of Medicine\\
+
+\end{center}
+
+\begin{center}
+zhaoyuqi616 at ucla.edu, zeyneb at ucla.edu, icestrike at ucla.edu,
+xyang123 at ucla.edu
+\end{center}
+
+
+
+If you use mergeomics in published research, please cite:
+
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+
+
+\tableofcontents
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Introduction}
+
+The recent revolution in genomic technologies has enabled the generation of
+massive amount of molecular data encompassing genetic, transcriptomic,
+epigenomic, metabolomics, and proteomics, which have become easily accessible
+in public domains and private sectors. It is increasingly recognized that
+analysis of individual types of data separately only reveals a fraction of
+the complex biology and often misses the key players driving diseases, making
+multi-dimensional big data integration an urgent need. Mergeomics package is
+developed as a capable and flexible pipeline to integrate various types of
+disease association data such as genetic association (e.g, GWAS or exome
+sequencing), transcriptome-wide association (e.g., TWAS from microarray or
+RNA sequencing studies), and epigenetic association (e.g., EWAS from methylome
+association studies), functional genomics (such as eQTLs and ENCODE
+annotations), biological pathways, and gene networks to identify
+disease-associated gene sub-networks and key regulatory genes.
+
+In this tutorial, we will assume that you have downloaded the standalone
+Mergeomics package from
+
+http://mergeomics.research.idre.ucla.edu/Download/Package/
+
+Mergeomics.tar.gz and install it using the following commands:
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+install.packages("Mergeomics.tar.gz", repos = NULL,
+type="source")
+## or from bioconductor3.3 release, use following lines:
+source("https://bioconductor.org/biocLite.R")
+biocLite("Mergeomics")
+@
+
+However, if you want, you can also try out these examples with our webserver at
+http://mergeomics.research.idre.ucla.edu. If you have any questions regarding
+this document or the described software, please contact us: Xia Yang
+(xyang123 at ucla.edu).
+
+\section{Tutorials}
+
+The whole Mergeomics pipeline can be divided into two major components:
+1) Marker Set Enrichment Analysis (MSEA), 2) Weighted Key Driver Analysis
+(wKDA). In the following step, we will illustrate how to implement Mergeomics
+using one sample dataset, the descriptions of which are as follows.
+
+\subsection{Input Data Preparation}
+
+In this part we described the datasets used by Mergeomics. Mergeomics is
+designed to integrate summary level data or curated resources, thus light
+data-preprocessing work is expected for user to convert datasets into
+Mergeomics ready format. For the convenience of users, we also deposited a
+rich collection of datasets that can be directly used for analysis in the
+Mergeomics webserver. Of note, Mergeomics is not able to directly process raw
+data from omics studies (e.g., sequencing reads).
+
+Mergeomics calls for five types of datasets. All files should contain tab
+delimited plain text.
+
+\textbf{1. Marker-disease association summary result:} Summary result files
+from association studies such as GWAS, TWAS or EWAS. Only two columns are
+required, MARKER and VALUE. The MARKER column gives the IDs of the markers
+(e.g., reference SNP ID), whereas the VALUE column contains the -log10
+transformed association P values for the markers. The package provides a
+sample summary file from the Genome-wide association studies (GWAS) of
+high-density lipoprotein cholesterol (HDLC) from Global Lipids Genetics
+Consortium [1].
+
+\textbf{2. Gene-marker mapping file: } Data that defines the mapping of genes
+and markers. Two columns are required, GENE and MARKER. Common resources for
+the file is chromosomal distance based mapping, eQTL based mapping and ENCODE
+based mapping. The Mergeomics webserver provides a comprehensive collection
+of above-mentioned mapping files. Experienced users can also generate their
+own mapping files from publicly available resources such as GTEx and ENCODE.
+The package provides a sample mapping file where the GWAS loci were projected
+to genes within 40 Kb distance from gene region.
+
+\textbf{3. Functionally related gene-sets:} Two columns are required, MODULE
+and GENE. The MODULE column defines the grouping of genes, normally by their
+functional relatedness. Gene-sets can be derived from canonical pathways,
+co-expression modules or differential genes under experimental conditions.
+The sample gene-sets in the package is the coexpression networks re-constructed
+ in mouse/human liver tissues, containing 2674 modules[2-6].
+
+\textbf{4. Gene regulatory networks:} Three columns are required, HEAD, TAIL
+and WEIGHT. If the user is using a directional network, then the starting
+nodes should be put in the HEAD column, while the ending nodes should be put
+in the TAIL column. If a non-directional network is used, users have to set
+the corresponding wKDA parameter to ``non-directional'', and are then safe to
+switch the HEAD and TAIL column. The WEIGHT column normally contains the
+confidence score or consensus score (if users merge multiple networks into
+one) of edges. If no weight information is available, simply fix all WEIGHT
+value to 1. The packages provides a sample Bayesian networks generated from
+mouse/human liver tissues [2-6].
+
+\textbf{5. Marker dependency structure file (Optional):} This file is needed
+if users plan to use the MDPrune module. Three columns are required, MARKERa,
+MARKERb and WEIGHT. The WEIGHT column describes the degree of dependency
+(e.g., linkage disequilibrium R2) between MARKERa and MARKERb.
+
+\subsection{MDPrune}
+
+Before taking any further steps, we recommend users to preprocess datasets to
+filter dependency structure among markers (e.g. Linkage disequilibrium),
+as marker dependency may cause artifacts and biases in the downstream analysis.
+We have provided an external C++ program named MDPrune
+(http://mergeomics.research.idre.ucla.edu/Download/MDPrune/), which can remove
+marker dependency while preferentially keeping those with a
+strong statistical association with traits.
+
+The commands are listed as follows:
+
+\begin{center}
+mdprune MARFile MAPFile MDSFile OutPath [Cutoff]
+\end{center}
+
+The file format request for MDPrune is the same as main Mergeomics pipeline,
+where;
+
+MARFile is the association study summary file
+
+MAPFile is the Gene-marker mapping file;
+
+MDSFile is the marker dependency structure file.
+
+Here, we chose a moderate cutoff (R\texttwosuperior \textgreater 0.7) for
+the dataset.
+
+\subsection{One-step Mergeomics}
+
+In this section, we first provide an one-step way to run the whole pipeline.
+One-step running of the pipeline can be accomplished either with a built-in
+function including all function calls in it or by explicitly calling/running
+all the required functions related with the pipeline processes one-by-one
+sequentially.
+
+We demonstrated the either ways of one-step Mergeomics pipeline with the
+code chunks given in this section. Then, the step-by-step processing of
+the pipeline will be described in detail in separate sections.
+
+The first way of one-step pipeline calling is given in the following
+code chunk. It is achieved by one built-in function call (MSEA.KDA.onestep).
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+####### One-step analysis for Mergeomics - 1st way ##
+###########################################################
+## Import library scripts.
+library(Mergeomics)
+
+## first, give the module info file, genefile, marker file, and network file
+## paths for the pipeline
+plan <- list()
+plan$label <- "hdlc"
+plan$folder <- "Results"
+plan$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+plan$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+plan$netfile <- system.file("extdata",
+"network.mouseliver.mouse.txt", package="Mergeomics")
+
+## second, define pipeline parameters (e.g. permutation #, seed for random #
+## generator, min and max module sizes, max overlapping ratio, etc.)
+plan$permtype <- "gene" ## default setting is gene permutation
+plan$nperm <- 100 ## default value is 20000
+plan$mingenes <- 10 ## default value is 10
+plan$maxgenes <- 500 ## default value is 500
+
+## then, call the one-step pipeline function including both MSEA and KDA steps
+plan <- MSEA.KDA.onestep(plan, apply.MSEA=TRUE, apply.KDA=TRUE,
+maxoverlap.genesets=0.20, symbol.transfer.needed=TRUE,
+sym.from=c("HUMAN", "MOUSE"), sym.to=c("HUMAN", "MOUSE"))
+## NOTE: default value of maxoverlap.genesets=0.33; but in all the examples of
+## this tutorial it is 0.2 (see job.msea$rmax<- 0.2 in the following example)
+## maxoverlap.genesets=0.33 (or job.msea$rmax<- 0.33) is recommended to use.
+@
+
+The second way of one-step pipeline calling is given in the following
+code chunk. Here, we need to explicitly and sequentially call main functions
+of both MSEA and KDA processes.
+
+<<echo=TRUE,print=FALSE,eval=TRUE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+####### One-step analysis for Mergeomics - 2nd way ##
+###########################################################
+## Import library scripts.
+library(Mergeomics)
+################ MSEA (Marker set enrichment analysis) ###
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+######### Create intermediary datasets for KDA ###########
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+
+## default and recommended rmax=0.33.
+## min.module.count is the number of the pathways to be taken from the MSEA
+## results to merge. If it is not specified (NULL), all the pathways having
+## MSEA-FDR value less than 0.25 will be considered for merging if they are
+## overlapping with the given ratio rmax.
+job.kda <- ssea2kda(job.msea, rmax=0.2, symbols=syms, min.module.count=NULL)
+####### wKDA (Weighted key driver analysis) ##########
+job.kda$netfile <- system.file("extdata",
+"network.mouseliver.mouse.txt", package="Mergeomics")
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+###### Prepare network files for visualization #########
+## Creates the input files for Cytoscape (http://www.cytoscape.org/)
+job.kda <- kda2cytoscape(job.kda)
+@
+
+
+After that, we focused on each pipeline process in the following sections.
+
+\subsection{Marker set enrichment analysis (MSEA)}
+The purpose of the MSEA is to leverage genome/epigenome wide association data,
+function genomics, canonical pathways and/or data-driven gene modules for
+identifying causal subnetworks for disease/traits.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## Import Mergeomics library.
+library("Mergeomics")
+## create an empty list for setting parameters
+job.msea <- list()
+## Next, label your project
+job.msea$label <- "HDLC"
+## The pathway size varies from 1 to a few thousands and will
+## introduce bias to the analysis. We set criteria for the
+## min. (mingenes) and max. (maxgenes) gene size for the pathways.
+job.msea$maxgenes <- 500
+job.msea$mingenes <- 10
+## The permutation setting and number of permutations. We recommend
+## using gene permutation due to its robust performance.
+## The alternative is locus permutation.
+job.msea$permtype <- "gene"
+job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+## set the output folder
+job.msea$folder <- "./Results"
+## The parameter genfile defines the Marker-to-Gene mapping file
+## It contains two columns, GENE and MARKER, delimited by tab
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## The parameter marfile defines the Disease association data file
+## It contains two columns, MARKER and VALUE, delimited by tab
+## Here, the marfile comes from the GWAS file after marker
+## dependency pruning, so the VALUE is the minus log10 transformed
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## The modfile defines the pathway information, which could come
+## from knowledge-based databases (such as KEGG, and Reactome)
+## or data-driven data sets (such as co-expression modules).
+## It contains two columns, MOUDLE and GENE, delimited by tab
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+## The inffile provides the basic descriptions for the pathways
+## It contains three columns, MODULE, SOURCE, and DESCR, which
+## provide information for pathway IDs corresponding to the
+## pathway names in modfile, the sources of the pathways, and
+## pathway annotations
+job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+package="Mergeomics")
+## Then, MSEA will run for ~30 minutes to ~2 hours
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+###########################################################
+@
+
+
+\begin{table}
+\centering
+\caption{Summary result of MSEA}
+\label{MSEAres}
+\begin{tabular}{llll}
+\hline \textbf{Pathways} & \textbf{Pvalues} & \textbf{FDRs} &
+\textbf{Descriptions} \\
+\hline 5099 & 3.48E-04 & 6.05\% & 5099:liver \\
+\hline 4588 & 1.05E-03 & 13.64\% & 4588:liver \\
+\hline 4737 & 2.01E-03 & 21.02\% & 4737:liver \\
+\hline 4128 & 4.44E-03 & 30.24\% & 4128:liver \\
+\hline 5117 & 5.22E-03 & 32.03\% & 5117:liver \\
+\hline 6645 & 5.24E-03 & 32.05\% & 6645:liver \\
+\hline 5104 & 5.50E-03 & 32.29\% & 5104:liver \\
+\hline 4094 & 7.65E-03 & 33.93\% & 4094:liver \\
+\hline 4932 & 7.95E-03 & 34.12\% & 4932:liver
+\end{tabular}
+\end{table}
+
+A part of the original results is listed in Table 1. The pathways
+satisfying certain cutoffs (e.g. FDR\textless 30\%) will be used for the next
+step and can be further annotated using diverse tools, such as DAVID
+(http://david.abcc.ncifcrf.gov/).
+
+\subsubsection{ModuleMerge}
+
+Usually, pathways/modules collected from different sources will have certain
+degree of redundancies, or refer to the same processes. In this case, users
+could merge the disease-related pathways into relatively independent gene
+sets, which exceed a given FDR cutoff after running MSEA.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+job <-list()
+job$folder <- c("module_merge")
+## The moddata and modinfo either come from the significant pathways found in
+## any previous MSEA run or these files can be manually curated by the user.
+## moddata is an obligatory input file; while modinfo is an optional input.
+## It is recommended to merge the overlapping pathways among significant
+## ones before applying KDA to proceed with the independent gene sets.
+moddata <- tool.read(system.file("extdata", "Significant_pathways.txt",
+package="Mergeomics"), c("MODULE","GENE"))
+modinfo <- tool.read(system.file("extdata", "Significant_pathways.info.txt",
+package="Mergeomics"),c("MODULE","SOURCE","DESCR"))
+
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+moddata$GENE <- tool.translate(words=moddata$GENE, from=syms$FROM,
+to=syms$TO)
+
+## Merge and trim overlapping modules.
+rmax <- 0.2
+moddata$OVERLAP <- moddata$MODULE
+moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+rcutoff=rmax)
+moddata$MODULE <- moddata$CLUSTER
+moddata$GENE <- moddata$ITEM
+moddata$OVERLAP <- moddata$GROUPS
+moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+moddata <- unique(moddata)
+modinfo <- modinfo[which(!is.na(match(modinfo[,1], moddata[,1]))), ]
+
+## Mark modules with overlaps.
+for(i in which(moddata$MODULE != moddata$OVERLAP)){
+ modinfo[which(modinfo[,"MODULE"] == moddata[i,"MODULE"]),
+ "MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+ moddata[i,"MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+}
+## Save merged module data and info for KDA.
+modfile <- "mergedModules.txt"
+modinfofile <- "mergedModules.info.txt"
+moddata$NODE <- moddata$GENE
+tool.save(frame=unique(moddata[,c("MODULE", "GENE", "OVERLAP", "NODE")]),
+file=modfile, directory=job$folder)
+tool.save(frame=unique(modinfo[,c("MODULE","SOURCE","DESCR")]),
+file=modinfofile, directory=job$folder)
+###########################################################
+@
+
+Users are also recommended to analyze the merged genesets with MSEA again
+to confirm that they are still significantly associated with disease.
+
+\subsubsection{Meta-MSEA}
+
+When multiple disease association datasets are available, meta-MSEA can be
+used to conduct meta-analysis at pathway or network level. This function
+allows user to achieve maximal power by combining results from independent
+association studies of different ethnicity, platform or even species, while
+evading the technical difficulties when performing meta-analysis directly
+on the marker-level association data.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## Assume there are three MSEA objects passed down by
+## ssea.finish()
+# job.metamsea = list()
+# job.metamsea$job1 = job.msea1
+# job.metamsea$job2 = job.msea2
+# job.metamsea$job3 = job.msea3
+# job.metamsea = ssea.meta(job.metamsea,"meta_label",
+# "meta_folder")
+###########################################################
+@
+
+\subsection{Weighted key driver analysis (wKDA)}
+
+wKDA aims to pinpoint key regulator genes (or key drivers) of the disease
+related gene sets using gene network topology and edge weight information.
+In specific, wKDA first screen the network for candidate hub genes. Then
+the disease gene-sets are overlaid onto the subnetworks of the candidate
+hubs to identify key drivers whose neighbors are enriched with disease genes.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"./Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile <- system.file("extdata", "network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns,
+## MODULE, NODE, delimited by tab
+job.kda$modfile<- system.file("extdata",
+"mergedModules.txt", package="Mergeomics")
+## Annotation file for the gene sets
+## if module or pathway annotation is not available, skip this:
+job.kda$inffile<-system.file("extdata",
+"mergedModules.info.txt", package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is
+## opposite.
+job.kda$edgefactor<-0.5 ## default value
+## The searching depth for the KDA
+job.kda$depth<-1 ## default value
+## "0" means we do not consider the directions of the
+## regulatory interactions
+## while 1 is opposite.
+job.kda$direction<-0 ## default value
+## Let us run KDA!
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+###########################################################
+@
+
+\begin{table}
+\centering
+\caption{Summary result of wKDA}
+\label{wKDAres}
+\begin{tabular}{lllll}
+\hline \textbf{Key Drivers} & \textbf{MODULES} & \textbf{Pvalues} &
+\textbf{FDRs} & \textbf{Descriptions}\\
+\hline Itih4 & 4932,.. & 2.90E-14 & 0.00\% & 4932:liver \\
+\hline Clstn3 & 136 & 6.92E-14 & 0.00\% & 136:liver \\
+\hline Pmvk & 4407 & 1.40E-13 & 0.00\% & 4407:liver \\
+\hline Acss2 & 4407 & 1.54E-13 & 0.00\% & 4407:liver \\
+\hline Gpam & 4407 & 3.82E-13 & 0.00\% & 4407:liver \\
+\hline Aqp8 & 4407 & 8.26E-13 & 0.00\% & 4407:liver \\
+\hline Itih4 & 5099 & 1.56E-12 & 0.00\% & 5099:liver \\
+\hline Lpin1 & 4084 & 2.21E-12 & 0.00\% & 4084:liver
+\end{tabular}
+\end{table}
+
+\subsection{Network visualization}
+For the current version, we generate Cytoscape-ready
+(http://www.cytoscape.org/) input files to visualize the network of key
+driver (KD) genes. Cytoscape-ready files are generated for illustrating
+either the neighborhoods of top five KDs of each module or the neighborhoods
+of a particular gene (node) list. By default, log files for each module's
+top five KDs' neighborhoods are prepared. Number of the top KDs can be
+specified by the user. Node and edge list files are created separately
+for Cytoscape. Additionally, top key driver name list for each module and
+color map of the modules are logged in the relevant files. kda2cytoscape
+function provides users:
+
+\begin{itemize}
+\item To illustrate top five KD neighborhoods for the modules, whose
+pathway or module name is listed within ``modules'' parameter (all modules
+are selected by default),
+\item To specify a particular number of KDs for each module with ``ndrivers''
+(default value is five),
+\item To search and illustrate the neighborhoods of a given node list with
+``node.list'' parameter (default value is NULL; if this is not specified by
+the user, top 5 KDs of each module and their neighborhoods will be
+illustrated; if ``node.list'' parameter is specified, it will be prioritized
+rather than top 5 KDs illustration),
+\item To examine the KD neighborhoods for a given depth (default depth is 1;
+hence, only the directly connected neighbor nodes of key drivers are taken
+into account).
+\end{itemize}
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## run following line after finishing the KDA process
+## i.e., after the kda.finish() function concluded
+job.kda <- kda2cytoscape (job.kda, node.list=NULL,
+modules=NULL, ndrivers=5, depth=1)
+###########################################################
+@
+
+Generated file names and their descriptions are given below:
+
+\begin{itemize}
+\item \textbf{kda2cytoscape.top.kds.txt:} Top KDs of the modules are listed
+in this file. Number of the key drivers can be set by user with ``ndrivers''
+parameter.
+\item \textbf{kda2cytoscape.edges.txt:} Edge lists of the generated graph.
+This file should be imported as network within the Cytoscape.
+\item \textbf{kda2cytoscape.nodes.txt:} Node lists of the generated graph.
+This file includes the optional formatting information for the nodes of the
+graph. It should be imported as node table within the Cytoscape.
+Field/attribute list provided for each node is as follows:
+\begin{itemize}
+\item NODE: gene symbol
+\item LABEL: node label (same as above)
+\item COLOR: background color of the node, it also shows the module
+membership of the node. If a node is member of multiple modules/pathways,
+only one color among them will be assigned as background color, but in the
+URL field this multiple module membership will be represented by a pie chart
+(node is divided into several sectors to show the multi-membership of the
+node). If a node is not a member for any of the pathways, the color will
+be ``grey''
+\item SIZE: size of the node. If the node is a KD it will be twice larger
+than ordinary nodes.
+\item SHAPE: shape of the node. If the node is a KD, it will be in
+diamond-shape, otherwise it is illustrated with circle.
+\item URL: pie chart created by Google chart tools. If the node is member of
+multiple modules, node circle will be divided as a pie chart and colored
+by the module colors it belongs to
+\item LABELSIZE: text size of the node. If the node is a KD, its font size
+will be larger than ordinary nodes.
+\end{itemize}
+\item \textbf{module.color.mapping.txt:} Color mapping for the modules, i.e.
+one color is assigned to each module.
+\end{itemize}
+
+An example graph for top 5 KDs\textquoteright neighborhoods of a given pathway
+is illustrated in Figure 1. Different colors represent
+modules/pathways, while the larger nodes are the key driver genes.
+
+Note that: SHAPE feature is not used in the Cytoscape in Fig 1. All the
+listed attributes for nodes are optional.
+
+\begin{figure}
+\centering
+\includegraphics[width=6in]{top5KDs}
+\caption{The key driver gene subnetwork}
+\label{top5KDnets}
+\end{figure}
+
+\section{Citation}
+If you use Mergeomics in published work, please cite: Shu L, Zhao Y,
+Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M,
+Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources
+to identify pathogenic perturbations to biological systems.
+
+bioRxiv doi: http://dx.doi.org/10.1101/036012.
+
+\section{References}
+
+[1] Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, et al. (2013)
+Discovery and refinement of loci associated with lipid levels.
+Nature Genetics 45.
+
+[2] Derry JMJ, Zhong H, Molony C, MacNeil D, GuhaThakurta D, et al. (2010)
+Identification of genes and networks driving cardiovascular and metabolic
+phenotypes in a mouse F2 intercross.
+PLoS ONE 5: e14319. doi:10.1371/journal.pone.0014319.
+
+[3] Wang SS, Schadt EE, Wang H, Wang X, Ingram-Drake L, et al. (2007)
+Identification of pathways for atherosclerosis in mice: integration of
+quantitative trait locus analysis and global gene expression data.
+Circ Res 101: e11-e30. doi:10.1161/CIRCRESAHA.107.152975.
+
+[4] Yang X, Schadt EE, Wang S, Wang H, Arnold AP, et al. (2006)
+Tissue-specific expression and regulation of sexually dimorphic genes
+in mice. Genome Res 16: 995-1004. doi:10.1101/gr.5217506.
+
+[5] Schadt EE, Molony C, Chudin E, Hao K, Yang X, et al. (2008)
+Mapping the genetic architecture of gene expression in human liver.
+PLoS Biol 6: e107. doi:10.1371/journal.pbio.0060107.
+
+[6] Tu ZZ, Keller MPM, Zhang CC, Rabaglia MEM, Greenawalt DMD, et al. (2012)
+Integrative analysis of a cross-loci regulation network identifies App
+as a gene regulating insulin secretion from pancreatic islets.
+PLoS Genet 8: e1003107-e1003107. doi:10.1371/journal.pgen.1003107.
+
+\end{document}
diff --git a/inst/doc/Mergeomics.pdf b/inst/doc/Mergeomics.pdf
new file mode 100644
index 0000000..90dd8e8
Binary files /dev/null and b/inst/doc/Mergeomics.pdf differ
diff --git a/inst/extdata/Significant_pathways.info.txt b/inst/extdata/Significant_pathways.info.txt
new file mode 100644
index 0000000..a75d8c8
--- /dev/null
+++ b/inst/extdata/Significant_pathways.info.txt
@@ -0,0 +1,17 @@
+MODULE SOURCE DESCR
+105 mouse 105:liver
+4360 mouse 4360:liver
+4407 mouse 4407:liver
+4464 mouse 4464:liver
+4511 mouse 4511:liver
+4588 mouse 4588:liver
+4708 mouse 4708:liver
+4746 mouse 4746:liver
+5085 mouse 5085:liver
+5099 mouse 5099:liver
+5104 mouse 5104:liver
+5582 mouse 5582:liver
+6643 mouse 6643:liver
+6648 mouse 6648:liver
+6675 mouse 6675:liver
+6699 mouse 6699:liver
diff --git a/inst/extdata/Significant_pathways.txt b/inst/extdata/Significant_pathways.txt
new file mode 100644
index 0000000..4b6a9d2
--- /dev/null
+++ b/inst/extdata/Significant_pathways.txt
@@ -0,0 +1,2351 @@
+MODULE GENE
+105 ACOT12
+105 FGF1
+105 CHRNA2
+105 HADHB
+105 ACAT1
+105 ABCB4
+105 ACADM
+105 DECR1
+105 FIBP
+105 FBP2
+105 HMGCS2
+105 CYP4A11
+105 ABHD6
+105 MMD
+105 TMEM56
+105 PCSK6
+105 ACADL
+105 DCI
+105 ECH1
+105 ETFDH
+105 CPT1A
+4360 HECW2
+4360 E2F7
+4360 PTGFR
+4360 PPARGC1A
+4360 RNF152
+4360 ELL2
+4360 PPL
+4360 USP1
+4360 PQLC1
+4360 PRKD3
+4360 PDE4B
+4360 HDC
+4360 FKBP5
+4360 PAQR7
+4360 EGLN3
+4360 EIF1AX
+4360 Contig398_RC
+4360 IL12RB1
+4360 ETV6
+4360 UAP1
+4360 IL6R
+4360 OSBPL11
+4360 TMEM2
+4360 P2RY5
+4360 RASD1
+4360 HERPUD1
+4360 HISPPD1
+4360 PPAPDC3
+4360 TNS3
+4360 PTP4A1
+4360 HNF4A
+4360 UBE2G2
+4360 KLF3
+4360 FAM35A
+4360 RFX4
+4360 FAM89A
+4360 RGS4
+4360 SLC25A32
+4360 EFNA1
+4360 ADAMTS1
+4360 RNF43
+4360 CKS2
+4360 ETS2
+4360 GJA1
+4360 NM_130469
+4360 NR2F2
+4360 GNPDA1
+4360 hCT1831218
+4360 TMEM87B
+4360 PDLIM1
+4360 PIGA
+4360 TNS1
+4360 RXRG
+4360 ADRA2B
+4360 TSC22D2
+4360 CLDN3
+4360 TSC22D3
+4360 CR749513
+4360 ZNF276
+4360 DUSP16
+4360 ZNF467
+4360 RIPK4
+4360 CHIC2
+4360 ADCY6
+4360 KIAA1853
+4360 KLF2
+4360 SDC4
+4360 SEMA4C
+4360 FAM46C
+4360 SKIL
+4360 FIGN
+4360 SLC10A6
+4360 ASAH3L
+4360 SLC39A14
+4360 KIAA0247
+4360 FZD1
+4360 NM_032495
+4360 GADD45B
+4360 C8orf4
+4360 GJA4
+4360 GNAI1
+4360 TMEM23
+4360 CDC37L1
+4360 CBX4
+4360 TMC7
+4360 TNFRSF12A
+4360 CDKN1A
+4360 MCART1
+4360 TACC2
+4360 TP53INP1
+4360 CLDN14
+4360 CDKN2D
+4360 COBLL1
+4360 RAPGEF4
+4360 IMPACT
+4360 INHBB
+4360 KCTD15
+4360 KIAA0182
+4360 FOXO1A
+4360 KIAA1754
+4360 KIF5B
+4360 S100G
+4360 AMOTL2
+4360 SETDB2
+4360 SFRS5
+4360 ARL6IP2
+4360 LPIN1
+4360 MAP3K6
+4360 MAPKAPK2
+4360 FOXA3
+4360 NM_014859
+4360 ATP1B1
+4360 NKD1
+4360 C9orf77
+4360 TDO2
+4360 NM_032718
+4360 C9orf150
+4360 TMEM100
+4360 DDX28
+4360 DDX5
+4360 C16orf76
+4360 C1orf210
+4360 C21orf55
+4360 ANGPTL4
+4360 APBB3
+4360 ZBTB12
+4360 ARRDC2
+4360 LPIN2
+4360 LMNB1
+4360 BCAR3
+4360 C14orf93
+4360 TFAP4
+4360 NAB2
+4360 GADD45A
+4360 NM_019000
+4360 ARHGEF3
+4360 THOC2
+4360 XLKD1
+4360 BAIAP2
+4360 BAIAP2L1
+4360 MBNL2
+4360 MIDN
+4360 MAP3K5
+4360 CREM
+4360 AMICA1
+4360 ZBTB10
+4360 KLHL25
+4360 MADD
+4360 KLF9
+4407 TPCN1
+4407 FGF10
+4407 FDPS
+4407 DHCR7
+4407 TRIM9
+4407 GAS2
+4407 FASN
+4407 TMPRSS7
+4407 RDH11
+4407 FADS1
+4407 C12orf38
+4407 C14orf1
+4407 ACACA
+4407 GALE
+4407 TSR1
+4407 STARD4
+4407 ACAT2
+4407 FAM96B
+4407 ACACB
+4407 FAM107A
+4407 AQP8
+4407 QDPR
+4407 RDH16
+4407 HMGCR
+4407 ACLY
+4407 CDH23
+4407 SQLE
+4407 MMAB
+4407 TALDO1
+4407 TFPI2
+4407 LACTB2
+4407 TM7SF2
+4407 KHK
+4407 AACS
+4407 GSTM2
+4407 ROPN1L
+4407 MSMO1
+4407 UNG
+4407 WDR5
+4407 ZC3H12A
+4407 GPAM
+4407 CYP2R1
+4407 GPX5
+4407 ALLC
+4407 TKT
+4407 ELOVL6
+4407 BET1
+4407 ATP5S
+4407 C2
+4407 SLC45A2
+4407 AK098740
+4407 PMVK
+4407 KCNH8
+4407 GRAMD3
+4407 PNPLA5
+4407 THRSP
+4407 DERL2
+4407 DERL3
+4407 HOXB1
+4407 ACSL5
+4407 GNGT1
+4407 MVD
+4407 GPR84
+4407 CYB5B
+4407 FADS2
+4407 EAF2
+4407 NM_017518
+4407 LSS
+4407 SC5DL
+4407 MID1IP1
+4407 DNAJC17
+4407 ADIPOR2
+4407 CYB5R3
+4407 IRS1
+4407 TLCD1
+4407 PAOX
+4407 DUSP9
+4407 ELOVL5
+4407 LOC728392
+4407 HMGCS1
+4407 NSDHL
+4407 IDH1
+4407 ACSS2
+4407 DNTT
+4407 PGK1
+4407 MX2
+4407 PCSK9
+4407 LRRC14
+4407 NM_033408
+4407 NR1I3
+4407 SCARB1
+4407 IDI1
+4407 DBI
+4407 PDHA1
+4407 SETBP1
+4407 CNN2
+4407 ACSL4
+4407 PGD
+4407 ECHDC1
+4407 Contig53615_RC
+4407 CS
+4407 DLAT
+4407 PDK1
+4407 CYP51A1
+4407 OR1N1
+4407 PDZK1IP1
+4407 INSIG1
+4407 OPRS1
+4464 TARDBP
+4464 SYNPO
+4464 KIF17
+4464 FAM214B
+4464 TCOF1
+4464 TAOK3
+4464 KIFC2
+4464 TRIM45
+4464 SLC10A6
+4464 TAF9
+4464 SIRT4
+4464 KIAA1853
+4464 FBXO21
+4464 BCMO1
+4464 TDO2
+4464 TRIP13
+4464 MYLK
+4464 FBXO31
+4464 THAP3
+4464 ZFP36
+4464 LGR5
+4464 BCL6B
+4464 SYNCRIP
+4464 FCGR2B
+4464 SLC2A4
+4464 LIPG
+4464 TNS1
+4464 TACC2
+4464 KIAA1754
+4464 SHB
+4464 BCL3
+4464 DST
+4464 DUSP14
+4464 LITAF
+4464 SLC15A4
+4464 SLC16A6
+4464 TFAP4
+4464 KLF12
+4464 TNS3
+4464 KIAA0247
+4464 TP53BP2
+4464 JAM2
+4464 KLHL25
+4464 TLR12P
+4464 SKIL
+4464 DYRK1A
+4464 FCHSD2
+4464 FEM1B
+4464 KLF15
+4464 KIAA0226
+4464 TIMP3
+4464 LRRC16
+4464 MADD
+4464 MAFK
+4464 TM4SF1
+4464 TMC7
+4464 TNIP2
+4464 IL6R
+4464 CR749513
+4464 TOPBP1
+4464 INSIG2
+4464 KIAA1622
+4464 TPR
+4464 TIPARP
+4464 MAN2B1
+4464 MAP3K5
+4464 MAP3K6
+4464 SLC12A7
+4464 EFHD2
+4464 FGF1
+4464 JUNB
+4464 FIGN
+4464 FKBP5
+4464 FNDC3B
+4464 TMEM2
+4464 TMEM87B
+4464 SLC2A1
+4464 C16orf76
+4464 C16orf87
+4464 TMEM100
+4464 TMEM106B
+4464 TMEM110
+4464 FH
+4464 KIAA0182
+4464 ST3GAL5
+4464 ZDHHC20
+4464 KIAA1018
+4464 MYADM
+4464 MYB
+4464 ZNF276
+4464 MYO1E
+4464 NARF
+4464 MAP3K8
+4464 IL1R1
+4464 MSH2
+4464 MT1A
+4464 SLC19A1
+4464 SLC19A2
+4464 IRF1
+4464 KLF3
+4464 KLF6
+4464 MDM1
+4464 MYCT1
+4464 MEST
+4464 MID1IP1
+4464 C17orf96
+4464 MAPKAPK2
+4464 EGLN3
+4464 SLC18B1
+4464 LOC81691
+4464 KLF2
+4464 LRP2BP
+4464 HSPA8
+4464 HSPB8
+4464 TMPRSS2
+4464 C7orf11
+4464 TNFAIP2
+4464 C19orf36
+4464 LMCD1
+4464 LMNB1
+4464 SERPINA7
+4464 MT2A
+4464 MTFMT
+4464 MTMR11
+4464 MTSS1
+4464 KIAA0460
+4464 DSCR1
+4464 NM_181708
+4464 NM_138492
+4464 DUSP16
+4464 BICD2
+4464 MRPS25
+4464 ZMYM1
+4464 C3orf59
+4464 MBD1
+4464 MBNL2
+4464 MCART1
+4464 MCM4
+4464 KLF4
+4464 EMD
+4464 EML1
+4464 MIDN
+4464 E2F7
+4464 MARVELD2
+4464 TMEM16H
+4464 TMEM25
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+4511 SLC22A1
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+4588 C4B
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+4708 USMG5
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+4708 SLC12A3
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+4746 P2RX5
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+4746 GNA15
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+4746 HCN2
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+6699 UROC1
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diff --git a/inst/extdata/coexpr.info.txt b/inst/extdata/coexpr.info.txt
new file mode 100644
index 0000000..58fd0b3
--- /dev/null
+++ b/inst/extdata/coexpr.info.txt
@@ -0,0 +1,21 @@
+MODULE SOURCE DESCR
+104 mouse 104:liver
+105 mouse 105:liver
+106 mouse 106:liver
+107 mouse 107:liver
+108 mouse 108:liver
+4360 mouse 4360:liver
+4407 mouse 4407:liver
+4464 mouse 4464:liver
+4511 mouse 4511:liver
+4588 mouse 4588:liver
+4708 mouse 4708:liver
+4746 mouse 4746:liver
+5085 mouse 5085:liver
+5099 mouse 5099:liver
+5104 mouse 5104:liver
+5582 mouse 5582:liver
+6643 mouse 6643:liver
+6648 mouse 6648:liver
+6675 mouse 6675:liver
+6699 mouse 6699:liver
diff --git a/inst/extdata/genes.hdlc_040kb_ld70.human_eliminated.txt b/inst/extdata/genes.hdlc_040kb_ld70.human_eliminated.txt
new file mode 100644
index 0000000..44a3017
--- /dev/null
+++ b/inst/extdata/genes.hdlc_040kb_ld70.human_eliminated.txt
@@ -0,0 +1,132706 @@
+GENE MARKER
+LIPA rs304494
+LIPA rs7096890
+LIPA rs11203124
+LIPA rs17121329
+LIPA rs10749599
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+KIRREL2 rs17777002
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diff --git a/inst/extdata/marker.hdlc_040kb_ld70.human_eliminated.txt b/inst/extdata/marker.hdlc_040kb_ld70.human_eliminated.txt
new file mode 100644
index 0000000..3cb76ac
--- /dev/null
+++ b/inst/extdata/marker.hdlc_040kb_ld70.human_eliminated.txt
@@ -0,0 +1,76867 @@
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diff --git a/inst/extdata/mergedModules.info.txt b/inst/extdata/mergedModules.info.txt
new file mode 100644
index 0000000..68c6c8f
--- /dev/null
+++ b/inst/extdata/mergedModules.info.txt
@@ -0,0 +1,16 @@
+MODULE SOURCE DESCR
+105 mouse 105:liver
+4360 mouse 4360:liver
+4407 mouse 4407:liver
+4464 mouse 4464:liver
+4511 mouse 4511:liver
+4588,.. mouse 4588:liver
+4708 mouse 4708:liver
+4746 mouse 4746:liver
+5085 mouse 5085:liver
+5104 mouse 5104:liver
+5582 mouse 5582:liver
+6643 mouse 6643:liver
+6648 mouse 6648:liver
+6675 mouse 6675:liver
+6699 mouse 6699:liver
diff --git a/inst/extdata/mergedModules.txt b/inst/extdata/mergedModules.txt
new file mode 100644
index 0000000..f57d35e
--- /dev/null
+++ b/inst/extdata/mergedModules.txt
@@ -0,0 +1,2320 @@
+MODULE GENE OVERLAP NODE
+6699 Adk 6699 Adk
+6699 Adra1b 6699 Adra1b
+6699 AK081405 6699 AK081405
+6699 Aldh1b1 6699 Aldh1b1
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+4464 Myadm 4464 Myadm
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+4464 Narf 4464 Narf
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+4464 Nfe2l2 4464 Nfe2l2
+4464 Nfkbib 4464 Nfkbib
+4464 Nfkbiz 4464 Nfkbiz
+4464 Ngef 4464 Ngef
+4464 Ngrn 4464 Ngrn
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+4464 Nit2 4464 Nit2
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+4464 Nr1i2 4464 Nr1i2
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+4464 Nt5c2 4464 Nt5c2
+4464 Nthl1 4464 Nthl1
+4464 Odc1 4464 Odc1
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+4464 Phf13 4464 Phf13
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+4464 Pole4 4464 Pole4
+4464 Poli 4464 Poli
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+4464 Prkag2 4464 Prkag2
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+4464 Rab17 4464 Rab17
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+4464 Rai17 4464 Rai17
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+4464 Rapgef4 4464 Rapgef4
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+4464 Rasgef1b 4464 Rasgef1b
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+4464 Rasl11b 4464 Rasl11b
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+4464 Rsn 4464 Rsn
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+4464 Rufy3 4464 Rufy3
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+4464 Sesn3 4464 Sesn3
+4464 Setd8 4464 Setd8
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+4464 Slc12a7 4464 Slc12a7
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+4464 Slc19a2 4464 Slc19a2
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+4464 Slc25a32 4464 Slc25a32
+4464 Slc29a1 4464 Slc29a1
+4464 Slc2a1 4464 Slc2a1
+4464 Slc2a4 4464 Slc2a4
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+4464 Slc36a1 4464 Slc36a1
+4464 Slc38a2 4464 Slc38a2
+4464 Slc39a14 4464 Slc39a14
+4464 Slc41a1 4464 Slc41a1
+4464 Slc41a2 4464 Slc41a2
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+4464 Slc6a18 4464 Slc6a18
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+4464 Taf9 4464 Taf9
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+4464 Wdr67 4464 Wdr67
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+4464 Xlkd1 4464 Xlkd1
+4464 Xrcc3 4464 Xrcc3
+4464 Xrcc6bp1 4464 Xrcc6bp1
+4464 Ypel3 4464 Ypel3
+4464 Zbtb12 4464 Zbtb12
+4464 Zbtb40 4464 Zbtb40
+4464 Zdhhc20 4464 Zdhhc20
+4464 Zfp276 4464 Zfp276
+4464 Zfp306 4464 Zfp306
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+4407 0610007P14Rik 4407 0610007P14Rik
+4407 1110019N10Rik 4407 1110019N10Rik
+4407 1110020L19Rik 4407 1110020L19Rik
+4407 1700012G19Rik 4407 1700012G19Rik
+4407 4432405B04Rik 4407 4432405B04Rik
+4407 5133400G04Rik 4407 5133400G04Rik
+4407 6330403K07Rik 4407 6330403K07Rik
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+4407 Elovl5 4407 Elovl5
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+4407 Fads1 4407 Fads1
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+4407 Fasn 4407 Fasn
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+4407 Hoxb1 4407 Hoxb1
+4407 Idh1 4407 Idh1
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+4407 Insig1 4407 Insig1
+4407 Irs1 4407 Irs1
+4407 Kcnh8 4407 Kcnh8
+4407 Khk 4407 Khk
+4407 Lactb2 4407 Lactb2
+4407 Lrrc14 4407 Lrrc14
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+4407 Mid1ip1 4407 Mid1ip1
+4407 Mmab 4407 Mmab
+4407 Mvd 4407 Mvd
+4407 Mx1 4407 Mx1
+4407 Nr1i3 4407 Nr1i3
+4407 Nsdhl 4407 Nsdhl
+4407 Olfr351 4407 Olfr351
+4407 Oprs1 4407 Oprs1
+4407 Paox 4407 Paox
+4407 Pcsk9 4407 Pcsk9
+4407 Pdha1 4407 Pdha1
+4407 Pdk1 4407 Pdk1
+4407 Pdzk1ip1 4407 Pdzk1ip1
+4407 Pgd 4407 Pgd
+4407 Pgk1 4407 Pgk1
+4407 Pmvk 4407 Pmvk
+4407 Pnpla5 4407 Pnpla5
+4407 Qdpr 4407 Qdpr
+4407 Rdh11 4407 Rdh11
+4407 Rdh16 4407 Rdh16
+4407 Ropn1l 4407 Ropn1l
+4407 Sc4mol 4407 Sc4mol
+4407 Sc5d 4407 Sc5d
+4407 Scarb1 4407 Scarb1
+4407 Setbp1 4407 Setbp1
+4407 Slc45a2 4407 Slc45a2
+4407 Sqle 4407 Sqle
+4407 Stard4 4407 Stard4
+4407 Taldo1 4407 Taldo1
+4407 Tfpi2 4407 Tfpi2
+4407 Thrsp 4407 Thrsp
+4407 Tkt 4407 Tkt
+4407 Tlcd1 4407 Tlcd1
+4407 Tm7sf2 4407 Tm7sf2
+4407 Tmprss7 4407 Tmprss7
+4407 Tpcn1 4407 Tpcn1
+4407 Trim9 4407 Trim9
+4407 Tsr1 4407 Tsr1
+4407 Ung 4407 Ung
+4407 Wdr5 4407 Wdr5
+4407 Zc3h12a 4407 Zc3h12a
+4360 1500001A10Rik 4360 1500001A10Rik
+4360 1810011O10Rik 4360 1810011O10Rik
+4360 1810015C04Rik 4360 1810015C04Rik
+4360 2310031A18Rik 4360 2310031A18Rik
+4360 2610039E05Rik 4360 2610039E05Rik
+4360 2610301K12Rik 4360 2610301K12Rik
+4360 2610528J11Rik 4360 2610528J11Rik
+4360 3110001K24Rik 4360 3110001K24Rik
+4360 4930431B09Rik 4360 4930431B09Rik
+4360 4931414P19Rik 4360 4931414P19Rik
+4360 4931419K03Rik 4360 4931419K03Rik
+4360 4933426M11Rik 4360 4933426M11Rik
+4360 5730446C15Rik 4360 5730446C15Rik
+4360 Adamts1 4360 Adamts1
+4360 Adcy6 4360 Adcy6
+4360 Adra2b 4360 Adra2b
+4360 AK004798 4360 AK004798
+4360 Amica1 4360 Amica1
+4360 Amotl2 4360 Amotl2
+4360 Angptl4 4360 Angptl4
+4360 Apbb3 4360 Apbb3
+4360 Arhgef3 4360 Arhgef3
+4360 Arl6ip2 4360 Arl6ip2
+4360 Arrdc2 4360 Arrdc2
+4360 Asah3l 4360 Asah3l
+4360 Atp1b1 4360 Atp1b1
+4360 AU040829 4360 AU040829
+4360 Baiap2 4360 Baiap2
+4360 Baiap2l1 4360 Baiap2l1
+4360 BC005561 4360 BC005561
+4360 BC063749 4360 BC063749
+4360 Bcar3 4360 Bcar3
+4360 C79267 4360 C79267
+4360 Cbx4 4360 Cbx4
+4360 Cdc37l1 4360 Cdc37l1
+4360 Cdkn1a 4360 Cdkn1a
+4360 Cdkn2d 4360 Cdkn2d
+4360 Chic2 4360 Chic2
+4360 Cks2 4360 Cks2
+4360 Cldn14 4360 Cldn14
+4360 Cldn3 4360 Cldn3
+4360 Cobll1 4360 Cobll1
+4360 Crem 4360 Crem
+4360 D4Bwg0951e 4360 D4Bwg0951e
+4360 Ddx28 4360 Ddx28
+4360 Ddx5 4360 Ddx5
+4360 Dusp16 4360 Dusp16
+4360 E2f7 4360 E2f7
+4360 Efna1 4360 Efna1
+4360 Egln3 4360 Egln3
+4360 Eif1a 4360 Eif1a
+4360 Ell2 4360 Ell2
+4360 Ets2 4360 Ets2
+4360 Etv6 4360 Etv6
+4360 Fign 4360 Fign
+4360 Fkbp5 4360 Fkbp5
+4360 Foxa3 4360 Foxa3
+4360 Foxo1 4360 Foxo1
+4360 Fzd1 4360 Fzd1
+4360 Gadd45a 4360 Gadd45a
+4360 Gadd45b 4360 Gadd45b
+4360 Gja1 4360 Gja1
+4360 Gja4 4360 Gja4
+4360 Gnai1 4360 Gnai1
+4360 Gnpda1 4360 Gnpda1
+4360 Gse1 4360 Gse1
+4360 Hdc 4360 Hdc
+4360 Hecw2 4360 Hecw2
+4360 Herpud1 4360 Herpud1
+4360 Hisppd1 4360 Hisppd1
+4360 Hnf4a 4360 Hnf4a
+4360 Il12rb1 4360 Il12rb1
+4360 Il6ra 4360 Il6ra
+4360 Impact 4360 Impact
+4360 Inhbb 4360 Inhbb
+4360 Kctd15 4360 Kctd15
+4360 Kif5b 4360 Kif5b
+4360 Klf2 4360 Klf2
+4360 Klf3 4360 Klf3
+4360 Klf9 4360 Klf9
+4360 Klhl25 4360 Klhl25
+4360 Lmnb1 4360 Lmnb1
+4360 Lpin1 4360 Lpin1
+4360 Lpin2 4360 Lpin2
+4360 Madd 4360 Madd
+4360 Map3k5 4360 Map3k5
+4360 Map3k6 4360 Map3k6
+4360 Mapkapk2 4360 Mapkapk2
+4360 Mbnl2 4360 Mbnl2
+4360 Mcart1 4360 Mcart1
+4360 Midn 4360 Midn
+4360 MMT00001293 4360 MMT00001293
+4360 MMT00033342 4360 MMT00033342
+4360 Nab2 4360 Nab2
+4360 Nkd1 4360 Nkd1
+4360 Nr2f2 4360 Nr2f2
+4360 ORF28 4360 ORF28
+4360 Osbpl11 4360 Osbpl11
+4360 P2ry5 4360 P2ry5
+4360 Paqr7 4360 Paqr7
+4360 Pde4b 4360 Pde4b
+4360 Pdlim1 4360 Pdlim1
+4360 Piga 4360 Piga
+4360 Ppapdc3 4360 Ppapdc3
+4360 Ppargc1a 4360 Ppargc1a
+4360 Ppl 4360 Ppl
+4360 Pqlc1 4360 Pqlc1
+4360 Prkcn 4360 Prkcn
+4360 Ptgfr 4360 Ptgfr
+4360 Ptp4a1 4360 Ptp4a1
+4360 Rapgef4 4360 Rapgef4
+4360 Rasd1 4360 Rasd1
+4360 Rfx4 4360 Rfx4
+4360 Rgs4 4360 Rgs4
+4360 Ripk4 4360 Ripk4
+4360 Rnf152 4360 Rnf152
+4360 Rnf43 4360 Rnf43
+4360 Rxrg 4360 Rxrg
+4360 S100g 4360 S100g
+4360 Sdc4 4360 Sdc4
+4360 Sema4c 4360 Sema4c
+4360 Setdb2 4360 Setdb2
+4360 Sfrs5 4360 Sfrs5
+4360 Skil 4360 Skil
+4360 Slc10a6 4360 Slc10a6
+4360 Slc25a32 4360 Slc25a32
+4360 Slc39a14 4360 Slc39a14
+4360 Sp9 4360 Sp9
+4360 Tacc2 4360 Tacc2
+4360 Tcfap4 4360 Tcfap4
+4360 Tdo2 4360 Tdo2
+4360 Thbd 4360 Thbd
+4360 Tmc7 4360 Tmc7
+4360 Tmem100 4360 Tmem100
+4360 Tmem2 4360 Tmem2
+4360 Tmem23 4360 Tmem23
+4360 Tnfrsf12a 4360 Tnfrsf12a
+4360 Tns1 4360 Tns1
+4360 Tns3 4360 Tns3
+4360 Trp53inp1 4360 Trp53inp1
+4360 Tsc22d2 4360 Tsc22d2
+4360 Tsc22d3 4360 Tsc22d3
+4360 Uap1 4360 Uap1
+4360 Ube2g2 4360 Ube2g2
+4360 Xlkd1 4360 Xlkd1
+4360 Zbtb10 4360 Zbtb10
+4360 Zbtb12 4360 Zbtb12
+4360 Zfp276 4360 Zfp276
+4360 Zfp467 4360 Zfp467
+105 Abcb4 105 Abcb4
+105 Abhd6 105 Abhd6
+105 Acadl 105 Acadl
+105 Acadm 105 Acadm
+105 Acat1 105 Acat1
+105 Acot12 105 Acot12
+105 Chrna2 105 Chrna2
+105 Cpt1a 105 Cpt1a
+105 Cyp4a10 105 Cyp4a10
+105 Dci 105 Dci
+105 Decr1 105 Decr1
+105 Ech1 105 Ech1
+105 Etfdh 105 Etfdh
+105 Fbp2 105 Fbp2
+105 Fgf1 105 Fgf1
+105 Fibp 105 Fibp
+105 Hadhb 105 Hadhb
+105 Hmgcs2 105 Hmgcs2
+105 Mmd 105 Mmd
+105 Pcsk6 105 Pcsk6
+105 Tmem56 105 Tmem56
+4588,.. 1110061N23Rik 4588,5099 1110061N23Rik
+4588,.. 1700021K02Rik 4588,5099 1700021K02Rik
+4588,.. 1810033B17Rik 4588,5099 1810033B17Rik
+4588,.. 2610507B11Rik 4588,5099 2610507B11Rik
+4588,.. 4930403J07Rik 4588,5099 4930403J07Rik
+4588,.. 4931428F04Rik 4588,5099 4931428F04Rik
+4588,.. 9530077C05Rik 4588,5099 9530077C05Rik
+4588,.. A2m 4588,5099 A2m
+4588,.. Abcc3 4588,5099 Abcc3
+4588,.. AI428936 4588,5099 AI428936
+4588,.. Ak7 4588,5099 Ak7
+4588,.. Alkbh6 4588,5099 Alkbh6
+4588,.. Ambp 4588,5099 Ambp
+4588,.. Ankrd41 4588,5099 Ankrd41
+4588,.. Apcs 4588,5099 Apcs
+4588,.. Atp2a1 4588,5099 Atp2a1
+4588,.. B230342M21Rik 4588,5099 B230342M21Rik
+4588,.. BC046404 4588,5099 BC046404
+4588,.. Bmper 4588,5099 Bmper
+4588,.. C4a 4588,5099 C4a
+4588,.. C4b 4588,5099 C4b
+4588,.. Camp 4588,5099 Camp
+4588,.. Casp12 4588,5099 Casp12
+4588,.. Casp4 4588,5099 Casp4
+4588,.. Cd302 4588,5099 Cd302
+4588,.. Cd6 4588,5099 Cd6
+4588,.. Cp 4588,5099 Cp
+4588,.. Cpb2 4588,5099 Cpb2
+4588,.. Crp 4588,5099 Crp
+4588,.. Cxcl1 4588,5099 Cxcl1
+4588,.. Cxcl11 4588,5099 Cxcl11
+4588,.. Cxcl14 4588,5099 Cxcl14
+4588,.. Cyb561 4588,5099 Cyb561
+4588,.. Cyp3a13 4588,5099 Cyp3a13
+4588,.. D15Ertd682e 4588,5099 D15Ertd682e
+4588,.. Dhcr7 4588,5099 Dhcr7
+4588,.. Dnajc12 4588,5099 Dnajc12
+4588,.. Efcab1 4588,5099 Efcab1
+4588,.. Fabp5 4588,5099 Fabp5
+4588,.. Fga 4588,5099 Fga
+4588,.. Fgb 4588,5099 Fgb
+4588,.. Fgg 4588,5099 Fgg
+4588,.. Fgl1 4588,5099 Fgl1
+4588,.. Flot1 4588,5099 Flot1
+4588,.. Fn1 4588,5099 Fn1
+4588,.. Glp1r 4588,5099 Glp1r
+4588,.. Gmds 4588,5099 Gmds
+4588,.. Gnat2 4588,5099 Gnat2
+4588,.. Hectd1 4588,5099 Hectd1
+4588,.. Hp 4588,5099 Hp
+4588,.. Hpgd 4588,5099 Hpgd
+4588,.. Il6 4588,5099 Il6
+4588,.. Il8rb 4588,5099 Il8rb
+4588,.. Insrr 4588,5099 Insrr
+4588,.. Itih3 4588,5099 Itih3
+4588,.. Itih4 4588,5099 Itih4
+4588,.. Kif17 4588,5099 Kif17
+4588,.. Lbp 4588,5099 Lbp
+4588,.. Lcn2 4588,5099 Lcn2
+4588,.. Lect1 4588,5099 Lect1
+4588,.. Lipc 4588,5099 Lipc
+4588,.. Lrg1 4588,5099 Lrg1
+4588,.. Mcf2l 4588,5099 Mcf2l
+4588,.. MMT00046222 4588,5099 MMT00046222
+4588,.. Mosc2 4588,5099 Mosc2
+4588,.. Mpo 4588,5099 Mpo
+4588,.. Mvp 4588,5099 Mvp
+4588,.. Mybl2 4588,5099 Mybl2
+4588,.. Nanos3 4588,5099 Nanos3
+4588,.. Nope 4588,5099 Nope
+4588,.. Nucb2 4588,5099 Nucb2
+4588,.. Nup93 4588,5099 Nup93
+4588,.. Orm1 4588,5099 Orm1
+4588,.. Orm2 4588,5099 Orm2
+4588,.. Pap 4588,5099 Pap
+4588,.. Pdia5 4588,5099 Pdia5
+4588,.. Ppap2c 4588,5099 Ppap2c
+4588,.. Prdm15 4588,5099 Prdm15
+4588,.. Prg4 4588,5099 Prg4
+4588,.. Rprc1 4588,5099 Rprc1
+4588,.. S100a8 4588,5099 S100a8
+4588,.. S100a9 4588,5099 S100a9
+4588,.. Saa1 4588,5099 Saa1
+4588,.. Saa3 4588,5099 Saa3
+4588,.. Scara5 4588,5099 Scara5
+4588,.. Serpina10 4588,5099 Serpina10
+4588,.. Serpina3n 4588,5099 Serpina3n
+4588,.. Slc13a5 4588,5099 Slc13a5
+4588,.. Slc2a3 4588,5099 Slc2a3
+4588,.. Slc30a2 4588,5099 Slc30a2
+4588,.. Slc37a1 4588,5099 Slc37a1
+4588,.. Slc3a1 4588,5099 Slc3a1
+4588,.. Slc41a2 4588,5099 Slc41a2
+4588,.. Socs3 4588,5099 Socs3
+4588,.. Srd5a1 4588,5099 Srd5a1
+4588,.. Stat3 4588,5099 Stat3
+4588,.. Steap4 4588,5099 Steap4
+4588,.. Tff3 4588,5099 Tff3
+4588,.. Tmprss2 4588,5099 Tmprss2
+4588,.. Tspan4 4588,5099 Tspan4
+4588,.. Ubc 4588,5099 Ubc
diff --git a/inst/extdata/modules.mousecoexpr.liver.human.txt b/inst/extdata/modules.mousecoexpr.liver.human.txt
new file mode 100644
index 0000000..0bdb1ca
--- /dev/null
+++ b/inst/extdata/modules.mousecoexpr.liver.human.txt
@@ -0,0 +1,2907 @@
+MODULE GENE
+104 HDDC3
+104 NGRN
+104 AQP11
+104 SULT1A1
+104 RKHD3
+104 RRM1
+104 MESDC1
+104 TRIM34
+104 TRIM68
+104 RAB6A
+104 ACSM3
+104 THUMPD1
+104 QPRT
+104 CRYM
+104 KCNE3
+105 ACOT12
+105 FGF1
+105 CHRNA2
+105 HADHB
+105 ACAT1
+105 ABCB4
+105 ACADM
+105 DECR1
+105 FIBP
+105 FBP2
+105 HMGCS2
+105 CYP4A11
+105 ABHD6
+105 MMD
+105 TMEM56
+105 PCSK6
+105 ACADL
+105 DCI
+105 ECH1
+105 ETFDH
+105 CPT1A
+106 SYT12
+106 SLC39A7
+106 SPCS2
+106 UBXD2
+106 FDX1
+106 TMED3
+106 ENTPD7
+106 FNBP1L
+106 FIBCD1
+106 COG6
+106 SLC30A5
+106 SLC38A3
+106 C9orf7
+106 METTL7A
+106 DNAJB11
+106 MAP2K6
+106 ZNF467
+106 SYNCRIP
+106 METTL1
+106 UBE2G2
+106 GORASP2
+106 SEPT11
+106 MAGEH1
+106 YARS
+106 LTV1
+106 FN3K
+106 FAM98A
+106 TXLNA
+106 TARS
+106 GLT8D1
+106 TK1
+106 SFRS3
+106 STX18
+106 CCDC75
+106 CCT3
+106 C7orf42
+106 CAD
+106 SLC35B1
+106 SRP19
+106 GBF1
+106 TBRG1
+106 GOLGA1
+106 GOLGA5
+106 LMAN1
+106 DNAJC3
+106 SURF4
+106 DHDDS
+106 DPP9
+106 FNTB
+106 TFG
+106 NRN1
+106 STT3A
+106 SEC24D
+106 ALG2
+106 FAM114A1
+106 RECQL
+106 KIAA0776
+106 VPS37C
+106 PDIA4
+106 LGALS8
+106 ATF6
+106 GNAT2
+106 LRRC59
+106 CDK2AP2
+106 SRM
+106 SRPRB
+106 FNDC3B
+106 EIF2B4
+106 ORMDL2
+106 C20orf59
+106 NME6
+106 DDX39A
+106 DENR
+106 MGAT2
+106 ARMET
+106 NGEF
+106 POLG2
+106 NARS
+106 MARS
+106 MCFD2
+106 Contig43807_RC
+106 GFPT1
+106 OSBP
+106 UGCGL1
+106 SEC23B
+106 PCBP1
+106 C21orf25
+106 CALR
+106 ERO1LB
+106 ARCN1
+106 PSMC2
+106 PSMD14
+106 EIF2AK3
+106 TASP1
+106 STCH
+106 MRPS18B
+106 DERL3
+106 BET1
+106 HSD3B2
+106 CREBL1
+106 NM_019116
+106 NM_003715
+106 PDIA3
+106 TMEM39A
+106 ARF4
+106 TSSC4
+106 SYVN1
+106 EDEM1
+106 ACBD3
+106 SEC13L1
+106 UPP2
+106 SDF2L1
+106 KIAA0528
+106 WFS1
+106 ARFGAP3
+106 C4orf34
+106 PRPF31
+106 COPB2
+106 RP11-217H1.1
+106 PAPSS2
+106 NM_021237
+106 LARS
+106 NEK6
+106 MRPL48
+106 MORF4L2
+106 MTMR4
+106 ATP2A2
+106 ANP32E
+106 NM_015939
+106 CRELD2
+106 RAB1A
+106 SEC11L1
+106 SCFD1
+106 PREB
+106 PPAPDC1B
+106 AMIGO3
+106 NM_021227
+106 DNPEP
+106 NKTR
+106 KDELR2
+106 SSR3
+106 NM_014445
+106 ZW10
+106 PIGA
+106 AARS
+106 HSS00154957
+106 IBTK
+106 DDX19B
+106 SCFD2
+106 KIAA0310
+106 PGM3
+106 HSPA5
+106 SSR2
+107 TRIM24
+107 TNFRSF19
+107 UBE2E2
+107 TUFT1
+107 TP53INP2
+107 UBQLN3
+107 TMEM53
+107 ULK4
+107 LAMA3
+107 LARP6
+107 ANXA5
+107 ACTG1
+107 CERS6
+107 ABLIM3
+107 TIMP1
+107 AL832149
+107 ARL6IP1
+107 UGT2B15
+107 UGT2B28
+107 UNC119
+107 SERF1A
+107 VEGFB
+107 VLDLR
+107 LEP
+107 WDR19
+107 Contig57896_RC
+107 UNC84B
+107 AKR1C1
+107 NOTCH3
+107 Contig57226_RC
+107 SERPINA9
+107 SERTAD4
+107 WFDC2
+107 ABCD2
+107 NM_014185
+107 ACSL6
+107 ZPLD1
+107 ADRB2
+107 ABCB10
+107 SFTPB
+107 SGPL1
+107 NM_174924
+107 TM6SF2
+107 AK092393
+107 TMC3
+107 ACCN4
+107 LCN12
+107 LCT
+107 ACSM2A
+107 C5orf62
+107 CAMSAP1
+107 WDR1
+107 NM_152760
+107 NM_173815
+107 C2orf50
+107 NPAL1
+107 SERPINA4
+107 SH2D3C
+107 SH3YL1
+107 FMO2
+107 FMO4
+107 TMCO5A
+107 SHOX2
+107 ITGA3
+107 KLF5
+107 NKX2-5
+107 ABCB1
+107 ARSB
+107 NM_007076
+107 LCK
+107 ADAMTS6
+107 LOC730098
+107 WDR32
+107 CD248
+107 XBP1
+107 CDCP1
+107 CDH1
+107 FAM126A
+107 SERPINE2
+107 AK056572
+107 TGFBR3
+107 THADA
+107 SHOC2
+107 SIGIRR
+107 TMEM173
+107 ANKRD13B
+107 AAK1
+107 KIF9
+107 ARNT
+107 ARRDC4
+107 ZFP91
+107 C8B
+107 ZNF536
+107 ZP3
+107 SCARA5
+107 SDHA
+107 SELENBP1
+107 SERPINA12
+107 ERAL1
+107 F11
+107 F2R
+107 FBLN2
+107 FBP1
+107 FIGN
+107 SIRT3
+107 NDUFA3
+107 IRF6
+107 KIF2B
+107 ZDHHC20
+107 NFE2L2
+107 NFYB
+107 NHLRC1
+107 SLC25A45
+107 hCT2345384
+107 ZNF25
+107 ACOT4
+107 SMARCD3
+107 SMOC1
+107 SMYD4
+107 CASP14
+107 SNX12
+107 CASZ1
+107 CCNI
+107 NMB
+107 SPSB1
+107 NOX4
+107 STOML1
+107 SERPINA1
+107 MNS1
+107 TES
+107 TMEM117
+107 SLC16A7
+107 AADAT
+107 KCTD15
+107 AATK
+107 NCLN
+107 GIT2
+107 GLB1L
+107 OPCML
+107 LMOD3
+107 LGTN
+107 WIF1
+107 SULT1C1
+107 NRGN
+107 MOCS1
+107 MORC4
+107 MYF5
+107 SLC1A4
+107 IQGAP3
+107 IQCG
+107 ZFP57
+107 CPLX3
+107 CRKRS
+107 CRSP9
+107 C11orf95
+107 EPB49
+107 MSX2
+107 MTMR11
+107 TM4SF4
+107 IDH2
+107 SLC16A5
+107 MYO1D
+107 IRX2
+107 NEK7
+107 GAS6
+107 GCKR
+107 SLC22A16
+107 SLC22A7
+107 SLC25A30
+107 GGA2
+107 C8A
+107 SLC4A2
+107 ACOT1
+107 CA2
+107 CACNB3
+107 CAMKMT
+107 SMYD5
+107 ENST00000297060
+107 GSTP1
+107 CCHCR1
+107 CCRL2
+107 SPRED3
+107 SOX15
+107 SRD5A1
+107 CDC2
+107 HEXB
+107 FAM117A
+107 C20orf26
+107 FAM62B
+107 MRS2L
+107 OGN
+107 CHST11
+107 TCEAL3
+107 FNDC1
+107 NFATC2IP
+107 ILVBL
+107 C17orf96
+107 CPA4
+107 ZFP95
+107 ZIM2
+107 ZMYND10
+107 MEIG1
+107 CXCL12
+107 OR2L13
+107 OR5AK4P
+107 LGALS1
+107 LPP-AS2
+107 LRRC16
+107 LRRTM4
+107 WIPI1
+107 MAN2B1
+107 CENTG2
+107 NUDT7
+107 CKAP4
+107 CLCN5
+107 FRMPD1
+107 IRF2
+107 ZBTB39
+107 GDF3
+107 C6orf72
+107 MEF2D
+107 MEN1
+107 MGST3
+107 S100G
+107 SALL1
+107 C2orf62
+107 EPS8L1
+107 C14orf58
+107 C17orf97
+107 C18orf17
+107 FAM107B
+107 C1QTNF4
+107 C20orf79
+107 C2
+107 CHCHD6
+107 TCN2
+107 C2orf81
+107 CMTM1
+107 COL6A2
+107 GAB3
+107 NAV3
+107 C7orf50
+107 GCN1L1
+107 MED12L
+107 SLC6A15
+107 GP5
+107 GPR152
+107 SPAG16
+107 SORT1
+107 GYPC
+107 HAO1
+107 SST
+107 HBZ
+107 HGFAC
+107 HHIP
+107 HOXB7
+107 NT5E
+107 CHAT
+107 TCEAL8
+107 Contig21835_RC
+107 IL1R1
+107 IL1R2
+107 IL36B
+107 COL8A2
+107 SLC17A1
+107 BEX4
+107 DUSP11
+107 CX3CL1
+107 CXorf38
+107 OSBPL7
+107 MAGEB1
+107 NRP1
+107 HTATIP2
+107 FUS
+107 YWHAQ
+107 ZAR1
+107 BMF
+107 RAD51B
+107 NM_024922
+107 C11orf75
+107 RYR1
+107 C14orf149
+107 PCDHB1
+107 C19orf71
+107 SUSD4
+107 CHMP4C
+107 HSS00347162
+107 HTRA4
+107 MYH10
+107 YBX2
+107 C6
+107 DNASE1L3
+107 RABIF
+107 C9orf3
+107 C9
+107 GNA14
+107 ELF4
+107 BACH2
+107 PAQR3
+107 PCSK5
+107 hCT1823092.1
+107 C19orf57
+107 CHAD
+107 BCL7C
+107 CIRH1A
+107 RAB7B
+107 PVT1
+107 CYLD
+107 CYP11B2
+107 ELOVL3
+107 ENPEP
+107 HOXB9
+107 HPS5
+107 SYNE1
+107 DENND3
+107 RARRES1
+107 PRLR
+107 PTAFR
+107 DIRC2
+107 PTPRS
+107 PALLD
+107 HSD17B13
+107 BTG1
+107 RXRG
+107 OTX1
+107 EGFR
+107 MCM10
+107 CYP1A2
+107 PPME1
+107 PP2D1
+107 DIO1
+107 PLCXD3
+107 BRP44L
+107 RPIA
+107 PRUNE
+107 PRTG
+107 DDIT3
+107 C12orf56
+107 CYP27A1
+107 PLEK2
+107 HSD17B2
+107 BRD4
+107 CYP2F1
+107 PLEKHK1
+107 RUVBL2
+107 ELK1
+107 MMP25
+107 EIF4EBP3
+107 MARCH7
+107 EDG1
+107 DKK4
+107 PLEKHA2
+107 ROBO1
+107 RP5-1022P6.2
+107 RP11-50D16.3
+107 PPL
+107 MAOA
+107 PHYH
+107 DEFA4
+107 RBBP4
+107 MBD1
+107 PHF19
+107 DUSP9
+107 CYP7B1
+107 DEFB116
+107 PRND
+107 DAPK1
+107 PLTP
+107 PKP3
+107 PGRMC2
+107 DLL4
+107 DHRS7
+107 PLCB1
+107 DACT2
+107 DOCK7
+107 PLCG1
+108 CYP21A1P
+108 PPAT
+108 C16orf87
+108 NOVA1
+108 CD207
+108 AHNAK
+108 TMEM34
+108 C14orf83
+108 HSD11B1
+108 RPS6KB2
+108 SNRPF
+108 ATP12A
+4360 HECW2
+4360 E2F7
+4360 PTGFR
+4360 PPARGC1A
+4360 RNF152
+4360 ELL2
+4360 PPL
+4360 USP1
+4360 PQLC1
+4360 PRKD3
+4360 PDE4B
+4360 HDC
+4360 FKBP5
+4360 PAQR7
+4360 EGLN3
+4360 EIF1AX
+4360 Contig398_RC
+4360 IL12RB1
+4360 ETV6
+4360 UAP1
+4360 IL6R
+4360 OSBPL11
+4360 TMEM2
+4360 P2RY5
+4360 RASD1
+4360 HERPUD1
+4360 HISPPD1
+4360 PPAPDC3
+4360 TNS3
+4360 PTP4A1
+4360 HNF4A
+4360 UBE2G2
+4360 KLF3
+4360 FAM35A
+4360 RFX4
+4360 FAM89A
+4360 RGS4
+4360 SLC25A32
+4360 EFNA1
+4360 ADAMTS1
+4360 RNF43
+4360 CKS2
+4360 ETS2
+4360 GJA1
+4360 NM_130469
+4360 NR2F2
+4360 GNPDA1
+4360 hCT1831218
+4360 TMEM87B
+4360 PDLIM1
+4360 PIGA
+4360 TNS1
+4360 RXRG
+4360 ADRA2B
+4360 TSC22D2
+4360 CLDN3
+4360 TSC22D3
+4360 CR749513
+4360 ZNF276
+4360 DUSP16
+4360 ZNF467
+4360 RIPK4
+4360 CHIC2
+4360 ADCY6
+4360 KIAA1853
+4360 KLF2
+4360 SDC4
+4360 SEMA4C
+4360 FAM46C
+4360 SKIL
+4360 FIGN
+4360 SLC10A6
+4360 ASAH3L
+4360 SLC39A14
+4360 KIAA0247
+4360 FZD1
+4360 NM_032495
+4360 GADD45B
+4360 C8orf4
+4360 GJA4
+4360 GNAI1
+4360 TMEM23
+4360 CDC37L1
+4360 CBX4
+4360 TMC7
+4360 TNFRSF12A
+4360 CDKN1A
+4360 MCART1
+4360 TACC2
+4360 TP53INP1
+4360 CLDN14
+4360 CDKN2D
+4360 COBLL1
+4360 RAPGEF4
+4360 IMPACT
+4360 INHBB
+4360 KCTD15
+4360 KIAA0182
+4360 FOXO1A
+4360 KIAA1754
+4360 KIF5B
+4360 S100G
+4360 AMOTL2
+4360 SETDB2
+4360 SFRS5
+4360 ARL6IP2
+4360 LPIN1
+4360 MAP3K6
+4360 MAPKAPK2
+4360 FOXA3
+4360 NM_014859
+4360 ATP1B1
+4360 NKD1
+4360 C9orf77
+4360 TDO2
+4360 NM_032718
+4360 C9orf150
+4360 TMEM100
+4360 DDX28
+4360 DDX5
+4360 C16orf76
+4360 C1orf210
+4360 C21orf55
+4360 ANGPTL4
+4360 APBB3
+4360 ZBTB12
+4360 ARRDC2
+4360 LPIN2
+4360 LMNB1
+4360 BCAR3
+4360 C14orf93
+4360 TFAP4
+4360 NAB2
+4360 GADD45A
+4360 NM_019000
+4360 ARHGEF3
+4360 THOC2
+4360 XLKD1
+4360 BAIAP2
+4360 BAIAP2L1
+4360 MBNL2
+4360 MIDN
+4360 MAP3K5
+4360 CREM
+4360 AMICA1
+4360 ZBTB10
+4360 KLHL25
+4360 MADD
+4360 KLF9
+4407 TPCN1
+4407 FGF10
+4407 FDPS
+4407 DHCR7
+4407 TRIM9
+4407 GAS2
+4407 FASN
+4407 TMPRSS7
+4407 RDH11
+4407 FADS1
+4407 C12orf38
+4407 C14orf1
+4407 ACACA
+4407 GALE
+4407 TSR1
+4407 STARD4
+4407 ACAT2
+4407 FAM96B
+4407 ACACB
+4407 FAM107A
+4407 AQP8
+4407 QDPR
+4407 RDH16
+4407 HMGCR
+4407 ACLY
+4407 CDH23
+4407 SQLE
+4407 MMAB
+4407 TALDO1
+4407 TFPI2
+4407 LACTB2
+4407 TM7SF2
+4407 KHK
+4407 AACS
+4407 GSTM2
+4407 ROPN1L
+4407 MSMO1
+4407 UNG
+4407 WDR5
+4407 ZC3H12A
+4407 GPAM
+4407 CYP2R1
+4407 GPX5
+4407 ALLC
+4407 TKT
+4407 ELOVL6
+4407 BET1
+4407 ATP5S
+4407 C2
+4407 SLC45A2
+4407 AK098740
+4407 PMVK
+4407 KCNH8
+4407 GRAMD3
+4407 PNPLA5
+4407 THRSP
+4407 DERL2
+4407 DERL3
+4407 HOXB1
+4407 ACSL5
+4407 GNGT1
+4407 MVD
+4407 GPR84
+4407 CYB5B
+4407 FADS2
+4407 EAF2
+4407 NM_017518
+4407 LSS
+4407 SC5DL
+4407 MID1IP1
+4407 DNAJC17
+4407 ADIPOR2
+4407 CYB5R3
+4407 IRS1
+4407 TLCD1
+4407 PAOX
+4407 DUSP9
+4407 ELOVL5
+4407 LOC728392
+4407 HMGCS1
+4407 NSDHL
+4407 IDH1
+4407 ACSS2
+4407 DNTT
+4407 PGK1
+4407 MX2
+4407 PCSK9
+4407 LRRC14
+4407 NM_033408
+4407 NR1I3
+4407 SCARB1
+4407 IDI1
+4407 DBI
+4407 PDHA1
+4407 SETBP1
+4407 CNN2
+4407 ACSL4
+4407 PGD
+4407 ECHDC1
+4407 Contig53615_RC
+4407 CS
+4407 DLAT
+4407 PDK1
+4407 CYP51A1
+4407 OR1N1
+4407 PDZK1IP1
+4407 INSIG1
+4407 OPRS1
+4464 TARDBP
+4464 SYNPO
+4464 KIF17
+4464 FAM214B
+4464 TCOF1
+4464 TAOK3
+4464 KIFC2
+4464 TRIM45
+4464 SLC10A6
+4464 TAF9
+4464 SIRT4
+4464 KIAA1853
+4464 FBXO21
+4464 BCMO1
+4464 TDO2
+4464 TRIP13
+4464 MYLK
+4464 FBXO31
+4464 THAP3
+4464 ZFP36
+4464 LGR5
+4464 BCL6B
+4464 SYNCRIP
+4464 FCGR2B
+4464 SLC2A4
+4464 LIPG
+4464 TNS1
+4464 TACC2
+4464 KIAA1754
+4464 SHB
+4464 BCL3
+4464 DST
+4464 DUSP14
+4464 LITAF
+4464 SLC15A4
+4464 SLC16A6
+4464 TFAP4
+4464 KLF12
+4464 TNS3
+4464 KIAA0247
+4464 TP53BP2
+4464 JAM2
+4464 KLHL25
+4464 TLR12P
+4464 SKIL
+4464 DYRK1A
+4464 FCHSD2
+4464 FEM1B
+4464 KLF15
+4464 KIAA0226
+4464 TIMP3
+4464 LRRC16
+4464 MADD
+4464 MAFK
+4464 TM4SF1
+4464 TMC7
+4464 TNIP2
+4464 IL6R
+4464 CR749513
+4464 TOPBP1
+4464 INSIG2
+4464 KIAA1622
+4464 TPR
+4464 TIPARP
+4464 MAN2B1
+4464 MAP3K5
+4464 MAP3K6
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+6675 CYP26A1
+6675 PCYT2
+6675 PDZRN4
+6675 PEMT
+6675 PHTF1
+6675 PLA2G4F
+6675 PMVK
+6675 PNPLA5
+6675 UBL5
+6675 UQCRC2
+6675 WNT9A
+6675 INSIG1
+6675 LAMP1
+6675 ROBO2
+6675 ROPN1L
+6675 ROR2
+6675 RPL19
+6675 RPL3
+6675 SPRY1
+6675 SREBF1
+6675 ST7L
+6675 RUTBC2
+6675 SC5DL
+6675 SCNM1
+6675 SHFM1
+6675 SLC22A15
+6675 SLC25A1
+6675 SLC30A7
+6675 SMAD7
+6675 SPRR4
+6675 MAT2A
+6675 HSD17B13
+6675 STARD4
+6675 PET112L
+6675 IDI1
+6675 ZNF420
+6675 DEPDC2
+6675 SQLE
+6675 HSD3B2
+6675 TTPA
+6675 TMEM97
+6675 TMEM14C
+6675 HSD3B1
+6675 HSD17B7
+6675 PCBD1
+6699 ALDH1B1
+6699 GCK
+6699 GFRA4
+6699 APOC3
+6699 ALDOB
+6699 ADK
+6699 ADRA1B
+6699 CNKSR2
+6699 LDHD
+6699 BHMT
+6699 GAPDH
+6699 PDHA1
+6699 PKLR
+6699 RND2
+6699 SNRPN
+6699 MDH1
+6699 KHK
+6699 ATP8B1
+6699 DBH
+6699 ENO1
+6699 EPHB4
+6699 TM6SF2
+6699 FOXA3
+6699 SLC23A1
+6699 TACC1
+6699 ATP5G3
+6699 TMEM86B
+6699 FBXW8
+6699 CYP17A1
+6699 DAK
+6699 FADS2
+6699 TALDO1
+6699 NME7
+6699 UROC1
+6699 FBP1
diff --git a/inst/extdata/msea2kda.nodes.txt b/inst/extdata/msea2kda.nodes.txt
new file mode 100644
index 0000000..a73efd3
--- /dev/null
+++ b/inst/extdata/msea2kda.nodes.txt
@@ -0,0 +1,14879 @@
+NODE GENE MARKER VALUE
+A1cf A1CF rs4488147 2.0995
+A2m A2M rs12426061 1.1782
+Gm7298 A2ML1 rs7137125 1.5698
+A3galt2 A3GALT2 rs4653027 1.1128
+A4galt A4GALT rs130334 1.7978
+A4gnt A4GNT rs329386 0.8589
+Aaas AAAS rs10876431 2.9923
+Aacs AACS rs11058036 1.9408
+Aadac AADAC rs3914815 1.0861
+Aadacl2 AADACL2 rs4679933 2.3435
+Aadacl3 AADACL3 rs4240904 1.8094
+Gm13177 AADACL4 rs3010936 2.7217
+Aadat AADAT rs2928818 1.0382
+Aagab AAGAB rs3743342 1.4331
+Aak1 AAK1 rs2175131 1.2978
+Aamp AAMP rs17572109 1.4019
+Aanat AANAT rs3826287 1.8219
+0610011L14Rik AAR2 rs6089041 0.8167
+Aard AARD rs17664597 1.8938
+Aars AARS rs12149660 1.4564
+Aars2 AARS2 rs2233434 2.6832
+Aarsd1 AARSD1 rs692198 1.1313
+Aasdh AASDH rs13147900 1.1749
+Aasdhppt AASDHPPT rs17093811 1.2297
+Aass AASS rs1405237 1.0139
+Aatf AATF rs9897257 2.1655
+Aatk AATK rs12103867 1.4116
+Abat ABAT rs12445515 1.9481
+Abca1 ABCA1 rs1883025 32.756
+4833417A11Rik ABCA12 rs765161 1.8035
+Abca13 ABCA13 rs7788249 2.274
+Abca17 ABCA17P rs6600201 1.8371
+Abca2 ABCA2 rs11790360 2.3242
+Abca3 ABCA3 rs12598641 1.6503
+Abca4 ABCA4 rs3789442 1.8233
+Abca5 ABCA5 rs17776943 1.1931
+Abca6 ABCA6 rs763920 1.2715
+Abca7 ABCA7 rs3752240 1.561
+Abca8b ABCA8 rs4148008 9.7469
+Abca9 ABCA9 rs4147951 1.965
+Abcb1a ABCB1 rs6946379 1.3216
+Abcb11 ABCB11 rs2287619 2.2997
+Abcb4 ABCB4 rs2051950 0.959
+Abcb5 ABCB5 rs4719620 1.1913
+Abcb8 ABCB8 rs1799983 4.0004
+Abcb9 ABCB9 rs10847980 3.9278
+Abcc1 ABCC1 rs9932506 2.5722
+Abcc10 ABCC10 rs3734688 1.3787
+Abcc12 ABCC12 rs7187621 1.137
+Abcc2 ABCC2 rs2756111 2.2674
+Abcc3 ABCC3 rs11568591 1.0903
+Abcc4 ABCC4 rs1564353 2.73
+Abcc5 ABCC5 rs7620457 2.3161
+Abcc6 ABCC6 rs7186601 1.6366
+Abcc8 ABCC8 rs916829 1.3465
+Abcc9 ABCC9 rs1492138 1.4852
+Abcd2 ABCD2 rs4129477 1.9359
+Abcd3 ABCD3 rs17410745 1.4916
+Abcd4 ABCD4 rs1052006 1.0137
+Abce1 ABCE1 rs968936 1.6899
+Abcf1 ABCF1 rs13195066 1.636
+Abcf2 ABCF2 rs1122979 1.2444
+Abcf3 ABCF3 rs843360 1.8066
+Abcg1 ABCG1 rs482303 2.1434
+Abcg2 ABCG2 rs11932727 1.751
+Abcg4 ABCG4 rs612132 1.5369
+Abcg5 ABCG5 rs4131228 1.6934
+Abcg8 ABCG8 rs4131228 1.6934
+Abhd1 ABHD1 rs7588926 1.3223
+Abhd10 ABHD10 rs812149 0.8645
+Abhd11 ABHD11 rs875342 0.9618
+Abhd12 ABHD12 rs11697524 1.3123
+Abhd12b ABHD12B rs17123178 2.0919
+Abhd13 ABHD13 rs9514825 2.5979
+Abhd14a ABHD14A rs323874 1.1723
+Abhd14b ABHD14B rs323874 1.1723
+Abhd15 ABHD15 rs548731 1.558
+Abhd16a ABHD16A rs3117583 4.5605
+Abhd16b ABHD16B rs2236152 0.9104
+Abhd2 ABHD2 rs420017 2.3047
+Abhd3 ABHD3 rs4616373 1.5504
+Abhd4 ABHD4 rs1384962 1.5845
+Abhd5 ABHD5 rs7645010 2.1752
+Abhd6 ABHD6 rs7626140 2.2162
+Abhd8 ABHD8 rs7250266 2.0722
+Abi1 ABI1 rs7094825 1.7894
+Abi2 ABI2 rs17410408 2.3308
+Abi3 ABI3 rs7222840 2.3984
+Abi3bp ABI3BP rs4928058 1.2064
+Abl1 ABL1 rs11244096 2.8857
+Abl2 ABL2 rs12133855 2.2097
+Ablim1 ABLIM1 rs2901044 3.4609
+Ablim2 ABLIM2 rs4696773 1.8111
+Ablim3 ABLIM3 rs10072074 1.5233
+Abo ABO rs8176693 3.6855
+Abp1 ABP1 rs10235739 4.1172
+Abr ABR rs3892291 1.6476
+Abra ABRA rs2035515 1.8636
+3110003A17Rik ABRACL rs9403028 2.8324
+Abt1 ABT1 rs9379892 1.3604
+Abtb1 ABTB1 rs782448 2.2447
+Abtb2 ABTB2 rs1983642 1.6716
+Acaa1a ACAA1 rs172111 1.1772
+Acaa2 ACAA2 rs2040293 15.125
+Acaca ACACA rs9894479 1.6209
+Acacb ACACB rs7974710 3.1386
+Acad10 ACAD10 rs10744774 1.6139
+Acad11 ACAD11 rs9877018 1.5168
+Acad8 ACAD8 rs1031381 1.144
+Acadl ACADL rs263693 1.423
+Acadm ACADM rs6686290 1.9772
+Acads ACADS rs696338 1.8788
+Acadsb ACADSB rs6599641 2.2384
+Acadvl ACADVL rs222850 0.9263
+Acan ACAN rs4932434 1.207
+Acap1 ACAP1 rs2292068 1.4675
+Acap2 ACAP2 rs11715578 1.2818
+Acat1 ACAT1 rs2280332 1.7005
+Acat2 ACAT2 rs763561 1.6229
+Acbd3 ACBD3 rs9645314 0.9147
+Acbd4 ACBD4 rs2285427 2.1238
+Acbd5 ACBD5 rs3802526 1.0312
+Acbd6 ACBD6 rs357047 2.8133
+9230116B18Rik ACBD7 rs7099479 1.186
+Accs ACCS rs178514 1.2057
+Accsl ACCSL rs178514 1.2057
+Acd ACD rs6499137 19.597
+Ace ACE rs4324 2.8202
+Acer1 ACER1 rs11085169 2.1246
+Acer2 ACER2 rs7020745 1.5022
+Acer3 ACER3 rs7925135 2.0803
+Ache ACHE rs314376 2.3639
+Acin1 ACIN1 rs2295689 3.0717
+Acmsd ACMSD rs10496731 2.2158
+Aco1 ACO1 rs12554770 1.5047
+Aco2 ACO2 rs11704973 1.9454
+Acot3 ACOT1 rs8015976 1.2103
+Acot11 ACOT11 rs2133003 2.3967
+Acot12 ACOT12 rs13357760 1.2713
+Acot13 ACOT13 rs17491647 3.1126
+Acot1 ACOT2 rs8015976 1.2103
+Acot4 ACOT4 rs8015976 1.2103
+Acot6 ACOT6 rs42923 1.3928
+Acot7 ACOT7 rs761109 1.4685
+Acot8 ACOT8 rs6032556 1.7622
+Acox1 ACOX1 rs2608882 2.025
+Acox2 ACOX2 rs9876744 1.3614
+Acox3 ACOX3 rs17194178 2.1006
+Acoxl ACOXL rs952249 5.1309
+Acp1 ACP1 rs17713879 2.4944
+Acp2 ACP2 rs3824866 15.777
+Acp5 ACP5 rs7253363 1.1298
+Acp6 ACP6 rs11240101 1.7491
+Acpl2 ACPL2 rs12630390 2.5682
+Acpp ACPP rs11924491 2.8492
+Acpt ACPT rs2247216 1.988
+Acr ACR rs3865766 0.9543
+Acrbp ACRBP rs11064303 1.8283
+Acsbg1 ACSBG1 rs4887024 1.3032
+Acsbg2 ACSBG2 rs10425093 1.1605
+Acsf2 ACSF2 rs7221450 1.5198
+Acsf3 ACSF3 rs12443551 1.0016
+Acsl1 ACSL1 rs11727009 1.5024
+Acsl3 ACSL3 rs17780761 1.3047
+Acsl5 ACSL5 rs6585151 4.2569
+Acsl6 ACSL6 rs17616702 1.5047
+Acsm1 ACSM1 rs163274 0.9855
+Acsm2 ACSM2A rs6416612 1.1974
+Acsm3 ACSM3 rs12597620 1.3437
+Acsm4 ACSM4 rs12231468 1.6792
+Acsm5 ACSM5 rs6416610 0.9834
+Acss1 ACSS1 rs6083731 1.0356
+Acss2 ACSS2 rs6088638 1.7361
+Acss3 ACSS3 rs3811170 1.63
+Acta1 ACTA1 rs10799528 1.2475
+Acta2 ACTA2 rs1388 2.5099
+Actb ACTB rs6463491 2.2409
+Actbl2 ACTBL2 rs4145581 2.1404
+Actc1 ACTC1 rs16959871 1.7951
+Actg2 ACTG2 rs13397133 1.3999
+Actl6b ACTL6B rs221786 1.0272
+Actl7a ACTL7A rs7026291 1.608
+Actl7b ACTL7B rs7026291 1.608
+Gm16385 ACTL8 rs6695001 1.9796
+Actl9 ACTL9 rs301412 1.0396
+Actn1 ACTN1 rs7149926 2.5597
+Actn2 ACTN2 rs12751677 1.8948
+Actn3 ACTN3 rs3867132 1.1085
+Actn4 ACTN4 rs3786837 2.7126
+Actr1a ACTR1A rs11191293 1.0654
+Actr1b ACTR1B rs2071038 0.8959
+Actr2 ACTR2 rs13427744 2.8309
+Actr3 ACTR3 rs10165272 1.498
+Actr3b ACTR3B rs4352759 0.8576
+Actr5 ACTR5 rs2250446 1.5262
+Actr6 ACTR6 rs7962842 1.181
+Actr8 ACTR8 rs3774616 1.3592
+Actrt2 ACTRT2 rs12566421 1.0628
+Acvr1 ACVR1 rs17798043 1.8283
+Acvr1b ACVR1B rs11169962 1.0817
+Acvr1c ACVR1C rs10171477 2.3822
+Acvr2a ACVR2A rs6737271 2.6225
+Acvrl1 ACVRL1 rs706830 1.0841
+Acy1 ACY1 rs323874 1.1723
+Acy3 ACY3 rs12800028 1.7924
+Acyp1 ACYP1 rs17493927 1.289
+Acyp2 ACYP2 rs1118618 3.5226
+Ada ADA rs2057638 2.0595
+Adad1 ADAD1 rs6827839 1.1256
+Adad2 ADAD2 rs3743642 1.2856
+Adal ADAL rs488756 0.9965
+Adam10 ADAM10 rs424346 5.3065
+Adam11 ADAM11 rs17682536 1.9763
+Adam12 ADAM12 rs2930123 1.6657
+Adam15 ADAM15 rs11589479 2.1627
+Adam17 ADAM17 rs12474969 1.4245
+Adam18 ADAM18 rs6980957 2.4927
+Adam19 ADAM19 rs10076465 3.19
+Adam2 ADAM2 rs7825533 1.2325
+Adam20 ADAM20 rs7150354 2.1179
+Adam21 ADAM21 rs8011370 3.0326
+Adam22 ADAM22 rs4728713 1.4132
+Adam23 ADAM23 rs16838403 1.9205
+Adam28 ADAM28 rs1877217 1.9739
+Adam24 ADAM29 rs13117033 2.5645
+Adam30 ADAM30 rs3795666 0.961
+Adam32 ADAM32 rs11995576 1.0912
+Adam33 ADAM33 rs511898 1.2513
+Adam3 ADAM3A rs10095526 1.4566
+Adam7 ADAM7 rs7821675 1.5445
+Adam8 ADAM8 rs1984262 1.4768
+Adam9 ADAM9 rs11776561 1.1122
+Adamdec1 ADAMDEC1 rs4559243 1.2503
+Adamts1 ADAMTS1 rs9976082 0.959
+Adamts10 ADAMTS10 rs4804311 4.4901
+Adamts12 ADAMTS12 rs2453289 1.9094
+Adamts13 ADAMTS13 rs2073933 2.2327
+Adamts14 ADAMTS14 rs12221473 1.5638
+Adamts15 ADAMTS15 rs7106030 1.0562
+Adamts16 ADAMTS16 rs2913663 1.584
+Adamts17 ADAMTS17 rs751103 3.0606
+Adamts18 ADAMTS18 rs12934894 1.7378
+Adamts19 ADAMTS19 rs30645 1.5109
+Adamts2 ADAMTS2 rs163321 1.613
+Adamts20 ADAMTS20 rs11182117 1.6887
+Adamts3 ADAMTS3 rs4694480 2.5653
+Adamts4 ADAMTS4 rs1136224 1.5842
+Adamts5 ADAMTS5 rs2830593 1.2074
+Adamts6 ADAMTS6 rs264783 1.4515
+Adamts7 ADAMTS7 rs4886982 0.8778
+Adamts8 ADAMTS8 rs11222085 1.8324
+Adamts9 ADAMTS9 rs6445415 3.0642
+Adamtsl1 ADAMTSL1 rs714875 1.6451
+Adamtsl2 ADAMTSL2 rs10993774 1.3007
+Adamtsl3 ADAMTSL3 rs2401173 2.7891
+Adamtsl5 ADAMTSL5 rs2292455 1.3293
+Adap1 ADAP1 rs10264122 2.8693
+Adap2 ADAP2 rs3760318 1.7794
+Adar ADAR rs6699825 1.9731
+Adarb1 ADARB1 rs7278357 2.6341
+Adarb2 ADARB2 rs7906527 2.3996
+Adat1 ADAT1 rs11648478 1.572
+Adat2 ADAT2 rs223592 1.1663
+Adat3 ADAT3 rs12981256 3.3698
+Adck1 ADCK1 rs1972615 2.1537
+Adck3 ADCK3 rs12145777 2.8102
+Adck4 ADCK4 rs2561537 2.969
+Adcy1 ADCY1 rs1006299 1.8312
+Adcy10 ADCY10 rs203835 1.2513
+Adcy2 ADCY2 rs4702484 1.6192
+Adcy3 ADCY3 rs13390269 4.4118
+Adcy4 ADCY4 rs3940231 1.7162
+Adcy5 ADCY5 rs9861425 3.3444
+Adcy6 ADCY6 rs6580694 2.0148
+Adcy7 ADCY7 rs9926131 1.517
+Adcy8 ADCY8 rs7845934 3.0789
+Adcy9 ADCY9 rs12933423 2.6007
+Adcyap1 ADCYAP1 rs17501715 1.8791
+Adcyap1r1 ADCYAP1R1 rs740334 1.4013
+Add1 ADD1 rs16843535 2.4094
+Add2 ADD2 rs4852705 1.0123
+Add3 ADD3 rs12416036 1.3231
+9130014G24Rik ADGB rs1115208 1.467
+Adh1 ADH1C rs4147541 1.4528
+Adh4 ADH4 rs2602836 3.1246
+Adh5 ADH5 rs2602836 3.1246
+Adh6a ADH6 rs4699726 1.5471
+Adh7 ADH7 rs1154473 1.523
+Adhfe1 ADHFE1 rs10112574 2.3455
+Adi1 ADI1 rs7606870 1.2142
+Adig ADIG rs4812025 1.316
+Adipoq ADIPOQ rs2293243 1.5143
+Adipor1 ADIPOR1 rs6672643 0.9743
+Adipor2 ADIPOR2 rs4140993 1.0417
+Adk ADK rs41405146 1.5728
+Adm2 ADM2 rs8138406 1.095
+Adnp ADNP rs6096135 1.5387
+Adnp2 ADNP2 rs612913 1.8948
+Adora1 ADORA1 rs10920569 2.2215
+Adora2a ADORA2A rs17650937 1.1146
+Adora2b ADORA2B rs859252 1.6444
+Adora3 ADORA3 rs10857896 1.3722
+Adpgk ADPGK rs8032586 2.6351
+Adprh ADPRH rs1385521 0.9809
+Adprhl1 ADPRHL1 rs9577550 1.163
+Adprhl2 ADPRHL2 rs558120 1.2021
+2310004I24Rik ADPRM rs201621 1.8348
+Adra1a ADRA1A rs17055965 3.3711
+Adra1b ADRA1B rs1019920 1.6627
+Adra1d ADRA1D rs4815668 2.2855
+Adra2a ADRA2A rs7085088 1.6098
+Adra2b ADRA2B rs10172762 0.9052
+Adra2c ADRA2C rs4916613 1.553
+Adrb1 ADRB1 rs7076938 3.8351
+Adrb2 ADRB2 rs2053044 1.8069
+Adrb3 ADRB3 rs2319760 0.8374
+Adrbk2 ADRBK2 rs10212026 1.4909
+Adrm1 ADRM1 rs6062177 1.1276
+Adsl ADSL rs12484951 1.3525
+Adss ADSS rs12084013 2.2317
+Adssl1 ADSSL1 rs9783669 0.967
+9530008L14Rik ADTRP rs4711432 1.9237
+Aebp1 AEBP1 rs4640970 1.0725
+Aebp2 AEBP2 rs11044531 1.751
+Aen AEN rs8038865 1.9706
+Aes AES rs7249809 1.5907
+MMS010211961 AF086294 rs6899003 2.0364
+Afap1 AFAP1 rs4696687 1.0979
+Afap1l1 AFAP1L1 rs6889207 1.9817
+Afap1l2 AFAP1L2 rs11196736 2.0674
+Aff1 AFF1 rs442177 6.5619
+Aff3 AFF3 rs11680587 1.8248
+Aff4 AFF4 rs39830 0.9108
+Afg3l2 AFG3L2 rs12956849 1.1739
+Afm AFM rs7790 1.2658
+Afmid AFMID rs4789546 0.8401
+Afp AFP rs7670052 1.4089
+Aftph AFTPH rs17029100 1.5161
+Aga AGA rs10520378 1.3792
+Agap1 AGAP1 rs6755747 3.5894
+Agap2 AGAP2 rs1479498 1.2853
+Agap3 AGAP3 rs6464127 3.2599
+Agbl1 AGBL1 rs12438353 2.3419
+Agbl2 AGBL2 rs11600974 1.3141
+Agbl3 AGBL3 rs2718146 3.4238
+Agbl4 AGBL4 rs1418952 2.5933
+Agbl5 AGBL5 rs11678188 2.1821
+Ager AGER rs17493811 1.0915
+Agfg1 AGFG1 rs6738421 2.1844
+Aggf1 AGGF1 rs10068040 1.3267
+Agk AGK rs6958386 1.0413
+Agl AGL rs2035958 1.3254
+Agmo AGMO rs10248127 2.2429
+Agpat1 AGPAT1 rs17493811 1.0915
+Agpat3 AGPAT3 rs7278317 1.9045
+Agpat4 AGPAT4 rs9458161 1.5447
+Agpat5 AGPAT5 rs2928576 2.8383
+Agpat9 AGPAT9 rs4518236 2.1811
+Agphd1 AGPHD1 rs8031948 1.4755
+Agps AGPS rs4893961 0.8948
+Agr2 AGR2 rs818805 1.8564
+Agr3 AGR3 rs1099971 3.1201
+Agrn AGRN rs3121561 1.475
+Agrp AGRP rs13334182 2.2873
+Agt AGT rs11568046 2.0457
+Agtpbp1 AGTPBP1 rs7860075 1.1159
+Agtr1a AGTR1 rs5183 2.5438
+Agtrap AGTRAP rs4845875 1.2497
+Agxt AGXT rs6719991 2.0401
+Agxt2 AGXT2 rs41474544 2.3541
+2900006B13Rik AGXT2L2 rs6868483 2.0844
+Ahctf1 AHCTF1 rs3935608 1.8058
+Ahcy AHCY rs1205357 1.0785
+Ahcyl1 AHCYL1 rs390923 4.1443
+Ahcyl2 AHCYL2 rs10954231 2.1708
+Ahi1 AHI1 rs4896142 2.1713
+Ahnak AHNAK rs2428541 1.9027
+Ahr AHR rs10247158 3.1306
+Ahrr AHRR rs3822759 1.4041
+Ahsa1 AHSA1 rs11623826 1.6108
+Ahsg AHSG rs16860933 1.6623
+Ahsp AHSP rs4477723 1.7902
+Aicda AICDA rs11046277 1.027
+Aida AIDA rs3008634 1.9792
+Aif1 AIF1 rs9469027 3.0645
+Aif1l AIF1L rs6597673 1.7185
+Aifm2 AIFM2 rs1046747 1.9813
+Aifm3 AIFM3 rs178274 1.8713
+Aig1 AIG1 rs4342440 1.8251
+Aim1 AIM1 rs12207423 1.2587
+Aim1l AIM1L rs11583897 2.0078
+Aimp1 AIMP1 rs2949646 0.8897
+Aimp2 AIMP2 rs3815383 1.5421
+Aip AIP rs17501521 1.0215
+Aipl1 AIPL1 rs12449580 1.7031
+Aire AIRE rs2155724 1.4012
+Airn AIRN rs9295117 3.6765
+Ajap1 AJAP1 rs4654454 1.62
+AK087483 AK022898 rs1775587 1.1054
+1810026B05Rik AK024092 rs41439544 2.4382
+4922502B01Rik AK055364 rs806736 1.5902
+AI852064 AK055386 rs3091737 1.2929
+Msr2 AK057438 rs16839472 1.8199
+5430425K12Rik AK091866 rs12522282 1.029
+MMT00054852 AK094561 rs17034606 1.5772
+Tspan11 AK095365 rs11062614 1.2455
+Ak1 AK1 rs913984 1.7528
+Ak2 AK2 rs4412566 2.1253
+Ak3 AK3 rs1385454 1.9727
+Ak4 AK4 rs17127422 1.2328
+Ak5 AK5 rs6684022 2.0001
+Ak7 AK7 rs8019115 1.4204
+Ak8 AK8 rs215156 3.6523
+Akap1 AKAP1 rs1007462 1.3758
+Akap10 AKAP10 rs9914273 2.235
+Akap11 AKAP11 rs9594732 3.8681
+Akap12 AKAP12 rs926561 1.9559
+Akap13 AKAP13 rs7359282 2.4903
+Akap2 AKAP2 rs495004 2.6955
+Akap5 AKAP5 rs8022447 0.9034
+Akap6 AKAP6 rs17099538 3.0618
+Akap7 AKAP7 rs1746469 1.8187
+Akap8 AKAP8 rs2024164 1.1729
+Akap8l AKAP8L rs2024164 1.1729
+Akap9 AKAP9 rs11772585 1.7921
+D930014E17Rik AKIP1 rs1025438 0.9702
+Akirin1 AKIRIN1 rs11810357 1.8959
+Akirin2 AKIRIN2 rs2787929 1.3349
+AI597013 AKNA rs4979373 5.1844
+Aknad1 AKNAD1 rs10857972 4.3865
+Akr1a1 AKR1A1 rs518216 2.0086
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+Asb4 ASB4 rs12533957 1.6101
+Asb5 ASB5 rs13129947 2.059
+Asb6 ASB6 rs16931378 1.5168
+Asb7 ASB7 rs8042302 1.7233
+Asb8 ASB8 rs11168437 1.1962
+Ascc1 ASCC1 rs5786062 1.1874
+Ascc2 ASCC2 rs4823054 2.1903
+Ascc3 ASCC3 rs10485299 2.0435
+Ascl1 ASCL1 rs4764926 3.4394
+Ascl2 ASCL2 rs7929293 1.023
+Ascl4 ASCL4 rs1816009 1.9122
+Ascl5 ASCL5 rs6699862 2.1427
+Asf1a ASF1A rs7775275 3.1757
+Asgr1 ASGR1 rs314246 0.8162
+Asgr2 ASGR2 rs12150391 1.8536
+Ash1l ASH1L rs10908468 1.087
+Ash2l ASH2L rs2517390 1.1004
+Accn2 ASIC1 rs836177 2.5291
+Accn1 ASIC2 rs16969055 3.9996
+Accn3 ASIC3 rs1808593 1.9574
+Accn4 ASIC4 rs16859981 1.4113
+Accn5 ASIC5 rs2229202 2.3342
+a ASIP rs6142129 1.1702
+Asl ASL rs160648 1.6123
+Asna1 ASNA1 rs2066489 0.821
+Asns ASNS rs17344459 1.443
+Asnsd1 ASNSD1 rs10931429 2.8716
+Aspa ASPA rs2318036 2.4012
+Aspdh ASPDH rs1862479 1.4725
+Aspg ASPG rs1744297 1.1767
+Asph ASPH rs41397746 1.7729
+Asphd1 ASPHD1 rs12917712 0.8097
+Asphd2 ASPHD2 rs650276 2.9094
+Aspm ASPM rs12085377 2.6692
+Asprv1 ASPRV1 rs10196546 0.9901
+Aspscr1 ASPSCR1 rs9903371 1.1444
+Asrgl1 ASRGL1 rs12270299 1.3154
+Ass1 ASS1 rs10901326 2.2387
+Aste1 ASTE1 rs13066072 1.6169
+Astl ASTL rs2969490 1.1484
+Astn1 ASTN1 rs10913280 1.7878
+Astn2 ASTN2 rs10983621 2.4362
+4933424B01Rik ASUN rs7967504 2.3667
+Asxl1 ASXL1 rs1737887 1.9851
+Asxl2 ASXL2 rs934073 1.0638
+Asxl3 ASXL3 rs7232237 2.0776
+Asz1 ASZ1 rs10249651 2.3289
+Atad1 ATAD1 rs11202575 1.0065
+Atad2 ATAD2 rs7005105 1.5207
+Atad2b ATAD2B rs2339920 3.1632
+Atad3a ATAD3A rs3128342 1.187
+Atad5 ATAD5 rs11080134 1.8523
+Atat1 ATAT1 rs13195066 1.636
+Atcay ATCAY rs9789330 1.4244
+Ate1 ATE1 rs10886983 2.4245
+Atf1 ATF1 rs11169551 3.2012
+Atf2 ATF2 rs3771925 1.6048
+Atf3 ATF3 rs3125292 1.4962
+Atf4 ATF4 rs17400681 1.1886
+Atf5 ATF5 rs4801810 2.2732
+Atf6 ATF6 rs3767635 1.1789
+Atf6b ATF6B rs1150754 3.7924
+Atf7 ATF7 rs1716966 2.6828
+Atf7ip ATF7IP rs4576875 2.902
+Atf7ip2 ATF7IP2 rs9674298 1.8069
+Atg10 ATG10 rs13435939 1.1333
+Atg12 ATG12 rs12521324 2.0099
+Atg13 ATG13 rs10838613 2.687
+Atg14 ATG14 rs11627272 1.2185
+Atg16l1 ATG16L1 rs6758317 2.8752
+Atg16l2 ATG16L2 rs7936074 1.2372
+Atg2a ATG2A rs11606204 2.3351
+Atg2b ATG2B rs11624919 1.6356
+Atg4b ATG4B rs7600642 1.3239
+Atg4c ATG4C rs2184192 1.4653
+Atg4d ATG4D rs7246953 1.6304
+Atg5 ATG5 rs4946729 1.2267
+Atg7 ATG7 rs2606762 4.4026
+Atg9b ATG9B rs1799983 4.0004
+Athl1 ATHL1 rs14408 1.8573
+Atic ATIC rs10498033 1.914
+Atl1 ATL1 rs7154254 1.4204
+Atl2 ATL2 rs13339787 1.1904
+Atl3 ATL3 rs640908 1.4276
+Atm ATM rs611646 2.2877
+Atmin ATMIN rs2257378 1.2528
+Atn1 ATN1 rs11064445 0.8184
+Atoh1 ATOH1 rs17362732 2.1136
+Atoh7 ATOH7 rs881976 3.0576
+Atoh8 ATOH8 rs869778 1.4785
+Atox1 ATOX1 rs9324708 1.7251
+Atp10a ATP10A rs10873612 2.2471
+Atp10b ATP10B rs17058079 1.2843
+Atp10d ATP10D rs13139804 2.1357
+Atp11a ATP11A rs425329 1.5042
+Atp11b ATP11B rs2717272 0.961
+Atp12a ATP12A rs9553400 1.9759
+Atp13a1 ATP13A1 rs16996227 1.4688
+Atp13a2 ATP13A2 rs2281376 1.4255
+Atp13a3 ATP13A3 rs923931 2.465
+Atp13a4 ATP13A4 rs10804954 1.7488
+Atp13a5 ATP13A5 rs1017973 2.7418
+Atp1a1 ATP1A1 rs41329745 2.4953
+Atp1a2 ATP1A2 rs7551983 1.1525
+Atp1a3 ATP1A3 rs1075728 1.2096
+Atp1a4 ATP1A4 rs4656889 2.2217
+Atp1b1 ATP1B1 rs10919053 1.1295
+Atp1b2 ATP1B2 rs2955617 3.1015
+Atp1b3 ATP1B3 rs6440052 1.1567
+Atp2a1 ATP2A1 rs6565259 2.3576
+Atp2a2 ATP2A2 rs3026484 1.2738
+Atp2a3 ATP2A3 rs9912501 2.1655
+Atp2b1 ATP2B1 rs12817819 1.9003
+Atp2b2 ATP2B2 rs7633661 1.8318
+Atp2b4 ATP2B4 rs7554335 1.6499
+Atp2c1 ATP2C1 rs2685189 1.8219
+1810010G06Rik ATP2C2 rs1023628 1.8496
+Atp4a ATP4A rs12460932 1.828
+Atp4b ATP4B rs11164142 0.9122
+Atp5a1 ATP5A1 rs1866322 1.156
+Atp5c1 ATP5C1 rs4237383 1.14
+Atp5e ATP5E rs151348 1.9158
+Atp5g1 ATP5G1 rs12938102 1.5923
+Atp5h ATP5H rs4789125 1.0136
+Atp5k ATP5I rs3895658 1.2084
+Atp5j ATP5J rs2829866 0.8703
+Atp5o ATP5O rs743337 0.997
+Atp5s ATP5S rs17716174 0.8716
+Atp5sl ATP5SL rs4803462 0.9215
+Atp6ap1l ATP6AP1L rs226200 1.2635
+Atp6v0a1 ATP6V0A1 rs647397 1.3183
+Atp6v0a2 ATP6V0A2 rs7980300 3.8536
+Atp6v0a4 ATP6V0A4 rs746998 2.0082
+Atp6v0b ATP6V0B rs1859728 1.9031
+Atp6v0c ATP6V0C rs4786285 1.2213
+Atp6v0d1 ATP6V0D1 rs7206718 3.1061
+Atp6v0d2 ATP6V0D2 rs11775441 1.2041
+Atp6v0e ATP6V0E1 rs6863129 1.2872
+Atp6v0e2 ATP6V0E2 rs10270385 1.4679
+Atp6v1a ATP6V1A rs13068857 2.2773
+Atp6v1b1 ATP6V1B1 rs3771391 2.8318
+Atp6v1b2 ATP6V1B2 rs11987726 1.9796
+Atp6v1c1 ATP6V1C1 rs2017133 1.5622
+Atp6v1c2 ATP6V1C2 rs6756672 1.9859
+Atp6v1e1 ATP6V1E1 rs1080199 1.088
+Atp6v1e2 ATP6V1E2 rs6732523 2.5589
+Atp6v1f ATP6V1F rs3823538 1.1929
+Atp6v1g1 ATP6V1G1 rs10982299 1.5553
+Atp6v1g2 ATP6V1G2 rs6929796 2.5474
+Atp6v1g3 ATP6V1G3 rs4915297 1.2671
+Atp6v1h ATP6V1H rs41321146 1.1635
+Atp7b ATP7B rs9526810 1.5345
+Atp8a1 ATP8A1 rs11939228 1.776
+Atp8a2 ATP8A2 rs4770904 3.4429
+Atp8b1 ATP8B1 rs2663845 2.2614
+Atp8b2 ATP8B2 rs12121180 1.4466
+Atp8b3 ATP8B3 rs7246629 1.8955
+Atp8b4 ATP8B4 rs11630182 1.5429
+Atp8b5 ATP8B5P rs7040391 1.1103
+Atp9a ATP9A rs4381832 2.6722
+Atp9b ATP9B rs7243874 1.4674
+Atpaf1 ATPAF1 rs6671527 1.9855
+Atpaf2 ATPAF2 rs4925137 0.9935
+Atpif1 ATPIF1 rs7516655 1.8817
+Atr ATR rs10804682 1.8595
+0610007C21Rik ATRAID rs1275521 1.2551
+Atrn ATRN rs658709 2.0639
+Atrnl1 ATRNL1 rs1272383 1.4728
+Atxn1 ATXN1 rs3765273 2.0063
+Atxn10 ATXN10 rs9614750 1.4399
+Atxn1l ATXN1L rs6499542 2.5995
+Atxn2 ATXN2 rs653178 5.7986
+Atxn3 ATXN3 rs2295172 2.1
+Atxn7 ATXN7 rs35679464 1.6821
+Atxn7l1 ATXN7L1 rs497105 1.813
+Atxn7l2 ATXN7L2 rs485660 1.3607
+Atxn7l3 ATXN7L3 rs7220138 1.2367
+Atxn7l3b ATXN7L3B rs10879805 1.2661
+Atxn8os ATXN8OS rs7985103 1.7328
+Auh AUH rs10991817 2.0201
+Aurka AURKA rs2426621 0.9066
+Aurkaip1 AURKAIP1 rs2649588 1.2546
+Aurkb AURKB rs8064449 3.2797
+Aurkc AURKC rs2215048 1.6048
+Auts2 AUTS2 rs951392 1.9974
+Aven AVEN rs713202 2.9052
+Avil AVIL rs8176345 1.3226
+Avl9 AVL9 rs17170151 1.7113
+Avp AVP rs2326056 0.8811
+Avpi1 AVPI1 rs7915721 1.869
+Avpr1a AVPR1A rs7314634 2.8277
+BC072620 AX747191 rs8100329 1.5406
+9430070O13Rik AXDND1 rs10798686 2.0399
+Axin1 AXIN1 rs11866815 2.104
+Axin2 AXIN2 rs12942924 2.4533
+Axl AXL rs12978323 1.5369
+Azgp1 AZGP1 rs11974610 1.6139
+Azi1 AZI1 rs12103867 1.4116
+Azi2 AZI2 rs13101128 1.1817
+Azin1 AZIN1 rs10112564 1.8918
+B2m B2M rs2412916 0.9496
+B3galt1 B3GALT1 rs836682 2.5481
+B3galt2 B3GALT2 rs12085373 1.2958
+B3galt4 B3GALT4 rs457565 1.555
+B3galt5 B3GALT5 rs606414 2.7018
+B3galtl B3GALTL rs9805284 4.5827
+B3gat1 B3GAT1 rs2119299 1.4674
+B3gat2 B3GAT2 rs9455243 2.567
+B3gat3 B3GAT3 rs3888173 1.9543
+B3gnt1 B3GNT1 rs2276106 1.1389
+B3gnt2 B3GNT2 rs4319920 1.2219
+B3gnt3 B3GNT3 rs3212798 1.3594
+B3gnt4 B3GNT4 rs11060167 2.3563
+B3gnt6 B3GNT6 rs10899346 1.561
+B3gnt7 B3GNT7 rs11692601 1.4424
+B3gnt8 B3GNT8 rs4803462 0.9215
+B3gnt9-ps B3GNT9 rs13312735 5.2378
+6030413G23Rik B3GNTL1 rs9303016 2.4574
+B4galnt1 B4GALNT1 rs11613457 3.7375
+B4galnt2 B4GALNT2 rs9902755 1.3784
+B4galnt3 B4GALNT3 rs11063536 2.5619
+B4galnt4 B4GALNT4 rs10794314 1.8309
+B4galt1 B4GALT1 rs10971409 1.5339
+B4galt2 B4GALT2 rs1859728 1.9031
+B4galt3 B4GALT3 rs3813619 1.3813
+B4galt4 B4GALT4 rs10934486 2.2287
+B4galt5 B4GALT5 rs6122808 1.4156
+B4galt6 B4GALT6 rs8093931 1.957
+B4galt7 B4GALT7 rs7727897 1.4743
+B9d1 B9D1 rs11656071 2.0986
+Baalc BAALC rs2515185 1.7153
+Baat BAAT rs2417250 1.5768
+5430437P03Rik BABAM1 rs7250266 2.0722
+Bace1 BACE1 rs548381 4.7997
+Bace2 BACE2 rs1041448 1.8111
+Bach1 BACH1 rs411697 2.1302
+Bach2 BACH2 rs10944477 1.4226
+Bad BAD rs2302263 2.1145
+Bag1 BAG1 rs555751 1.0067
+Bag2 BAG2 rs7774488 2.0308
+Bag3 BAG3 rs3847486 1.5746
+Bag4 BAG4 rs3015656 1.0447
+Bag5 BAG5 rs8013911 1.3914
+Bag6 BAG6 rs3117583 4.5605
+Bahcc1 BAHCC1 rs899289 1.4153
+Bahd1 BAHD1 rs12440317 3.0409
+Bai1 BAI1 rs13276323 0.8608
+Bai2 BAI2 rs4949466 2.2232
+Bai3 BAI3 rs2802699 1.4391
+Baiap2 BAIAP2 rs8072124 1.6073
+Baiap2l1 BAIAP2L1 rs3801258 2.5438
+Baiap2l2 BAIAP2L2 rs3827354 1.3882
+Baiap3 BAIAP3 rs8045459 1.2734
+Bak1 BAK1 rs9469474 2.2706
+Bambi BAMBI rs10826492 1.9747
+Banf1 BANF1 rs10791838 1.4466
+Banf2 BANF2 rs6080767 2.2824
+Bank1 BANK1 rs17199964 4.1608
+Banp BANP rs7403882 1.8894
+Bap1 BAP1 rs12163565 0.8422
+Bard1 BARD1 rs280623 1.5345
+Barhl1 BARHL1 rs370721 1.5219
+Barhl2 BARHL2 rs12116456 1.656
+Barx1 BARX1 rs10992977 1.517
+Barx2 BARX2 rs11604150 1.207
+Basp1 BASP1 rs11133892 1.5146
+Batf BATF rs11620668 1.1123
+Batf2 BATF2 rs240569 1.2667
+Batf3 BATF3 rs17674590 2.1947
+Bax BAX rs905238 1.1775
+Baz1a BAZ1A rs6571682 2.2353
+Baz1b BAZ1B rs7811265 5.919
+Baz2a BAZ2A rs10747768 1.4262
+Baz2b BAZ2B rs12473369 2.6264
+Bbc3 BBC3 rs2032809 2.0351
+Bbox1 BBOX1 rs10501071 1.5547
+Bbs1 BBS1 rs3867132 1.1085
+Bbs10 BBS10 rs7980676 1.4873
+Bbs12 BBS12 rs398418 1.7284
+Bbs2 BBS2 rs16968331 1.9352
+Bbs4 BBS4 rs17826767 1.0739
+Bbs5 BBS5 rs2353182 1.3329
+Bbs7 BBS7 rs17005251 1.3627
+Bbs9 BBS9 rs17727583 1.7361
+Bbx BBX rs10933904 1.4038
+A130075O10Rik BC008215 rs13399558 1.1403
+R74862 BC019904 rs708564 1.3649
+MMT00036147 BC031884 rs11063582 1.3699
+4930412O13Rik BC036297 rs412681 0.8468
+2610316D01Rik BC040219 rs7693962 1.6405
+Bcam BCAM rs4605275 4.2198
+Bcan BCAN rs2236601 1.6759
+Bcap29 BCAP29 rs4730250 2.3513
+Bcar1 BCAR1 rs2278020 2.4019
+Bcar3 BCAR3 rs3767966 3.1068
+Bcas1 BCAS1 rs465096 2.573
+Bcas2 BCAS2 rs222497 2.1438
+Bcas3 BCAS3 rs4968546 3.3818
+Bcat1 BCAT1 rs7954529 1.3461
+Bcat2 BCAT2 rs499765 1.1535
+Bccip BCCIP rs12259918 0.8719
+Bcdin3d BCDIN3D rs7138803 1.3415
+Bche BCHE rs9790044 1.8139
+Bckdha BCKDHA rs4803462 0.9215
+Bckdhb BCKDHB rs11961389 1.0087
+Bckdk BCKDK rs11865499 1.1896
+Bcl10 BCL10 rs17124345 1.7093
+Bcl11a BCL11A rs17331129 1.4092
+Bcl11b BCL11B rs1257432 2.1662
+Bcl2 BCL2 rs4987765 1.6983
+Bcl2a1a BCL2A1 rs11636338 2.4638
+Bcl2l1 BCL2L1 rs6058259 1.4437
+Bcl2l10 BCL2L10 rs4776002 2.7991
+Bcl2l11 BCL2L11 rs11691193 1.1991
+Bcl2l12 BCL2L12 rs7251 1.1906
+Bcl2l13 BCL2L13 rs1080199 1.088
+Bcl2l14 BCL2L14 rs12424479 1.978
+Bcl2l2 BCL2L2 rs10144278 1.5275
+Bcl3 BCL3 rs1531517 3.7852
+Bcl6 BCL6 rs1523474 2.6304
+Bcl6b BCL6B rs312456 1.9101
+Bcl7a BCL7A rs7976716 1.4275
+Bcl7b BCL7B rs17145738 8.9252
+Bcl7c BCL7C rs11649653 2.73
+Bcl9 BCL9 rs1553004 2.398
+Bcl9l BCL9L rs12363277 1.231
+Bclaf1 BCLAF1 rs811753 2.0729
+Bcmo1 BCMO1 rs12925557 1.8239
+Bco2 BCO2 rs4084132 1.3535
+Bcr BCR rs17531828 1.5854
+Bcs1l BCS1L rs33946522 0.965
+Bdh1 BDH1 rs2686095 1.8901
+Bdh2 BDH2 rs5008412 1.3218
+Bdkrb1 BDKRB1 rs2069586 2.0969
+Bdkrb2 BDKRB2 rs2069586 2.0969
+Bdnf BDNF rs12273363 1.28
+Bdp1 BDP1 rs277970 1.4825
+Bean1 BEAN1 rs37167 3.1727
+Begain BEGAIN rs7493924 2.1014
+Bend3 BEND3 rs1665901 2.0337
+Bend5 BEND5 rs4926749 2.0145
+Bend6 BEND6 rs4490669 1.0996
+Bend7 BEND7 rs2399972 2.4955
+Best1 BEST1 rs195157 3.1135
+Best2 BEST2 rs2066489 0.821
+Best3 BEST3 rs12369068 0.8362
+Bet1 BET1 rs2109839 1.5064
+Bet1l BET1L rs4980319 1.4276
+Bfsp1 BFSP1 rs742428 2.2253
+Bfsp2 BFSP2 rs10935059 1.7104
+Bhlha15 BHLHA15 rs6969321 2.1576
+Bhlha9 BHLHA9 rs869203 1.2442
+Bhlhe22 BHLHE22 rs10097877 0.8545
+Bhlhe23 BHLHE23 rs4809443 1.2484
+Bhlhe40 BHLHE40 rs7628683 1.2896
+Bhlhe41 BHLHE41 rs10842677 0.9889
+Bhmt BHMT rs567754 2.3632
+Bhmt2 BHMT2 rs402701 2.2254
+Bicc1 BICC1 rs1626606 2.817
+Bicd1 BICD1 rs904639 3.3086
+Bicd2 BICD2 rs10992461 1.0746
+Bid BID rs5746469 1.0062
+Bik BIK rs5759165 1.5955
+Bin1 BIN1 rs9287568 1.0765
+Bin2 BIN2 rs4761995 1.1896
+Bin3 BIN3 rs13272264 1.6813
+Birc3 BIRC3 rs11225198 1.0102
+Birc6 BIRC6 rs6747906 1.7328
+Birc7 BIRC7 rs6122403 1.6751
+Bivm BIVM rs11839583 1.2453
+Blcap BLCAP rs6018925 1.3986
+Blk BLK rs2252386 2.8586
+Blm BLM rs8038275 1.3574
+Blmh BLMH rs17767328 3.2266
+Blnk BLNK rs1902699 1.6519
+Bloc1s1 BLOC1S1 rs2293414 1.1448
+Bloc1s2 BLOC1S2 rs17729876 3.3729
+Bloc1s3 BLOC1S3 rs16979707 1.659
+Bloc1s4 BLOC1S4 rs4689034 1.2355
+Bloc1s5 BLOC1S5 rs197126 2.3215
+Bloc1s6 BLOC1S6 rs12442114 1.1175
+Blvra BLVRA rs1181600 0.9614
+Blvrb BLVRB rs444241 1.6407
+Blzf1 BLZF1 rs1064274 1.2103
+Bmf BMF rs495592 0.8533
+Bmp1 BMP1 rs2070687 1.1863
+Bmp10 BMP10 rs17036178 1.1301
+Bmp2 BMP2 rs11699950 1.5867
+Bmp2k BMP2K rs2288255 1.043
+Bmp3 BMP3 rs2685462 1.9205
+Bmp4 BMP4 rs17127103 2.2504
+Bmp5 BMP5 rs9367653 2.7073
+Bmp6 BMP6 rs17673852 1.8758
+Bmp7 BMP7 rs16980768 1.8459
+Bmp8a BMP8A rs11584399 2.3787
+Bmp8b BMP8B rs524469 1.7014
+Bmper BMPER rs10228524 1.8775
+Bmpr1a BMPR1A rs12415784 1.8765
+Bmpr1b BMPR1B rs4426786 1.9329
+Bmpr2 BMPR2 rs4303700 1.9248
+Bms1 BMS1 rs1592158 1.6434
+Bnc1 BNC1 rs4386103 1.0441
+Bnc2 BNC2 rs7046433 2.0253
+Bnip1 BNIP1 rs17730451 2.694
+Bnip2 BNIP2 rs6151582 1.6511
+Bnip3 BNIP3 rs10430614 0.9378
+Bnip3l BNIP3L rs2046222 1.8283
+Bnipl BNIPL rs7532312 1.1035
+Boc BOC rs9874798 1.6743
+Bod1 BOD1 rs41408753 1.3654
+Bod1l BOD1L1 rs13105197 1.4253
+Gm7843 BOD1L2 rs17062376 1.3702
+Bok BOK rs4675807 1.6084
+Bola1 BOLA1 rs626785 1.9481
+Bola2 BOLA2 rs235633 2.1056
+Bola3 BOLA3 rs6738709 1.561
+Boll BOLL rs13015772 2.0241
+Bop1 BOP1 rs4557742 1.8887
+6720463M24Rik BORA rs9530080 1.2394
+Bpgm BPGM rs12538734 1.0075
+Bphl BPHL rs3799207 1.1934
+Bpi BPI rs2185558 2.813
+Bpifa1 BPIFA1 rs1407017 1.0225
+Bpifa2 BPIFA2 rs6059150 1.1234
+Bpifa3 BPIFA3 rs6059150 1.1234
+Bpifb2 BPIFB2 rs1133358 1.9
+Bpifb3 BPIFB3 rs2070316 1.1391
+Bpifb4 BPIFB4 rs2424943 0.8633
+Bpifb6 BPIFB6 rs1133358 1.9
+Bpifc BPIFC rs880867 1.1777
+Bptf BPTF rs7221651 1.6983
+Braf BRAF rs4725829 0.8781
+Brap BRAP rs11065987 3.2345
+Baat1 BRAT1 rs6979501 1.4836
+Brca1 BRCA1 rs8176234 1.6045
+Brca2 BRCA2 rs206079 3.4275
+Brd1 BRD1 rs138864 1.2763
+Brd2 BRD2 rs150359 2.3042
+Brd3 BRD3 rs2810490 1.4885
+Brd4 BRD4 rs11668977 1.2026
+Brd7 BRD7 rs8060063 1.9446
+AL022779 BRD9 rs11950677 1.9112
+Brdt BRDT rs4512703 1.6774
+Bre BRE rs12470242 3.1455
+Brf1 BRF1 rs1008628 1.2526
+Brf2 BRF2 rs6998793 1.0744
+Bri3 BRI3 rs11771397 2.0683
+Bri3bp BRI3BP rs7959530 2.4371
+6720456B07Rik BRK1 rs779812 1.3922
+Brms1 BRMS1 rs479315 2.3097
+Brms1l BRMS1L rs10134750 0.9115
+Brox BROX rs11487815 0.9974
+Brpf1 BRPF1 rs3891072 1.1322
+Brpf3 BRPF3 rs9462164 0.9872
+Brsk1 BRSK1 rs8113016 2.4566
+Brsk2 BRSK2 rs11029667 1.5867
+Brwd1 BRWD1 rs4818007 1.7525
+Bscl2 BSCL2 rs7386 2.2897
+Bsdc1 BSDC1 rs8176781 1.3647
+AI325538 BSG rs3764938 0.8791
+Bsn BSN rs3870336 1.5133
+Bsnd BSND rs4339899 1.1655
+Bsph1 BSPH1 rs12972045 0.9961
+Bspry BSPRY rs1998503 2.1305
+Bst2 BST2 rs9576 1.421
+Bsx BSX rs930982 1.5167
+Btaf1 BTAF1 rs11186798 0.93
+Btbd1 BTBD1 rs1568657 2.4506
+Btbd10 BTBD10 rs4356206 3.0345
+Btbd11 BTBD11 rs11113413 2.6562
+Btbd16 BTBD16 rs7082865 1.7959
+Btbd17 BTBD17 rs4789057 1.1041
+Gm13718 BTBD18 rs9420 1.0711
+Btbd2 BTBD2 rs2041120 1.3097
+Btbd3 BTBD3 rs4813064 3.6405
+Btbd6 BTBD6 rs1008628 1.2526
+Btbd7 BTBD7 rs12889883 1.4702
+Btbd9 BTBD9 rs11754110 2.055
+Btc BTC rs13131487 1.5928
+Btd BTD rs6774663 1.5971
+Btg1 BTG1 rs11609268 1.0213
+Btg2 BTG2 rs4077764 1.6478
+Btg3 BTG3 rs2846881 1.7111
+Btg4 BTG4 rs17585834 2.8049
+Btla BTLA rs2705535 1.3454
+Btn1a1 BTN1A1 rs9393725 1.2476
+Btn2a2 BTN2A2 rs6933583 1.8791
+Btnl4 BTN3A2 rs6933583 1.8791
+Btnl2 BTNL2 rs1980493 4.4007
+Btnl9 BTNL9 rs4385169 0.963
+Btrc BTRC rs911560 1.0127
+Bub3 BUB3 rs17104692 3.2462
+Bud13 BUD13 rs964184 46.283
+Bzrap1 BZRAP1 rs9905604 2.3801
+Bzw2 BZW2 rs11770872 3.104
+8430408G22Rik C10orf10 rs17157321 1.7778
+Gm17455 C10orf105 rs1227071 1.8052
+1700040L02Rik C10orf107 rs12778088 2.4562
+1700112E06Rik C10orf11 rs1875160 2.0563
+A930004D18Rik C10orf114 rs11012709 1.8072
+A630007B06Rik C10orf118 rs12268028 1.5847
+Gm340 C10orf12 rs17112245 0.9492
+4933402N03Rik C10orf120 rs2947594 0.8315
+1810011H11Rik C10orf128 rs2804928 1.2421
+2700050L05Rik C10orf137 rs12414594 1.3804
+BC016423 C10orf18 rs3209286 3.2749
+Peo1 C10orf2 rs735137 1.5547
+2010012O05Rik C10orf32 rs17724534 1.7488
+2010107G23Rik C10orf35 rs10998872 1.4408
+1700024G13Rik C10orf53 rs1055421 2.2234
+4632428N05Rik C10orf54 rs10762481 1.5738
+4933411K16Rik C10orf62 rs1105999 0.971
+3425401B19Rik C10orf71 rs17774672 1.4246
+9130011E15Rik C10orf76 rs12221307 1.1332
+1700019N19Rik C10orf82 rs11197786 0.841
+2310057M21Rik C10orf88 rs7085513 2.6119
+D7Ertd443e C10orf90 rs2002950 1.9344
+2610528A11Rik C10orf99 rs7923369 1.1576
+BC051019 C11orf16 rs1025438 0.9702
+1810055G02Rik C11orf24 rs674857 1.6925
+2210018M11Rik C11orf30 rs17134961 1.4399
+2700094K13Rik C11orf31 rs9420 1.0711
+1600029D21Rik C11orf34 rs3018471 1.0384
+1600016N20Rik C11orf35 rs3740651 2.1787
+Gm5901 C11orf42 rs10839527 1.2839
+1810009A15Rik C11orf48 rs7386 2.2897
+1110051M20Rik C11orf49 rs11039024 15.606
+1810046K07Rik C11orf53 rs1568202 0.9234
+AU019823 C11orf57 rs7935331 1.4073
+4931429I11Rik C11orf63 rs4936758 1.7277
+4930550C14Rik C11orf65 rs611646 2.2877
+AI837181 C11orf68 rs679147 3.3254
+EG434402 C11orf71 rs4936295 1.1322
+0610007P06Rik C11orf73 rs7937189 1.8162
+B230118H07Rik C11orf74 rs1498336 1.5312
+4632434I11Rik C11orf82 rs7924546 1.2906
+AI462493 C11orf83 rs7386 2.2897
+AI846148 C11orf84 rs7942889 1.0034
+1700123I01Rik C11orf85 rs35079323 2.2192
+2010003K11Rik C11orf86 rs4930187 1.8315
+AI593442 C11orf87 rs1439508 1.5478
+4833427G06Rik C11orf88 rs17585834 2.8049
+A930018P22Rik C11orf91 rs831612 0.8807
+1700029I15Rik C11orf94 rs2292910 3.3325
+Gm13889 C11orf96 rs4755757 2.1599
+AI597468 C12orf23 rs4640029 1.6556
+4930430F08Rik C12orf29 rs1960667 2.15
+B230216G23Rik C12orf39 rs7138234 1.6326
+D6Wsu163e C12orf4 rs12831479 1.358
+CN725425 C12orf40 rs11614400 2.0172
+1700113H08Rik C12orf42 rs11111603 2.3573
+2210016L21Rik C12orf43 rs1183910 2.3972
+D10Wsu102e C12orf45 rs11112269 1.2272
+2410131K14Rik C12orf49 rs2172953 2.0229
+9630033F20Rik C12orf5 rs12301377 1.9927
+1700017N19Rik C12orf50 rs1960667 2.15
+1110008J03Rik C12orf52 rs12828094 0.8371
+1700031M16Rik C12orf54 rs9268 1.1118
+4930485B16Rik C12orf55 rs3825265 1.2239
+1500017I17Rik C12orf56 rs6581549 1.5461
+BC049715 C12orf60 rs17821751 1.4057
+2810006K23Rik C12orf65 rs10773000 2.845
+BC048403 C12orf66 rs12424248 1.0052
+AI836003 C12orf68 rs11168445 1.6218
+1190007I07Rik C12orf73 rs4135119 2.0718
+1500009L16Rik C12orf75 rs11608724 1.2877
+1700011H14Rik C14orf105 rs11844933 1.2283
+1700123O20Rik C14orf119 rs17126602 1.8133
+D430019H16Rik C14orf132 rs12436590 1.514
+AK010878 C14orf142 rs1268371 0.8617
+9030617O03Rik C14orf159 rs11622455 2.5196
+2700060E02Rik C14orf166 rs12432630 3.2592
+Gm6772 C14orf166B rs760262 1.8477
+2410016O06Rik C14orf169 rs2302041 1.604
+A530016L24Rik C14orf180 rs4983599 1.4916
+2010107E04Rik C14orf2 rs1885073 0.9872
+4921506M07Rik C14orf25 rs17107295 1.9408
+Gm527 C14orf28 rs1950303 1.9014
+3632451O06Rik C14orf37 rs17094108 1.8871
+4930447C04Rik C14orf39 rs1951114 0.8617
+BC022687 C14orf79 rs2033932 1.6904
+4930427A07Rik C14orf80 rs11849001 1.8841
+4931414P19Rik C14orf93 rs12590429 1.6247
+1700026D08Rik C15orf26 rs1509559 2.5491
+AI118078 C15orf27 rs16967858 1.5627
+4930432H15Rik C15orf32 rs7170647 1.8111
+2610034B18Rik C15orf38 rs1256826 1.9266
+1700017B05Rik C15orf39 rs7170378 1.3983
+3110040N11Rik C15orf40 rs6603044 1.3223
+BC052040 C15orf41 rs4923719 2.4233
+A430105I19Rik C15orf52 rs28602975 1.1929
+6030419C18Rik C15orf59 rs16958177 1.225
+2410076I21Rik C15orf60 rs17185294 2.2618
+2300009A05Rik C15orf61 rs17240463 1.8674
+Gm14137 C15orf62 rs11855560 2.3641
+0610011F06Rik C16orf13 rs9929621 1.1517
+2900011O08Rik C16orf45 rs7206414 1.8094
+1700030J22Rik C16orf46 rs804905 1.419
+BC030336 C16orf52 rs9788859 0.8199
+AI467606 C16orf54 rs235633 2.1056
+4732415M23Rik C16orf55 rs164741 1.9825
+BC017158 C16orf58 rs4477723 1.7902
+1600002H07Rik C16orf59 rs4786285 1.2213
+9030624J02Rik C16orf62 rs371889 1.8407
+D230025D16Rik C16orf70 rs13312735 5.2378
+4930451G09Rik C16orf71 rs12926158 1.9314
+1810013L24Rik C16orf72 rs2313980 0.9404
+1190005I06Rik C16orf74 rs871290 2.6123
+4933402J07Rik C16orf78 rs187504 2.5245
+Gtl3 C16orf80 rs11076220 0.9179
+4933405L10Rik C16orf86 rs6499137 19.597
+AU021092 C16orf89 rs2241060 1.3217
+1700037C18Rik C16orf90 rs40535 1.1025
+BC003965 C16orf91 rs3826226 1.0838
+1700018B08Rik C16orf95 rs1974880 1.9634
+4930562C15Rik C16orf96 rs2438983 1.4379
+Gm16515 C17orf103 rs6587128 1.7857
+Gm1564 C17orf104 rs12949217 1.6639
+1700006E09Rik C17orf105 rs2023794 2.1135
+4930544D05Rik C17orf107 rs11654952 1.2197
+Gm11492 C17orf47 rs6503869 1.1109
+1700020L24Rik C17orf50 rs2240903 0.9052
+BC030867 C17orf53 rs7220138 1.2367
+2310047M10Rik C17orf59 rs8064449 3.2797
+BC017643 C17orf62 rs7406694 1.1231
+Gm525 C17orf67 rs7223071 1.2755
+2310003H01Rik C17orf70 rs8068511 1.9914
+2310007L24Rik C17orf72 rs885633 1.0566
+4933402P03Rik C17orf74 rs11652328 2.8827
+5730455P16Rik C17orf75 rs16967261 0.9303
+Gm11437 C17orf78 rs12601443 0.9172
+D11Wsu47e C17orf80 rs11544800 1.173
+1200014J11Rik C17orf85 rs4300684 1.2055
+1810043H04Rik C17orf89 rs2466773 1.1856
+E130012A19Rik C17orf96 rs968113 0.9439
+1700016K19Rik C17orf97 rs4130140 1.4504
+1700001P01Rik C17orf98 rs8076343 1.0752
+6030468B19Rik C17orf99 rs450474 1.5748
+8030462N17Rik C18orf25 rs11873572 0.9481
+BC031181 C18orf32 rs12963212 8.3966
+A330050F15Rik C18orf42 rs2991286 1.9326
+4930503L19Rik C18orf54 rs10164112 1.0033
+Gm17266 C18orf63 rs12970715 1.5555
+3110002H16Rik C18orf8 rs7241000 2.403
+D17Wsu104e C19orf10 rs8109860 1.1385
+1600014C10Rik C19orf12 rs10403744 2.2096
+2900092C05Rik C19orf18 rs3810129 1.3137
+1600002K03Rik C19orf24 rs2379240 1.6478
+2310011J03Rik C19orf25 rs4807927 1.348
+2200002D01Rik C19orf33 rs41465446 1.0192
+AB124611 C19orf38 rs1541596 2.6156
+C230052I12Rik C19orf40 rs4805021 2.8639
+2310036O22Rik C19orf43 rs2066489 0.821
+1700030K09Rik C19orf44 rs10518278 1.5084
+1700019B03Rik C19orf45 rs2287916 2.1734
+2310022A10Rik C19orf47 rs8108272 1.527
+2410002F23Rik C19orf48 rs2247216 1.988
+1810026J23Rik C19orf52 rs11667234 1.2777
+D8Ertd738e C19orf53 rs378189 1.1026
+BC024978 C19orf54 rs2604913 2.4279
+4930432K21Rik C19orf57 rs897786 1.0056
+1810033B17Rik C19orf59 rs1862513 0.9606
+2810428I15Rik C19orf60 rs3170474 3.1899
+A230050P20Rik C19orf66 rs1979262 1.1248
+4930404N11Rik C19orf71 rs7253540 1.055
+Mtag2 C19orf73 rs1009214 0.9194
+2210404O07Rik C19orf77 rs4807470 2.716
+Gm6484 C19orf80 rs737337 8.5082
+1700008O03Rik C19orf81 rs11665802 2.9759
+C1galt1 C1GALT1 rs6956105 2.2614
+1700016C15Rik C1orf100 rs12084013 2.2317
+Gm7068 C1orf101 rs3123697 1.8386
+4930558K02Rik C1orf105 rs2227198 2.7113
+5730559C18Rik C1orf106 rs10920059 1.7515
+9930104L06Rik C1orf109 rs11585866 1.2671
+1700084C01Rik C1orf110 rs12044618 1.467
+1700015E13Rik C1orf111 rs11577628 0.8693
+BC055324 C1orf112 rs12025581 1.67
+C130074G19Rik C1orf115 rs17008302 1.1299
+AA986860 C1orf116 rs11119976 1.6413
+1110065P20Rik C1orf122 rs582811 1.2912
+0610037L13Rik C1orf123 rs7554022 1.6326
+Gm572 C1orf127 rs9430163 1.6038
+2810004N23Rik C1orf131 rs556132 1.3894
+4921539E11Rik C1orf141 rs1885276 1.4254
+1700012P22Rik C1orf158 rs4240904 1.8094
+9430015G10Rik C1orf159 rs3121561 1.475
+I830077J02Rik C1orf162 rs9025 1.0845
+1700024P16Rik C1orf168 rs12133173 1.8989
+1810019J16Rik C1orf172 rs17162333 6.8551
+4922501L14Rik C1orf173 rs12239097 1.782
+A430005L14Rik C1orf174 rs4648426 1.862
+BC055111 C1orf177 rs886738 2.0821
+4930522H14Rik C1orf185 rs12744230 1.2147
+1700094D03Rik C1orf189 rs12026177 1.3009
+1700009P17Rik C1orf192 rs16832872 1.9462
+1700013F07Rik C1orf194 rs17838321 0.8294
+2310022B05Rik C1orf198 rs11805329 2.0034
+1700025G04Rik C1orf21 rs17577136 1.5764
+2610528J11Rik C1orf210 rs34783763 1.3189
+5730409E04Rik C1orf216 rs12563583 0.8551
+1700022P22Rik C1orf227 rs6700342 1.4665
+Gm1661 C1orf228 rs7538419 1.9776
+2310033P09Rik C1orf35 rs3768331 1.4325
+4933434E20Rik C1orf43 rs3811454 1.0499
+AU022252 C1orf50 rs4660657 1.0818
+Gm129 C1orf51 rs7511649 0.8604
+2410004B18Rik C1orf52 rs11161548 1.7296
+2310009B15Rik C1orf53 rs12122995 1.1245
+BC028528 C1orf54 rs7511649 0.8604
+Gm128 C1orf56 rs7532312 1.1035
+Gm694 C1orf64 rs732286 1.902
+4922505E12Rik C1orf65 rs866096 0.9477
+A130010J15Rik C1orf74 rs669694 1.9996
+2310006M14Rik C1orf81 rs7551481 1.5378
+0610031J06Rik C1orf85 rs2230194 0.8492
+2610002J02Rik C1orf86 rs2292857 2.0711
+ENSMUST00000054501 C1orf87 rs681724 1.4868
+CK137956 C1orf94 rs10914879 0.9062
+6330403A02Rik C1orf95 rs751685 1.7104
+C1qa C1QA rs294189 1.4487
+C1qb C1QB rs294189 1.4487
+C1qbp C1QBP rs3026143 1.3339
+C1qc C1QC rs294189 1.4487
+C1ql1 C1QL1 rs17547180 1.6863
+C1ql2 C1QL2 rs7583824 1.4049
+C1ql3 C1QL3 rs17138741 1.1674
+C1ql4 C1QL4 rs11837234 3.4465
+C1qtnf2 C1QTNF2 rs4921132 1.7029
+C1qtnf3 C1QTNF3 rs15612 1.8891
+C1qtnf6 C1QTNF6 rs229565 1.2594
+C1qtnf7 C1QTNF7 rs16891760 2.1974
+C1qtnf9 C1QTNF9 rs9511199 2.1383
+C1ra C1R rs7965055 1.0494
+C1rl C1RL rs3813736 1.2216
+Gm5077 C1S rs7965055 1.0494
+C2 C2 rs3869145 2.4438
+8430427H17Rik C20orf112 rs1737887 1.9851
+1700020A23Rik C20orf141 rs6051416 1.5427
+B230312C02Rik C20orf166 rs4969526 1.0593
+6430550D23Rik C20orf173 rs8121389 1.076
+4930402H24Rik C20orf194 rs2422862 1.5193
+BC051628 C20orf195 rs7268068 1.1196
+1110034G24Rik C20orf196 rs1699229 1.2025
+4930526D03Rik C20orf201 rs6011280 1.6592
+BC052486 C20orf202 rs6074272 1.4157
+4930529M08Rik C20orf26 rs11908058 2.1848
+1700037H04Rik C20orf27 rs6139201 1.8993
+1700021F07Rik C20orf85 rs2903011 1.0919
+6820408C15Rik C20orf96 rs406449 1.1958
+1810043G02Rik C21orf2 rs2838549 1.1016
+2610028H24Rik C21orf58 rs11700398 1.2021
+1110004E09Rik C21orf59 rs1782962 1.0477
+4932438H23Rik C21orf62 rs6517112 1.8755
+D16Ertd472e C21orf91 rs10482890 0.8811
+Gm867 C22orf15 rs6003870 1.5067
+MMT00006053 C22orf26 rs7511013 2.2054
+2510002D24Rik C22orf39 rs2073730 1.135
+C2cd2 C2CD2 rs2277793 2.8739
+C2cd2l C2CD2L rs1784460 3.2948
+C2cd3 C2CD3 rs11236024 1.3075
+C2cd4a C2CD4A rs7164482 1.1837
+C2cd4b C2CD4B rs4775474 3.5354
+C2cd4c C2CD4C rs1115033 1.721
+C2cd4d C2CD4D rs12045886 0.8801
+4932415D10Rik C2orf16 rs13392197 2.5166
+1500015O10Rik C2orf40 rs13016988 1.1761
+C87436 C2orf42 rs6546572 1.2547
+1110057K04Rik C2orf43 rs4666453 2.6426
+BC068281 C2orf44 rs11125371 1.808
+9430016H08Rik C2orf47 rs17629127 1.5363
+2410004P03Rik C2orf50 rs11892684 1.8145
+1700110M21Rik C2orf53 rs7588926 1.3223
+2310007B03Rik C2orf54 rs6719991 2.0401
+1700019O17Rik C2orf57 rs12990343 2.056
+1700011E24Rik C2orf61 rs6750222 2.2431
+Gm216 C2orf62 rs7587403 1.157
+0610030E20Rik C2orf68 rs10206961 0.8573
+1700066M21Rik C2orf69 rs17629127 1.5363
+1700001C02Rik C2orf70 rs751651 1.3156
+BC027072 C2orf71 rs6742110 1.626
+2810459M11Rik C2orf72 rs11692681 1.3195
+4930505A04Rik C2orf73 rs4389358 2.9792
+1700093K21Rik C2orf74 rs4672416 2.5753
+3110009E18Rik C2orf76 rs3769663 1.4896
+D630023F18Rik C2orf80 rs2885602 1.62
+1700003E16Rik C2orf81 rs13396033 2.5805
+3110079O15Rik C2orf82 rs895432 1.0508
+1700019D03Rik C2orf88 rs16832401 2.3806
+C3 C3 rs4807895 1.3005
+C3ar1 C3AR1 rs2231745 2.3708
+BC027231 C3orf17 rs11707498 1.0089
+6430571L13Rik C3orf18 rs420536 0.8216
+4930590J08Rik C3orf20 rs9840023 1.8392
+MMT00009530 C3orf27 rs1806722 2.4465
+4930435E12Rik C3orf30 rs6807110 2.4477
+E130311K13Rik C3orf33 rs358717 1.3987
+8430410A17Rik C3orf37 rs4853 0.8162
+4930453N24Rik C3orf38 rs12629581 1.2947
+BC016579 C3orf52 rs9811691 0.9638
+1190002N15Rik C3orf58 rs13320111 1.7986
+4930452B06Rik C3orf67 rs6804703 2.2346
+2510009E07Rik C3orf70 rs744306 1.1289
+C4a C4A rs4151651 2.004
+C4b C4B rs2228296 1.2919
+C4bp C4BPA rs6673109 1.4848
+C4bp-ps1 C4BPB rs6673109 1.4848
+4930579F01Rik C4orf17 rs11933667 3.5661
+0610040J01Rik C4orf19 rs705099 2.0073
+4930422G04Rik C4orf21 rs17606550 1.7809
+1700007G11Rik C4orf22 rs2087019 1.5077
+Gm1045 C4orf26 rs6835992 1.0283
+2700029M09Rik C4orf27 rs13111508 1.0683
+1810037I17Rik C4orf3 rs12645965 4.9385
+D3Ertd751e C4orf33 rs13111663 1.66
+1700016H13Rik C4orf36 rs10015121 3.8114
+4930432K09Rik C4orf40 rs1399252 1.9393
+Gm17359 C4orf45 rs9995727 1.4024
+1700029J07Rik C4orf47 rs11940393 1.6126
+Gm1673 C4orf48 rs7377932 1.4769
+1700011L22Rik C4orf51 rs950333 1.7326
+Hc C5 rs16910280 1.755
+C5ar1 C5AR1 rs10406676 1.7335
+9530068E07Rik C5orf15 rs10123 1.0989
+MMT00069885 C5orf20 rs874158 1.2638
+6030458C11Rik C5orf22 rs16901235 1.8111
+4732471D19Rik C5orf25 rs13163755 1.3348
+EG633640 C5orf28 rs7718627 0.8726
+D1Ertd622e C5orf30 rs17155171 1.213
+4833420G17Rik C5orf34 rs7718627 0.8726
+3010026O09Rik C5orf45 rs166624 1.5109
+Gm94 C5orf46 rs6884703 2.8904
+4930524B15Rik C5orf47 rs13168379 1.7089
+1700065I17Rik C5orf48 rs7701818 1.9918
+1700001L19Rik C5orf49 rs1046014 1.6126
+AW549877 C5orf51 rs7713580 1.1494
+4921536K21Rik C5orf52 rs10071628 1.3068
+4930469K13Rik C5orf58 rs12055330 1.7135
+C330018D20Rik C5orf63 rs17165199 0.8976
+C6 C6 rs10071904 1.4063
+AI413582 C6orf1 rs1776877 2.0729
+BC051142 C6orf10 rs6913309 2.6842
+D17Wsu92e C6orf106 rs2814944 8.4186
+1700010I14Rik C6orf118 rs6903922 2.2145
+1600012H06Rik C6orf120 rs3823465 2.1051
+AI661453 C6orf132 rs12211359 1.6647
+2310061I04Rik C6orf136 rs2285320 1.5852
+9130008F23Rik C6orf141 rs12176464 1.1041
+2300002M23Rik C6orf15 rs3094222 3.0336
+Gm136 C6orf163 rs1203163 1.3701
+1700003M02Rik C6orf165 rs9450716 1.099
+D630037F22Rik C6orf170 rs9490185 2.7149
+MMT00079324 C6orf195 rs17135340 2.0146
+4933417A18Rik C6orf201 rs9392552 1.4122
+1700021F05Rik C6orf203 rs3099605 1.1369
+1700052N19Rik C6orf211 rs9397054 1.3431
+4930539E08Rik C6orf222 rs12189786 2.2712
+2310039H08Rik C6orf226 rs9471932 1.0323
+G6b C6orf25 rs400547 2.5546
+D17H6S53E C6orf47 rs3117583 4.5605
+1110038B12Rik C6orf48 rs9368699 1.3581
+1110058L19Rik C6orf57 rs9455190 2.5558
+2310057J18Rik C6orf58 rs1355624 1.6017
+BC005537 C6orf62 rs17491647 3.1126
+2210404J11Rik C6orf70 rs2274952 1.6996
+BC004004 C6orf89 rs41408445 0.9326
+C7 C7 rs324064 1.9496
+5033411D12Rik C7orf10 rs12701832 1.5291
+AW209491 C7orf25 rs582214 1.8083
+E130309D02Rik C7orf26 rs4418238 1.7775
+4921507P07Rik C7orf31 rs4722430 1.2389
+1700034O15Rik C7orf34 rs4252400 1.6885
+2410066E13Rik C7orf41 rs724576 3.4048
+BC037034 C7orf43 rs4134895 1.1281
+3110062M04Rik C7orf49 rs12707218 2.8576
+3110082I17Rik C7orf50 rs10274367 3.9796
+1110001J03Rik C7orf55 rs6965212 0.8557
+Gm11992 C7orf57 rs7789930 2.518
+B630005N14Rik C7orf60 rs2396570 1.2563
+4921511H03Rik C7orf62 rs17164368 2.3268
+A330021E22Rik C7orf63 rs13247172 2.1492
+4930415F15Rik C7orf72 rs1911753 1.2413
+1810058I24Rik C7orf73 rs7798256 3.4484
+C8a C8A rs607268 2.8788
+C8b C8B rs607268 2.8788
+C8g C8G rs884113 1.2378
+A830018L16Rik C8orf34 rs11986007 2.5376
+2610301B20Rik C8orf37 rs7844993 1.8601
+1810011O10Rik C8orf4 rs16888839 0.8758
+2610019F03Rik C8orf42 rs17065117 1.6251
+3110035E14Rik C8orf46 rs10112574 2.3455
+BC030476 C8orf47 rs2255464 1.5931
+AI429214 C8orf48 rs7000363 1.4905
+9930012K11Rik C8orf58 rs3758038 1.8256
+Gm4540 C8orf59 rs4150944 2.4382
+4930578I06Rik C8orf74 rs11996321 1.3894
+C9 C9 rs835207 1.8814
+MMT00082066 C9orf102 rs2297009 1.7003
+D2Wsu81e C9orf114 rs11515530 2.6338
+1700007K13Rik C9orf116 rs535 1.7354
+1700019L03Rik C9orf117 rs564754 1.4466
+3110001D03Rik C9orf123 rs2890762 3.0289
+1700022I11Rik C9orf131 rs3824552 0.9169
+1700028P14Rik C9orf135 rs4744944 1.5777
+BC029214 C9orf142 rs11790360 2.3242
+D630039A03Rik C9orf152 rs16914837 1.7786
+4921517D22Rik C9orf153 rs1535757 1.3261
+5830415F09Rik C9orf156 rs1561958 1.3451
+1110008P14Rik C9orf16 rs7047254 1.3447
+1700101E01Rik C9orf171 rs11243789 2.1284
+Gm996 C9orf172 rs2811749 1.5594
+1110017D15Rik C9orf24 rs10758259 1.5991
+2010111I01Rik C9orf3 rs4647551 1.8167
+D030056L22Rik C9orf40 rs2769068 1.4468
+4933430I17Rik C9orf43 rs8177813 1.1795
+1700001O22Rik C9orf50 rs10125020 1.4844
+C330006A16Rik C9orf69 rs10858224 1.3203
+3110043O21Rik C9orf72 rs10812614 1.6436
+BC005624 C9orf78 rs13297359 1.6432
+AI481877 C9orf84 rs384408 3.8722
+1110059E24Rik C9orf85 rs4745148 0.9512
+1110007C09Rik C9orf89 rs7046328 0.9706
+1700026L06Rik C9orf9 rs1050700 2.6778
+6330416G13Rik C9orf91 rs6478092 1.7959
+Gm711 C9orf96 rs7040821 1.4449
+Car1 CA1 rs1032551 1.3987
+Car10 CA10 rs11653979 1.8798
+Car11 CA11 rs2292342 1.8551
+Car12 CA12 rs16946801 4.4263
+Car13 CA13 rs4150944 2.4382
+Car2 CA2 rs9329911 1.7153
+Car3 CA3 rs2452679 0.8456
+Car4 CA4 rs8079003 0.8645
+Car5a CA5A rs11642531 1.7794
+Car6 CA6 rs12564569 2.0498
+Car7 CA7 rs444373 1.9944
+Car8 CA8 rs16925993 2.197
+Car9 CA9 rs10972557 1.4008
+5830433M19Rik CAAP1 rs10967547 2.0061
+Cab39 CAB39 rs10193165 1.2147
+Cab39l CAB39L rs11620398 1.4277
+Cabc1 CABC1 rs2000399 2.2326
+Cables1 CABLES1 rs750071 5.2372
+Cables2 CABLES2 rs6089219 1.5938
+Cabp1 CABP1 rs11065194 1.3302
+Cabp2 CABP2 rs2276118 1.1834
+Cabp4 CABP4 rs17501521 1.0215
+Cabp5 CABP5 rs2560967 1.3866
+Cabp7 CABP7 rs715595 2.7989
+4931407G18Rik CABS1 rs17714076 2.212
+Cabyr CABYR rs9961527 1.0561
+Cacfd1 CACFD1 rs2073933 2.2327
+Cachd1 CACHD1 rs12025177 1.5331
+Cacna1a CACNA1A rs4926290 1.8128
+Cacna1b CACNA1B rs4076712 1.6185
+Cacna1c CACNA1C rs10774045 2.1941
+Cacna1d CACNA1D rs2680670 2.7825
+Cacna1e CACNA1E rs12567235 2.7768
+Cacna1g CACNA1G rs198542 1.2642
+Cacna1h CACNA1H rs8058550 0.963
+Cacna1i CACNA1I rs12484428 1.2513
+Cacna1s CACNA1S rs6699862 2.1427
+Cacna2d1 CACNA2D1 rs2040977 1.5889
+Cacna2d2 CACNA2D2 rs12488468 1.7849
+Cacna2d3 CACNA2D3 rs525736 3.8453
+Cacna2d4 CACNA2D4 rs11062008 1.4878
+Cacnb1 CACNB1 rs16530 2.9041
+Cacnb2 CACNB2 rs1277729 2.5237
+Cacnb3 CACNB3 rs3730071 1.2267
+Cacnb4 CACNB4 rs13016984 1.6631
+Cacng1 CACNG1 rs1507504 2.2008
+Cacng2 CACNG2 rs5756311 1.767
+Cacng3 CACNG3 rs16973528 1.1481
+Cacng4 CACNG4 rs1507504 2.2008
+Cacng5 CACNG5 rs4791023 2.0633
+Cacng6 CACNG6 rs7247590 1.6055
+Cacng7 CACNG7 rs7251033 2.5967
+Cacng8 CACNG8 rs7251033 2.5967
+2510012J08Rik CACTIN rs8100350 0.9442
+2700078E11Rik CACUL1 rs12413025 1.8801
+Cacybp CACYBP rs10489330 1.4265
+Cad CAD rs1275521 1.2551
+Cadm1 CADM1 rs12279110 1.7989
+Cadm2 CADM2 rs9837914 1.9454
+Cadm3 CADM3 rs2746042 1.2533
+Cadm4 CADM4 rs400058 2.2827
+Cadps CADPS rs2251423 2.1164
+Cadps2 CADPS2 rs13242616 3.2857
+Cage1 CAGE1 rs1324502 1.3855
+B930003M22Rik CAHM rs6939187 2.0887
+Calb1 CALB1 rs11778036 1.4038
+Calb2 CALB2 rs11075884 1.3465
+Calca CALCA rs11023382 1.2323
+Calcb CALCB rs4132974 1.0961
+Calcoco1 CALCOCO1 rs11170674 1.564
+Calcoco2 CALCOCO2 rs12938102 1.5923
+Calcr CALCR rs3802051 1.2065
+Calcrl CALCRL rs840617 1.159
+Cald1 CALD1 rs2718146 3.4238
+Calhm1 CALHM1 rs755577 2.0556
+Calhm2 CALHM2 rs755577 2.0556
+Calm1 CALM1 rs17126685 1.2585
+Calm2 CALM2 rs6750222 2.2431
+Calm3 CALM3 rs4274528 0.8739
+Calml3 CALML3 rs12359191 1.1866
+Calml4 CALML4 rs10152713 2.1314
+Calm4 CALML5 rs12359191 1.1866
+Caln1 CALN1 rs1808379 1.7122
+Calr3 CALR3 rs17796594 2.0286
+Calu CALU rs2290228 1.2577
+Caly CALY rs11101683 2.0436
+Camk1 CAMK1 rs3891072 1.1322
+Camk1d CAMK1D rs10752268 2.8851
+Camk1g CAMK1G rs17014889 1.7833
+Camk2a CAMK2A rs6896288 3.6929
+Camk2b CAMK2B rs12535503 1.3789
+Camk2d CAMK2D rs757175 2.0348
+Camk2n1 CAMK2N1 rs721360 0.9066
+Camk2n2 CAMK2N2 rs13315366 1.1534
+Camk4 CAMK4 rs306096 1.5809
+Camkk1 CAMKK1 rs4790547 1.3179
+Camkk2 CAMKK2 rs2686386 2.4869
+1700106N22Rik CAMKMT rs698826 3.1366
+Camkv CAMKV rs10865959 2.3433
+Camp CAMP rs2889854 1.2021
+Camsap1 CAMSAP1 rs11103209 1.1804
+Camsap2 CAMSAP2 rs16847180 0.9205
+Camsap3 CAMSAP3 rs548664 1.048
+Camta1 CAMTA1 rs6577435 2.2362
+Cand1 CAND1 rs10506542 1.669
+Cand2 CAND2 rs906671 1.5306
+Cant1 CANT1 rs6501272 0.9446
+Canx CANX rs11747643 1.0615
+Cap1 CAP1 rs6699941 2.1512
+Cap2 CAP2 rs7773451 2.0213
+Capg CAPG rs11674612 3.0878
+Capn1 CAPN1 rs3825072 3.8162
+Capn10 CAPN10 rs4494742 1.3342
+Capn11 CAPN11 rs11967633 2.6672
+Capn12 CAPN12 rs4802744 1.4925
+Capn13 CAPN13 rs6707864 2.2734
+Capn2 CAPN2 rs10916078 1.7208
+Capn3 CAPN3 rs2412710 4.2097
+Capn5 CAPN5 rs10899346 1.561
+Capn7 CAPN7 rs17480974 2.4276
+Capn8 CAPN8 rs10916078 1.7208
+Capn9 CAPN9 rs2478540 1.7602
+Capns1 CAPNS1 rs17777328 1.0453
+Capns2 CAPNS2 rs9934904 1.2256
+Caprin1 CAPRIN1 rs4756101 1.273
+Caprin2 CAPRIN2 rs11051065 1.6341
+AK015008 CAPS2 rs7135867 0.9465
+Capsl CAPSL rs1645181 1.329
+Capza1 CAPZA1 rs7550315 1.0064
+Capza2 CAPZA2 rs41775 4.3492
+Capzb CAPZB rs17477654 2.2457
+Card10 CARD10 rs133705 1.4322
+Card11 CARD11 rs11768944 1.8108
+Card14 CARD14 rs7207000 1.5571
+Card6 CARD6 rs6868112 1.5346
+Card9 CARD9 rs10870201 1.6269
+Carhsp1 CARHSP1 rs1058982 1.5289
+Carkd CARKD rs3759463 1.7488
+Carm1 CARM1 rs1541596 2.6156
+Carns1 CARNS1 rs1064876 1.339
+Cars CARS rs739401 3.9183
+Cars2 CARS2 rs3759463 1.7488
+BB093062 CARTPT rs16871468 1.9208
+Casc1 CASC1 rs7973136 1.7508
+Casc3 CASC3 rs16965666 1.4633
+Casc4 CASC4 rs11856569 1.0857
+Casc5 CASC5 rs3092980 1.2821
+Casd1 CASD1 rs10277520 0.9543
+Caskin1 CASKIN1 rs8062006 1.1593
+Caskin2 CASKIN2 rs3934780 1.6503
+Casp1 CASP1 rs12293622 1.4814
+Casp12 CASP12 rs475931 0.971
+Casp14 CASP14 rs17377848 1.9698
+Casp2 CASP2 rs3212147 1.0615
+Casp3 CASP3 rs871253 1.63
+Casp4 CASP4 rs12293622 1.4814
+Casp6 CASP6 rs2301717 2.3451
+Casp7 CASP7 rs12415607 1.219
+Casp8 CASP8 rs13402616 2.0013
+Casp8ap2 CASP8AP2 rs369328 1.7199
+Casp9 CASP9 rs16851662 0.9408
+Casq1 CASQ1 rs4656889 2.2217
+Casq2 CASQ2 rs7538337 1.8383
+Casr CASR rs1354162 1.2158
+Cass4 CASS4 rs16984830 1.419
+Cast CAST rs9687185 20.496
+Casz1 CASZ1 rs11121621 1.6383
+Cat CAT rs7931494 1.8877
+Catsper1 CATSPER1 rs10791838 1.4466
+Catsper2 CATSPER2 rs11635852 1.2377
+Catsper3 CATSPER3 rs888685 0.8265
+Catsper4 CATSPER4 rs8768 1.8027
+Catsperb CATSPERB rs1704684 1.7552
+Tmem146 CATSPERD rs7257191 1.2541
+Catsperg2 CATSPERG rs2074979 0.8108
+Cav1 CAV1 rs7791593 3.5123
+Cav2 CAV2 rs7791593 3.5123
+Cav3 CAV3 rs237864 0.9119
+Cbfa2t2 CBFA2T2 rs12625776 1.5646
+Cbfa2t3 CBFA2T3 rs12596491 2.739
+Cbfb CBFB rs11639620 4.9825
+Cbl CBL rs12417928 1.9094
+Cblb CBLB rs2120105 1.6934
+Cblc CBLC rs1531517 3.7852
+Cbll1 CBLL1 rs6973044 0.8162
+Cbln1 CBLN1 rs41210 1.2475
+Cbln2 CBLN2 rs639932 1.4657
+Cbln3 CBLN3 rs4981519 1.2555
+Cbln4 CBLN4 rs6024535 1.0453
+Cbr1 CBR1 rs2845727 2.2328
+Cbr3 CBR3 rs12626189 2.0543
+Cbr4 CBR4 rs17543620 1.3526
+Cbs CBS rs11702511 1.5547
+Cbwd1 CBWD1 rs3008126 0.8761
+Cbx1 CBX1 rs10491183 4.4033
+Cbx2 CBX2 rs2036316 0.9512
+Cbx3 CBX3 rs41374744 1.1953
+Cbx5 CBX5 rs2071391 2.3205
+Cbx6 CBX6 rs5757318 1.547
+Cbx7 CBX7 rs17000751 1.6682
+Cbx8 CBX8 rs2036316 0.9512
+Cby1 CBY1 rs11089893 1.5028
+Cby3 CBY3 rs11744173 0.8671
+Cc2d1a CC2D1A rs1057193 1.467
+Cc2d2a CC2D2A rs2314589 1.0061
+Ccar1 CCAR1 rs1163205 1.3192
+Ccbe1 CCBE1 rs688519 2.8204
+Ccbl1 CCBL1 rs11515530 2.6338
+Ccbl2 CCBL2 rs13376225 1.8891
+Ccbp2 CCBP2 rs6790095 0.8831
+Ccdc101 CCDC101 rs7193402 1.063
+Ccdc102a CCDC102A rs10775336 3.4843
+Ccdc103 CCDC103 rs8067254 0.9454
+Ccdc104 CCDC104 rs8080 1.4171
+Ccdc105 CCDC105 rs17377848 1.9698
+Ccdc106 CCDC106 rs12460543 1.6203
+Ccdc107 CCDC107 rs10972557 1.4008
+Ccdc108 CCDC108 rs359977 1.0837
+Ccdc109b CCDC109B rs6821382 2.5948
+Ccdc11 CCDC11 rs1562004 1.4932
+Ccdc110 CCDC110 rs4318692 2.07
+Ccdc111 CCDC111 rs871253 1.63
+Ccdc112 CCDC112 rs267296 0.9344
+Ccdc113 CCDC113 rs2550374 1.5654
+BC013491 CCDC114 rs4801753 1.3769
+Ccdc115 CCDC115 rs11542415 1.1487
+Ccdc116 CCDC116 rs447001 2.7637
+Ccdc117 CCDC117 rs5752792 2.8225
+Ccdc12 CCDC12 rs724448 2.1312
+Ccdc121 CCDC121 rs2068834 2.1142
+Ccdc122 CCDC122 rs9533623 1.5845
+Ccdc124 CCDC124 rs7245612 1.4584
+Ccdc125 CCDC125 rs4362903 1.1893
+Ccdc126 CCDC126 rs227943 1.7224
+Ccdc127 CCDC127 rs9312960 1.1697
+Ccdc129 CCDC129 rs2191337 2.7823
+Ccdc130 CCDC130 rs378189 1.1026
+Ccdc132 CCDC132 rs10233804 2.2001
+Ccdc135 CCDC135 rs8045126 1.5909
+Ccdc136 CCDC136 rs7799186 2.3596
+Ccdc137 CCDC137 rs3088016 1.0063
+Ccdc138 CCDC138 rs2169813 0.8891
+Ccdc14 CCDC14 rs12638706 1.3373
+Ccdc141 CCDC141 rs1394129 1.7493
+Ccdc142 CCDC142 rs13396033 2.5805
+Ccdc146 CCDC146 rs1880909 2.0043
+Ccdc147 CCDC147 rs7083064 2.1365
+Ccdc148 CCDC148 rs12612640 1.0472
+Ccdc149 CCDC149 rs17552548 1.9698
+Ccdc15 CCDC15 rs7130442 1.8831
+Ccdc150 CCDC150 rs2577583 0.9352
+Ccdc151 CCDC151 rs167479 1.1759
+Ccdc152 CCDC152 rs11950811 0.9259
+Ccdc153 CCDC153 rs12417928 1.9094
+Ccdc154 CCDC154 rs3826226 1.0838
+Ccdc155 CCDC155 rs7260611 1.6631
+Ccdc157 CCDC157 rs5994293 1.9427
+Ccdc158 CCDC158 rs963509 1.4476
+Ccdc159 CCDC159 rs3816864 1.3527
+Ccdc163 CCDC163P rs11211115 0.9927
+2410075B13Rik CCDC166 rs3204170 1.044
+1110021J02Rik CCDC167 rs11752757 1.1159
+A730037C10Rik CCDC169 rs7321473 0.959
+Ccdc17 CCDC17 rs3014210 1.0196
+Gm221 CCDC170 rs9397054 1.3431
+4930473A06Rik CCDC171 rs1472941 2.8894
+1700011F14Rik CCDC172 rs1860400 2.605
+Ccdc18 CCDC18 rs12118262 6.3873
+Ccdc19 CCDC19 rs2501318 0.8199
+Ccdc23 CCDC23 rs11809083 1.0207
+Ccdc24 CCDC24 rs1859728 1.9031
+Ccdc25 CCDC25 rs10081527 1.6463
+Ccdc27 CCDC27 rs1181872 3.4342
+Ccdc28a CCDC28A rs7763314 0.9226
+Ccdc28b CCDC28B rs1358823 0.857
+Ccdc3 CCDC3 rs7913819 1.9686
+Ccdc30 CCDC30 rs7513162 1.1744
+Ccdc33 CCDC33 rs381751 1.67
+2810027O19Rik CCDC34 rs7935021 2.3407
+Ccdc36 CCDC36 rs4499638 2.1233
+Ccdc37 CCDC37 rs13100907 2.1548
+Ccdc38 CCDC38 rs7968332 1.5809
+Ccdc39 CCDC39 rs13079402 1.5168
+Ccdc40 CCDC40 rs4533317 1.3688
+4921537D05Rik CCDC41 rs11107531 1.18
+Ccdc42 CCDC42 rs4309437 1.3291
+Ccdc43 CCDC43 rs8079488 0.8315
+Ccdc47 CCDC47 rs721575 1.0399
+Ccdc50 CCDC50 rs150503 1.8353
+Ccdc53 CCDC53 rs10860836 1.0185
+Ccdc57 CCDC57 rs6502051 1.352
+Ccdc58 CCDC58 rs6803098 1.5016
+Ccdc59 CCDC59 rs7974148 1.2053
+Ccdc6 CCDC6 rs10509113 1.347
+Ccdc60 CCDC60 rs4433636 2.3581
+Ccdc61 CCDC61 rs8108561 1.2286
+Ccdc62 CCDC62 rs12314544 3.8986
+Ccdc63 CCDC63 rs7960825 1.6276
+Ccdc64 CCDC64 rs17499109 2.1521
+Ccdc64b CCDC64B rs2257295 1.637
+Ccdc65 CCDC65 rs12122 1.1656
+Ccdc66 CCDC66 rs9811046 2.5296
+Ccdc69 CCDC69 rs4958899 2.2704
+Ccdc7 CCDC7 rs3006745 1.3143
+Ccdc70 CCDC70 rs17075988 1.8465
+Ccdc71 CCDC71 rs4499638 2.1233
+2010109K11Rik CCDC71L rs342189 1.0218
+Ccdc73 CCDC73 rs198270 1.9352
+2310015A05Rik CCDC74A rs12463867 0.8303
+Ccdc74a CCDC74B rs11685415 1.3815
+Ccdc77 CCDC77 rs7955258 0.9104
+Ccdc78 CCDC78 rs2071951 2.7371
+Ccdc79 CCDC79 rs1548538 0.8105
+Ccdc8 CCDC8 rs393760 1.3587
+Ccdc80 CCDC80 rs2705514 0.9031
+Ccdc81 CCDC81 rs7951433 2.0199
+Ccdc82 CCDC82 rs4753750 0.9918
+Ccdc83 CCDC83 rs11897178 1.5667
+Ccdc84 CCDC84 rs4456264 3.2666
+Ccdc85a CCDC85A rs17268799 2.233
+Ccdc85b CCDC85B rs679147 3.3254
+Ccdc85c CCDC85C rs3918048 1.3697
+Ccdc86 CCDC86 rs10792297 2.6891
+Ccdc87 CCDC87 rs1110707 1.6731
+Ccdc88a CCDC88A rs2589071 1.2688
+Ccdc88b CCDC88B rs11231749 2.0274
+Ccdc88c CCDC88C rs8018903 2.187
+Ccdc89 CCDC89 rs7938015 1.273
+Ccdc9 CCDC9 rs2032809 2.0351
+2310015N07Rik CCDC90B rs555363 1.5393
+Ccdc91 CCDC91 rs10843141 0.9355
+Ccdc92 CCDC92 rs4765127 9.5396
+Ccdc93 CCDC93 rs6722346 1.1467
+Ccdc94 CCDC94 rs8107226 1.991
+Ccdc96 CCDC96 rs11731231 0.9205
+Cchcr1 CCHCR1 rs3823417 3.5312
+Ccin CCIN rs10814336 0.9731
+Cck CCK rs9879040 2.401
+Cckar CCKAR rs2071011 1.3354
+Cckbr CCKBR rs11606755 1.5229
+Ccl1 CCL1 rs2282692 1.401
+Ccl11 CCL11 rs11650048 1.2858
+Ccl17 CCL17 rs3958205 1.5867
+Ccl19 CCL19 rs13301836 1.0838
+Ccl12 CCL2 rs9889296 0.8861
+Ccl20 CCL20 rs7604823 1.819
+Ccl21b CCL21 rs10814138 1.7129
+Ccl22 CCL22 rs1645890 3.6191
+Ccl24 CCL24 rs7784660 1.0143
+Ccl25 CCL25 rs17202419 2.0344
+Ccl26 CCL26 rs12534640 1.8407
+Ccl27a CCL27 rs3739882 0.9303
+Ccl3 CCL3 rs1102934 2.1093
+Ccl4 CCL4 rs8080548 1.9048
+Ccl7 CCL7 rs12613163 1.2773
+Ccm2 CCM2 rs1866579 1.9352
+Ccna1 CCNA1 rs9576061 1.1405
+Ccna2 CCNA2 rs17005251 1.3627
+Ccnb1 CCNB1 rs2932767 2.3798
+Ccnb1ip1 CCNB1IP1 rs1713432 1.1129
+Ccnb2 CCNB2 rs4775116 2.2153
+Ccnc CCNC rs7744845 1.1959
+Ccnd1 CCND1 rs12273574 0.9329
+Ccnd2 CCND2 rs12301377 1.9927
+Ccnd3 CCND3 rs6901077 2.479
+Ccne1 CCNE1 rs10403515 1.4245
+Ccnf CCNF rs6600201 1.8371
+Ccng1 CCNG1 rs1421835 0.8908
+Ccng2 CCNG2 rs4302483 2.0986
+Ccnh CCNH rs10051750 1.2683
+Ccnj CCNJ rs10736098 1.2872
+Ccnjl CCNJL rs4921132 1.7029
+Ccnk CCNK rs3918048 1.3697
+Ccnl1 CCNL1 rs13314825 2.5289
+Ccnl2 CCNL2 rs2649588 1.2546
+Ccno CCNO rs12110186 1.6584
+Ccnt1 CCNT1 rs6580689 3.3264
+Ccnt2 CCNT2 rs12151827 1.5715
+Ccnyl1 CCNYL1 rs2551968 2.2023
+6330503K22Rik CCP110 rs371889 1.8407
+Ccpg1 CCPG1 rs3203152 1.3779
+Ccr1 CCR1 rs3136671 2.1935
+Ccr10 CCR10 rs8079242 2.9987
+Ccr2 CCR2 rs17765307 2.373
+Ccr3 CCR3 rs3136671 2.1935
+Ccr5 CCR5 rs17765307 2.373
+Ccr6 CCR6 rs4710179 1.3938
+Ccr7 CCR7 rs8067673 1.1541
+Ccr8 CCR8 rs17038753 1.2449
+Ccr9 CCR9 rs12633699 0.9401
+Ccrl1 CCRL1 rs9818117 1.4029
+Ccrl2 CCRL2 rs3181039 1.1337
+Ccs CCS rs1110707 1.6731
+1700054N08Rik CCSAP rs237775 1.5131
+Cct2 CCT2 rs710764 2.3619
+Cct3 CCT3 rs2230194 0.8492
+Cct4 CCT4 rs17513722 1.6619
+Cct5 CCT5 rs6889488 1.144
+Cct6b CCT6B rs1482103 1.1642
+Cct7 CCT7 rs909065 0.8173
+Cct8l1 CCT8L2 rs5747968 1.8778
+Ccz1 CCZ1 rs7798766 2.413
+Cd101 CD101 rs10494192 1.7471
+Cd109 CD109 rs7753766 0.9027
+Cd160 CD160 rs1297552 1.8213
+Cd163 CD163 rs6488336 1.767
+Cd164 CD164 rs752727 1.4141
+Cd177 CD177 rs7246785 1.7462
+Cd180 CD180 rs1697137 1.4594
+Cd1d1 CD1D rs385597 0.9682
+Cd2 CD2 rs10489634 1.3467
+Cd200 CD200 rs6787547 2.8297
+Cd200r1 CD200R1 rs9858931 1.5399
+Cd200r2 CD200R1L rs2087286 2.2068
+Cd207 CD207 rs17006436 1.2721
+Cd209a CD209 rs2287887 1.4593
+Cd22 CD22 rs41407744 1.3203
+Cd226 CD226 rs12604328 1.6708
+Cd244 CD244 rs4656942 1.3429
+Cd247 CD247 rs13376681 2.1505
+Cd248 CD248 rs479315 2.3097
+Cd27 CD27 rs2364488 1.1099
+E130112N10Rik CD27-AS1 rs2364488 1.1099
+Cd274 CD274 rs6476976 2.6519
+Cd276 CD276 rs16958177 1.225
+Cd28 CD28 rs41412947 1.656
+Cd2ap CD2AP rs11970178 1.6998
+Cd300a CD300A rs4789661 1.4976
+Cd300c CD300C rs2382845 0.8993
+AF251704 CD300E rs11657185 1.3441
+Cd300lb CD300LB rs2382845 0.8993
+Cd300lf CD300LF rs8081794 1.238
+Cd300lg CD300LG rs11867362 4.1635
+Cd320 CD320 rs2241590 2.7312
+Cd33 CD33 rs1710398 1.3319
+Cd34 CD34 rs17010071 2.263
+Cd36 CD36 rs13225492 2.9208
+Cd37 CD37 rs10422677 0.9727
+Cd38 CD38 rs6858536 1.3466
+Cd3d CD3D rs4938507 1.0676
+Cd3e CD3E rs4938506 0.901
+Cd3eap CD3EAP rs12610134 1.6209
+Cd3g CD3G rs4938507 1.0676
+Cd4 CD4 rs2301333 2.0057
+Cd40 CD40 rs6065921 4.9586
+Cd44 CD44 rs11033042 2.0621
+Cd46 CD46 rs2761421 1.6674
+Cd47 CD47 rs7640424 2.4303
+Cd48 CD48 rs11265472 1.422
+Cd5 CD5 rs175133 1.7328
+Cd52 CD52 rs6598955 1.2391
+Cd53 CD53 rs1578956 2.7077
+AW476241 CD55 rs11120753 0.8425
+Cd59b CD59 rs831598 1.5421
+Cd5l CD5L rs1555843 1.4646
+Cd6 CD6 rs11230548 0.8639
+Cd63 CD63 rs2293414 1.1448
+Cd68 CD68 rs8068222 2.1304
+Cd69 CD69 rs10772137 2.179
+Cd7 CD7 rs7212093 1.2822
+Cd70 CD70 rs348347 1.1052
+Cd72 CD72 rs10972546 1.3806
+Cd74 CD74 rs15251 2.0199
+Cd79a CD79A rs2075751 2.1193
+Cd79b CD79B rs3744287 1.6019
+Cd80 CD80 rs11922005 1.93
+Cd81 CD81 rs708564 1.3649
+Cd82 CD82 rs3781757 1.4431
+Cd83 CD83 rs9476517 1.7129
+Cd84 CD84 rs2134704 1.6472
+Cd86 CD86 rs2715265 0.991
+Cd8a CD8A rs13004867 1.5137
+Cd8b1 CD8B rs10200219 1.4856
+Cd9 CD9 rs2363878 0.9508
+Cd93 CD93 rs17682515 1.9465
+Cd96 CD96 rs4682281 1.7602
+Cd97 CD97 rs3815716 1.413
+Cda CDA rs2072671 2.6696
+Cdadc1 CDADC1 rs1924658 0.8814
+Cdc123 CDC123 rs876089 1.9686
+Cdc14a CDC14A rs7529831 1.8496
+Cdc14b CDC14B rs16911041 1.4454
+Cdc16 CDC16 rs7998576 0.8986
+Cdc20 CDC20 rs11210834 0.9337
+Cdc20b CDC20B rs336084 2.1294
+Cdc23 CDC23 rs2107682 1.1506
+Cdc25a CDC25A rs3731544 1.0031
+Cdc25b CDC25B rs6139201 1.8993
+Cdc25c CDC25C rs3756765 1.7176
+Cdc26 CDC26 rs10733604 1.7875
+Cdc27 CDC27 rs16941650 1.743
+Cdc34 CDC34 rs3745924 1.1985
+Cdc37 CDC37 rs11879191 1.8514
+Cdc37l1 CDC37L1 rs1385454 1.9727
+Cdc40 CDC40 rs6927697 1.2768
+Cdc42 CDC42 rs3768574 1.9112
+Cdc42bpa CDC42BPA rs6675572 1.6743
+Cdc42bpb CDC42BPB rs1211497 1.2547
+Cdc42bpg CDC42BPG rs524386 2.4167
+Cdc42ep1 CDC42EP1 rs8137687 1.2763
+Cdc42ep2 CDC42EP2 rs4149841 1.6643
+Cdc42ep3 CDC42EP3 rs12615921 1.3866
+Cdc42ep4 CDC42EP4 rs12885 1.1717
+Cdc42ep5 CDC42EP5 rs1035452 1.2796
+Cdc42se1 CDC42SE1 rs7532312 1.1035
+Cdc42se2 CDC42SE2 rs4706015 1.3056
+Cdc45 CDC45 rs41455044 1.3621
+Cdc5l CDC5L rs2274832 1.2855
+Cdc7 CDC7 rs484417 2.302
+Cdc73 CDC73 rs12085373 1.2958
+Cdca2 CDCA2 rs12675625 0.9014
+Cdca3 CDCA3 rs12302904 1.2145
+Cdca4 CDCA4 rs2033932 1.6904
+Cdca5 CDCA5 rs1300845 2.5488
+Cdca7 CDCA7 rs6433401 0.901
+Cdca7l CDCA7L rs17145715 1.5764
+Cdca8 CDCA8 rs6426031 1.42
+Cdcp1 CDCP1 rs1520078 1.0237
+Cdcp2 CDCP2 rs17109968 1.1263
+Cdh1 CDH1 rs3114408 1.5678
+Cdh10 CDH10 rs2319566 1.442
+Cdh11 CDH11 rs7189108 1.347
+Cdh12 CDH12 rs436639 2.2429
+Cdh13 CDH13 rs16960303 3.9559
+Cdh15 CDH15 rs1112463 1.195
+Cdh16 CDH16 rs2271025 1.6923
+Cdh17 CDH17 rs2607058 1.3764
+B230220E17Rik CDH18 rs4286675 4.1641
+Cdh19 CDH19 rs6566218 1.0745
+Cdh2 CDH2 rs11564341 2.0055
+Cdh20 CDH20 rs686445 1.2808
+Cdh22 CDH22 rs2425785 2.2423
+Cdh23 CDH23 rs2487067 2.4759
+Cdh24 CDH24 rs2295689 3.0717
+Cdh26 CDH26 rs194999 1.9076
+Cdh3 CDH3 rs3114407 2.6467
+Cdh4 CDH4 rs6061815 2.7575
+Cdh5 CDH5 rs11644739 1.7921
+Cdh6 CDH6 rs6450837 1.5036
+Cdh7 CDH7 rs7232924 1.2444
+Cdh8 CDH8 rs4485351 2.066
+Cdh9 CDH9 rs6869941 2.5637
+Cdhr1 CDHR1 rs11595934 1.6052
+Cdhr2 CDHR2 rs3762966 2.2429
+Cdhr3 CDHR3 rs17357622 2.2272
+Cdhr4 CDHR4 rs6446297 1.6548
+Cdhr5 CDHR5 rs12419694 2.7284
+Cdipt CDIPT rs12917712 0.8097
+Cdk1 CDK1 rs3213077 0.9722
+Cdk10 CDK10 rs2437956 1.1084
+Cdk12 CDK12 rs11655550 2.2712
+Cdk13 CDK13 rs1608500 1.4605
+Cdk14 CDK14 rs3808237 3.1443
+Cdk15 CDK15 rs16838312 2.2441
+Cdk17 CDK17 rs12049922 1.263
+Cdk18 CDK18 rs17418898 2.3286
+Cdk19 CDK19 rs17622461 2.5738
+Cdk2 CDK2 rs11171710 1.4055
+Cdk20 CDK20 rs9410652 1.2406
+Cdk2ap1 CDK2AP1 rs11057244 6.8038
+Cdk2ap2 CDK2AP2 rs2276118 1.1834
+Cdk3 CDK3 rs2665964 1.7542
+Cdk4 CDK4 rs8176345 1.3226
+Cdk5 CDK5 rs11766855 1.2793
+Cdk5r1 CDK5R1 rs17806303 1.1889
+Cdk5r2 CDK5R2 rs359983 1.3805
+Cdk5rap1 CDK5RAP1 rs291669 1.8199
+Cdk5rap2 CDK5RAP2 rs10818462 1.4543
+Cdk5rap3 CDK5RAP3 rs2597177 1.2906
+Cdk6 CDK6 rs41459146 1.9694
+Cdk7 CDK7 rs4362903 1.1893
+Cdk8 CDK8 rs998910 1.2082
+Cdk9 CDK9 rs3217739 1.5566
+Cdkal1 CDKAL1 rs7744319 2.5984
+Cdkl2 CDKL2 rs6835992 1.0283
+Cdkl3 CDKL3 rs17167407 2.6906
+Cdkn1a CDKN1A rs3176344 2.1918
+Cdkn1b CDKN1B rs11055018 1.4114
+Cdkn1c CDKN1C rs400643 1.6428
+Cdkn2a CDKN2A rs3731217 2.755
+Cdkn2aip CDKN2AIP rs4241774 1.2162
+Cdkn2b CDKN2B rs3731217 2.755
+Cdkn2d CDKN2D rs2043306 0.9519
+Cdkn3 CDKN3 rs1950202 1.4961
+Cdo1 CDO1 rs12521324 2.0099
+Cdon CDON rs7131418 1.2294
+2210021J22Rik CDPF1 rs6007662 1.0959
+Cdr2l CDR2L rs4789125 1.0136
+Cdr3 CDR3 rs361435 2.5837
+Cdrt4 CDRT4 rs8069152 1.6229
+Cds2 CDS2 rs11699947 0.8283
+Cdsn CDSN rs3823417 3.5312
+Cdt1 CDT1 rs555812 2.6338
+Cdx1 CDX1 rs17110948 1.1816
+Cdx2 CDX2 rs2481952 1.4793
+Cdyl CDYL rs17138966 1.5207
+Cdyl2 CDYL2 rs12444274 2.3593
+Ceacam16 CEACAM16 rs754340 3.0125
+Ceacam18 CEACAM18 rs11672925 1.3868
+Ceacam19 CEACAM19 rs6509170 3.392
+Ceacam20 CEACAM20 rs17800819 3.0648
+Ceacam3 CEACAM3 rs10413359 2.5733
+Ceacam14 CEACAM7 rs10401378 1.5504
+Cebpa CEBPA rs12975577 2.8248
+Cebpe CEBPE rs11625112 2.6225
+Cebpg CEBPG rs17530508 4.0439
+Cebpz CEBPZ rs3845783 1.7011
+Cecr2 CECR2 rs1860309 2.1458
+Cecr5 CECR5 rs4819962 1.7607
+Cecr6 CECR6 rs4819962 1.7607
+Cel CEL rs6597598 1.9566
+Cela2a CELA2A rs2473355 1.0031
+Gm13011 CELA3A rs3768574 1.9112
+Cela3b CELA3B rs6681693 1.8395
+Celf1 CELF1 rs12269874 0.9359
+Celf2 CELF2 rs17149643 1.6661
+Celf3 CELF3 rs1196466 1.1341
+Celf4 CELF4 rs4799934 2.4946
+Celf5 CELF5 rs17674370 1.291
+Celf6 CELF6 rs16956661 1.3358
+Celsr1 CELSR1 rs5768880 1.4802
+Celsr2 CELSR2 rs629301 7.2084
+Celsr3 CELSR3 rs2255532 1.1337
+Cend1 CEND1 rs7945912 0.8271
+Cenpa CENPA rs2060390 1.0341
+Cenpb CENPB rs6139201 1.8993
+Cenpc1 CENPC1 rs7699834 1.4715
+Cenpe CENPE rs6533058 1.1958
+Cenpf CENPF rs12121403 0.9226
+Cenph CENPH rs2932767 2.3798
+Cenpk CENPK rs6895641 1.3329
+Cenpl CENPL rs2092926 3.6891
+Cenpm CENPM rs5751188 1.1444
+2610510J17Rik CENPN rs17670718 1.4589
+Cenpo CENPO rs1550116 3.2937
+Cenpp CENPP rs10283572 1.1686
+Cenpt CENPT rs7198357 24.33
+Cenpv CENPV rs2286666 1.3069
+Cenpw CENPW rs647463 0.9263
+BC046331 CEP104 rs4648426 1.862
+Ccdc46 CEP112 rs232126 1.4893
+Cep120 CEP120 rs4073779 2.377
+4930534B04Rik CEP128 rs179243 1.2957
+Cep135 CEP135 rs2702334 1.2461
+Cep152 CEP152 rs12102149 1.1289
+Cep164 CEP164 rs552498 5.3965
+Cep170 CEP170 rs6429416 2.0779
+1500031L02Rik CEP19 rs13061191 1.2064
+Cep192 CEP192 rs550315 1.9578
+Cep250 CEP250 rs8121389 1.076
+Cep290 CEP290 rs1960667 2.15
+Tsga14 CEP41 rs11972262 1.7201
+BC088983 CEP44 rs17060459 1.0358
+Cep55 CEP55 rs4919250 3.3748
+Cep57 CEP57 rs1727164 1.0516
+Cep57l1 CEP57L1 rs11962095 1.6872
+Cep63 CEP63 rs11918922 0.9842
+Cep68 CEP68 rs1009358 3.4916
+Cep70 CEP70 rs9289562 1.6747
+4933440J22Rik CEP72 rs1709548 2.1748
+Cep76 CEP76 rs4797700 1.5349
+Cep78 CEP78 rs11137561 1.3317
+Ccdc21 CEP85 rs6699798 1.9763
+Gm9766 CEP85L rs1936059 1.7153
+Ccdc123 CEP89 rs4805021 2.8639
+Ccdc45 CEP95 rs2872315 1.0169
+Cept1 CEPT1 rs2484459 1.19
+Cer1 CER1 rs3819004 0.8918
+Cercam CERCAM rs2240943 1.001
+Cerk CERK rs16995595 2.0917
+Cerkl CERKL rs7602326 1.0857
+Lass2 CERS2 rs267738 2.5605
+Lass3 CERS3 rs2062815 1.64
+Lass4 CERS4 rs8110054 1.9378
+Lass5 CERS5 rs836177 2.5291
+Lass6 CERS6 rs4668089 2.0202
+Ces1g CES1 rs35918553 1.3882
+Ces2h CES2 rs8045523 2.0879
+Ces1d CES3 rs8045523 2.0879
+Ces4a CES4A rs11860308 4.93
+Ces5a CES5A rs6499792 1.4316
+Cetn1 CETN1 rs12607285 1.2412
+Cetn3 CETN3 rs12516976 1.4046
+Cetn4 CETN4P rs398418 1.7284
+Cfb CFB rs9332739 2.0606
+Cfd CFD rs12460890 1.2608
+Cfdp1 CFDP1 rs13337397 1.9682
+Cfh CFH rs1360144 1.7841
+Cfhr1 CFHR1 rs388338 1.2929
+Cfi CFI rs998539 2.121
+Cfl1 CFL1 rs679147 3.3254
+Cfl2 CFL2 rs6571682 2.2353
+Cftr CFTR rs17547853 3.1071
+Cga CGA rs9359730 1.3535
+Cggbp1 CGGBP1 rs12629581 1.2947
+Cgnl1 CGNL1 rs2934441 2.209
+Cgref1 CGREF1 rs7588926 1.3223
+Cgrrf1 CGRRF1 rs17127595 1.3401
+Ch25h CH25H rs10887930 2.5667
+Chac1 CHAC1 rs1869907 2.5711
+2510006C20Rik CHAC2 rs3770396 1.5772
+Chadl CHADL rs139477 0.9014
+Zfp828 CHAMP1 rs9525207 2.0219
+Chat CHAT rs1055421 2.2234
+Chchd1 CHCHD1 rs10509311 1.1235
+Chchd10 CHCHD10 rs6003870 1.5067
+Chchd3 CHCHD3 rs4728270 1.808
+Chchd5 CHCHD5 rs10197847 1.0725
+Chchd6 CHCHD6 rs7638306 2.9598
+Chchd7 CHCHD7 rs11986542 1.202
+Chd1l CHD1L rs17356024 0.9531
+Chd2 CHD2 rs41439544 2.4382
+Chd4 CHD4 rs1057510 1.6972
+Chd5 CHD5 rs11121407 1.461
+Chd6 CHD6 rs6102415 4.697
+Chd7 CHD7 rs12542177 1.1826
+Chd9 CHD9 rs11075915 1.927
+Chdh CHDH rs1829423 1.8401
+Chek2 CHEK2 rs5752792 2.8225
+Cherp CHERP rs12459238 1.4868
+Chfr CHFR rs7297261 2.3725
+Chga CHGA rs2402226 1.9234
+Chgb CHGB rs16991393 0.8444
+Chi3l1 CHI3L1 rs10920573 1.6605
+Chia CHIA rs2820070 1.4557
+Chic2 CHIC2 rs13103605 1.367
+Chit1 CHIT1 rs4077764 1.6478
+Chka CHKA rs7127090 1.1467
+Chkb CHKB rs2516557 1.1529
+BC090627 CHKB-CPT1B rs2516557 1.1529
+Chl1 CHL1 rs6442827 1.2497
+Chm CHM rs12118044 0.8145
+Chml CHML rs3753223 1.0256
+Chmp1a CHMP1A rs164741 1.9825
+Chmp1b CHMP1B rs7228231 1.3972
+Chmp2a CHMP2A rs10413586 1.2161
+Chmp2b CHMP2B rs1488366 1.7279
+Chmp3 CHMP3 rs7559738 1.3117
+Chmp4b CHMP4B rs6088300 1.5375
+2310010I16Rik CHMP4C rs3735701 1.7051
+Chmp6 CHMP6 rs9899178 1.4737
+Chmp7 CHMP7 rs7830709 2.012
+Chn1 CHN1 rs4972458 1.2493
+Chn2 CHN2 rs12700939 2.4526
+Chodl CHODL rs7279503 1.9477
+Chordc1 CHORDC1 rs12792416 1.2213
+1500003O03Rik CHP1 rs494490 1.0098
+2010110P09Rik CHP2 rs187406 0.9772
+Chpf CHPF rs7597875 1.3109
+Chpf2 CHPF2 rs1122979 1.2444
+Chpt1 CHPT1 rs41402844 1.0639
+Chrac1 CHRAC1 rs2977451 1.8184
+Chrd CHRD rs9944 1.3203
+Chrdl2 CHRDL2 rs4944937 1.4658
+Chrm1 CHRM1 rs11605665 1.0605
+Chrm2 CHRM2 rs11767373 2.3318
+Chrm3 CHRM3 rs12132662 1.783
+Chrm4 CHRM4 rs11038871 2.3187
+Chrm5 CHRM5 rs6495419 1.1842
+Chrna1 CHRNA1 rs12472540 1.4819
+Chrna10 CHRNA10 rs2271589 1.5131
+Chrna2 CHRNA2 rs7819756 1.1215
+Chrna3 CHRNA3 rs8029939 2.2226
+Chrna4 CHRNA4 rs1044396 2.5385
+Chrna5 CHRNA5 rs8029939 2.2226
+Chrna6 CHRNA6 rs11783289 1.5935
+Chrna7 CHRNA7 rs41481945 1.4001
+Chrna9 CHRNA9 rs6842752 0.9539
+Chrnb1 CHRNB1 rs11652328 2.8827
+Chrnb2 CHRNB2 rs3766924 1.6643
+Chrnb3 CHRNB3 rs11783289 1.5935
+Chrnb4 CHRNB4 rs8029939 2.2226
+Chrnd CHRND rs2117770 1.783
+Chrne CHRNE rs11654952 1.2197
+Chrng CHRNG rs2117770 1.783
+Chst1 CHST1 rs7929114 1.8306
+Chst10 CHST10 rs6716367 1.2858
+Chst11 CHST11 rs1922269 1.8182
+Chst12 CHST12 rs17793630 1.1241
+1110067M19Rik CHST13 rs17691504 1.547
+Chst14 CHST14 rs3803357 2.5421
+Chst15 CHST15 rs4397784 1.6821
+Chst3 CHST3 rs4148941 2.7726
+Chst4 CHST4 rs7188299 0.988
+Chst5 CHST5 rs2453141 1.494
+Chst8 CHST8 rs284340 3.5369
+Chst9 CHST9 rs1426878 2.4619
+Chsy1 CHSY1 rs11638966 1.5771
+Chsy3 CHSY3 rs30271 1.6388
+Chtf8 CHTF8 rs8044066 0.9838
+Chuk CHUK rs2862954 4.0887
+Churc1 CHURC1 rs1951489 0.9034
+Ciapin1 CIAPIN1 rs223839 2.6216
+Cib1 CIB1 rs16944099 0.819
+Cib2 CIB2 rs7179651 1.162
+Cib3 CIB3 rs206231 3.8431
+Cib4 CIB4 rs751651 1.3156
+Cidea CIDEA rs11659813 2.0871
+Cideb CIDEB rs3940231 1.7162
+Cidec CIDEC rs203563 2.2186
+C2ta CIITA rs8063850 1.2691
+Cinp CINP rs4906191 1.2982
+Cirbp CIRBP rs2379240 1.6478
+Cirh1a CIRH1A rs17624199 1.5699
+Cisd1 CISD1 rs17620218 1.2749
+Cisd2 CISD2 rs223366 0.9991
+Cisd3 CISD3 rs228274 1.3168
+Cit CIT rs17527257 0.8353
+Cited2 CITED2 rs6920963 2.5874
+Ciz1 CIZ1 rs2229917 1.7747
+Ckap2l CKAP2L rs2048876 0.9423
+Ckap4 CKAP4 rs1896555 2.7926
+Ckap5 CKAP5 rs3136441 17.458
+Ckb CKB rs8013911 1.3914
+Cklf CKLF rs662089 1.8356
+Ckm CKM rs7260359 2.2874
+Ckmt2 CKMT2 rs17294084 1.2867
+Cks2 CKS2 rs3211681 1.0705
+Clasp1 CLASP1 rs6751025 1.7891
+Clasp2 CLASP2 rs9876692 0.8761
+Clasrp CLASRP rs874743 1.766
+Clc CLC rs2108686 0.9485
+Clca1 CLCA1 rs969788 2.2743
+Clca5 CLCA2 rs969788 2.2743
+AI747448 CLCA4 rs12750427 1.9314
+Clcc1 CLCC1 rs839859 2.2476
+Clcf1 CLCF1 rs2066490 0.8374
+Clcn1 CLCN1 rs4489232 1.7657
+Clcn2 CLCN2 rs9944 1.3203
+Clcn3 CLCN3 rs6828248 1.1992
+Clcn6 CLCN6 rs2076003 1.7943
+Clcn7 CLCN7 rs3826226 1.0838
+Clcnka CLCNKA rs732286 1.902
+Clcnkb CLCNKB rs732286 1.902
+Cldn1 CLDN1 rs6805979 2.7836
+Cldn10 CLDN10 rs9556487 1.6396
+Cldn11 CLDN11 rs6774505 2.3578
+Cldn12 CLDN12 rs6465258 3.9329
+Cldn14 CLDN14 rs2071050 2.6449
+Cldn15 CLDN15 rs3735337 1.7133
+Cldn16 CLDN16 rs1955089 2.8623
+Cldn17 CLDN17 rs7277502 1.3617
+Cldn18 CLDN18 rs4678399 1.9355
+Cldn19 CLDN19 rs4660657 1.0818
+Cldn22 CLDN22 rs907366 1.4645
+Cldn23 CLDN23 rs7824019 1.395
+Cldn24 CLDN24 rs907366 1.4645
+Cldn3 CLDN3 rs11764572 2.4229
+Cldn4 CLDN4 rs9647713 1.0723
+Cldn5 CLDN5 rs5748260 1.2725
+Cldn6 CLDN6 rs2257295 1.637
+Cldn7 CLDN7 rs222850 0.9263
+Cldn8 CLDN8 rs576728 2.281
+Cldn9 CLDN9 rs2257295 1.637
+Cldn25 CLDND1 rs1729969 1.1135
+Cldnd2 CLDND2 rs11881372 0.8911
+Mgl2 CLEC10A rs312456 1.9101
+Clec11a CLEC11A rs17800333 0.8105
+Clec12a CLEC12A rs1323461 1.38
+Clec12b CLEC12B rs1323461 1.38
+Clec14a CLEC14A rs7152252 1.5983
+Clec16a CLEC16A rs17803907 1.2491
+Clec18a CLEC18A rs9673566 0.9007
+Clec1a CLEC1A rs1532087 2.1987
+Clec1b CLEC1B rs1323461 1.38
+Clec2i CLEC2D rs7302498 1.5921
+Clec2l CLEC2L rs6953625 1.8771
+Clec3a CLEC3A rs2735400 1.0237
+Clec3b CLEC3B rs9873373 0.841
+Clec4a2 CLEC4A rs10492105 1.8726
+Clec4a1 CLEC4C rs7969455 1.7187
+Clec4f CLEC4F rs17006436 1.2721
+Clec4g CLEC4G rs4804805 1.019
+Clec5a CLEC5A rs11767119 0.848
+Clec4n CLEC6A rs4520670 1.6838
+Clec7a CLEC7A rs1532087 2.1987
+Clec9a CLEC9A rs11053573 1.9454
+Clic1 CLIC1 rs400547 2.5546
+Clic3 CLIC3 rs11790360 2.3242
+Clic4 CLIC4 rs7525139 1.764
+Clic5 CLIC5 rs9381416 1.156
+Clic6 CLIC6 rs2834608 1.0091
+Clint1 CLINT1 rs436964 2.2889
+Clip1 CLIP1 rs11057405 2.49
+Clip2 CLIP2 rs5874 1.6657
+Clip3 CLIP3 rs12462868 1.5102
+Clip4 CLIP4 rs1452920 1.4042
+Clk1 CLK1 rs17467700 1.1239
+Clk2 CLK2 rs8177970 0.8677
+Clk4 CLK4 rs6889322 0.8324
+Clmn CLMN rs3742332 2.5859
+9030425E11Rik CLMP rs12284852 1.5249
+Cln5 CLN5 rs599115 1.0382
+Cln6 CLN6 rs10152713 2.1314
+Cln8 CLN8 rs41376544 1.1154
+Clnk CLNK rs13112750 2.0097
+Clns1a CLNS1A rs7482006 1.6012
+Clock CLOCK rs10462028 2.1144
+Clp1 CLP1 rs11229087 1.2466
+Clpb CLPB rs11235499 2.5972
+Clpp CLPP rs1982082 1.67
+Clps CLPS rs2766560 3.2526
+Gm749 CLPSL2 rs2766560 3.2526
+Clptm1 CLPTM1 rs4420638 20.357
+Clptm1l CLPTM1L rs2242652 0.994
+Clpx CLPX rs12438475 1.1452
+Clrn1 CLRN1 rs6806039 1.5627
+Clrn2 CLRN2 rs13138275 1.9014
+Clrn3 CLRN3 rs575552 1.2104
+Clspn CLSPN rs12563583 0.8551
+Clstn2 CLSTN2 rs16849596 2.2159
+Clstn3 CLSTN3 rs3813736 1.2216
+Cltb CLTB rs13162111 0.9594
+Cltc CLTC rs10515169 1.3923
+Clu CLU rs1532275 1.3308
+Cluap1 CLUAP1 rs1124285 1.6653
+MMT00070731 CLUL1 rs4798418 1.6094
+Clvs1 CLVS1 rs6985494 1.1318
+Clvs2 CLVS2 rs9375212 1.0793
+Clybl CLYBL rs6491518 1.9031
+Cma1 CMA1 rs8020496 0.8462
+Cmas CMAS rs11046263 1.3829
+Cmbl CMBL rs10038129 1.1797
+Cmc1 CMC1 rs13101128 1.1817
+2310061C15Rik CMC2 rs17670718 1.4589
+Cmip CMIP rs2925979 10.681
+Cmklr1 CMKLR1 rs11113797 1.2844
+Cmpk1 CMPK1 rs17103095 1.2953
+Cmpk2 CMPK2 rs771175 1.0938
+Cmtm1 CMTM1 rs662089 1.8356
+Cmtm2a CMTM2 rs355924 2.871
+Cmtm3 CMTM3 rs355924 2.871
+Cmtm4 CMTM4 rs355924 2.871
+Cmtm6 CMTM6 rs2304593 2.1983
+Cmtm7 CMTM7 rs2304593 2.1983
+Cmtm8 CMTM8 rs1398100 2.6726
+Cmya5 CMYA5 rs16876996 2.6015
+MMT00025179 CNBD1 rs10282857 2.1487
+Cnbp CNBP rs9842978 1.1441
+Cndp1 CNDP1 rs7506051 2.3076
+Cndp2 CNDP2 rs8084058 2.2099
+Tmem188 CNEP1R1 rs9929913 0.9465
+Cnga1 CNGA1 rs1371732 0.8781
+Cnga3 CNGA3 rs7602422 0.9574
+Cnga4 CNGA4 rs11040799 1.1417
+Cngb1 CNGB1 rs11867123 2.3467
+Cngb3 CNGB3 rs4960985 2.42
+Cnih CNIH rs6572956 1.4077
+Cnih2 CNIH2 rs479315 2.3097
+Cnih3 CNIH3 rs6690131 2.9551
+Cnih4 CNIH4 rs11903231 1.0675
+Cnksr1 CNKSR1 rs8768 1.8027
+Cnksr2 CNKSR2 rs12944757 1.328
+Cnksr3 CNKSR3 rs13209273 1.7016
+Cnn1 CNN1 rs11880637 1.0059
+Cnn2 CNN2 rs3752240 1.561
+Cnn3 CNN3 rs17112700 2.0933
+Cnnm1 CNNM1 rs41535452 0.9838
+Cnnm2 CNNM2 rs2274341 2.9714
+Cnnm3 CNNM3 rs6756482 1.9547
+Cnnm4 CNNM4 rs6756482 1.9547
+Cnot1 CNOT1 rs4784046 2.0448
+Cnot10 CNOT10 rs7650578 1.1761
+Cnot2 CNOT2 rs12308706 1.3686
+Cnot3 CNOT3 rs2576452 2.9531
+Cnot4 CNOT4 rs11765559 2.3303
+Cnot6 CNOT6 rs937919 2.4681
+Cnot6l CNOT6L rs9654194 0.9825
+Cnot7 CNOT7 rs17502404 1.5646
+Cnot8 CNOT8 rs12300 1.9961
+Cnp CNP rs4260106 1.384
+Cnpy1 CNPY1 rs10488639 1.43
+Cnpy3 CNPY3 rs4711731 1.927
+Cnpy4 CNPY4 rs4134895 1.1281
+Cnr1 CNR1 rs4707433 1.2585
+Cnr2 CNR2 rs2502959 1.0932
+Cnrip1 CNRIP1 rs7588136 1.4489
+Cnst CNST rs7518651 2.4159
+Cntf CNTF rs12574639 3.3546
+Cntfr CNTFR rs16931623 0.8342
+Cntln CNTLN rs2200599 1.5748
+Cntn1 CNTN1 rs17128696 2.3748
+Cntn2 CNTN2 rs3795559 1.3987
+Cntn3 CNTN3 rs1488811 2.7312
+Cntn4 CNTN4 rs954882 3.0884
+Cntn5 CNTN5 rs2045058 3.1589
+Cntn6 CNTN6 rs9854448 2.2344
+Cntnap1 CNTNAP1 rs8079242 2.9987
+Cntnap2 CNTNAP2 rs1034865 2.5678
+Cntnap3 CNTNAP3 rs9298992 1.1535
+Cntnap4 CNTNAP4 rs12325173 2.5507
+Cntnap5a CNTNAP5 rs12468133 2.7615
+Cep110 CNTRL rs16910280 1.755
+Coasy COASY rs676387 2.3565
+Cobl COBL rs6945400 4.0507
+Cobll1 COBLL1 rs12328675 9.5662
+Coch COCH rs1124181 2.9606
+Cog1 COG1 rs11544800 1.173
+Cog2 COG2 rs11122563 2.6254
+Cog3 COG3 rs2985996 2.0447
+Cog4 COG4 rs11075766 0.967
+Cog5 COG5 rs4730250 2.3513
+Cog6 COG6 rs17529725 1.482
+Cog7 COG7 rs585589 1.4475
+Cog8 COG8 rs3852689 1.5064
+Coil COIL rs2586519 1.7796
+Col10a1 COL10A1 rs1547030 1.2377
+Col11a1 COL11A1 rs7553904 1.6486
+Col11a2 COL11A2 rs2854028 1.4432
+Col12a1 COL12A1 rs7762653 1.5976
+Col13a1 COL13A1 rs2894090 3.2469
+Col14a1 COL14A1 rs10505380 2.4669
+Col15a1 COL15A1 rs10988510 2.8368
+Col16a1 COL16A1 rs4949466 2.2232
+Col17a1 COL17A1 rs1747676 1.5874
+Col18a1 COL18A1 rs2274808 1.8422
+Col19a1 COL19A1 rs10945198 2.2935
+Col1a1 COL1A1 rs2586502 1.2615
+Col1a2 COL1A2 rs10241133 1.0833
+Col20a1 COL20A1 rs1044396 2.5385
+Col22a1 COL22A1 rs6992562 1.7902
+Col23a1 COL23A1 rs890808 1.7886
+Col24a1 COL24A1 rs12089500 2.4136
+Col25a1 COL25A1 rs1724465 1.945
+Col27a1 COL27A1 rs4978567 3.2997
+Col28a1 COL28A1 rs6950096 1.4604
+Col2a1 COL2A1 rs7134203 1.6484
+Col3a1 COL3A1 rs1122869 1.1061
+Col4a1 COL4A1 rs7335117 1.6602
+Col4a2 COL4A2 rs9521732 2.5039
+Col4a3 COL4A3 rs7601647 1.8914
+Col4a3bp COL4A3BP rs6453133 1.7169
+Col4a4 COL4A4 rs2222869 1.6457
+Col5a1 COL5A1 rs4240706 1.5686
+Col5a2 COL5A2 rs4667270 1.7833
+Col5a3 COL5A3 rs3745592 1.2463
+Col6a1 COL6A1 rs1475888 1.5408
+Col6a2 COL6A2 rs3810590 1.6925
+Col6a3 COL6A3 rs7558030 1.4029
+Col6a4 COL6A4P1 rs3864041 2.5067
+Col6a5 COL6A5 rs819094 2.5929
+Col6a6 COL6A6 rs6795367 1.5704
+Col7a1 COL7A1 rs7615170 1.218
+Col8a1 COL8A1 rs13079346 1.4593
+Col8a2 COL8A2 rs558120 1.2021
+Col9a1 COL9A1 rs11760185 1.704
+Col9a2 COL9A2 rs6682701 1.4528
+Col9a3 COL9A3 rs33992134 1.045
+Colec10 COLEC10 rs16891959 2.1809
+Colec11 COLEC11 rs13004318 2.8633
+Colec12 COLEC12 rs4798128 2.7706
+Colq COLQ rs2061187 2.4125
+Commd1 COMMD1 rs17018666 1.4237
+Commd10 COMMD10 rs4920905 1.1386
+Commd5 COMMD5 rs2953874 1.6455
+Commd6 COMMD6 rs953109 1.2034
+Commd7 COMMD7 rs2424898 2.1108
+Commd8 COMMD8 rs1136022 1.3218
+Commd9 COMMD9 rs4756302 2.5867
+Comt COMT rs8141691 1.8894
+Comtd1 COMTD1 rs879155 2.5686
+Copa COPA rs10159339 1.0948
+Copb1 COPB1 rs7121538 3.9763
+Copb2 COPB2 rs6439849 3.3996
+Copg2 COPG2 rs12706933 1.0979
+Cops2 COPS2 rs11070683 1.0059
+Cops3 COPS3 rs4399589 1.5948
+Cops4 COPS4 rs7654829 1.0044
+Cops5 COPS5 rs11785782 1.4514
+Cops6 COPS6 rs4729577 1.0678
+Cops8 COPS8 rs2078469 1.402
+Copz1 COPZ1 rs11170881 1.6635
+Copz2 COPZ2 rs10491183 4.4033
+Coq3 COQ3 rs6917584 0.9292
+Coq4 COQ4 rs3003600 0.994
+Coq5 COQ5 rs580016 1.6968
+Coq6 COQ6 rs727523 1.4686
+Coq7 COQ7 rs4072393 0.9555
+Coq9 COQ9 rs223839 2.6216
+Corin CORIN rs11941639 1.601
+Coro1b CORO1B rs1064876 1.339
+Coro1c CORO1C rs8179137 2.3738
+Coro2a CORO2A rs4408014 2.3994
+Coro2b CORO2B rs1352833 1.8283
+Coro6 CORO6 rs2289629 2.3484
+Coro7 CORO7 rs3747576 2.3563
+Cort CORT rs11121566 1.0638
+Cotl1 COTL1 rs8061519 3.1561
+Cox10 COX10 rs11078225 1.0913
+Cox11 COX11 rs41455544 1.5089
+2310016M24Rik COX14 rs836177 2.5291
+Cox16 COX16 rs1044527 3.808
+Cox17 COX17 rs6773852 1.1045
+2810437L13Rik COX19 rs10264122 2.8693
+Fam36a COX20 rs751715 1.1948
+Cox4i1 COX4I1 rs2733954 0.8207
+Cox4i2 COX4I2 rs6058259 1.4437
+Cox5a COX5A rs6495131 1.515
+Cox5b COX5B rs2071038 0.8959
+Cox6a1 COX6A1 rs3901251 1.21
+Cox6b1 COX6B1 rs2311871 1.2562
+Cox6b2 COX6B2 rs8113016 2.4566
+AW476018 COX6C rs13280341 1.7607
+Cox7a1 COX7A1 rs10410162 1.0411
+Cox7a2 COX7A2 rs240421 1.9718
+Cox7b2 COX7B2 rs1512128 2.8147
+Cox7c COX7C rs16902295 1.3322
+Cox8a COX8A rs11231649 0.8748
+Cox8c COX8C rs12889883 1.4702
+Cp CP rs4681169 2.5334
+Cpa1 CPA1 rs11972262 1.7201
+Cpa3 CPA3 rs3772598 1.2409
+Cpa4 CPA4 rs7781250 1.0673
+Cpa5 CPA5 rs4731690 1.2765
+Cpa6 CPA6 rs6995909 2.0498
+Cpb1 CPB1 rs3772598 1.2409
+Cpb2 CPB2 rs9534294 1.6101
+Cpd CPD rs2011614 1.6776
+Cpe CPE rs2053277 2.0821
+Cpeb1 CPEB1 rs8032281 1.2189
+Cpeb2 CPEB2 rs12643066 1.0601
+Cpeb3 CPEB3 rs11186798 0.93
+Cpeb4 CPEB4 rs13168379 1.7089
+A430107O13Rik CPED1 rs10277559 1.9133
+Cplx1 CPLX1 rs7661962 1.7187
+Cplx2 CPLX2 rs10516132 1.034
+Cplx3 CPLX3 rs872263 1.7523
+Cplx4 CPLX4 rs10503024 0.863
+Cpm CPM rs12367652 1.2799
+Cpn1 CPN1 rs11597528 2.8871
+Cpn2 CPN2 rs923931 2.465
+Cpne1 CPNE1 rs6060558 3.539
+Cpne2 CPNE2 rs4783972 5.0824
+Cpne3 CPNE3 rs997426 1.3353
+Cpne4 CPNE4 rs13076253 3.0936
+Cpne5 CPNE5 rs4711465 1.5223
+Cpne6 CPNE6 rs8005834 1.0509
+Cpne7 CPNE7 rs455527 1.4861
+Cpne8 CPNE8 rs2653738 1.1178
+Cpne9 CPNE9 rs9879089 1.1631
+Cpox CPOX rs11709315 1.909
+Cpped1 CPPED1 rs11647868 2.3346
+Pgcp CPQ rs6983702 2.0837
+Cps1 CPS1 rs6750325 4.3489
+Cpsf2 CPSF2 rs12895003 1.7867
+Cpsf3 CPSF3 rs12474647 0.9055
+Cpsf4l CPSF4L rs12885 1.1717
+Cpsf6 CPSF6 rs2231696 0.848
+Cpt1a CPT1A rs11228372 3.1412
+Cpt1b CPT1B rs2516557 1.1529
+Cpt1c CPT1C rs7251 1.1906
+Cpt2 CPT2 rs7554022 1.6326
+Cpvl CPVL rs12700939 2.4526
+Cpxm1 CPXM1 rs6051416 1.5427
+Cpxm2 CPXM2 rs1235048 2.238
+Cpz CPZ rs10938726 1.3614
+Crry CR1 rs1323720 1.8778
+Cr2 CR2 rs2025935 1.2298
+Crabp1 CRABP1 rs2056307 1.0238
+Crabp2 CRABP2 rs2236601 1.6759
+Cradd CRADD rs11107202 2.1502
+Crat CRAT rs10988217 1.9194
+Crb1 CRB1 rs10922213 2.1715
+Crb2 CRB2 rs1970585 2.2066
+Crb3 CRB3 rs1663803 2.1806
+Crbn CRBN rs1669362 1.6613
+Crcp CRCP rs160648 1.6123
+Crct1 CRCT1 rs6665952 1.9666
+Creb1 CREB1 rs2464978 1.6048
+Creb3l1 CREB3L1 rs6485674 5.3087
+Creb3l2 CREB3L2 rs274012 2.2977
+Creb3l3 CREB3L3 rs352494 2.7627
+Creb3l4 CREB3L4 rs6413529 0.9842
+Creb5 CREB5 rs6978323 3.1386
+Crebbp CREBBP rs17136507 1.8254
+Crebl2 CREBL2 rs11054983 1.2879
+A930001N09Rik CREBRF rs17730451 2.694
+Crebzf CREBZF rs17743826 0.8941
+Creg1 CREG1 rs13376681 2.1505
+Creg2 CREG2 rs6716306 1.3757
+Creld1 CRELD1 rs13097123 2.0881
+Crh CRH rs9643575 1.2047
+Crhbp CRHBP rs16873930 2.1639
+Crhr1 CRHR1 rs12940092 2.8102
+Crhr2 CRHR2 rs255121 2.4793
+Crim1 CRIM1 rs3770818 2.5887
+Crip1 CRIP1 rs11849001 1.8841
+Crip2 CRIP2 rs11849001 1.8841
+Crip3 CRIP3 rs4714674 0.9901
+Cript CRIPT rs896213 1.4339
+Crisp4 CRISP1 rs12197213 0.9838
+Crisp2 CRISP2 rs521492 0.8665
+Crisp1 CRISP3 rs511454 1.2239
+Crispld1 CRISPLD1 rs16939013 1.1651
+Crispld2 CRISPLD2 rs8048917 2.7742
+Crk CRK rs7208647 1.071
+Crkl CRKL rs4822687 1.039
+Crlf1 CRLF1 rs3170474 3.1899
+Crlf3 CRLF3 rs11080134 1.8523
+Crls1 CRLS1 rs6076910 1.075
+Crmp1 CRMP1 rs6826649 1.7014
+Crnkl1 CRNKL1 rs200175 1.4525
+Crocc CROCC rs10888011 1.965
+Crot CROT rs2051950 0.959
+Crp CRP rs1800947 1.3029
+Crtac1 CRTAC1 rs11189441 0.8671
+Crtam CRTAM rs4936758 1.7277
+Crtap CRTAP rs4678713 0.9935
+Crtc1 CRTC1 rs739850 1.4928
+Crtc2 CRTC2 rs6413529 0.9842
+Crtc3 CRTC3 rs12914981 1.7742
+Crx CRX rs4427918 0.8935
+Cry1 CRY1 rs12368868 2.7844
+Cry2 CRY2 rs10838519 5.6925
+Cryaa CRYAA rs9975438 2.3409
+Cryab CRYAB rs4252589 0.9259
+Cryba1 CRYBA1 rs8614 1.0581
+Cryba2 CRYBA2 rs359983 1.3805
+Cryba4 CRYBA4 rs7290642 1.2314
+Crybb1 CRYBB1 rs2283838 1.3059
+Crybb2 CRYBB2 rs2213882 1.7768
+Crybb3 CRYBB3 rs2213882 1.7768
+Cryga CRYGA rs2885602 1.62
+Crygb CRYGB rs2885602 1.62
+Crygc CRYGC rs2885602 1.62
+Crygd CRYGD rs2885602 1.62
+Crygn CRYGN rs7785613 1.8592
+Crygs CRYGS rs16860878 2.4215
+Cryl1 CRYL1 rs4341630 1.3247
+Crym CRYM rs7202110 1.2686
+Cryz CRYZ rs12239097 1.782
+Cryzl1 CRYZL1 rs9647067 0.9935
+Csad CSAD rs1465056 2.3702
+Csdc2 CSDC2 rs5751124 1.7233
+Csde1 CSDE1 rs2207701 1.314
+Csf1 CSF1 rs333947 4.1236
+Csf1r CSF1R rs17798680 1.3696
+Csf2 CSF2 rs27438 1.1843
+Csf2rb CSF2RB rs2075726 1.5955
+Csf3 CSF3 rs2302776 3.6582
+Csf3r CSF3R rs6667860 0.9329
+Csgalnact1 CSGALNACT1 rs4921667 7.7929
+Csgalnact2 CSGALNACT2 rs1272142 0.9767
+Csmd1 CSMD1 rs1230767 4.2121
+Csmd2 CSMD2 rs6684352 2.823
+Csmd3 CSMD3 rs16883864 3.7592
+Csn1s1 CSN1S1 rs1231466 1.659
+Csn1s2b CSN1S2BP rs1399252 1.9393
+Csn2 CSN2 rs1231466 1.659
+Csn3 CSN3 rs13141970 3.117
+Csnk1a1 CSNK1A1 rs422329 2.0265
+Csnk1d CSNK1D rs7212093 1.2822
+Csnk1e CSNK1E rs41456252 1.3396
+Csnk1g1 CSNK1G1 rs7163558 1.0182
+Csnk1g2 CSNK1G2 rs2028262 1.7447
+Csnk1g3 CSNK1G3 rs6595461 1.3722
+Csnk2a1 CSNK2A1 rs4815707 2.0059
+Csnk2a2 CSNK2A2 rs16959786 1.0607
+Csnk2b CSNK2B rs3117583 4.5605
+Cspg4 CSPG4 rs8031394 1.3194
+Cspp1 CSPP1 rs11986053 1.27
+Csrnp1 CSRNP1 rs1274963 2.3268
+Csrnp2 CSRNP2 rs10876135 1.4675
+Csrnp3 CSRNP3 rs2221999 2.5701
+Csrp1 CSRP1 rs10920200 1.6686
+Csrp2 CSRP2 rs11115929 1.4799
+Csrp2bp CSRP2BP rs3828013 1.3973
+Csrp3 CSRP3 rs9888275 2.3426
+Cst3 CST3 rs2405391 1.1828
+Cst10 CST5 rs6083308 1.1263
+Cst6 CST6 rs10791838 1.4466
+Cst7 CST7 rs4273288 0.8693
+Cst9 CST9L rs2405391 1.1828
+EG433016 CSTA rs2001548 1.8579
+Cstb CSTB rs9978643 1.3118
+Cstf1 CSTF1 rs2426621 0.9066
+Cstf2t CSTF2T rs6480412 1.3938
+Cstf3 CSTF3 rs373499 1.5491
+Ctage5 CTAGE5 rs10162550 1.2225
+Ctbp1 CTBP1 rs730830 0.9052
+Ctbp2 CTBP2 rs7899253 2.38
+Ctbs CTBS rs4906999 2.3561
+Ctc1 CTC1 rs1476161 3.0105
+Ctcf CTCF rs6499137 19.597
+Ctcfl CTCFL rs2902992 2.6209
+Ctdnep1 CTDNEP1 rs222850 0.9263
+Ctdp1 CTDP1 rs8098000 1.6356
+Ctdsp1 CTDSP1 rs2276631 1.1314
+Ctdsp2 CTDSP2 rs1021469 1.3121
+Ctdspl CTDSPL rs11712794 1.6302
+Ctdspl2 CTDSPL2 rs17586255 1.1432
+Ctf1 CTF1 rs11649653 2.73
+Ctgf CTGF rs1931000 1.8055
+Cth CTH rs672203 1.2727
+Cthrc1 CTHRC1 rs2687350 0.8474
+Ctif CTIF rs9958543 3.1686
+Ctla4 CTLA4 rs11571319 1.644
+Ctnna1 CTNNA1 rs3749663 2.016
+Ctnna2 CTNNA2 rs9808084 4.0779
+Ctnna3 CTNNA3 rs9415874 2.6038
+Ctnnal1 CTNNAL1 rs3750460 1.3731
+Ctnnb1 CTNNB1 rs890415 1.54
+Ctnnbl1 CTNNBL1 rs6125916 1.6302
+Ctnnd1 CTNND1 rs11570210 1.1741
+Ctnnd2 CTNND2 rs2727603 2.95
+Ctns CTNS rs222757 1.8884
+Ctps CTPS1 rs10889750 1.1362
+Ctr9 CTR9 rs10840483 1.9469
+Ctrb1 CTRB1 rs2278020 2.4019
+Ctrc CTRC rs1883426 2.7231
+Ctrl CTRL rs16942887 32.076
+Ctsa CTSA rs4810479 20.464
+Ctsb CTSB rs1299525 1.1358
+Ctsc CTSC rs660470 1.0999
+Ctsd CTSD rs17834326 1.2514
+Ctsf CTSF rs3867132 1.1085
+Ctsh CTSH rs11072818 1.6817
+Ctsk CTSK rs10305724 1.4533
+Ctsl CTSL1 rs11141951 1.9654
+Ctsll3 CTSL1P8 rs10868658 0.965
+Ctso CTSO rs10020154 0.994
+Ctsw CTSW rs679147 3.3254
+Ctsz CTSZ rs151348 1.9158
+Cttn CTTN rs4367974 0.9846
+Cttnbp2 CTTNBP2 rs6466624 1.7073
+Cttnbp2nl CTTNBP2NL rs2492495 2.0057
+Ctu1 CTU1 rs10403942 1.1069
+Ctu2 CTU2 rs4597303 1.4721
+Ctxn1 CTXN1 rs4804833 3.2673
+Ctxn3 CTXN3 rs7734486 1.6269
+Cubn CUBN rs1276715 3.5403
+Cuedc1 CUEDC1 rs4793574 1.3991
+Cuedc2 CUEDC2 rs7076748 1.3418
+Cul1 CUL1 rs6973481 2.0669
+Cul3 CUL3 rs6736188 2.4737
+Cul4a CUL4A rs2296066 0.8306
+Cul5 CUL5 rs7947408 1.6428
+Cul7 CUL7 rs3793021 1.2604
+Cul9 CUL9 rs7832 2.0181
+Cuta CUTA rs2747479 1.041
+Cux1 CUX1 rs11974778 1.9983
+Cux2 CUX2 rs1265566 2.372
+Cwc15 CWC15 rs10831290 1.3662
+Cwc22 CWC22 rs2584201 1.4657
+Cwc25 CWC25 rs228293 1.763
+Cwc27 CWC27 rs7733835 1.9931
+Cwf19l1 CWF19L1 rs17729876 3.3729
+Cwf19l2 CWF19L2 rs10890730 1.4915
+Cwh43 CWH43 rs1463366 1.8874
+Cx3cl1 CX3CL1 rs223819 2.2197
+Cx3cr1 CX3CR1 rs871144 2.5642
+Cxadr CXADR rs2846881 1.7111
+Cxcl1 CXCL1 rs17811212 1.4012
+Cxcl10 CXCL10 rs4129780 1.5224
+Cxcl11 CXCL11 rs4129780 1.5224
+Cxcl12 CXCL12 rs4948876 1.6225
+Cxcl13 CXCL13 rs17416271 1.7284
+Cxcl14 CXCL14 rs11950696 0.9763
+Cxcl16 CXCL16 rs754814 2.6297
+Cxcl2 CXCL3 rs352041 1.0414
+Cxcl5 CXCL6 rs16850073 0.9512
+Cxcl9 CXCL9 rs4859583 1.3686
+Cxcr1 CXCR1 rs3092968 1.1862
+Cxcr4 CXCR4 rs13395686 1.5862
+Cxcr5 CXCR5 rs12363277 1.231
+Cxcr7 CXCR7 rs6431471 1.0117
+Cxxc1 CXXC1 rs1562004 1.4932
+Cxxc5 CXXC5 rs6580190 1.1897
+Cyb561 CYB561 rs17687734 1.3198
+Cyb561d1 CYB561D1 rs485660 1.3607
+Cyb561d2 CYB561D2 rs709210 2.4806
+Cyb5 CYB5A rs3794948 1.1784
+Cyb5b CYB5B rs11867084 1.2921
+Cyb5d2 CYB5D2 rs4328477 1.1415
+Cyb5r1 CYB5R1 rs6672643 0.9743
+Cyb5r2 CYB5R2 rs4758216 3.1183
+Cyb5r3 CYB5R3 rs4822171 1.7833
+Cyb5r4 CYB5R4 rs1925462 1.0345
+Cyb5rl CYB5RL rs946447 1.3144
+Cyba CYBA rs899729 1.5645
+Cybrd1 CYBRD1 rs6729179 1.4658
+Cycs CYCS rs12531950 1.4355
+Cyfip1 CYFIP1 rs4778455 1.6893
+Cyfip2 CYFIP2 rs10076465 3.19
+Cyhr1 CYHR1 rs2721166 2.5933
+4930488P18Rik CYLC2 rs10512309 1.667
+Cym CYMP rs41354749 1.261
+Cyp11a1 CYP11A1 rs2959003 1.5514
+Cyp11b1 CYP11B1 rs3819497 1.3517
+Cyp11b2 CYP11B2 rs3097 0.9179
+Cyp17a1 CYP17A1 rs12356821 1.9516
+Cyp19a1 CYP19A1 rs2445762 2.6664
+Cyp1a1 CYP1A1 rs4986885 0.8502
+Cyp1a2 CYP1A2 rs4986885 0.8502
+Cyp1b1 CYP1B1 rs336039 1.9133
+Cyp20a1 CYP20A1 rs4673253 1.7464
+Cyp21a1 CYP21A1P rs2228296 1.2919
+Cyp24a1 CYP24A1 rs2762941 1.1264
+Cyp26a1 CYP26A1 rs8211 2.6232
+Cyp26b1 CYP26B1 rs2041666 1.2242
+Cyp26c1 CYP26C1 rs8211 2.6232
+Cyp27a1 CYP27A1 rs4674345 1.4153
+Cyp27b1 CYP27B1 rs8176345 1.3226
+Cyp2a5 CYP2A7 rs4124633 2.0121
+Cyp2b10 CYP2B6 rs7251950 1.7685
+Cyp2c50 CYP2C18 rs11818797 1.0882
+Cyp2c29 CYP2C19 rs4362080 1.9462
+Cyp2c55 CYP2C9 rs12772884 1.0021
+Cyp2e1 CYP2E1 rs7077457 1.994
+Cyp2f2 CYP2F1 rs1709128 1.1717
+Cyp2g1 CYP2G1P rs10422346 1.3056
+Cyp2j6 CYP2J2 rs1593461 1.0687
+Cyp2r1 CYP2R1 rs10766197 3.3107
+Cyp2s1 CYP2S1 rs8101985 1.3626
+Cyp2u1 CYP2U1 rs6826111 1.1605
+Cyp2w1 CYP2W1 rs10264122 2.8693
+Cyp39a1 CYP39A1 rs6919150 1.2283
+Cyp3a13 CYP3A4 rs12721617 2.7823
+Cyp3a57 CYP3A43 rs472660 1.0587
+Cyp3a41a CYP3A5 rs7792939 1.5712
+Cyp46a1 CYP46A1 rs10873508 1.3416
+Cyp4a10 CYP4A11 rs1078311 1.3859
+Cyp4a12b CYP4A22 rs7512729 2.074
+Cyp4b1 CYP4B1 rs632645 1.4548
+Cyp4f40 CYP4F11 rs11882524 0.8986
+Cyp4f14 CYP4F12 rs10409750 1.9512
+Cyp4f18 CYP4F2 rs6417255 2.9897
+Cyp4f39 CYP4F22 rs1273511 1.4003
+Cyp4f13 CYP4F3 rs2163295 1.8459
+Cyp4v3 CYP4V2 rs3736455 1.7373
+Cyp4x1 CYP4X1 rs1078311 1.3859
+Cyp7a1 CYP7A1 rs10110183 1.9219
+Cyp7b1 CYP7B1 rs13280706 2.0348
+Cyp8b1 CYP8B1 rs6790095 0.8831
+Cyr61 CYR61 rs10782552 1.5711
+Cys1 CYS1 rs7569312 1.137
+Cysltr2 CYSLTR2 rs12184704 2.0267
+Cyth1 CYTH1 rs12946167 1.9755
+Cyth2 CYTH2 rs1799257 3.3125
+Cyth3 CYTH3 rs6463529 1.5157
+Cyth4 CYTH4 rs5995416 1.4504
+Cytip CYTIP rs1459219 3.0896
+Cytl1 CYTL1 rs4392464 2.1332
+Cyyr1 CYYR1 rs219652 2.0419
+D2hgdh D2HGDH rs4675794 1.5666
+Daam1 DAAM1 rs1957406 2.465
+Daam2 DAAM2 rs11754450 1.7657
+Dab1 DAB1 rs849480 2.1724
+Dab2 DAB2 rs13179493 2.3008
+Dab2ip DAB2IP rs6478524 2.4127
+Dach1 DACH1 rs1931465 2.2639
+Dact1 DACT1 rs12878996 1.3164
+Dact2 DACT2 rs2306285 0.9735
+Dact3 DACT3 rs931647 1.0094
+9330104G04Rik DACT3-AS1 rs931647 1.0094
+Dad1 DAD1 rs1384962 1.5845
+Dag1 DAG1 rs3870336 1.5133
+Dagla DAGLA rs12274157 3.2195
+Daglb DAGLB rs702485 5.862
+Dalrd3 DALRD3 rs4974081 0.9416
+2700023E23Rik DANCR rs10517266 1.5936
+Dao DAO rs11114089 1.2981
+Dap DAP rs7973 2.117
+Dapk1 DAPK1 rs11141951 1.9654
+Dapk2 DAPK2 rs12592616 2.0927
+Dapk3 DAPK3 rs737204 4.1622
+Dapl1 DAPL1 rs11684616 2.9727
+Dapp1 DAPP1 rs17539393 2.1145
+Darc DARC rs2746042 1.2533
+Dars DARS rs309143 2.1627
+Daxx DAXX rs6906691 1.1757
+Dazap1 DAZAP1 rs4807927 1.348
+Dazap2 DAZAP2 rs10783424 1.0609
+Dazl DAZL rs17029188 1.9076
+Dbc1 DBC1 rs7036405 1.3438
+Dbf4 DBF4 rs11982729 2.0429
+Dbh DBH rs1074052 1.7242
+Dbi DBI rs3769663 1.4896
+Dbil5 DBIL5P rs1741275 0.9598
+Dbn1 DBN1 rs2287694 0.8398
+Dbndd1 DBNDD1 rs3785181 1.1144
+Dbndd2 DBNDD2 rs3761195 2.6087
+Dbnl DBNL rs4640970 1.0725
+Dbp DBP rs2292342 1.8551
+Dbt DBT rs17454070 1.2826
+Dbx1 DBX1 rs12804935 1.2391
+Dbx2 DBX2 rs11182800 1.2908
+Dcaf11 DCAF11 rs11574503 1.442
+Dcaf12 DCAF12 rs10971942 0.927
+Dcaf13 DCAF13 rs17803441 1.7474
+Dcaf15 DCAF15 rs10409785 1.5467
+Dcaf4 DCAF4 rs8022709 1.5834
+Dcaf5 DCAF5 rs34264436 1.8658
+Dcaf6 DCAF6 rs201539 1.528
+Dcaf7 DCAF7 rs11655956 2.323
+Dcaf8 DCAF8 rs16831591 1.8817
+Dcakd DCAKD rs9916632 0.8321
+Dcbld1 DCBLD1 rs12197399 2.3373
+Dcbld2 DCBLD2 rs10935518 1.5399
+Dcc DCC rs1531347 2.5776
+Dcdc2a DCDC2 rs2296536 2.0489
+Dcdc2b DCDC2B rs1358823 0.857
+Dcdc5 DCDC5 rs7117695 1.137
+Dchs1 DCHS1 rs11040933 1.8716
+Dchs2 DCHS2 rs17301300 1.2152
+Dck DCK rs6836323 1.3044
+Dclk1 DCLK1 rs12100412 2.3303
+Dclk2 DCLK2 rs10001645 1.4646
+Dclk3 DCLK3 rs1543226 1.1667
+Dclre1a DCLRE1A rs11196530 1.9003
+Dclre1c DCLRE1C rs4556439 1.0713
+Dcn DCN rs544968 1.0124
+Dcp1a DCP1A rs687346 1.9205
+Dcp1b DCP1B rs2246581 1.1963
+Dcp2 DCP2 rs1422222 2.3821
+Dcps DCPS rs17135399 2.7881
+Dcst1 DCST1 rs11589479 2.1627
+Dcst2 DCST2 rs6682611 1.8716
+Dcstamp DCSTAMP rs10808389 3.0898
+Dct DCT rs17196233 1.5882
+Dctd DCTD rs4742 1.2001
+Dctn4 DCTN4 rs12657686 1.4935
+Dctn5 DCTN5 rs703767 1.1213
+Dcun1d1 DCUN1D1 rs16833613 1.099
+AW495713 DCUN1D2 rs9577550 1.163
+Dcun1d4 DCUN1D4 rs6837103 1.3919
+Dcun1d5 DCUN1D5 rs17099939 1.25
+Dcxr DCXR rs4969483 1.5018
+Dda1 DDA1 rs1529482 1.8681
+Ddah1 DDAH1 rs12029474 2.6084
+Ddah2 DDAH2 rs400547 2.5546
+Ddb2 DDB2 rs3824866 15.777
+Ddc DDC rs12718527 2.2134
+Ddhd1 DDHD1 rs8016154 1.2706
+1700011N24Rik DDI1 rs260852 2.0301
+Ddit4l DDIT4L rs11097696 1.4637
+Ddn DDN rs7311091 1.1649
+Ddo DDO rs4945856 1.2256
+Ddost DDOST rs647812 1.672
+Ddr1 DDR1 rs2844659 3.8431
+Ddr2 DDR2 rs1510311 1.0472
+Ddrgk1 DDRGK1 rs2422862 1.5193
+Ddt DDT rs2858908 1.3544
+Ddx1 DDX1 rs13432677 1.433
+Ddx10 DDX10 rs11212816 1.5983
+Ddx11 DDX11 rs4031375 1.9682
+Ddx17 DDX17 rs763121 2.2817
+Ddx18 DDX18 rs17566210 2.2853
+Ddx19a DDX19A rs12149660 1.4564
+Ddx19b DDX19B rs12149660 1.4564
+Ddx20 DDX20 rs565522 1.7491
+Ddx21 DDX21 rs4746804 0.9496
+Ddx23 DDX23 rs1057908 0.9101
+Ddx24 DDX24 rs10138711 1.2212
+Ddx27 DDX27 rs6067024 1.5845
+Ddx31 DDX31 rs13294925 1.5662
+Ddx39 DDX39A rs3815716 1.413
+Ddx39b DDX39B rs3094005 4.2846
+Ddx41 DDX41 rs179810 0.9694
+BG089777 DDX43 rs311678 1.1782
+Ddx47 DDX47 rs11615058 1.8251
+Ddx5 DDX5 rs2872315 1.0169
+Ddx50 DDX50 rs3740596 1.6576
+Ddx52 DDX52 rs9892460 1.4458
+Ddx54 DDX54 rs2701626 2.2793
+Ddx55 DDX55 rs4456350 1.9179
+Ddx58 DDX58 rs11795343 2.6623
+Ddx59 DDX59 rs10919982 1.6119
+Ddx6 DDX6 rs2510897 1.3346
+Ddx60 DDX60 rs1117105 2.6891
+Deaf1 DEAF1 rs2246614 2.573
+Decr1 DECR1 rs1805889 0.8554
+Decr2 DECR2 rs540 1.4272
+Dedd DEDD rs11265554 1.3805
+Dedd2 DEDD2 rs17750207 1.0246
+Def6 DEF6 rs6899744 1.7102
+Def8 DEF8 rs17784583 2.3156
+Defa1 DEFA1 rs2702916 0.9987
+MMT00080249 DEFA4 rs41396548 1.4802
+Defb1 DEFB1 rs2702840 2.4572
+Defb29 DEFB116 rs4292148 1.6657
+4930525K10Rik DEFB118 rs11905693 0.9851
+Defb19 DEFB119 rs11905693 0.9851
+Defb45 DEFB122 rs765736 0.8864
+Defb36 DEFB123 rs6059440 1.1248
+Defb25 DEFB124 rs1543438 1.2484
+Defb26 DEFB125 rs2196239 1.0244
+Defb20 DEFB128 rs6039778 0.8193
+Defb23 DEFB129 rs406449 1.1958
+Defb47 DEFB130 rs6601667 1.3589
+Defb43 DEFB131 rs13114077 1.2422
+Defb30 DEFB135 rs11774568 1.4309
+Defb42 DEFB136 rs7016119 1.848
+Degs1 DEGS1 rs6686449 2.0774
+Degs2 DEGS2 rs12588659 1.4634
+Dennd1a DENND1A rs4836940 3.4068
+BC042698 DENND1B rs2477073 2.5092
+Dennd1c DENND1C rs1663803 2.1806
+Dennd2a DENND2A rs7781853 2.9038
+Dennd2c DENND2C rs222497 2.1438
+Dennd2d DENND2D rs2484459 1.19
+Dennd3 DENND3 rs3739239 3.1856
+Dennd4a DENND4A rs2572207 2.4629
+Dennd4b DENND4B rs6413529 0.9842
+1700065A05Rik DENND4C rs2383093 1.4681
+Dennd5a DENND5A rs9666585 2.1615
+Dennd5b DENND5B rs792871 1.4506
+Denr DENR rs2256572 4.2827
+Depdc1a DEPDC1 rs34349147 0.9855
+Depdc1b DEPDC1B rs2045355 1.8775
+Depdc5 DEPDC5 rs11703246 2.6772
+Depdc7 DEPDC7 rs10836004 0.9322
+Deptor DEPTOR rs7006901 1.9702
+Dera DERA rs2900367 1.6574
+Derl1 DERL1 rs7812931 1.6684
+Derl2 DERL2 rs16954705 1.1183
+Derl3 DERL3 rs9612480 1.2008
+Pppde1 DESI2 rs2819018 1.6341
+Det1 DET1 rs16942010 0.9359
+Dexi DEXI rs8063850 1.2691
+Dffa DFFA rs3828044 0.8505
+Dffb DFFB rs4648426 1.862
+Dfna5 DFNA5 rs2521766 2.1777
+Whrn DFNB31 rs4979373 5.1844
+Dfnb59 DFNB59 rs2288323 0.8884
+Dgat1 DGAT1 rs4557742 1.8887
+Dgat2 DGAT2 rs10793126 4.8486
+Dgcr14 DGCR14 rs2096055 1.2211
+Dgcr2 DGCR2 rs2096055 1.2211
+Dgcr6 DGCR6 rs376425 1.7305
+Dgcr8 DGCR8 rs389471 1.4628
+Dgka DGKA rs11171694 1.3512
+Dgkb DGKB rs12699614 3.1858
+Dgkd DGKD rs3828305 1.7395
+Dgke DGKE rs1992553 1.4772
+Dgkg DGKG rs9816226 1.8548
+Dgkh DGKH rs10492434 3.6225
+Dgki DGKI rs3823550 2.3341
+Dgkq DGKQ rs11724804 1.0655
+Dgkz DGKZ rs6485674 5.3087
+Dguok DGUOK rs831549 1.708
+Dhcr24 DHCR24 rs10489659 1.4216
+Dhcr7 DHCR7 rs2276360 1.6123
+Dhdds DHDDS rs11247954 2.6887
+Dhdh DHDH rs746075 2.4449
+Dhfr DHFR rs17263489 1.5599
+Dhodh DHODH rs889545 1.5761
+Dhrs1 DHRS1 rs3940231 1.7162
+Dhrs11 DHRS11 rs17693183 1.4965
+Dhrs13 DHRS13 rs8081659 1.2189
+Dhrs2 DHRS2 rs12434181 1.1259
+Dhrs3 DHRS3 rs12029485 3.035
+Dhrs4 DHRS4 rs7140888 1.2229
+Dhrs7 DHRS7 rs1254036 0.9642
+Dhrs7b DHRS7B rs2279164 1.7815
+Dhrs7c DHRS7C rs4791872 1.2297
+Dhrs9 DHRS9 rs1990703 1.1625
+Dhtkd1 DHTKD1 rs2062986 1.0301
+Dhx15 DHX15 rs6853518 1.8761
+Dhx16 DHX16 rs2285320 1.5852
+Dhx32 DHX32 rs12259918 0.8719
+Dhx33 DHX33 rs3026143 1.3339
+Dhx34 DHX34 rs4802349 2.3168
+Dhx35 DHX35 rs2867897 1.3271
+Dhx36 DHX36 rs13087488 1.4237
+Dhx37 DHX37 rs7957007 2.8222
+Dhx38 DHX38 rs217181 1.8392
+Dhx40 DHX40 rs10515169 1.3923
+Dhx58 DHX58 rs12600570 2.6598
+Dhx8 DHX8 rs4632174 1.9634
+Dhx9 DHX9 rs9425563 1.9252
+Diablo DIABLO rs11060167 2.3563
+Diap1 DIAPH1 rs10068400 1.367
+Diap3 DIAPH3 rs1337652 2.7342
+Dicer1 DICER1 rs10131275 3.7086
+Dido1 DIDO1 rs6062729 1.7545
+Diexf DIEXF rs674346 1.288
+Dimt1 DIMT1 rs153867 0.9378
+Dio1 DIO1 rs11206230 1.4065
+Dio2 DIO2 rs17766126 1.9935
+Dio3 DIO3 rs11623978 1.2437
+Dio3os DIO3OS rs11623978 1.2437
+Dip2a DIP2A rs9984082 1.5382
+AI317237 DIP2B rs11169551 3.2012
+Dip2c DIP2C rs17221323 2.2473
+Diras1 DIRAS1 rs1001328 1.6678
+Diras2 DIRAS2 rs529543 1.1591
+Dis3 DIS3 rs7996083 1.0708
+Dis3l DIS3L rs17257440 0.8996
+Dis3l2 DIS3L2 rs893740 1.8649
+Disc1 DISC1 rs2793102 2.9855
+Disp1 DISP1 rs2378600 1.1662
+Disp2 DISP2 rs17670745 1.1444
+Dixdc1 DIXDC1 rs2105619 2.2339
+4930488L21Rik DKFZP434H168 rs11641551 3.3482
+Dkk2 DKK2 rs10155071 1.9303
+Dkk3 DKK3 rs16910327 2.1111
+Dkk4 DKK4 rs10096210 0.9263
+Dkkl1 DKKL1 rs7260611 1.6631
+Dlc1 DLC1 rs6530634 2.7929
+Dld DLD rs2395932 1.8523
+Dlec1 DLEC1 rs11922130 1.0919
+Dleu2 DLEU2 rs7318395 1.3456
+Dleu7 DLEU7 rs11841145 2.4852
+Dlg1 DLG1 rs9825631 1.0375
+Dlg2 DLG2 rs7125294 2.8529
+Dlg4 DLG4 rs222850 0.9263
+Dlg5 DLG5 rs11002285 2.6028
+Dlgap1 DLGAP1 rs8090376 3.2053
+Dlgap2 DLGAP2 rs2286217 1.6745
+Dlgap3 DLGAP3 rs12076918 1.5479
+Dlgap4 DLGAP4 rs2076692 1.7476
+Dlgap5 DLGAP5 rs8004787 1.0041
+Dlk1 DLK1 rs10139403 2.9961
+Dlk2 DLK2 rs3734688 1.3787
+Dll1 DLL1 rs7452607 1.413
+Dll3 DLL3 rs3212275 1.0661
+Dll4 DLL4 rs4924514 1.988
+Dlx1 DLX1 rs10497378 1.5571
+Dlx2 DLX2 rs10497378 1.5571
+Dlx3 DLX3 rs7224701 1.8959
+Dlx4 DLX4 rs7224701 1.8959
+Dlx5 DLX5 rs917047 1.2475
+Dlx6 DLX6 rs917047 1.2475
+Dmap1 DMAP1 rs17414912 1.3019
+Dmbt1 DMBT1 rs6606641 1.2706
+Dmbx1 DMBX1 rs12567374 1.7251
+Dmc1 DMC1 rs763121 2.2817
+Dmgdh DMGDH rs402701 2.2254
+Dmkn DMKN rs12460932 1.828
+Dmp1 DMP1 rs2736980 1.0969
+Dmpk DMPK rs8100208 1.4514
+Dmrt1 DMRT1 rs16925431 1.2557
+Dmrt2 DMRT2 rs4741035 1.7501
+Dmrt3 DMRT3 rs7047346 1.5245
+Dmrta1 DMRTA1 rs1329395 1.0408
+Dmrtc2 DMRTC2 rs2075751 2.1193
+Dmtf1 DMTF1 rs17161329 0.8383
+Dmwd DMWD rs8100208 1.4514
+Dmxl1 DMXL1 rs11241487 3.3393
+Dna2 DNA2 rs10998105 2.1187
+Lrrc50 DNAAF1 rs3743642 1.2856
+1110034A24Rik DNAAF2 rs1011373 0.852
+6030429G01Rik DNAAF3 rs3810351 0.9187
+Dnahc1 DNAH1 rs699469 2.9462
+Dnahc10 DNAH10 rs6488910 6.9512
+Dnahc11 DNAH11 rs17145715 1.5764
+Dnahc12 DNAH12 rs11707555 1.4492
+Dnahc14 DNAH14 rs10915742 1.8259
+Dnahc17 DNAH17 rs17642548 6.4721
+Dnahc2 DNAH2 rs2543541 2.4919
+Dnahc3 DNAH3 rs858212 1.9237
+Dnahc5 DNAH5 rs11133773 1.3526
+Dnahc7a DNAH7 rs341948 1.2144
+Dnahc8 DNAH8 rs9470961 1.7765
+Dnahc9 DNAH9 rs1014323 2.207
+Dnaic1 DNAI1 rs10972139 1.3512
+Dnaic2 DNAI2 rs1477792 0.8884
+Dnaja1 DNAJA1 rs3818644 1.3096
+Dnaja2 DNAJA2 rs12445911 0.8652
+Dnaja3 DNAJA3 rs2270363 2.0391
+Dnaja4 DNAJA4 rs4887024 1.3032
+Dnajb1 DNAJB1 rs3810255 1.5825
+Dnajb11 DNAJB11 rs16860878 2.4215
+Dnajb12 DNAJB12 rs4433500 1.4476
+Dnajb13 DNAJB13 rs2632725 1.248
+Dnajb14 DNAJB14 rs2866175 1.3746
+Dnajb3 DNAJB3 rs17868337 2.0383
+Dnajb4 DNAJB4 rs4543852 1.6919
+Dnajb5 DNAJB5 rs4879848 0.9252
+Dnajb6 DNAJB6 rs4716707 1.6354
+Dnajb7 DNAJB7 rs5758106 1.2289
+Dnajb8 DNAJB8 rs2659690 2.3242
+Dnajb9 DNAJB9 rs4727713 1.3169
+Dnajc1 DNAJC1 rs11596672 1.0601
+Dnajc10 DNAJC10 rs288341 0.9194
+Dnajc11 DNAJC11 rs11892 1.3938
+Dnajc13 DNAJC13 rs11924491 2.8492
+Dnajc17 DNAJC17 rs11855560 2.3641
+Dnajc19 DNAJC19 rs1805614 1.3919
+Dnajc2 DNAJC2 rs3757507 0.8626
+Dnajc21 DNAJC21 rs41474544 2.3541
+Dnajc22 DNAJC22 rs10506285 1.3191
+Dnajc24 DNAJC24 rs11031375 1.4062
+Dnajc25 DNAJC25 rs13285010 2.02
+Dnajc27 DNAJC27 rs6726261 1.9694
+Dnajc3 DNAJC3 rs10467347 1.6189
+Dnajc30 DNAJC30 rs799160 1.4142
+Dnajc4 DNAJC4 rs1011219 1.1014
+Dnajc5 DNAJC5 rs817377 1.334
+Dnajc5b DNAJC5B rs17397411 2.7378
+Dnajc5g DNAJC5G rs2083363 0.8891
+Dnajc6 DNAJC6 rs9326066 1.6868
+Dnajc7 DNAJC7 rs4796756 1.4436
+Dnajc8 DNAJC8 rs7516655 1.8817
+Dnajc9 DNAJC9 rs17737927 1.3571
+Dnalc1 DNAL1 rs4635279 2.1401
+Dnalc4 DNAL4 rs5750678 1.1939
+Dnase1l2 DNASE1L2 rs27382 0.8395
+Dnase1l3 DNASE1L3 rs34267423 2.2141
+Dnase2b DNASE2B rs12144715 1.3396
+Dner DNER rs6754103 2.1278
+Dnlz DNLZ rs10870201 1.6269
+Dnm1 DNM1 rs2229917 1.7747
+Dnm1l DNM1L rs12372562 1.1718
+Dnm2 DNM2 rs7254425 5.32
+Dnm3 DNM3 rs2227198 2.7113
+Dnmbp DNMBP rs11190368 1.4795
+Dnmt1 DNMT1 rs2305792 0.8306
+Dnmt3a DNMT3A rs7581217 2.1184
+Dnmt3b DNMT3B rs2424898 2.1108
+Dnmt3l DNMT3L rs2155724 1.4012
+Dnpep DNPEP rs7600361 0.8176
+Dntt DNTT rs17297837 0.8219
+Dnttip1 DNTTIP1 rs12480887 5.8453
+Dnttip2 DNTTIP2 rs3761912 1.0108
+Doc2b DOC2B rs4890199 1.4872
+Doc2g DOC2GP rs11227871 1.382
+Dock1 DOCK1 rs11018116 2.5366
+Dock10 DOCK10 rs12995498 2.6317
+Dock2 DOCK2 rs17646824 1.7467
+Dock3 DOCK3 rs10510757 2.5248
+Dock4 DOCK4 rs29465 2.1409
+Dock5 DOCK5 rs2709628 2.163
+Dock6 DOCK6 rs737337 8.5082
+Dock7 DOCK7 rs11207998 3.5322
+Dock8 DOCK8 rs577854 1.6052
+Dock9 DOCK9 rs9517574 1.6743
+Dohh DOHH rs10413312 1.3122
+Dok2 DOK2 rs11783414 3.178
+Dok3 DOK3 rs179810 0.9694
+Dok4 DOK4 rs223839 2.6216
+Dok5 DOK5 rs7270175 1.3978
+Dok6 DOK6 rs12604328 1.6708
+Dok7 DOK7 rs3468 1.9602
+Dolk DOLK rs9031 1.0474
+Dolpp1 DOLPP1 rs11564095 1.5835
+Dom3z DOM3Z rs9332739 2.0606
+Donson DONSON rs762185 1.247
+Dopey1 DOPEY1 rs9294271 1.0867
+Dopey2 DOPEY2 rs2284644 2.1846
+Dpagt1 DPAGT1 rs1784460 3.2948
+Dpcd DPCD rs11597334 0.8097
+Dpcr1 DPCR1 rs2844651 2.8011
+Dpep1 DPEP1 rs164741 1.9825
+Dpep3 DPEP3 rs4986970 7.1735
+Dpf1 DPF1 rs2304174 1.8441
+Dpf2 DPF2 rs4149841 1.6643
+Dpf3 DPF3 rs8012795 2.4423
+Dph1 DPH1 rs1051322 1.1305
+Dph2 DPH2 rs1859728 1.9031
+Dph3 DPH3 rs2672564 1.2275
+Dph3b-ps DPH3P1 rs6122316 0.8465
+Dpm1 DPM1 rs16995642 1.3855
+Dpm2 DPM2 rs7997 1.2138
+Dpm3 DPM3 rs7517158 2.0327
+Dpp10 DPP10 rs11904435 2.3287
+Dpp4 DPP4 rs4664446 0.9551
+Dpp6 DPP6 rs12703355 2.4254
+Dpp7 DPP7 rs7466635 1.0547
+Dpp8 DPP8 rs6494521 2.2535
+Dpp9 DPP9 rs10406145 1.1842
+Dppa2 DPPA2 rs7621590 3.4519
+Dppa3 DPPA3 rs7969455 1.7187
+Dppa4 DPPA4 rs7621590 3.4519
+Dppa5 DPPA5 rs9360664 0.9626
+Crxos1 DPRX rs17273141 0.9606
+Dpt DPT rs1415680 2.1855
+Dpy19l1 DPY19L1 rs328892 2.0322
+Dpy19l2 DPY19L2 rs4763089 2.6347
+Dpy19l3 DPY19L3 rs2012353 5.2747
+Dpy19l4 DPY19L4 rs4735324 1.5302
+Dpy30 DPY30 rs2064813 1.6728
+Dpyd DPYD rs17378455 2.4523
+Dpys DPYS rs10808389 3.0898
+Dpysl2 DPYSL2 rs12155555 1.5158
+Dpysl3 DPYSL3 rs17106688 1.8435
+Dpysl4 DPYSL4 rs7094598 1.6538
+Dpysl5 DPYSL5 rs10181777 1.3132
+Dr1 DR1 rs12563120 4.1728
+Dram1 DRAM1 rs10860821 0.9901
+Dram2 DRAM2 rs325898 1.7962
+Drap1 DRAP1 rs622614 3.3156
+Drd1a DRD1 rs266004 1.2185
+Drd2 DRD2 rs2234689 1.2623
+Drd3 DRD3 rs1394016 1.5193
+Drd4 DRD4 rs12419694 2.7284
+Drd5 DRD5 rs10939504 1.2845
+Drg1 DRG1 rs5749272 1.0519
+Drg2 DRG2 rs854790 1.2
+Drosha DROSHA rs16901235 1.8111
+Dsc1 DSC1 rs1789036 1.2695
+Dsc2 DSC2 rs1893962 1.4094
+Dsc3 DSC3 rs10502553 1.7515
+Dscam DSCAM rs13049995 3.2928
+Dscaml1 DSCAML1 rs552498 5.3965
+Dscc1 DSCC1 rs12707880 1.1826
+Dscr3 DSCR3 rs9976308 1.4568
+Dse DSE rs10485181 2.2492
+Dsel DSEL rs1061568 1.3241
+Dsg1a DSG1 rs2919995 1.6739
+Dsg2 DSG2 rs1449079 1.5656
+Dsg3 DSG3 rs2035037 0.8639
+Dsg4 DSG4 rs3859363 1.6548
+Dsn1 DSN1 rs6071999 0.841
+Dsp DSP rs2744373 1.8857
+Dspp DSPP rs7668491 2.7781
+Dst DST rs9464401 2.8655
+Dstn DSTN rs12479820 1.9094
+Dstyk DSTYK rs17417002 1.3967
+Dtd1 DTD1 rs1884783 1.9582
+Gm17384 DTHD1 rs4359926 3.4899
+Dtl DTL rs6657728 1.2993
+Dtna DTNA rs12955378 0.9266
+Dtnb DTNB rs17745893 2.3601
+Dtnbp1 DTNBP1 rs2743861 0.8908
+Dtwd2 DTWD2 rs10447237 1.6949
+Dtx1 DTX1 rs11066491 0.9201
+Dtx2 DTX2 rs10952935 0.8972
+Dtx3 DTX3 rs11613457 3.7375
+Dtx3l DTX3L rs17267388 1.9314
+Dtx4 DTX4 rs10896928 0.9784
+Dtymk DTYMK rs4675794 1.5666
+Duox1 DUOX1 rs12910807 1.6323
+Duox2 DUOX2 rs12910807 1.6323
+Duoxa1 DUOXA1 rs12910807 1.6323
+9030623N16Rik DUOXA2 rs12910807 1.6323
+Dus1l DUS1L rs4969483 1.5018
+Dus2l DUS2L rs2301814 22.623
+Dus3l DUS3L rs7257191 1.2541
+Dus4l DUS4L rs4730250 2.3513
+Dusp1 DUSP1 rs10516095 1.9337
+Dusp10 DUSP10 rs17491176 1.6158
+Dusp11 DUSP11 rs2685277 1.0587
+Dusp12 DUSP12 rs10917821 1.1575
+Dusp13 DUSP13 rs11001296 1.4244
+Dusp15 DUSP15 rs17340555 2.1077
+Dusp16 DUSP16 rs17313592 1.3891
+Dusp18 DUSP18 rs1005887 0.8213
+Dusp19 DUSP19 rs2705712 2.492
+Dusp2 DUSP2 rs2969490 1.1484
+Dusp22 DUSP22 rs815593 1.7975
+Dusp23 DUSP23 rs11584887 1.4927
+Dusp26 DUSP26 rs3888869 0.8897
+Dusp27 DUSP27 rs2025885 0.8204
+Dusp28 DUSP28 rs4494742 1.3342
+Dusp3 DUSP3 rs2023794 2.1135
+Dusp4 DUSP4 rs2124414 0.8706
+Dusp5 DUSP5 rs11195165 1.3972
+Dusp6 DUSP6 rs2279574 1.8857
+Dusp7 DUSP7 rs9817697 1.8925
+Dut DUT rs4775748 1.2084
+Dvl2 DVL2 rs222850 0.9263
+Dvl3 DVL3 rs843360 1.8066
+Dydc1 DYDC1 rs7090145 0.9751
+Dydc2 DYDC2 rs7090145 0.9751
+Dym DYM rs833514 3.9126
+Dync1h1 DYNC1H1 rs754598 1.3227
+Dync1i1 DYNC1I1 rs42078 1.7368
+Dync1i2 DYNC1I2 rs951917 1.5107
+Dync1li1 DYNC1LI1 rs6782149 1.263
+Dync2h1 DYNC2H1 rs7128981 1.3333
+Dync2li1 DYNC2LI1 rs713262 1.2181
+Dynll1 DYNLL1 rs580016 1.6968
+Dynll2 DYNLL2 rs11079337 1.4947
+Dynlrb1 DYNLRB1 rs6088515 2.0755
+Dynlrb2 DYNLRB2 rs11640067 0.9031
+Dynlt1b DYNLT1 rs4709236 1.574
+Dyrk1a DYRK1A rs2835763 1.6602
+Dyrk1b DYRK1B rs3810386 2.6449
+Dyrk2 DYRK2 rs2620756 1.3012
+Dyrk3 DYRK3 rs12096152 1.0319
+Dyrk4 DYRK4 rs2907492 1.1763
+Dysf DYSF rs12471152 1.7212
+Dytn DYTN rs16838403 1.9205
+Dyx1c1 DYX1C1 rs3203152 1.3779
+6330439K17Rik DZANK1 rs1543455 3.1458
+Dzip1 DZIP1 rs9590305 1.5909
+Dzip1l DZIP1L rs11925123 1.6949
+Dzip3 DZIP3 rs13078271 1.2456
+E2f2 E2F2 rs1009590 3.8044
+E2f3 E2F3 rs9368188 2.4786
+E2f4 E2F4 rs2233455 8.8128
+E2f5 E2F5 rs4150944 2.4382
+E2f7 E2F7 rs310855 2.0143
+E2f8 E2F8 rs721067 1.4142
+E4f1 E4F1 rs27382 0.8395
+Eaf1 EAF1 rs6807885 2.808
+Eaf2 EAF2 rs9868875 1.011
+Eapp EAPP rs9805947 1.3455
+Ears2 EARS2 rs867131 1.037
+Ebag9 EBAG9 rs17450206 1.6098
+Ebf1 EBF1 rs6420101 1.2
+Ebf2 EBF2 rs901174 3.0553
+Ebf3 EBF3 rs11592303 2.6304
+Ebf4 EBF4 rs6051233 2.3634
+Ebna1bp2 EBNA1BP2 rs4660721 1.1089
+Ebpl EBPL rs6561552 1.8955
+Ecd ECD rs12249189 1.4437
+Ece1 ECE1 rs12562197 1.6661
+Ece2 ECE2 rs13315366 1.1534
+Ecel1 ECEL1 rs790039 1.5827
+Ech1 ECH1 rs8100321 1.2167
+Echdc1 ECHDC1 rs4895820 1.1047
+Echdc2 ECHDC2 rs557715 1.4033
+Echdc3 ECHDC3 rs3829154 1.1387
+Echs1 ECHS1 rs7475905 1.0409
+Eci1 ECI1 rs27382 0.8395
+Eci2 ECI2 rs9392552 1.4122
+Ecm1 ECM1 rs4970972 0.8441
+Ecsit ECSIT rs11880637 1.0059
+Ect2 ECT2 rs7374229 1.2195
+Ect2l ECT2L rs17068057 1.4444
+Edar EDAR rs4676046 1.2464
+Edaradd EDARADD rs16833722 1.3577
+Edc4 EDC4 rs16942887 32.076
+Eddm3b EDDM3B rs17211691 1.9714
+Edem1 EDEM1 rs341986 1.4415
+Edem2 EDEM2 rs6058202 3.8327
+Edem3 EDEM3 rs10797982 1.2013
+Edf1 EDF1 rs2811749 1.5594
+Edil3 EDIL3 rs9293365 1.4077
+Edn1 EDN1 rs10755713 1.5444
+Edn2 EDN2 rs11584933 1.0665
+Edn3 EDN3 rs197174 1.5872
+Ednra EDNRA rs11734418 2.7496
+Eea1 EEA1 rs2264420 2.9996
+Eed EED rs7950735 0.9201
+Eef1a2 EEF1A2 rs10485825 1.3704
+Eef1d EEF1D rs4874150 0.9048
+Eef1e1 EEF1E1 rs9405369 1.0552
+Eef1g EEF1G rs3888173 1.9543
+Eef2 EEF2 rs737204 4.1622
+Eef2k EEF2K rs11649104 1.5923
+Eefsec EEFSEC rs1010786 2.7547
+Eepd1 EEPD1 rs2726049 2.6103
+Efcab10 EFCAB10 rs6943132 1.0574
+2610021K21Rik EFCAB11 rs6575087 2.9796
+BC060267 EFCAB12 rs13326661 1.5825
+Efcab2 EFCAB2 rs12143677 1.5971
+Efcab3 EFCAB3 rs12150277 1.2911
+Efcab4b EFCAB4B rs9300302 1.63
+Efcab5 EFCAB5 rs7213462 2.3335
+Efcab6 EFCAB6 rs137157 1.3831
+Efcab7 EFCAB7 rs217483 1.3816
+Efcab9 EFCAB9 rs6876698 1.1674
+Efemp1 EFEMP1 rs3791678 1.9792
+Efemp2 EFEMP2 rs679147 3.3254
+Efhb EFHB rs11128927 1.0604
+Efhc1 EFHC1 rs3804508 1.2752
+Efhd1 EFHD1 rs13410380 1.273
+Efhd2 EFHD2 rs1883426 2.7231
+Efna1 EFNA1 rs7517158 2.0327
+Efna2 EFNA2 rs10423270 1.9923
+Efna3 EFNA3 rs11589479 2.1627
+Efna4 EFNA4 rs11589479 2.1627
+Efna5 EFNA5 rs17159969 2.3805
+Efnb2 EFNB2 rs1407584 1.8649
+Efnb3 EFNB3 rs8064946 1.4988
+Efr3a EFR3A rs13263860 1.3512
+Efr3b EFR3B rs1077492 2.2943
+Eftud1 EFTUD1 rs16973457 1.2188
+Eftud2 EFTUD2 rs8067254 0.9454
+Egf EGF rs971695 1.2793
+Egfem1 EGFEM1P rs17736491 1.8697
+Egfl8 EGFL8 rs17493811 1.0915
+AU040377 EGFLAM rs17639823 1.6236
+Egfr EGFR rs2075106 2.2662
+Egln1 EGLN1 rs11587618 1.4331
+Egln2 EGLN2 rs4803372 1.4827
+Egln3 EGLN3 rs10149089 1.8114
+Egr1 EGR1 rs2269947 1.5943
+Egr3 EGR3 rs4520160 1.0276
+Egr4 EGR4 rs1317734 1.6349
+Ehbp1 EHBP1 rs4671052 1.4014
+Ehbp1l1 EHBP1L1 rs7946115 3.7696
+Ehd1 EHD1 rs524386 2.4167
+Ehd2 EHD2 rs10423006 2.0168
+Ehd3 EHD3 rs3769619 2.0375
+Ehd4 EHD4 rs1704401 2.6447
+Ehf EHF rs11032807 1.8752
+Ehhadh EHHADH rs1468949 0.959
+Ehmt1 EHMT1 rs9314635 1.8413
+Ehmt2 EHMT2 rs3869145 2.4438
+Eid1 EID1 rs2413916 1.6005
+Eid2 EID2 rs3212275 1.0661
+AK044722 EID2B rs3212275 1.0661
+Eid3 EID3 rs10735392 0.8438
+Eif1ad EIF1AD rs586921 1.3842
+Eif2a EIF2A rs9830228 1.7981
+Eif2ak1 EIF2AK1 rs3815383 1.5421
+Eif2ak2 EIF2AK2 rs4648234 2.0896
+Eif2ak3 EIF2AK3 rs11681299 1.4496
+Eif2ak4 EIF2AK4 rs11632930 1.1658
+Eif2b1 EIF2B1 rs786449 1.4832
+Eif2b4 EIF2B4 rs11689803 1.1015
+Eif2b5 EIF2B5 rs843360 1.8066
+Eif2d EIF2D rs12096152 1.0319
+Eif2s2 EIF2S2 rs6120513 1.9374
+Eif3a EIF3A rs3824830 1.6623
+Eif3b EIF3B rs2286204 1.0649
+Eif3d EIF3D rs2281086 1.3146
+Eif3e EIF3E rs2298604 2.5604
+Eif3f EIF3F rs10839961 1.2784
+Eif3g EIF3G rs1979262 1.1248
+Eif3h EIF3H rs6992589 1.6293
+Eif3i EIF3I rs1358823 0.857
+Eif3k EIF3K rs752067 1.2018
+Eif3m EIF3M rs201884 1.56
+Eif4a2 EIF4A2 rs17299991 1.9329
+Eif4a3 EIF4A3 rs2229221 1.0896
+Eif4b EIF4B rs2682327 1.0256
+Eif4e EIF4E rs4550929 1.7823
+Eif4e1b EIF4E1B rs13183320 1.0784
+Eif4e2 EIF4E2 rs2117770 1.783
+Eif4e3 EIF4E3 rs1349028 3.4949
+Eif4ebp2 EIF4EBP2 rs999551 1.1994
+Eif4enif1 EIF4ENIF1 rs5749272 1.0519
+Eif4g1 EIF4G1 rs9944 1.3203
+Eif4g2 EIF4G2 rs4243917 1.317
+Eif4g3 EIF4G3 rs6691824 2.0819
+Eif4h EIF4H rs415784 1.2405
+Eif5a EIF5A rs3809822 1.3322
+Eif5b EIF5B rs4851200 1.0555
+Eif6 EIF6 rs2425024 1.9658
+Elac1 ELAC1 rs8085716 1.0906
+Elac2 ELAC2 rs1981622 1.639
+Elane ELANE rs12460890 1.2608
+Elavl2 ELAVL2 rs7870903 2.3874
+Elavl3 ELAVL3 rs167479 1.1759
+Elavl4 ELAVL4 rs17381033 1.21
+Elf1 ELF1 rs17532301 2.3697
+Elf2 ELF2 rs3087936 0.8655
+Elf3 ELF3 rs6689634 1.2343
+Elf5 ELF5 rs1055981 0.8976
+Elfn1 ELFN1 rs10950352 1.8623
+Elfn2 ELFN2 rs739187 1.4369
+Elk3 ELK3 rs10492223 1.6878
+Elk4 ELK4 rs7529037 5.7089
+Ell ELL rs8111582 1.4588
+Ell2 ELL2 rs17085176 1.4965
+Ell3 ELL3 rs2228368 2.0663
+Elmo1 ELMO1 rs11532670 2.6059
+Elmo2 ELMO2 rs2274676 1.3051
+Elmo3 ELMO3 rs2233455 8.8128
+Elmod1 ELMOD1 rs12281067 1.3059
+Elmod3 ELMOD3 rs1053560 6.9974
+Eln ELN rs1091813 1.4938
+Elof1 ELOF1 rs7253363 1.1298
+Elovl1 ELOVL1 rs12731981 0.8145
+Elovl2 ELOVL2 rs16870896 2.0943
+Elovl4 ELOVL4 rs13211482 1.582
+Elovl5 ELOVL5 rs9474471 2.5197
+Elovl6 ELOVL6 rs10032613 2.0595
+Elovl7 ELOVL7 rs10058456 1.1506
+Elp2 ELP2 rs589213 0.9504
+Elp3 ELP3 rs13257057 1.436
+Elp4 ELP4 rs7928377 2.286
+Eltd1 ELTD1 rs1780768 3.1493
+Emb EMB rs6450063 1.4163
+C230096C10Rik EMC1 rs2294943 1.7457
+2310044H10Rik EMC10 rs1274688 1.4194
+Ttc35 EMC2 rs11985994 1.8665
+Tmem111 EMC3 rs13097123 2.0881
+Tmem85 EMC4 rs10519916 1.4623
+Tmem93 EMC6 rs222757 1.8884
+2900064A13Rik EMC7 rs11635377 1.4403
+Cox4nb EMC8 rs408126 1.6024
+Fam158a EMC9 rs11574503 1.442
+Emcn EMCN rs34596755 1.2708
+Eme1 EME1 rs2526524 1.3872
+Eme2 EME2 rs1065663 1.2005
+Emg1 EMG1 rs17788859 0.8775
+Emid1 EMID1 rs13056488 1.4492
+Emilin2 EMILIN2 rs9962679 1.7867
+Emilin3 EMILIN3 rs8118245 2.2438
+Eml1 EML1 rs11160563 3.257
+Eml2 EML2 rs11670462 4.0356
+Eml3 EML3 rs3888173 1.9543
+Eml4 EML4 rs10490557 1.7525
+Eml5 EML5 rs12431548 1.3953
+Eml6 EML6 rs13004573 2.9094
+Emp1 EMP1 rs4763326 0.9923
+Emp2 EMP2 rs9674298 1.8069
+Emr1 EMR1 rs17718517 1.7685
+Emx1 EMX1 rs11682000 1.8173
+Emx2 EMX2 rs242985 2.0816
+Emx2os EMX2OS rs451123 2.113
+En1 EN1 rs1866407 0.8938
+En2 EN2 rs1345511 1.3644
+Enah ENAH rs10753406 2.0962
+Enam ENAM rs3924573 0.8735
+Enc1 ENC1 rs300260 1.412
+Endod1 ENDOD1 rs673330 1.0088
+Endog ENDOG rs11515530 2.6338
+Endou ENDOU rs2239187 0.8969
+A730011L01Rik ENDOV rs9898470 1.7501
+Eng ENG rs913984 1.7528
+Engase ENGASE rs4789984 1.075
+Enho ENHO rs10972139 1.3512
+Enkur ENKUR rs11816696 0.9187
+CL302992 ENO1 rs12751621 1.2928
+Eno2 ENO2 rs11064445 0.8184
+Eno3 ENO3 rs7212695 1.0472
+Eno4 ENO4 rs740363 4.0633
+Enoph1 ENOPH1 rs4693030 1.3552
+Enox1 ENOX1 rs12867694 2.3803
+Enpep ENPEP rs10004650 1.7625
+Enpp1 ENPP1 rs1974201 1.6844
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+Fastk FASTK rs6464127 3.2599
+Fastkd1 FASTKD1 rs6752958 1.1393
+Fastkd2 FASTKD2 rs1862089 0.9062
+Fastkd3 FASTKD3 rs1046014 1.6126
+Fastkd5 FASTKD5 rs6084276 1.5009
+Fat1 FAT1 rs6820100 1.8824
+Fat2 FAT2 rs7723204 2.0946
+Fat3 FAT3 rs10466349 1.7542
+Fat4 FAT4 rs11941590 1.7142
+ENSMUST00000052867 FAU rs769442 2.4264
+6230409E13Rik FAXC rs13219146 1.9586
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+Fbrs FBRS rs1036696 1.6492
+Fbrsl1 FBRSL1 rs2323990 1.8851
+Fbxl13 FBXL13 rs12670723 0.9458
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+Fbxl15 FBXL15 rs7076748 1.3418
+Fbxl16 FBXL16 rs4984685 1.1576
+Fbxl17 FBXL17 rs17452992 3.4201
+Fbxl18 FBXL18 rs10046576 2.1193
+Fbxl19 FBXL19 rs11649653 2.73
+Fbxl2 FBXL2 rs9832065 1.1167
+Fbxl20 FBXL20 rs2338755 10.914
+Fbxl21 FBXL21 rs2520439 1.205
+Fbxl22 FBXL22 rs4074448 1.8066
+Fbxl3 FBXL3 rs599115 1.0382
+Fbxl4 FBXL4 rs9372961 1.4087
+Fbxl5 FBXL5 rs4267734 0.8864
+Fbxl7 FBXL7 rs6420027 1.8419
+Fbxl8 FBXL8 rs2233455 8.8128
+Fbxo10 FBXO10 rs4621903 2.2327
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+Fbxo15 FBXO15 rs12605962 2.2513
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+Fbxo25 FBXO25 rs6988801 2.0822
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+Fbxo47 FBXO47 rs11872093 1.5653
+Fbxo5 FBXO5 rs13199739 1.0046
+Fbxo6 FBXO6 rs12760507 1.3172
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+Fbxw10 FBXW10 rs12603319 1.2722
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+Fcamr FCAMR rs2883215 1.815
+Fcer1a FCER1A rs12097396 1.2934
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+Fcf1 FCF1 rs12431412 2.0843
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+Fcgr3 FCGR2A rs6698806 0.9654
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+Fchsd1 FCHSD1 rs1975988 1.6258
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+Fcna FCN1 rs3128639 1.8884
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+Fer1l6 FER1L6 rs7465584 1.4769
+Fermt1 FERMT1 rs4815890 0.8881
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+Fes FES rs2677737 0.8492
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+Fez1 FEZ1 rs12281482 1.5291
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+Fezf1 FEZF1 rs12706405 1.5931
+Fezf2 FEZF2 rs17687440 0.9226
+Ffar1 FFAR1 rs12462800 1.1947
+Ffar2 FFAR2 rs8107905 1.614
+Ffar3 FFAR3 rs12462800 1.1947
+Fga FGA rs1025154 1.5865
+Fgb FGB rs1025154 1.5865
+Fgd2 FGD2 rs2293381 1.087
+Fgd3 FGD3 rs10821056 1.8179
+Fgd4 FGD4 rs1004968 2.1268
+Fgd5 FGD5 rs6806067 1.3055
+Fgd6 FGD6 rs11107870 1.3766
+Fgf1 FGF1 rs7712968 2.0989
+Fgf10 FGF10 rs6883885 1.3632
+Fgf11 FGF11 rs11652328 2.8827
+Fgf12 FGF12 rs781414 1.9957
+Fgf14 FGF14 rs9557735 2.3618
+Fgf17 FGF17 rs7813420 2.5909
+Fgf18 FGF18 rs11740426 2.961
+Fgf15 FGF19 rs6606651 0.9698
+Fgf2 FGF2 rs7664357 1.3028
+Fgf20 FGF20 rs9918788 1.6153
+Fgf21 FGF21 rs499765 1.1535
+Fgf22 FGF22 rs10426061 2.0133
+Fgf23 FGF23 rs2909369 1.2775
+Fgf3 FGF3 rs9666758 2.1246
+Fgf4 FGF4 rs9666758 2.1246
+Fgf5 FGF5 rs1902859 1.5592
+Fgf6 FGF6 rs11614424 1.656
+Fgf7 FGF7 rs12595692 0.9208
+Fgf9 FGF9 rs264718 1.2745
+Fgfbp1 FGFBP1 rs6849167 1.6047
+Fgfr1 FGFR1 rs881301 1.6519
+Fgfr1op FGFR1OP rs239935 1.2814
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+Fgfr2 FGFR2 rs3135812 1.7555
+Fgfr3 FGFR3 rs2301293 0.8199
+Fgfr4 FGFR4 rs2546095 1.173
+Fgfrl1 FGFRL1 rs11941528 1.2135
+Fgg FGG rs2070008 1.2739
+Fggy FGGY rs3934551 2.0389
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+Fh1 FH rs2994977 1.0248
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+Ficd FICD rs1121639 1.721
+Fig4 FIG4 rs3799844 1.821
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+Fign FIGN rs12692700 2.1618
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+Filip1 FILIP1 rs2312930 1.5445
+Filip1l FILIP1L rs793444 1.1705
+Fip1l1 FIP1L1 rs6826663 1.7585
+Fis1 FIS1 rs4729671 1.1605
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+Fiz1 FIZ1 rs1423054 1.381
+Fjx1 FJX1 rs4755421 1.8342
+Fkbp10 FKBP10 rs13412 2.1228
+Fkbp11 FKBP11 rs12122 1.1656
+Fkbp14 FKBP14 rs10274617 1.1852
+Fkbp15 FKBP15 rs4352917 2.4337
+Fkbp1a FKBP1A rs13039228 1.6776
+Fkbp1b FKBP1B rs11125371 1.808
+Fkbp2 FKBP2 rs2302263 2.1145
+Fkbp3 FKBP3 rs10130377 1.5786
+Fkbp4 FKBP4 rs7296848 2.2143
+Fkbp5 FKBP5 rs9380525 2.3628
+Fkbp6 FKBP6 rs17339199 1.7102
+Fkbp7 FKBP7 rs2288323 0.8884
+Fkbp9 FKBP9 rs17170151 1.7113
+Fkbpl FKBPL rs2269426 1.555
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+Flg FLG rs11204981 1.6094
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+4933438B17Rik FLJ37505 rs4761025 2.4781
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+Flnb FLNB rs1718472 1.9104
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+Flot1 FLOT1 rs9295910 0.9059
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+Flrt1 FLRT1 rs11231698 3.6358
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+Flt1 FLT1 rs950860 1.5461
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+Flt4 FLT4 rs10085109 3.9492
+Mfsd7b FLVCR1 rs10864029 2.7438
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+Flywch1 FLYWCH1 rs7193548 1.3691
+Flywch2 FLYWCH2 rs7193548 1.3691
+Fmn1 FMN1 rs347921 3.0279
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+Fn3krp FN3KRP rs2252314 1.791
+Fnbp1 FNBP1 rs1108534 1.4772
+Fnbp1l FNBP1L rs3767966 3.1068
+Fnbp4 FNBP4 rs2869531 14.324
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+Foxa1 FOXA1 rs4900969 1.4464
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+Foxa3 FOXA3 rs10416943 1.6267
+Foxb1 FOXB1 rs1425933 2.3153
+Foxc1 FOXC1 rs2745579 1.8677
+Foxc2 FOXC2 rs4843390 1.791
+Foxd2 FOXD2 rs17103227 0.8614
+Foxd3 FOXD3 rs12046145 1.1583
+Foxe1 FOXE1 rs12342888 2.4455
+Foxe3 FOXE3 rs17103227 0.8614
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+Foxi2 FOXI2 rs12354844 0.8511
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+Foxk1 FOXK1 rs7784748 2.106
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+Foxn2 FOXN2 rs10153773 2.0008
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+Foxn4 FOXN4 rs3742023 3.0544
+Foxo3 FOXO3 rs12206094 2.9187
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+Foxred1 FOXRED1 rs3824924 1.9245
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+Foxs1 FOXS1 rs17340555 2.1077
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+Galnt10 GALNT10 rs17626419 3.1407
+Galnt11 GALNT11 rs6947297 1.9014
+Galnt12 GALNT12 rs7039118 1.2956
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+Galnt14 GALNT14 rs1862960 1.4843
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+Galntl6 GALNTL6 rs1503683 2.3419
+Galp GALP rs12980218 1.2079
+Galr1 GALR1 rs2717162 1.4751
+Galr2 GALR2 rs2256879 0.8542
+Galr3 GALR3 rs1894646 0.8179
+Galt GALT rs3739882 0.9303
+Gan GAN rs2290949 2.2582
+Ganab GANAB rs7386 2.2897
+Ganc GANC rs4924668 1.4304
+Gap43 GAP43 rs6805991 1.0154
+AK081405 GAPDH rs1043262 1.8283
+Gapdhs GAPDHS rs12460932 1.828
+Gapt GAPT rs2307117 2.1058
+Gapvd1 GAPVD1 rs7034096 1.9179
+Gar1 GAR1 rs998539 2.121
+Garnl3 GARNL3 rs3824415 1.9594
+Gars GARS rs38490 2.2387
+Gart GART rs2834233 1.1594
+Gas2 GAS2 rs16910106 1.259
+Gas2l2 GAS2L2 rs2240903 0.9052
+Gas2l3 GAS2L3 rs12816980 2.0694
+Gas6 GAS6 rs3088277 2.1655
+Gas7 GAS7 rs4791906 2.0982
+Gas8 GAS8 rs3785181 1.1144
+Gast GAST rs4523977 1.4396
+Gata2 GATA2 rs2659690 2.3242
+Gata3 GATA3 rs412681 0.8468
+Gata4 GATA4 rs904006 2.415
+Gata5 GATA5 rs6121611 1.0409
+Gata6 GATA6 rs10454095 0.9129
+Gatad1 GATAD1 rs6958957 0.8567
+Gatad2b GATAD2B rs10908475 4.1259
+Gatc GATC rs540520 1.6192
+Gatm GATM rs2461702 0.8649
+Gatsl3 GATSL3 rs10854598 1.6428
+mCT51200 GBA3 rs12507337 1.1134
+Gbas GBAS rs12671091 2.0054
+Gbe1 GBE1 rs9309872 2.1442
+Gbf1 GBF1 rs7076748 1.3418
+Gbgt1 GBGT1 rs10125492 1.0291
+Gbp2 GBP1 rs7911 1.3831
+Gbp1 GBP2 rs1969962 1.419
+Gbp5 GBP5 rs641429 1.6055
+BC057170 GBP6 rs4658311 0.8976
+Gbp7 GBP7 rs4348699 1.2068
+Gbx1 GBX1 rs2288651 1.8626
+Gc GC rs842990 2.466
+Gca GCA rs17715343 2.6055
+Gcat GCAT rs1894646 0.8179
+Gcc1 GCC1 rs6973507 0.8256
+Gcc2 GCC2 rs3806530 1.659
+AW146020 GCFC2 rs17011635 2.8804
+Gcg GCG rs12692647 1.522
+Gch1 GCH1 rs8011358 0.8752
+Gchfr GCHFR rs11855560 2.3641
+Gck GCK rs882020 0.8392
+Gckr GCKR rs2911711 1.4561
+Gclc GCLC rs510088 1.4097
+Gclm GCLM rs10874811 1.0749
+Gcm1 GCM1 rs2816340 1.8016
+Gcm2 GCM2 rs560869 1.3996
+Gcn1l1 GCN1L1 rs17499109 2.1521
+Gcnt1 GCNT1 rs11546569 2.1938
+Gcnt2 GCNT2 rs16870458 2.6047
+Gcnt3 GCNT3 rs4775190 1.1695
+Gcnt4 GCNT4 rs7707833 0.9905
+Gcnt7 GCNT7 rs16984830 1.419
+Myzap GCOM1 rs1690325 1.988
+Gcsh GCSH rs804905 1.419
+Gda GDA rs7863783 1.0919
+Gdap1 GDAP1 rs4738445 1.5945
+Gdap1l1 GDAP1L1 rs16985330 1.7833
+Gde1 GDE1 rs7200119 1.1822
+Gdf10 GDF10 rs743508 1.3645
+Gdf15 GDF15 rs1054564 1.5943
+Gdf2 GDF2 rs743508 1.3645
+Gdf3 GDF3 rs7969455 1.7187
+Gdf5 GDF5 rs13037376 1.9367
+Gdf6 GDF6 rs2466130 1.2409
+Gdf7 GDF7 rs4666453 2.6426
+Gdf9 GDF9 rs39830 0.9108
+Gdi2 GDI2 rs2797496 3.6453
+Gdnf GDNF rs10054159 1.2774
+Gdpd1 GDPD1 rs6503910 1.5574
+Gdpd2 GDPD2 rs41390849 0.8864
+Gdpd4 GDPD4 rs11237145 1.4355
+Gdpd5 GDPD5 rs1557473 1.5456
+Gem GEM rs2607058 1.3764
+Sip1 GEMIN2 rs17108815 1.3298
+Gemin4 GEMIN4 rs1741275 0.9598
+Gemin5 GEMIN5 rs12300 1.9961
+Gemin7 GEMIN7 rs6509179 1.3161
+Gen1 GEN1 rs7556983 2.0659
+Get4 GET4 rs11769388 1.6258
+Gfap GFAP rs3169733 1.3845
+Gfer GFER rs8055439 1.1127
+Gfi1 GFI1 rs7547145 1.2126
+Gfi1b GFI1B rs682437 1.5343
+Gfm2 GFM2 rs6453084 1.2178
+Gfod1 GFOD1 rs2115063 1.1464
+Gfod2 GFOD2 rs6499137 19.597
+Gfpt1 GFPT1 rs7585699 1.0999
+Gfpt2 GFPT2 rs729568 1.0939
+Gfra1 GFRA1 rs12413585 2.8986
+Gfra2 GFRA2 rs13273530 1.8321
+Gfra4 GFRA4 rs511898 1.2513
+Gfral GFRAL rs2055445 4.7479
+Gga1 GGA1 rs7290615 0.9809
+Gga2 GGA2 rs867131 1.037
+Gga3 GGA3 rs4789161 1.0457
+Ggct GGCT rs4270 1.1857
+Ggh GGH rs12541122 1.232
+Ggn GGN rs3745962 0.9318
+Ggnbp1 GGNBP1 rs9469491 1.4739
+Ggnbp2 GGNBP2 rs17693183 1.4965
+Ggt1 GGT1 rs5760492 0.8935
+Ggt5 GGT5 rs2275984 1.0371
+Ggt6 GGT6 rs3760194 1.1889
+Ggt7 GGT7 rs6088618 3.5989
+a GH1 rs3744287 1.6019
+Ghdc GHDC rs939881 1.9158
+Ghitm GHITM rs11594534 1.4671
+Ghr GHR rs3764451 1.6447
+Ghrhr GHRHR rs10225302 2.0758
+Ghrl GHRL rs1642974 1.8085
+Ghsr GHSR rs1403637 2.2716
+4933439F18Rik GID4 rs4925137 0.9935
+2310003C23Rik GID8 rs8115101 1.2279
+Gif GIF rs558660 1.0351
+Gigyf1 GIGYF1 rs221786 1.0272
+Gigyf2 GIGYF2 rs12694912 1.1221
+Gimap1 GIMAP1 rs10029840 1.2671
+Gimap4 GIMAP4 rs11763828 2.3205
+Gimap5 GIMAP5 rs10029840 1.2671
+Gimap6 GIMAP6 rs9768400 3.3407
+Gimap7 GIMAP7 rs10235836 1.1433
+Gimap8 GIMAP8 rs1916931 1.3089
+Gm5549 GIMD1 rs2949646 0.8897
+Gin1 GIN1 rs34774 1.3884
+BC013529 GINM1 rs9689569 0.8877
+4833427B12Rik GINS2 rs871290 2.6123
+Gins3 GINS3 rs1895522 0.909
+Gins4 GINS4 rs919033 1.021
+Gip GIP rs9899404 1.7064
+Gipc1 GIPC1 rs3810255 1.5825
+Gipc2 GIPC2 rs4543852 1.6919
+Gipc3 GIPC3 rs7246261 1.0939
+Gipr GIPR rs11670462 4.0356
+Git1 GIT1 rs1971157 1.797
+Git2 GIT2 rs9804905 1.0487
+Gja1 GJA1 rs41488349 1.5119
+Gja10 GJA10 rs369328 1.7199
+Gja3 GJA3 rs10870680 2.2707
+Gja4 GJA4 rs10489657 1.1476
+Gja8 GJA8 rs6672945 0.9893
+Gjb2 GJB2 rs10870680 2.2707
+Gjb3 GJB3 rs10489657 1.1476
+Gjb4 GJB4 rs10489657 1.1476
+Gjb5 GJB5 rs10489657 1.1476
+Gjb6 GJB6 rs6490527 1.0928
+Gjc1 GJC1 rs8064331 1.446
+Gjc2 GJC2 rs12116745 1.7802
+Gjc3 GJC3 rs6957718 1.862
+Gjd2 GJD2 rs10519956 1.2299
+Gjd3 GJD3 rs2715554 1.309
+Gk2 GK2 rs17003742 0.9215
+Gk5 GK5 rs9841672 1.4946
+Gkap1 GKAP1 rs7874980 3.5967
+Gkn2 GKN2 rs4419260 1.9151
+Glb1l2 GLB1L2 rs526507 2.3669
+4921509F24Rik GLB1L3 rs526507 2.3669
+Glcci1 GLCCI1 rs6947392 1.6538
+Glce GLCE rs17360351 1.2333
+Gldc GLDC rs1547256 1.739
+Gldn GLDN rs12148492 3.6066
+Gle1 GLE1 rs10513497 1.3354
+Glg1 GLG1 rs17338280 1.184
+Gli1 GLI1 rs3741414 7.7857
+Gli2 GLI2 rs2310895 1.8847
+Gli3 GLI3 rs7778774 1.7073
+Glipr1 GLIPR1 rs12819511 0.9083
+Glipr1l1 GLIPR1L1 rs7135867 0.9465
+Glipr2 GLIPR2 rs10814336 0.9731
+Glis1 GLIS1 rs4927033 2.6838
+Glis2 GLIS2 rs9938257 1.0441
+Glis3 GLIS3 rs2281729 4.1103
+Glmn GLMN rs1542685 0.8303
+Glo1 GLO1 rs1781731 1.3536
+Glod4 GLOD4 rs592294 1.2516
+Glp1r GLP1R rs9296292 3.1999
+Glp2r GLP2R rs4791906 2.0982
+Glra1 GLRA1 rs10079059 1.5748
+Glra3 GLRA3 rs7655252 2.838
+Glrb GLRB rs17035810 1.6531
+Glrx GLRX rs7736948 1.6975
+Glrx2 GLRX2 rs12131585 1.1543
+Glrx3 GLRX3 rs7906617 1.4447
+Glrx5 GLRX5 rs10484055 2.5701
+Gls GLS rs11696087 1.1188
+Gls2 GLS2 rs2657883 0.8133
+Glt1d1 GLT1D1 rs10847721 1.5805
+Glt6d1 GLT6D1 rs7861295 1.1209
+Glt8d2 GLT8D2 rs4135119 2.0718
+Gltp GLTP rs10850838 1.4237
+Gltpd2 GLTPD2 rs754814 2.6297
+Gltscr1 GLTSCR1 rs2974224 5.209
+Gltscr2 GLTSCR2 rs17703870 1.0197
+Glud1 GLUD1 rs7079587 1.015
+Glul GLUL rs6684297 2.1162
+Glyat GLYAT rs596847 2.0793
+Glycam1 GLYCAM1 rs2280426 1.8091
+Glyctk GLYCTK rs699469 2.9462
+Glyr1 GLYR1 rs6500635 2.9923
+Gm2a GM2A rs9324686 1.5296
+Gmds GMDS rs2745579 1.8677
+Gmeb1 GMEB1 rs6673481 1.2791
+Gmeb2 GMEB2 rs2738770 2.0248
+Gmfb GMFB rs11848784 1.62
+Gmfg GMFG rs8102889 1.3322
+Gmip GMIP rs2304129 1.1126
+Gml GML rs3819497 1.3517
+Gm606 GMNC rs9812368 1.8099
+Gmnn GMNN rs4712848 2.4605
+Gmppa GMPPA rs6724274 2.9821
+Gmppb GMPPB rs2291542 3.556
+Gmpr GMPR rs4716052 1.0653
+Gmpr2 GMPR2 rs1950496 1.0338
+Gmps GMPS rs6441025 1.7749
+Gna11 GNA11 rs1104737 1.1237
+Gna12 GNA12 rs9332401 1.8389
+Gna14 GNA14 rs12336835 2.6112
+Gna15 GNA15 rs12461827 1.604
+Gnai1 GNAI1 rs2714467 5.699
+Gnai2 GNAI2 rs2236942 2.7662
+Gnai3 GNAI3 rs7550711 3.9805
+Gnal GNAL rs12457818 2.2961
+Gnao1 GNAO1 rs3790106 5.036
+Gnaq GNAQ rs1808788 2.413
+Gnas GNAS rs6015380 1.3696
+Nespas GNAS-AS1 rs6015380 1.3696
+Gnat1 GNAT1 rs2236942 2.7662
+Gnat2 GNAT2 rs17024393 3.3509
+Gnat3 GNAT3 rs6974934 1.4268
+Gnaz GNAZ rs5751579 1.3115
+Gnb1 GNB1 rs6603794 0.8983
+Gnb1l GNB1L rs2238777 2.9274
+Gnb2 GNB2 rs221786 1.0272
+Gnb2l1 GNB2L1 rs254455 0.9412
+Gnb3 GNB3 rs12302904 1.2145
+Gnb4 GNB4 rs7618251 1.7597
+Gnb5 GNB5 rs4776002 2.7991
+Gng10 GNG10 rs13285010 2.02
+Gng11 GNG11 rs10234713 1.52
+Gng12 GNG12 rs506731 1.9901
+Gng2 GNG2 rs12883290 2.2301
+Gng3 GNG3 rs2187108 1.4925
+Gng4 GNG4 rs16832785 1.6799
+Gng5 GNG5 rs4512665 1.6117
+Gng7 GNG7 rs1001328 1.6678
+Gng8 GNG8 rs931647 1.0094
+Gngt1 GNGT1 rs2724081 1.7095
+Gngt2 GNGT2 rs7222840 2.3984
+Gnl1 GNL1 rs2524222 0.9333
+Gnmt GNMT rs2274517 3.8642
+BC030307 GNN rs2576954 1.8225
+Gnpat GNPAT rs556132 1.3894
+Gnpda1 GNPDA1 rs7737097 1.9654
+Gnpda2 GNPDA2 rs9884283 1.7498
+EG432486 GNPTAB rs759935 1.2193
+Gnptg GNPTG rs8045459 1.2734
+Gnrh1 GNRH1 rs4872309 2.1342
+Gnrhr GNRHR rs7671397 2.0487
+Gns GNS rs12820666 0.8389
+Golga2 GOLGA2 rs2229917 1.7747
+Golga3 GOLGA3 rs900443 2.021
+Golga4 GOLGA4 rs13099358 1.8623
+Golga5 GOLGA5 rs17128637 2.1176
+Golga7 GOLGA7 rs6993590 1.1812
+Golga7b GOLGA7B rs11189390 0.9355
+Golgb1 GOLGB1 rs6807711 1.0911
+Golim4 GOLIM4 rs17727923 2.2476
+Golm1 GOLM1 rs1535757 1.3261
+Golph3 GOLPH3 rs2279232 3.2736
+Golph3l GOLPH3L rs2055975 1.4674
+Golt1a GOLT1A rs12141465 1.2647
+Golt1b GOLT1B rs7138234 1.6326
+Gon4l GON4L rs4612618 1.969
+Gopc GOPC rs12197399 2.3373
+Gorasp1 GORASP1 rs6769051 1.6426
+Gorasp2 GORASP2 rs3770436 1.0077
+Gosr1 GOSR1 rs2011614 1.6776
+Gosr2 GOSR2 rs9911967 4.0492
+Got1 GOT1 rs17491356 1.0757
+Got1l1 GOT1L1 rs2319760 0.8374
+Got2 GOT2 rs10500407 1.1651
+Gp1ba GP1BA rs11654952 1.2197
+Gp2 GP2 rs6497459 1.7768
+Gp5 GP5 rs923931 2.465
+Gp6 GP6 rs11672111 2.1797
+Gp9 GP9 rs2341295 1.0897
+Gpa33 GPA33 rs17486769 0.862
+Gpam GPAM rs2250802 6.6661
+Gpank1 GPANK1 rs3117583 4.5605
+Gpatch1 GPATCH1 rs12462426 1.3092
+Gpatch2 GPATCH2 rs3790735 2.4936
+Gpatch3 GPATCH3 rs17162333 6.8551
+Gpatch4 GPATCH4 rs6694580 1.2152
+Gpatch8 GPATCH8 rs708383 1.7293
+Gpbar1 GPBAR1 rs17572109 1.4019
+Gpbp1l1 GPBP1L1 rs12757451 1.857
+Gpc1 GPC1 rs6744696 1.0299
+Gpc2 GPC2 rs11762932 1.3452
+Gpc5 GPC5 rs12430272 2.1765
+Gpc6 GPC6 rs12855239 3.3339
+Gpcpd1 GPCPD1 rs6053504 0.9801
+Gpd1 GPD1 rs836177 2.5291
+Gpd1l GPD1L rs12637969 1.1842
+Gpd2 GPD2 rs2288760 2.5855
+Gpr30 GPER rs10274367 3.9796
+Gpha2 GPHA2 rs35079323 2.2192
+Gphb5 GPHB5 rs17824786 1.5741
+Gphn GPHN rs1274047 1.5541
+Gpi1 GPI rs3786884 1.2943
+Gpihbp1 GPIHBP1 rs7815797 4.3528
+Gpld1 GPLD1 rs3761790 2.4645
+Gpm6a GPM6A rs9685455 2.4505
+Gpn1 GPN1 rs2068834 2.1142
+Gpn2 GPN2 rs17162333 6.8551
+Gpn3 GPN3 rs3759384 1.1021
+Gpnmb GPNMB rs1728312 2.9017
+Gpr1 GPR1 rs11681549 1.5372
+AI790205 GPR107 rs12344194 1.2206
+Gpr108 GPR108 rs4807895 1.3005
+Gpr110 GPR110 rs4714983 1.1773
+Gpr111 GPR111 rs41452951 1.1379
+Gpr113 GPR113 rs7568529 1.1308
+Gpr114 GPR114 rs10775336 3.4843
+Gpr115 GPR115 rs41452951 1.1379
+Gpr116 GPR116 rs9689298 1.3673
+Gpr12 GPR12 rs12874479 1.0246
+Gpr123 GPR123 rs11101928 1.4355
+Gpr124 GPR124 rs6998793 1.0744
+Gpr125 GPR125 rs4697305 1.7086
+Gpr126 GPR126 rs12529186 1.1536
+Gpr128 GPR128 rs1165260 1.1263
+Gpr132 GPR132 rs4633639 1.5712
+Gpr133 GPR133 rs10848273 2.0277
+Gpr135 GPR135 rs1253137 0.9682
+Gpr137 GPR137 rs2302263 2.1145
+Gpr137b GPR137B rs6658832 1.7233
+6330416L11Rik GPR137C rs4901288 1.0127
+Gpr139 GPR139 rs2764743 1.9642
+Gpr141 GPR141 rs12673355 1.2666
+Gpr142 GPR142 rs4789057 1.1041
+XM_355327 GPR144 rs10818971 2.0307
+Gpr146 GPR146 rs10274367 3.9796
+Gpr149 GPR149 rs13080908 3.2147
+Gpr15 GPR15 rs1729969 1.1135
+Gpr150 GPR150 rs17211585 1.1057
+Gpr151 GPR151 rs6580424 0.9048
+Gpr152 GPR152 rs17501521 1.0215
+Gpr155 GPR155 rs7593976 1.3935
+Gpr156 GPR156 rs6804257 3.1442
+Gpr157 GPR157 rs6577549 1.2074
+Gpr158 GPR158 rs1413394 2.173
+Gpr160 GPR160 rs879161 1.6981
+Gpr161 GPR161 rs11581009 1.2989
+Gpr162 GPR162 rs12302904 1.2145
+Gpr17 GPR17 rs6732660 1.3527
+Gpr171 GPR171 rs1231520 1.7027
+Gpr176 GPR176 rs7173582 1.6373
+Gpr18 GPR18 rs984477 0.9801
+Gpr180 GPR180 rs9556401 1.1273
+Gpr182 GPR182 rs3803018 1.4226
+Gpr183 GPR183 rs9513593 1.2385
+Gpr19 GPR19 rs11055018 1.4114
+Gpr20 GPR20 rs13260421 1.0951
+Gpr22 GPR22 rs6943051 1.5415
+Gpr25 GPR25 rs10920059 1.7515
+Gpr26 GPR26 rs7921171 1.3694
+Gpr27 GPR27 rs1349028 3.4949
+Gpr31b GPR31 rs2345968 0.8099
+XM_357749 GPR32 rs7257906 1.2974
+Gpr33 GPR33 rs8020452 1.2243
+Gpr35 GPR35 rs4676399 1.6576
+Gpr37 GPR37 rs9641748 1.5499
+Gpr39 GPR39 rs3109139 2.3364
+Gpr4 GPR4 rs2334253 2.8703
+Gpr45 GPR45 rs6758290 1.7802
+EG620246 GPR52 rs7516240 1.289
+Gpr55 GPR55 rs3098336 1.3768
+Gpr56 GPR56 rs13332410 2.8713
+Gpr6 GPR6 rs9384730 1.2245
+Gpr61 GPR61 rs7550711 3.9805
+Gpr62 GPR62 rs323874 1.1723
+Gpr63 GPR63 rs9487264 1.4215
+Gpr68 GPR68 rs11622455 2.5196
+Gpr75 GPR75 rs698853 2.0799
+Gpr83 GPR83 rs12795994 1.2798
+Gpr84 GPR84 rs11170881 1.6635
+Gpr85 GPR85 rs1610290 1.9245
+Gpr88 GPR88 rs12565245 1.9504
+Gpr89 GPR89A rs9728619 1.9427
+Gpr97 GPR97 rs8045126 1.5909
+Gpr98 GPR98 rs6452917 2.8254
+Gprc5a GPRC5A rs10845667 1.6645
+Gprc5b GPRC5B rs4782291 4.3118
+Gprc5c GPRC5C rs4789661 1.4976
+Gprc5d GPRC5D rs10845667 1.6645
+Gprc6a GPRC6A rs1567700 2.5662
+Gprin1 GPRIN1 rs3762966 2.2429
+Gprin3 GPRIN3 rs6532150 1.5452
+Gps1 GPS1 rs4969483 1.5018
+Gps2 GPS2 rs3809822 1.3322
+Gpsm1 GPSM1 rs10870201 1.6269
+Gpsm2 GPSM2 rs10857972 4.3865
+Gpsm3 GPSM3 rs17493811 1.0915
+Gpt GPT rs2721166 2.5933
+Gpt2 GPT2 rs1978320 0.8327
+Gpx1 GPX1 rs13078949 1.4594
+Gpx3 GPX3 rs10038350 1.1135
+Gpx4 GPX4 rs2024093 2.4686
+Gpx5 GPX5 rs13215054 1.2041
+Gpx6 GPX6 rs13215054 1.2041
+Gpx8 GPX8 rs336084 2.1294
+Gramd1a GRAMD1A rs35879596 2.6444
+Gramd1b GRAMD1B rs2846313 1.8564
+Gramd1c GRAMD1C rs4682509 1.5989
+Gramd2 GRAMD2 rs4777489 0.8353
+Gramd3 GRAMD3 rs13359059 1.7755
+Gramd4 GRAMD4 rs17151 1.2869
+Grap GRAP rs1624825 1.2206
+Grap2 GRAP2 rs107140 1.1925
+Grb10 GRB10 rs41430449 1.679
+Grb14 GRB14 rs6749902 1.4311
+Grb2 GRB2 rs4789177 2.4932
+Grb7 GRB7 rs907091 9.7066
+Greb1 GREB1 rs16857640 2.1215
+Greb1l GREB1L rs1019977 3.3316
+Grem1 GREM1 rs16966853 1.9931
+Grem2 GREM2 rs10926289 2.7447
+Grhl1 GRHL1 rs2357394 1.7552
+Grhl2 GRHL2 rs2030784 1.8047
+Grhl3 GRHL3 rs635539 1.1544
+Grhpr GRHPR rs17502738 2.0767
+Gria1 GRIA1 rs17519810 2.3692
+Gria2 GRIA2 rs17184008 1.4075
+Gria4 GRIA4 rs679914 1.9863
+Grid1 GRID1 rs7100188 3.3429
+Grid2 GRID2 rs4693325 2.6623
+Grid2ip GRID2IP rs6796 2.1198
+Grik1 GRIK1 rs41341044 2.5715
+Grik2 GRIK2 rs611348 1.7484
+Grik3 GRIK3 rs532719 1.1602
+Grik4 GRIK4 rs4938816 2.3166
+Grik5 GRIK5 rs4803522 1.8536
+Grin1 GRIN1 rs4880094 0.8102
+Grin2a GRIN2A rs11864073 2.0872
+Grin2b GRIN2B rs4764025 1.9435
+Grin2c GRIN2C rs10512600 1.1864
+Grin2d GRIN2D rs1643489 1.3195
+Grin3a GRIN3A rs10115481 3.5086
+Grin3b GRIN3B rs6510986 1.9223
+Grip1 GRIP1 rs41505246 1.5459
+Grip2 GRIP2 rs9863287 2.5251
+Grk1 GRK1 rs11164142 0.9122
+Gprk2l GRK4 rs34020100 1.9237
+Gprk5 GRK5 rs4752309 2.73
+Gprk6 GRK6 rs1801020 1.5835
+Grm1 GRM1 rs2300617 1.7948
+Grm2 GRM2 rs4687773 1.0733
+Grm3 GRM3 rs1024516 1.5532
+Grm4 GRM4 rs4713752 1.9278
+Grm5 GRM5 rs1903844 1.7433
+Grm6 GRM6 rs17078877 1.4132
+Grm7 GRM7 rs17694321 1.8908
+Grm8 GRM8 rs17867254 3.2197
+Grn GRN rs708383 1.7293
+Grp GRP rs6567071 0.8665
+Grpel1 GRPEL1 rs6836129 0.9197
+Grpel2 GRPEL2 rs6889207 1.9817
+Grwd1 GRWD1 rs1799257 3.3125
+Grxcr1 GRXCR1 rs7681586 1.8315
+Grxcr2 GRXCR2 rs11167913 1.6728
+Gsc GSC rs10142319 1.3782
+Gsc2 GSC2 rs807671 1.9718
+Gsdma GSDMA rs3894194 6.433
+Gsdmc GSDMC rs4733741 2.1757
+Gsdmd GSDMD rs4874150 0.9048
+Gsg1 GSG1 rs7308029 1.0152
+Gsg1l GSG1L rs6498039 2.4984
+Gsg2 GSG2 rs178323 1.4281
+Gsk3b GSK3B rs1719891 3.6096
+Gsn GSN rs4837817 2.222
+Gspt1 GSPT1 rs17810044 1.6728
+Gsr GSR rs13278923 1.6289
+Gss GSS rs6120777 1.5986
+Gsta2 GSTA1 rs9395826 2.7673
+Gsta3 GSTA3 rs13215455 3.2118
+Gsta4 GSTA4 rs606464 2.9055
+Gm3776 GSTA5 rs9395826 2.7673
+Gstcd GSTCD rs4699199 1.4585
+Gstm1 GSTM1 rs366631 3.0873
+Gstm2 GSTM2 rs574344 2.6291
+Gstm2-ps1 GSTM2P1 rs9386986 1.3801
+Gstm3 GSTM3 rs366631 3.0873
+Gstm4 GSTM4 rs574344 2.6291
+Gsto1 GSTO1 rs11191966 1.8983
+Gsto2 GSTO2 rs11191966 1.8983
+Gstt2 GSTT2 rs2858908 1.3544
+Gstz1 GSTZ1 rs3759733 3.2024
+Gsx1 GSX1 rs1923835 2.6641
+Gsx2 GSX2 rs2412535 1.8976
+Gtdc1 GTDC1 rs16823339 1.7706
+Gtf2a1 GTF2A1 rs1957546 2.1853
+Gtf2a1l GTF2A1L rs13022990 1.095
+Gtf2a2 GTF2A2 rs6151582 1.6511
+Gtf2b GTF2B rs1361594 2.6202
+Gtf2e1 GTF2E1 rs7650069 1.786
+Gtf2e2 GTF2E2 rs13278923 1.6289
+Gtf2f1 GTF2F1 rs7540 1.4981
+Gtf2f2 GTF2F2 rs12866440 1.3785
+Gtf2h1 GTF2H1 rs4757645 1.815
+Gtf2h3 GTF2H3 rs6551 1.2364
+Gtf2h4 GTF2H4 rs2844651 2.8011
+Gtf2h5 GTF2H5 rs11962291 1.1486
+Gtf2i GTF2I rs17145611 1.9727
+Gtf2ird1 GTF2IRD1 rs7800325 1.7476
+Gtf3a GTF3A rs9512681 1.8265
+Gtf3c1 GTF3C1 rs3093291 1.517
+Gtf3c2 GTF3C2 rs11689803 1.1015
+Gtf3c3 GTF3C3 rs4380257 1.1355
+Gtf3c4 GTF3C4 rs13294925 1.5662
+Gtf3c5 GTF3C5 rs6597598 1.9566
+Gtf3c6 GTF3C6 rs1704721 1.8601
+Gtpbp1 GTPBP1 rs3827358 1.3892
+Gtpbp10 GTPBP10 rs6465258 3.9329
+Gtpbp3 GTPBP3 rs1529482 1.8681
+Gtpbp4 GTPBP4 rs7906595 1.363
+Gtpbp8 GTPBP8 rs11707498 1.0089
+Gtse1 GTSE1 rs6008538 0.9763
+Gtsf1 GTSF1 rs7979707 1.9158
+Guca1a GUCA1A rs9381152 1.0658
+Guca1b GUCA1B rs9381152 1.0658
+Guca2a GUCA2A rs7539864 1.4676
+Guca2b GUCA2B rs7539864 1.4676
+Gucy1a2 GUCY1A2 rs17106305 2.5065
+Gucy1a3 GUCY1A3 rs12503180 1.8804
+Gucy1b2 GUCY1B2 rs17075173 1.9073
+Gucy1b3 GUCY1B3 rs2229202 2.3342
+Gucy2c GUCY2C rs10846029 0.9289
+Gucy2e GUCY2D rs8074694 1.4029
+Gucy2g GUCY2GP rs6585151 4.2569
+Guk1 GUK1 rs12116745 1.7802
+Gulp1 GULP1 rs7423378 1.4124
+Gusb GUSB rs12698510 1.2075
+Gvin1 GVINP1 rs7935848 1.0323
+Gxylt1 GXYLT1 rs3990966 1.5528
+Gyg GYG1 rs4681167 2.5897
+Gyltl1b GYLTL1B rs2292910 3.3325
+Gypc GYPC rs2016257 1.7333
+Gys1 GYS1 rs3764621 1.2762
+Gys2 GYS2 rs7138234 1.6326
+Gzma GZMA rs3136555 1.1939
+Gzmb GZMB rs20545 2.4783
+Gzme GZMH rs20545 2.4783
+Gzmk GZMK rs2112938 0.9052
+Gzmm GZMM rs3745924 1.1985
+H19 H19 rs7950932 1.7612
+H1f0 H1F0 rs1894646 0.8179
+H1fnt H1FNT rs17825134 1.6187
+H1fx H1FX rs4853 0.8162
+H2afj H2AFJ rs7970697 1.3436
+H2afv H2AFV rs1006231 2.7572
+H2afx H2AFX rs1784460 3.2948
+H2afy H2AFY rs2652089 1.4371
+H2afy2 H2AFY2 rs10999100 1.0866
+H2afz H2AFZ rs17613664 1.2506
+H3f3a H3F3A rs12126865 2.4774
+H3f3b H3F3B rs8069105 1.9658
+H3f3c H3F3C rs16918921 1.5897
+H6pd H6PD rs1009940 2.6312
+Haao HAAO rs17630329 1.1203
+Habp2 HABP2 rs2051111 2.63
+Hacl1 HACL1 rs2061187 2.4125
+Hadh HADH rs4245926 0.9176
+Hadha HADHA rs12611790 0.983
+Hadhb HADHB rs1107238 0.9237
+Hagh HAGH rs2473467 2.5638
+Haghl HAGHL rs2071951 2.7371
+Hal HAL rs1059845 1.7959
+Hand1 HAND1 rs2055422 1.5242
+Hand2 HAND2 rs13141356 1.1991
+Hao1 HAO1 rs2235248 1.0202
+Hao2 HAO2 rs879334 1.339
+Hap1 HAP1 rs4523977 1.4396
+Hapln1 HAPLN1 rs11955113 2.3764
+Hapln3 HAPLN3 rs8040435 1.0718
+Hapln4 HAPLN4 rs11555053 0.9614
+Has1 HAS1 rs8112223 2.2721
+Has2 HAS2 rs4612347 1.0547
+Has2as HAS2-AS1 rs6988139 1.4815
+Has3 HAS3 rs8044066 0.9838
+Hat1 HAT1 rs12622385 1.0126
+Haus1 HAUS1 rs4243278 1.5725
+Haus2 HAUS2 rs10454692 1.3805
+Haus3 HAUS3 rs3762942 2.273
+Haus5 HAUS5 rs3814983 1.1949
+Haus6 HAUS6 rs7022576 3.9318
+Haus8 HAUS8 rs1379281 1.0764
+Havcr1 HAVCR1 rs17054136 1.1875
+Havcr2 HAVCR2 rs3734021 1.2107
+Hax1 HAX1 rs11265425 3.988
+Hba-a2 HBA1 rs6600143 1.2236
+Hba-a1 HBA2 rs6600143 1.2236
+Hbb HBB rs10128556 2.5611
+Hbb-bh2 HBD rs916111 3.4669
+Hbb-y HBE1 rs916111 3.4669
+Hbegf HBEGF rs4912711 0.8242
+Hbb-bh1 HBG1 rs916111 3.4669
+Hbp1 HBP1 rs3779500 3.5429
+Hbq1 HBQ1 rs3918352 2.4299
+Hbs1l HBS1L rs2072827 2.605
+Hba-x HBZ rs11865131 1.504
+Gpr81 HCAR1 rs2454722 6.4961
+Niacr1 HCAR2 rs2454722 6.4961
+Hcfc1r1 HCFC1R1 rs2257295 1.637
+Hcfc2 HCFC2 rs17193219 1.3855
+5430410E06Rik HCG4 rs1737012 2.0747
+Hck HCK rs6058526 1.7675
+Hcls1 HCLS1 rs4676736 1.4295
+Hcn1 HCN1 rs1534391 2.3196
+Hcn2 HCN2 rs2283575 1.1281
+Hcn3 HCN3 rs8177970 0.8677
+Hcn4 HCN4 rs11857639 2.1944
+Hcrt HCRT rs939881 1.9158
+Hcrtr1 HCRTR1 rs4949448 0.9458
+Hcrtr2 HCRTR2 rs13196636 1.7731
+Hdac1 HDAC1 rs8176781 1.3647
+Hdac10 HDAC10 rs17248301 2.184
+Hdac11 HDAC11 rs468738 2.08
+Hdac2 HDAC2 rs6912132 1.165
+Hdac3 HDAC3 rs702379 1.0669
+Hdac4 HDAC4 rs519165 2.5372
+Hdac7 HDAC7 rs11168244 1.0283
+Hdac9 HDAC9 rs17346073 4.7739
+Hdc HDC rs12438041 1.5283
+Hddc2 HDDC2 rs9388411 2.1027
+Hddc3 HDDC3 rs2677737 0.8492
+Hdgf HDGF rs3806415 3.2961
+Hdgfl1 HDGFL1 rs9348526 1.0098
+Hdgfrp2 HDGFRP2 rs16992410 1.6334
+Hdgfrp3 HDGFRP3 rs17158063 1.2486
+Hdhd2 HDHD2 rs2635042 0.8136
+Hdhd3 HDHD3 rs1998503 2.1305
+Hdlbp HDLBP rs15129 3.1036
+Heatr1 HEATR1 rs10802551 1.8948
+Heatr2 HEATR2 rs6978481 1.5666
+XM_153454 HEATR4 rs2302041 1.604
+Heatr5a HEATR5A rs17097898 1.1726
+Heatr5b HEATR5B rs4670651 1.7335
+Heatr7b1 HEATR7B1 rs28899491 2.2277
+Hebp1 HEBP1 rs1291370 1.1919
+Hebp2 HEBP2 rs3756890 1.5553
+Heca HECA rs41391157 0.9638
+Hectd1 HECTD1 rs17097740 1.3066
+Hectd2 HECTD2 rs11186546 1.0904
+Hecw1 HECW1 rs2164038 2.4737
+Hecw2 HECW2 rs16846830 5.1666
+Heg1 HEG1 rs1909580 2.1923
+Helb HELB rs7978354 1.2654
+Hells HELLS rs11188040 1.5902
+Helq HELQ rs13128996 1.0317
+Helt HELT rs13130524 1.8551
+Helz HELZ rs11653020 4.0785
+Hemgn HEMGN rs1561958 1.3451
+Hemk1 HEMK1 rs420536 0.8216
+Hepacam HEPACAM rs7130442 1.8831
+Hepacam2 HEPACAM2 rs17165129 1.0187
+Hephl1 HEPHL1 rs1894165 2.0185
+Herc1 HERC1 rs12324720 2.4037
+Herc3 HERC3 rs2924941 2.1728
+Herc4 HERC4 rs41451145 1.2592
+Herc5 HERC6 rs13138145 1.4084
+Herpud1 HERPUD1 rs3764261 323.01
+Herpud2 HERPUD2 rs345404 0.9722
+Hes1 HES1 rs9840796 1.0733
+Hes5 HES5 rs4648646 0.9519
+Hes6 HES6 rs11694487 2.4629
+Hes7 HES7 rs9892383 1.3514
+Hesx1 HESX1 rs4325895 0.9097
+Hexa HEXA rs16956826 0.8368
+Hexb HEXB rs6453071 1.9817
+Hexdc HEXDC rs9648 1.1082
+Hexim1 HEXIM1 rs2285427 2.1238
+Hexim2 HEXIM2 rs2291447 2.0555
+Hey1 HEY1 rs2467781 1.754
+Hey2 HEY2 rs1739384 2.3187
+Heyl HEYL rs3916164 5.9516
+Hfe HFE rs2071303 1.4568
+Hfe2 HFE2 rs12088164 1.3305
+Hfm1 HFM1 rs282001 2.1429
+Hgd HGD rs2098781 1.2108
+Hgf HGF rs975360 2.4598
+Hgfac HGFAC rs4690099 1.5626
+Hgs HGS rs3088016 1.0063
+Hhat HHAT rs3765857 2.7097
+Hhatl HHATL rs339720 1.1576
+Hhip HHIP rs11942655 2.174
+Hhipl1 HHIPL1 rs10873508 1.3416
+Hhipl2 HHIPL2 rs17011636 1.4662
+Hhla1 HHLA1 rs1966701 1.437
+Hiatl1 HIATL1 rs2769119 0.8259
+Hibadh HIBADH rs6975900 1.279
+Hibch HIBCH rs16832401 2.3806
+Hic1 HIC1 rs1051322 1.1305
+Hic2 HIC2 rs465500 1.6661
+Hif1a HIF1A rs710038 1.4537
+Hif1an HIF1AN rs9420790 1.1145
+Hif3a HIF3A rs7250724 1.3098
+Higd1b HIGD1B rs2231651 0.8362
+Higd1c HIGD1C rs7306330 0.8844
+Higd2a HIGD2A rs13162111 0.9594
+Hils1 HILS1 rs2696297 0.8928
+Hinfp HINFP rs612132 1.5369
+Hint3 HINT3 rs1407590 1.8359
+Hip1 HIP1 rs12534640 1.8407
+Hip1r HIP1R rs3817094 6.8027
+Hipk1 HIPK1 rs6689205 1.4215
+Hipk2 HIPK2 rs4726715 2.1628
+Hipk3 HIPK3 rs7106825 1.5364
+Hipk4 HIPK4 rs744389 1.4602
+Hira HIRA rs2106144 1.5923
+Hist1h1b HIST1H1B rs6901303 1.6828
+Hist1h1d HIST1H1D rs806794 2.9801
+Hist1h1e HIST1H1E rs9379829 2.2716
+Hist1h1t HIST1H1T rs2071303 1.4568
+Hist1h2aa HIST1H2AA rs9379780 2.1562
+Hist1h2ac HIST1H2AC rs2071303 1.4568
+Hist1h2ad HIST1H2AD rs806794 2.9801
+Hist1h2ae HIST1H2AE rs806794 2.9801
+Hist1h2aj HIST1H2AJ rs12196939 1.0956
+Hist1h2ak HIST1H2AK rs6901303 1.6828
+Hist1h2af HIST1H2AL rs6901303 1.6828
+Hist1h2ba HIST1H2BA rs9379780 2.1562
+Hist1h2bc HIST1H2BC rs2071303 1.4568
+Hist1h2be HIST1H2BE rs806794 2.9801
+Hist1h2bf HIST1H2BF rs806794 2.9801
+Hist1h2bg HIST1H2BG rs806794 2.9801
+Hist1h2bh HIST1H2BH rs16891497 1.431
+Hist1h2bl HIST1H2BL rs742046 1.542
+Hist1h2bm HIST1H2BM rs12196939 1.0956
+Hist1h2bc HIST1H2BN rs6901303 1.6828
+Hist1h2bp HIST1H2BO rs200956 1.1761
+Hist1h3d HIST1H3D rs806794 2.9801
+Hist1h3e HIST1H3E rs806794 2.9801
+Hist1h3f HIST1H3F rs806794 2.9801
+Hist1h3g HIST1H3G rs6916289 2.4991
+Hist1h3h HIST1H3H rs742046 1.542
+Hist1h3i HIST1H3I rs6901303 1.6828
+Hist1h4c HIST1H4C rs2071303 1.4568
+Hist1h4d HIST1H4D rs806794 2.9801
+Hist1h4f HIST1H4F rs806794 2.9801
+Hist1h4h HIST1H4H rs6916289 2.4991
+Hist1h4h HIST1H4J rs6901303 1.6828
+EG621893 HIST2H2AB rs6700818 1.1777
+Hist2h2ac HIST2H2AC rs6700818 1.1777
+Hist2h2be HIST2H2BE rs6700818 1.1777
+Hist1h2ao HIST3H2A rs10733022 2.3463
+Hist3h2bb-ps HIST3H2BB rs10733022 2.3463
+Gm6421 HIST3H3 rs4581279 1.507
+Hist4h4 HIST4H4 rs7970697 1.3436
+Hivep1 HIVEP1 rs2327515 2.2576
+Hivep2 HIVEP2 rs2095369 2.267
+Hivep3 HIVEP3 rs349423 2.3518
+Hjurp HJURP rs28899491 2.2277
+Hk1 HK1 rs10159477 1.9289
+Hk2 HK2 rs41498345 1.7036
+Hk3 HK3 rs2884479 1.437
+Hkdc1 HKDC1 rs5030933 1.5394
+BC023409 HLA-B rs2523567 4.9045
+H2-D1 HLA-C rs2523567 4.9045
+H2-DMa HLA-DMA rs150359 2.3042
+H2-DMb1 HLA-DMB rs150359 2.3042
+H2-Oa HLA-DOA rs445704 1.9547
+H2-Ob HLA-DOB rs4947258 4.0967
+H2-Pa HLA-DPA1 rs9277196 2.5265
+H2-Pb HLA-DPB2 rs9277196 2.5265
+H2-Aa HLA-DQA1 rs9271562 4.6164
+H2-Ab1 HLA-DQB1 rs9271562 4.6164
+H2-Ea HLA-DRA rs3135363 2.7091
+H2-Eb1 HLA-DRB1 rs9269794 5.8193
+C920025E04Rik HLA-E rs13220159 1.3255
+H2-M3 HLA-F rs1611689 2.5248
+Gm7030 HLA-L rs1264622 2.7612
+Hlcs HLCS rs9985116 1.5983
+Hlf HLF rs4550511 1.4283
+Hltf HLTF rs4681167 2.5897
+Hlx HLX rs7545525 1.56
+H13 HM13 rs6119442 2.424
+Hmbs HMBS rs1784460 3.2948
+Hmcn1 HMCN1 rs12144532 1.2874
+Hmg20a HMG20A rs883627 1.0124
+Hmg20b HMG20B rs7246261 1.0939
+Hmga1 HMGA1 rs6922119 2.1091
+Hmga2 HMGA2 rs883605 2.1098
+Hmgb1 HMGB1 rs7989032 1.6631
+Hmgb2 HMGB2 rs1383629 1.2323
+Hmgb4 HMGB4 rs6425850 1.7242
+Hmgcll1 HMGCLL1 rs1502207 4.198
+Hmgcr HMGCR rs13356670 1.2378
+Hmgcs2 HMGCS2 rs3790693 2.1279
+Hmgn1 HMGN1 rs9981301 1.087
+Hmgn2 HMGN2 rs12741800 1.95
+Hmgn3 HMGN3 rs12214161 1.631
+Hmgxb3 HMGXB3 rs2282804 1.2228
+Hmha1 HMHA1 rs3752240 1.561
+Hmox1 HMOX1 rs735267 1.6946
+Hmox2 HMOX2 rs2270363 2.0391
+Hmx2 HMX2 rs17104692 3.2462
+Hmx3 HMX3 rs17104692 3.2462
+Hn1l HN1L rs8059926 1.3165
+Hnf1a HNF1A rs1183910 2.3972
+Hnf1b HNF1B rs2689 2.2885
+Hnf4a HNF4A rs1800961 14.978
+Hnf4g HNF4G rs3779741 1.0505
+Hnmt HNMT rs3791242 1.6609
+Hnrnpa1 HNRNPA1 rs2071391 2.3205
+Hnrnpa2b1 HNRNPA2B1 rs10270359 3.4094
+Hnrnpa3 HNRNPA3 rs6433653 1.6643
+Hnrnpab HNRNPAB rs6868483 2.0844
+Hnrnpc HNRNPC rs10483251 2.1252
+Hnrnpd HNRNPD rs1960667 2.15
+Hnrnpf HNRNPF rs4514358 1.3245
+Hnrnph1 HNRNPH1 rs4700812 3.4441
+Hnrnph3 HNRNPH3 rs7893969 2.0917
+Hnrnpk HNRNPK rs3737134 4.1881
+Hnrnpl HNRNPL rs8100321 1.2167
+Hnrnpm HNRNPM rs4804074 4.2411
+Hnrnpr HNRNPR rs10753554 1.7778
+Hnrnpu HNRNPU rs3191 1.1315
+AI503316 HNRNPU-AS1 rs751715 1.1948
+Hnrnpul1 HNRNPUL1 rs12978323 1.5369
+Hnrnpul2 HNRNPUL2 rs2187108 1.4925
+Hnrpdl HNRPDL rs1113576 1.1281
+Hnrpf HNRPF rs4514358 1.3245
+Hnrpll HNRPLL rs17022908 1.4375
+Hoga1 HOGA1 rs933057 2.3049
+Homer1 HOMER1 rs11747261 1.4417
+Homer2 HOMER2 rs7179000 1.6139
+Homez HOMEZ rs10144278 1.5275
+Hook1 HOOK1 rs11589193 0.9442
+Hook2 HOOK2 rs2066489 0.821
+Hook3 HOOK3 rs17622994 0.8094
+Hopx HOPX rs6849557 1.5438
+Hormad2 HORMAD2 rs5763751 3.5156
+2700086A05Rik HOXA-AS3 rs17500835 1.5977
+Hoxa10 HOXA10 rs17500835 1.5977
+Hoxa11 HOXA11 rs2285724 1.0419
+Hoxa11as HOXA11-AS rs2285724 1.0419
+Hoxa13 HOXA13 rs2285724 1.0419
+Hoxa2 HOXA2 rs17500605 0.9318
+Hoxa3 HOXA3 rs17500835 1.5977
+Hoxa4 HOXA4 rs17500835 1.5977
+Hoxa5 HOXA5 rs17500835 1.5977
+Hoxa6 HOXA6 rs17500835 1.5977
+Hoxa7 HOXA7 rs17500835 1.5977
+Hoxa9 HOXA9 rs17500835 1.5977
+Hoxb1 HOXB1 rs10514953 1.0624
+Hoxb13 HOXB13 rs9652867 1.584
+Hoxb2 HOXB2 rs10514953 1.0624
+Hoxb3 HOXB3 rs10514953 1.0624
+Hoxc10 HOXC10 rs1109391 1.3882
+Hoxc11 HOXC11 rs1109391 1.3882
+Hoxc13 HOXC13 rs7139337 1.5847
+Hoxc4 HOXC4 rs1109391 1.3882
+Hoxc5 HOXC5 rs1109391 1.3882
+Hoxc6 HOXC6 rs1109391 1.3882
+Hoxc8 HOXC8 rs1109391 1.3882
+Hoxc9 HOXC9 rs1109391 1.3882
+Hoxd1 HOXD1 rs1867863 1.4779
+Hoxd10 HOXD10 rs711814 1.0349
+E230017H14Rik HOXD11 rs711814 1.0349
+Hoxd12 HOXD12 rs711814 1.0349
+Hoxd13 HOXD13 rs711814 1.0349
+Hoxd3 HOXD3 rs6736786 1.5924
+Hoxd4 HOXD4 rs6736786 1.5924
+Hoxd8 HOXD8 rs6736786 1.5924
+Hoxd9 HOXD9 rs6736786 1.5924
+Hp HP rs217181 1.8392
+Hp1bp3 HP1BP3 rs10916853 1.7345
+Hpca HPCA rs7527330 1.3002
+Hpcal1 HPCAL1 rs11684791 1.8483
+Hpcal4 HPCAL4 rs784597 1.9893
+Hpd HPD rs895953 4.2226
+Hpdl HPDL rs10890324 0.9241
+Hpgd HPGD rs6835990 1.8225
+Hpgds HPGDS rs1386531 1.7929
+Hpn HPN rs35879596 2.6444
+Hp HPR rs217181 1.8392
+Hps3 HPS3 rs4681169 2.5334
+Hps4 HPS4 rs129845 1.1556
+Hps5 HPS5 rs10832911 1.758
+Hps6 HPS6 rs7090600 0.8567
+Hpse HPSE rs4410520 0.9516
+Hpse2 HPSE2 rs4145921 3.1213
+Hpx HPX rs1906656 1.3886
+Hr HR rs6557841 0.9927
+Hras1 HRAS rs4963176 1.7142
+Hrc HRC rs11667393 1.3431
+Hrct1 HRCT1 rs4271053 0.8496
+Hrg HRG rs6767451 1.6511
+Hrh1 HRH1 rs901865 1.7535
+Hrh2 HRH2 rs610101 1.754
+Hrh3 HRH3 rs2236528 1.2798
+Hrh4 HRH4 rs17259814 1.3923
+Hrnr HRNR rs12030180 2.3822
+Hrsp12 HRSP12 rs10504985 2.0799
+Hs1bp3 HS1BP3 rs2305458 2.767
+Hs2st1 HS2ST1 rs17418454 0.9462
+Hs3st1 HS3ST1 rs2058360 1.8242
+Hs3st2 HS3ST2 rs2157854 1.2745
+Hs3st3a1 HS3ST3A1 rs7217404 2.162
+Hs3st3b1 HS3ST3B1 rs719470 1.5991
+Hs3st4 HS3ST4 rs12598321 1.963
+Hs3st5 HS3ST5 rs12526401 1.4282
+Hs3st6 HS3ST6 rs1657137 1.6091
+Hs6st1 HS6ST1 rs840873 2.0073
+Hs6st3 HS6ST3 rs9513128 2.6951
+Hsbp1 HSBP1 rs8049765 1.9743
+Hscb HSCB rs5752792 2.8225
+Hsd11b1 HSD11B1 rs12059601 1.2965
+Hsd11b2 HSD11B2 rs7206718 3.1061
+Hsd17b1 HSD17B1 rs676387 2.3565
+Hsd17b11 HSD17B11 rs9647458 3.0207
+Hsd17b12 HSD17B12 rs17597037 2.0228
+Hsd17b13 HSD17B13 rs9647458 3.0207
+Hsd17b14 HSD17B14 rs17206686 1.6979
+Hsd17b2 HSD17B2 rs8059915 1.6349
+Hsd17b3 HSD17B3 rs10120552 1.8274
+Hsd17b4 HSD17B4 rs10051448 1.0915
+Hsd17b6 HSD17B6 rs6581104 1.2011
+Hsd17b7 HSD17B7 rs10917597 1.0558
+H2-Ke6 HSD17B8 rs2854028 1.4432
+Hsd3b1 HSD3B2 rs10923823 2.5756
+Hsd3b7 HSD3B7 rs4889606 2.7307
+Hsdl1 HSDL1 rs16962887 0.9154
+Hsdl2 HSDL2 rs7872317 1.5242
+Hsf1 HSF1 rs4557742 1.8887
+Hsf2 HSF2 rs13210569 1.0927
+Hsf2bp HSF2BP rs8127287 1.2788
+Hsf4 HSF4 rs2233455 8.8128
+Hsh2d HSH2D rs681059 1.6807
+Hsp90ab1 HSP90AB1 rs2233434 2.6832
+Hsp90b1 HSP90B1 rs2576954 1.8225
+Hspa12a HSPA12A rs740363 4.0633
+Hspa12b HSPA12B rs6139201 1.8993
+Hspa13 HSPA13 rs2822626 1.1957
+Hspa1a HSPA1A rs2736426 1.8342
+Hspa1b HSPA1B rs2736426 1.8342
+Hspa1l HSPA1L rs2736426 1.8342
+Hspa2 HSPA2 rs2045022 1.2043
+Hspa4 HSPA4 rs12517292 1.8242
+Hspa8 HSPA8 rs11218932 1.4063
+Hspb1 HSPB1 rs1076436 1.3918
+Hspb11 HSPB11 rs731828 1.2479
+Hspb2 HSPB2 rs4252589 0.9259
+Hspb3 HSPB3 rs17322880 1.8019
+Hspb7 HSPB7 rs732286 1.902
+Hspb8 HSPB8 rs2723880 2.1758
+Hspb9 HSPB9 rs12600570 2.6598
+Hspbp1 HSPBP1 rs12983684 1.7421
+Hspg2 HSPG2 rs6681693 1.8395
+Hsp110 HSPH1 rs3736861 3.8758
+Htatip2 HTATIP2 rs11025524 1.2979
+Htr1a HTR1A rs17484586 0.8386
+Htr1b HTR1B rs9350712 2.2097
+Htr1d HTR1D rs16828047 1.0238
+Htr1f HTR1F rs12491719 0.9454
+Htr2a HTR2A rs594242 1.111
+Htr2b HTR2B rs17586428 1.1073
+Htr3a HTR3A rs7104827 1.364
+Htr3b HTR3B rs1176740 1.1065
+Htr4 HTR4 rs17108437 1.9151
+Htr5a HTR5A rs3734966 2.9151
+Htr6 HTR6 rs3748749 1.4041
+Htr7 HTR7 rs1936621 1.5427
+Htra1 HTRA1 rs2292626 1.6704
+Htra3 HTRA3 rs4629434 1.4167
+Htra4 HTRA4 rs4076625 1.0424
+Htt HTT rs363075 2.2496
+Hunk HUNK rs2833609 1.5911
+Hus1 HUS1 rs3176493 2.7773
+Hus1b HUS1B rs6939459 1.0187
+Hvcn1 HVCN1 rs7974047 2.0166
+Hyal1 HYAL1 rs709210 2.4806
+Hyal2 HYAL2 rs709210 2.4806
+Hyal3 HYAL3 rs13100173 1.7749
+Hyal4 HYAL4 rs12672205 1.5268
+Hydin HYDIN rs1512613 1.6531
+Hyi HYI rs12129487 0.8468
+Hyou1 HYOU1 rs1784460 3.2948
+Iah1 IAH1 rs7580419 1.0561
+A230051G13Rik IBA57 rs12116745 1.7802
+Ibsp IBSP rs2732167 2.1022
+Ibtk IBTK rs9443964 2.0467
+Ica1 ICA1 rs7799828 2.2975
+Ica1l ICA1L rs13398418 1.3211
+Icam1 ICAM1 rs5030340 1.2874
+Icam2 ICAM2 rs885633 1.0566
+Icam4 ICAM4 rs5030340 1.2874
+Icam5 ICAM5 rs5030340 1.2874
+Ick ICK rs606464 2.9055
+Icmt ICMT rs4908863 0.9212
+Icos ICOS rs1978594 1.7791
+Icosl ICOSLG rs915776 1.7029
+Ict1 ICT1 rs4789125 1.0136
+Id2 ID2 rs16866757 1.5872
+Id3 ID3 rs2742952 1.3761
+Id4 ID4 rs932346 1.3214
+Ide IDE rs7068618 0.9706
+Idh1 IDH1 rs7580715 1.0093
+Idh3a IDH3A rs7179651 1.162
+Idh3b IDH3B rs5026920 0.8798
+Idi1 IDI1 rs11250250 2.0031
+ENSMUST00000021573 IDI2 rs7906595 1.363
+Idnk IDNK rs2811927 2.401
+Ido2 IDO2 rs2340952 1.6251
+Idua IDUA rs11724804 1.0655
+Ier2 IER2 rs752340 1.1111
+Ier3 IER3 rs9295910 0.9059
+Ier3ip1 IER3IP1 rs8096219 0.9901
+Ier5 IER5 rs17374514 1.272
+Ier5l IER5L rs10988229 2.1685
+Iffo1 IFFO1 rs1043262 1.8283
+Iffo2 IFFO2 rs4912056 1.4947
+Ifi204 IFI16 rs11580289 1.2819
+Ifi27l2b IFI27L2 rs10150227 1.4938
+Ifi35 IFI35 rs8076790 1.1148
+Ifi44 IFI44 rs166642 0.9935
+H28 IFI44L rs7531185 1.2021
+Ifih1 IFIH1 rs17715343 2.6055
+Ifit1 IFIT1 rs304494 1.102
+Ifit2 IFIT2 rs4934464 0.8489
+Ifitm1 IFITM1 rs14408 1.8573
+6330512M04Rik IFITM10 rs17834326 1.2514
+Ifitm5 IFITM5 rs14408 1.8573
+Ifltd1 IFLTD1 rs11048148 1.7778
+Gm12597 IFNA1 rs7864960 1.5188
+Ifna4 IFNA16 rs3091289 1.4134
+OTTMUSG00000007655 IFNA2 rs643070 1.4796
+Ifnar1 IFNAR1 rs2843705 0.9551
+Ifnar2 IFNAR2 rs2248412 1.7934
+Ifnb1 IFNB1 rs10811469 0.9393
+Ifne IFNE rs7849420 1.5502
+Ifng IFNG rs10784676 1.0184
+Ifngr1 IFNGR1 rs3799488 1.1909
+Ifngr2 IFNGR2 rs2012075 1.0582
+Ifnk IFNK rs903603 1.4366
+Ifnz IFNW1 rs10964842 1.4145
+Ifrd1 IFRD1 rs6955527 2.3677
+Ifrd2 IFRD2 rs13100173 1.7749
+Ift122 IFT122 rs13326661 1.5825
+Ift140 IFT140 rs1040499 1.0024
+Ift172 IFT172 rs11675428 1.5809
+Ift20 IFT20 rs7221462 1.4002
+1700019E19Rik IFT43 rs4252330 1.9465
+Ift46 IFT46 rs573971 1.1182
+Ift52 IFT52 rs285207 1.0328
+Ift57 IFT57 rs2966580 1.2527
+Ift74 IFT74 rs12003612 0.9543
+Ift80 IFT80 rs1141023 1.7865
+Ift81 IFT81 rs7314552 2.3175
+Ift88 IFT88 rs4341630 1.3247
+Igdcc3 IGDCC3 rs7182756 1.1872
+Igdcc4 IGDCC4 rs16948719 1.5461
+Igf1 IGF1 rs2072592 1.9187
+Igf1r IGF1R rs11247381 1.8233
+Igf2 IGF2 rs7932949 1.0631
+Igf2bp1 IGF2BP1 rs11079849 2.0427
+Igf2bp2 IGF2BP2 rs16860216 1.5814
+Igf2bp3 IGF2BP3 rs3087888 1.902
+Igf2r IGF2R rs9295117 3.6765
+Igfals IGFALS rs2473467 2.5638
+Igfbp1 IGFBP1 rs3763497 2.8153
+Igfbp2 IGFBP2 rs12478840 1.3425
+Igfbp3 IGFBP3 rs3763497 2.8153
+Igfbp4 IGFBP4 rs10305319 1.7916
+Igfbp5 IGFBP5 rs2024486 1.6728
+Igfbp6 IGFBP6 rs10876407 1.8356
+Igfbp7 IGFBP7 rs1713963 1.9983
+Igfbpl1 IGFBPL1 rs12684944 2.046
+Gm580 IGFL3 rs12459902 1.0439
+Igfn1 IGFN1 rs10920138 1.3304
+Igh-C IGH@ rs12232113 0.9531
+Igh-7 IGHE rs11628672 0.8404
+Ighg1 IGHG1 rs11628672 0.8404
+Ighmbp2 IGHMBP2 rs513359 4.1376
+Igl IGL@ rs17699428 1.2181
+Iglon5 IGLON5 rs10414689 1.066
+Igsf10 IGSF10 rs16863374 0.9355
+Igsf11 IGSF11 rs12636639 2.8857
+Igsf21 IGSF21 rs12131268 2.0417
+2210010C17Rik IGSF23 rs6509170 3.392
+Igsf5 IGSF5 rs2205203 1.7975
+Igsf6 IGSF6 rs41468646 1.3992
+Igsf9 IGSF9 rs2501318 0.8199
+Igsf9b IGSF9B rs7939140 1.2911
+Ihh IHH rs359977 1.0837
+Ikbip IKBIP rs7299837 0.8196
+Ikbkap IKBKAP rs7026291 1.608
+Ikbkb IKBKB rs17875671 0.862
+Ikbke IKBKE rs9242 4.3716
+Ikzf1 IKZF1 rs11185603 4.7513
+Ikzf2 IKZF2 rs6713784 2.9983
+Ikzf3 IKZF3 rs907091 9.7066
+Ikzf4 IKZF4 rs773114 1.0901
+Ikzf5 IKZF5 rs7085513 2.6119
+Il10 IL10 rs4845133 1.8791
+Il10ra IL10RA rs2512145 2.0967
+Il10rb IL10RB rs2248412 1.7934
+Il11 IL11 rs8105530 1.558
+Il11ra1 IL11RA rs3739882 0.9303
+Il12a IL12A rs2243123 1.9638
+Il12b IL12B rs3181225 1.4568
+Il12rb1 IL12RB1 rs11673460 2.0003
+Il12rb2 IL12RB2 rs17375360 1.6556
+Il13 IL13 rs2243261 1.3089
+Il15 IL15 rs9996712 2.3108
+Il15ra IL15RA rs593026 1.5547
+Il16 IL16 rs4778640 1.1522
+Il17a IL17A rs12195273 1.4531
+Il17b IL17B rs371488 1.648
+Il17c IL17C rs899729 1.5645
+Il17d IL17D rs12871071 1.1022
+Il17f IL17F rs10807440 1.7233
+Il17ra IL17RA rs4819962 1.7607
+Il17rb IL17RB rs3774616 1.3592
+Il17rc IL17RC rs13097123 2.0881
+Il17rd IL17RD rs1872947 1.636
+Il17re IL17RE rs13097123 2.0881
+Il18 IL18 rs5744261 0.8837
+Il18bp IL18BP rs10501408 0.9834
+Il18r1 IL18R1 rs17696274 1.3445
+Il18rap IL18RAP rs6760621 1.3293
+Il19 IL19 rs7529836 1.7755
+Il1a IL1A rs6722023 1.5077
+Il1b IL1B rs12469600 1.0031
+Il1f10 IL1F10 rs11123162 1.9535
+Il1r1 IL1R1 rs3917330 2.4715
+Il1r2 IL1R2 rs3218920 1.611
+Il1rap IL1RAP rs3935775 1.3232
+Il1rl1 IL1RL1 rs17696274 1.3445
+Il1rl2 IL1RL2 rs3917330 2.4715
+Il1rn IL1RN rs11123162 1.9535
+Il2 IL2 rs6827839 1.1256
+Il20 IL20 rs2243156 0.9727
+Il20ra IL20RA rs276500 2.2484
+Il20rb IL20RB rs6773440 2.2766
+Il21r IL21R rs4787426 1.5715
+Il22 IL22 rs12825571 2.7498
+Il22ra1 IL22RA1 rs10903034 0.8921
+Il22ra2 IL22RA2 rs276572 1.7095
+Il23r IL23R rs17375360 1.6556
+Il24 IL24 rs291110 1.4004
+Il2ra IL2RA rs10795763 1.5656
+Il2rb IL2RB rs2284033 1.2933
+Il3 IL3 rs27438 1.1843
+Il31 IL31 rs11060167 2.3563
+Il31ra IL31RA rs918626 1.5751
+Il33 IL33 rs2890704 1.1747
+Il34 IL34 rs11865804 0.9809
+Il1f6 IL36A rs13396054 1.2586
+Il1f8 IL36B rs17042712 1.5429
+Il1f9 IL36G rs13396054 1.2586
+Il1f5 IL36RN rs17042712 1.5429
+Il4 IL4 rs2243261 1.3089
+Il4i1 IL4I1 rs4801810 2.2732
+Il4ra IL4R rs11074852 1.815
+Il5 IL5 rs739718 1.616
+Il5ra IL5RA rs3913308 1.6645
+Il6 IL6 rs2069827 3.4715
+Il6ra IL6R rs1552482 0.9017
+Il6st IL6ST rs4865998 1.3279
+Il7 IL7 rs6473119 1.069
+Il7r IL7R rs975433 1.3273
+Cxcl15 IL8 rs10023573 1.6613
+Il9 IL9 rs25895 1.0444
+Ildr1 ILDR1 rs4602434 1.1266
+Ildr2 ILDR2 rs16858259 1.0696
+Ilf2 ILF2 rs6674929 2.8582
+Ilf3 ILF3 rs8106664 2.2957
+Ilk ILK rs11040933 1.8716
+Ilkap ILKAP rs13396611 2.3808
+Ilvbl ILVBL rs7251788 3.194
+Immp1l IMMP1L rs7928377 2.286
+Immp2l IMMP2L rs13310342 2.6444
+Immt IMMT rs17618119 1.6582
+Imp3 IMP3 rs11634402 0.8145
+Imp4 IMP4 rs11542415 1.1487
+Impa1 IMPA1 rs2955003 1.3286
+Impa2 IMPA2 rs619671 2.908
+Impact IMPACT rs17187528 0.8732
+Impad1 IMPAD1 rs4738559 1.0142
+Impdh2 IMPDH2 rs4974081 0.9416
+Impg1 IMPG1 rs1853936 1.3758
+Impg2 IMPG2 rs515307 1.4427
+Ina INA rs11191650 1.4862
+Inadl INADL rs2498962 2.6855
+Incenp INCENP rs2903922 1.0744
+Inf2 INF2 rs4983378 1.4814
+Ing1 ING1 rs7999702 1.5555
+Ing2 ING2 rs6850330 1.07
+Ing3 ING3 rs2244865 1.2399
+Ing4 ING4 rs11064303 1.8283
+Ing5 ING5 rs4675794 1.5666
+Inha INHA rs3731920 1.4403
+Inhba INHBA rs6962594 1.4522
+Inhbb INHBB rs6741251 0.8108
+Inhbc INHBC rs3741414 7.7857
+Inhbe INHBE rs3741414 7.7857
+1110054O05Rik INIP rs786990 1.8639
+Inmt INMT rs255121 2.4793
+Ino80 INO80 rs1869907 2.5711
+Ino80b INO80B rs13396033 2.5805
+Ino80c INO80C rs9957318 1.6605
+Ino80d INO80D rs6714031 1.3986
+Inpp1 INPP1 rs3791809 1.2341
+Inpp4a INPP4A rs4850877 2.019
+Inpp4b INPP4B rs1982965 3.0793
+Inpp5a INPP5A rs4481948 1.691
+Inpp5b INPP5B rs17531077 1.5842
+Inpp5d INPP5D rs4973608 1.2929
+Inpp5e INPP5E rs4487900 0.908
+Inpp5f INPP5F rs1008727 1.0545
+Inpp5j INPP5J rs2074739 3.5815
+Inpp5k INPP5K rs17833825 1.4947
+Ins2 INS rs4929966 1.3939
+Insc INSC rs1714376 2.256
+Insig1 INSIG1 rs9767875 1.4134
+Insig2 INSIG2 rs10490626 1.7162
+Insl3 INSL3 rs3212798 1.3594
+Insm1 INSM1 rs11908058 2.1848
+Insr INSR rs8101064 4.6919
+Insrr INSRR rs2768764 2.4583
+Ints1 INTS1 rs3735660 1.2261
+Ints10 INTS10 rs11986182 20.211
+Ints3 INTS3 rs10908475 4.1259
+Ints4 INTS4 rs24356 2.0332
+Ints5 INTS5 rs7386 2.2897
+Ints6 INTS6 rs944869 1.2565
+Ints7 INTS7 rs2998419 1.4573
+Intu INTU rs17012568 1.2399
+Invs INVS rs6479002 1.3221
+Ip6k1 IP6K1 rs2291542 3.556
+Ip6k3 IP6K3 rs10947433 2.4178
+Ipcef1 IPCEF1 rs13209273 1.7016
+Ipmk IPMK rs7077184 0.9961
+Ipo11 IPO11 rs153867 0.9378
+Ipo13 IPO13 rs1859728 1.9031
+Ipo5 IPO5 rs648659 1.3228
+Ipo7 IPO7 rs4910048 1.523
+Ipo8 IPO8 rs7974489 1.294
+Ipo9 IPO9 rs1043823 2.008
+Ipp IPP rs12757451 1.857
+Ippk IPPK rs10820995 0.8665
+Ipw IPW rs17115228 2.0235
+Iqca IQCA1 rs7597596 1.0118
+Iqcb1 IQCB1 rs9868875 1.011
+Iqcc IQCC rs1358823 0.857
+Iqcd IQCD rs12828094 0.8371
+Iqce IQCE rs2272287 1.3115
+Iqcf1 IQCF1 rs9853511 1.3214
+Iqcf3 IQCF3 rs9853511 1.3214
+Iqcf5 IQCF5 rs9853511 1.3214
+Iqcf6 IQCF6 rs11130296 1.0347
+Iqch IQCH rs8026627 2.1593
+Iqck IQCK rs11865403 4.4631
+Iqgap1 IQGAP1 rs12914981 1.7742
+Iqgap2 IQGAP2 rs3797390 1.7192
+Iqgap3 IQGAP3 rs4661178 1.6751
+Iqsec1 IQSEC1 rs2596910 3.8567
+Iqsec3 IQSEC3 rs2284328 1.1707
+Irak1bp1 IRAK1BP1 rs12196485 1.204
+Irak2 IRAK2 rs3844283 2.2548
+Irak3 IRAK3 rs11541076 3.311
+Irak4 IRAK4 rs10880568 1.6899
+Ireb2 IREB2 rs2568485 1.5396
+Irf1 IRF1 rs6897597 2.2559
+Irf2 IRF2 rs7657540 1.2759
+Irf2bp1 IRF2BP1 rs17651629 2.4544
+Irf2bp2 IRF2BP2 rs17519958 1.2812
+6430527G18Rik IRF2BPL rs4899632 1.4823
+Irf3 IRF3 rs7251 1.1906
+Irf4 IRF4 rs11757491 1.2946
+Irf6 IRF6 rs669694 1.9996
+Irf7 IRF7 rs12419694 2.7284
+Irf8 IRF8 rs6540231 1.6927
+Irf9 IRF9 rs11574503 1.442
+Irg1 IRG1 rs17385359 1.0899
+Irgc1 IRGC rs12610476 1.8881
+Irgq IRGQ rs2682587 1.0153
+Irs1 IRS1 rs12185555 1.6592
+Irs2 IRS2 rs9587980 1.2381
+Irx1 IRX1 rs2197506 2.1874
+Irx2 IRX2 rs924216 1.392
+Irx3 IRX3 rs1868689 1.4326
+Irx4 IRX4 rs4975753 0.862
+Irx5 IRX5 rs11076060 1.7411
+Irx6 IRX6 rs31044 0.8604
+Isca1 ISCA1 rs1535757 1.3261
+Iscu ISCU rs12317582 1.2011
+Isg15 ISG15 rs3128126 0.957
+Isg20 ISG20 rs8038865 1.9706
+Isg20l2 ISG20L2 rs3806415 3.2961
+Isl1 ISL1 rs12656456 2.4455
+Isl2 ISL2 rs4886792 1.3792
+Islr ISLR rs4887066 1.1267
+Islr2 ISLR2 rs11635599 0.9104
+Ism1 ISM1 rs6109807 1.6957
+Ism2 ISM2 rs11623826 1.6108
+Isoc1 ISOC1 rs6862373 1.8804
+Ispd ISPD rs6461225 3.0141
+2400003C14Rik IST1 rs17358402 2.1436
+Isx ISX rs9622125 1.8236
+Isy1 ISY1-RAB43 rs9842978 1.1441
+Isyna1 ISYNA1 rs11668719 0.8642
+Itch ITCH rs1205342 4.481
+Itfg1 ITFG1 rs12930708 1.7378
+Itfg2 ITFG2 rs4766000 1.1699
+Itfg3 ITFG3 rs3918352 2.4299
+Itga1 ITGA1 rs2648735 1.3931
+Itga10 ITGA10 rs17352281 2.2366
+Itga11 ITGA11 rs12915306 1.6981
+Itga2 ITGA2 rs246409 1.0332
+Itga2b ITGA2B rs708383 1.7293
+Itga3 ITGA3 rs847698 1.3058
+Itga4 ITGA4 rs155099 1.7545
+Itga6 ITGA6 rs1920979 2.3126
+Itga7 ITGA7 rs2293414 1.1448
+Itga8 ITGA8 rs1868996 2.4996
+Itga9 ITGA9 rs2844395 2.0526
+Itgad ITGAD rs11574636 1.5113
+Itgae ITGAE rs178323 1.4281
+Itgal ITGAL rs2073917 1.1129
+Itgam ITGAM rs11150612 1.7312
+Itgav ITGAV rs1020235 1.0244
+Itgax ITGAX rs11150612 1.7312
+Itgb1 ITGB1 rs10827166 1.6258
+Itgb1bp1 ITGB1BP1 rs12474647 0.9055
+Itgb2 ITGB2 rs235329 3.6936
+Itgb3 ITGB3 rs4968255 3.3099
+Itgb3bp ITGB3BP rs17319708 0.965
+Itgb4 ITGB4 rs4485425 1.7778
+Itgb5 ITGB5 rs3863065 2.8122
+Itgb6 ITGB6 rs10497211 2.4542
+Itgb7 ITGB7 rs1465056 2.3702
+Itgb8 ITGB8 rs9655193 1.4915
+Itgbl1 ITGBL1 rs7323652 1.2106
+Itih1 ITIH1 rs17331151 4.4108
+Itih2 ITIH2 rs7068109 1.4355
+Itih3 ITIH3 rs17331151 4.4108
+Itih4 ITIH4 rs17331151 4.4108
+Itih5 ITIH5 rs17142818 1.8268
+Itk ITK rs30112 2.5948
+Itln1 ITLN1 rs2199936 1.6796
+Itm2b ITM2B rs7996717 1.422
+Itm2c ITM2C rs3098336 1.3768
+Itpa ITPA rs2422862 1.5193
+Itpk1 ITPK1 rs3825683 2.6704
+Itpka ITPKA rs3743031 4.2148
+Itpkb ITPKB rs1288938 1.9104
+Itpkc ITPKC rs2561537 2.969
+Itpr1 ITPR1 rs11712379 2.3996
+Itpr2 ITPR2 rs7302344 3.424
+Itpr3 ITPR3 rs2296329 2.1892
+Itprip ITPRIP rs6584596 1.997
+Itpripl2 ITPRIPL2 rs3826249 1.4962
+Itsn1 ITSN1 rs762185 1.247
+Itsn2 ITSN2 rs11896896 1.2703
+Ivd IVD rs12440317 3.0409
+Ivl IVL rs11801996 1.4613
+Iyd IYD rs9384412 1.745
+Izumo1 IZUMO1 rs281384 2.5312
+Izumo2 IZUMO2 rs11881272 1.0685
+Jag1 JAG1 rs8123495 1.9678
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+Jagn1 JAGN1 rs203563 2.2186
+Jak1 JAK1 rs17127091 1.4448
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+Jak3 JAK3 rs3212798 1.3594
+Jakmip1 JAKMIP1 rs908013 2.5715
+Jakmip2 JAKMIP2 rs11745685 4.3688
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+Jam2 JAM2 rs2829866 0.8703
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+Jarid2 JARID2 rs6905698 1.7199
+Jazf1 JAZF1 rs10275982 2.3368
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+Jmjd6 JMJD6 rs10473 1.1037
+Jmjd7 JMJD7 rs4923917 3.9318
+Pla2g4b JMJD7-PLA2G4B rs4923917 3.9318
+Jmjd8 JMJD8 rs4984685 1.1576
+Jmy JMY rs17234734 1.3169
+Josd1 JOSD1 rs3827358 1.3892
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+Jph1 JPH1 rs4738445 1.5945
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+Jrk JRK rs4545075 2.8182
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+Jup JUP rs4629018 1.5343
+Kalrn KALRN rs10512628 3.2568
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+Kank4 KANK4 rs10789108 1.5143
+Kansl1 KANSL1 rs4997213 1.8333
+Kansl1l KANSL1L rs263693 1.423
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+Kars KARS rs11648478 1.572
+Kat2a KAT2A rs12600570 2.6598
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+Myst3 KAT6A rs4236920 1.3002
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+Kcnab1 KCNAB1 rs4679774 1.5131
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+Kcnh8 KCNH8 rs1442199 2.6402
+Kcnip1 KCNIP1 rs953601 2.5667
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+Kcnj1 KCNJ1 rs17137982 0.9935
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+Kcnk5 KCNK5 rs6918122 1.8465
+Kcnk7 KCNK7 rs7946115 3.7696
+Kcnk9 KCNK9 rs199215 1.5575
+Kcnma1 KCNMA1 rs1516510 2.2023
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+Kcnmb3 KCNMB3 rs7645550 1.7251
+Kcnmb4 KCNMB4 rs12319193 1.862
+Kcnn1 KCNN1 rs17724502 2.0564
+Kcnn2 KCNN2 rs1366088 1.4201
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+Kcnn4 KCNN4 rs10421887 1.4602
+Kcnq1 KCNQ1 rs11824323 1.7194
+Kcnq1ot1 KCNQ1OT1 rs11824323 1.7194
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+Kcnu1 KCNU1 rs11994612 2.604
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+Kcp KCP rs4731523 1.5608
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+Krt31 KRT31 rs1047448 1.4973
+Krt32 KRT32 rs12451652 0.8333
+Krt33b KRT33B rs757102 0.9154
+Krt34 KRT34 rs1047448 1.4973
+Krt35 KRT35 rs8081580 1.1809
+Krt36 KRT36 rs8081580 1.1809
+Krt39 KRT39 rs2058736 1.6666
+Krt4 KRT4 rs1552756 1.0982
+Krt40 KRT40 rs7212171 2.3399
+Krt5 KRT5 rs7303411 1.432
+Krt6a KRT6A rs7303411 1.432
+Krt6b KRT6B rs582365 1.2035
+Krt7 KRT7 rs4761782 2.1099
+Krt71 KRT71 rs3847845 1.8119
+Krt72 KRT72 rs3847845 1.8119
+Krt73 KRT73 rs3847845 1.8119
+Krt74 KRT74 rs3847845 1.8119
+Krt75 KRT75 rs2857658 1.1128
+Krt76 KRT76 rs1552756 1.0982
+Krt77 KRT77 rs2269327 1.3857
+Krt78 KRT78 rs11170295 2.5768
+Krt79 KRT79 rs11170295 2.5768
+Krt8 KRT8 rs17746685 1.7675
+Krt80 KRT80 rs11608409 0.9337
+Krt81 KRT81 rs4761858 2.5314
+Krt82 KRT82 rs7969516 1.3778
+Krt83 KRT83 rs4761858 2.5314
+Krt84 KRT84 rs1732313 1.4001
+Krt85 KRT85 rs1732313 1.4001
+Krt86 KRT86 rs4761858 2.5314
+Krtap1-4 KRTAP1-1 rs7212171 2.3399
+2310040M23Rik KRTAP1-3 rs7212171 2.3399
+Gm9508 KRTAP10-12 rs10854463 2.269
+Gm7137 KRTAP10-2 rs11911565 1.4281
+Krtap10-10 KRTAP10-3 rs11911565 1.4281
+Krtap4-6 KRTAP10-4 rs1847283 2.0029
+Gm9736 KRTAP10-9 rs2329835 1.267
+Krtap11-1 KRTAP11-1 rs13046197 1.2381
+Krtap12-1 KRTAP12-2 rs10854463 2.269
+2310034C09Rik KRTAP13-1 rs4816362 1.6605
+EG546672 KRTAP13-2 rs4816362 1.6605
+Krtap15 KRTAP15-1 rs999597 0.9539
+Krtap19-7 KRTAP19-7 rs2212300 0.9662
+Krtap21-1 KRTAP21-1 rs9361276 0.9296
+Krtap6-2 KRTAP21-2 rs2833014 1.9278
+Krtap24-1 KRTAP24-1 rs9980400 0.8435
+Krtap26-1 KRTAP26-1 rs9980400 0.8435
+Krtap3-1 KRTAP3-1 rs7212171 2.3399
+Krtap3-2 KRTAP3-2 rs7212171 2.3399
+Krtap3-3 KRTAP3-3 rs7212171 2.3399
+Krtap4-7 KRTAP4-11 rs1848811 1.3421
+Krtap4-1 KRTAP4-12 rs8075810 1.08
+Gm11595 KRTAP4-2 rs397782 0.9122
+Krtap9-5 KRTAP4-3 rs397782 0.9122
+2300006N05Rik KRTAP4-9 rs1848811 1.3421
+Krtap5-5 KRTAP5-5 rs11605284 1.6592
+5430433J05Rik KRTAP7-1 rs2833096 1.9735
+Krtap8-1 KRTAP8-1 rs2833096 1.9735
+Krtcap2 KRTCAP2 rs12137850 1.0366
+Krtcap3 KRTCAP3 rs11675428 1.5809
+Krtdap KRTDAP rs7259430 1.0259
+Ksr1 KSR1 rs2289567 1.3615
+Ksr2 KSR2 rs11068596 3.3925
+Ktn1 KTN1 rs12432344 0.908
+2810422J05Rik KXD1 rs2057649 2.3598
+Ky KY rs9818188 1.6895
+Kynu KYNU rs12993448 1.2124
+L1td1 L1TD1 rs12120440 1.5408
+L2hgdh L2HGDH rs17716174 0.8716
+L3mbtl1 L3MBTL1 rs2867725 1.2164
+L3mbtl2 L3MBTL2 rs139477 0.9014
+L3mbtl3 L3MBTL3 rs4897364 3.2886
+D930040M24Rik L3MBTL4 rs541088 1.3738
+Lace1 LACE1 rs1575676 2.0633
+Lactb LACTB rs2652834 8.058
+Lactb2 LACTB2 rs17760081 1.6801
+Lad1 LAD1 rs3923236 1.4359
+Lag3 LAG3 rs2301333 2.0057
+Lair1 LAIR1 rs1645784 10.263
+Lalba LALBA rs2409044 1.9401
+Lama1 LAMA1 rs522402 2.5303
+Lama2 LAMA2 rs17742838 2.6115
+Lama3 LAMA3 rs4044149 1.0952
+Lama4 LAMA4 rs17073310 1.5669
+Lama5 LAMA5 rs2427283 0.9759
+Lamb1 LAMB1 rs2072208 2.0705
+Lamb3 LAMB3 rs17014889 1.7833
+6030469F06Rik LAMB4 rs3893568 1.2646
+Lamc1 LAMC1 rs10797830 2.3303
+Lamc2 LAMC2 rs3768600 2.9062
+Lamc3 LAMC3 rs1414402 3.3781
+Lamp3 LAMP3 rs6771207 1.4476
+6330527O06Rik LAMP5 rs8125541 0.9129
+Lamtor3 LAMTOR3 rs2866175 1.3746
+Lancl1 LANCL1 rs2287431 2.6531
+Lap3 LAP3 rs9994673 1.7854
+Laptm4a LAPTM4A rs7602766 1.9901
+Laptm4b LAPTM4B rs11783750 1.6436
+Laptm5 LAPTM5 rs1188357 0.8517
+Large LARGE rs5998902 3.5408
+Larp1 LARP1 rs17641488 1.7994
+Larp4 LARP4 rs4768928 1.0563
+Larp4b LARP4B rs9783235 1.3617
+Larp7 LARP7 rs17606550 1.7809
+Lars2 LARS2 rs6777962 1.8055
+Lasp1 LASP1 rs11872093 1.5653
+Lat LAT rs3922668 4.181
+Lat2 LAT2 rs17146094 1.1099
+Lats2 LATS2 rs551714 1.8102
+Lax1 LAX1 rs12240088 1.34
+Layn LAYN rs17585834 2.8049
+Lbh LBH rs11694633 1.7846
+Lbp LBP rs1739638 0.8955
+Lbr LBR rs6686405 1.3211
+Lbx1 LBX1 rs701839 1.14
+Lca5 LCA5 rs4706790 1.5248
+Lca5l LCA5L rs4816624 1.5758
+Lcat LCAT rs4986970 7.1735
+Sprrl1 LCE3B rs12034595 0.8119
+Lce3b LCE3C rs4357532 0.8386
+Gm9782 LCE3D rs9645407 1.1254
+Lce6a LCE6A rs7519200 1.5414
+Lck LCK rs6425795 0.8389
+Lclat1 LCLAT1 rs17009676 1.991
+Lcmt1 LCMT1 rs7188975 2.133
+Lcmt2 LCMT2 rs488756 0.9965
+Lcn3 LCN1 rs535 1.7354
+Lcn10 LCN10 rs945379 0.9718
+Lcn12 LCN12 rs11790360 2.3242
+Lcn2 LCN2 rs7047254 1.3447
+Lcn6 LCN6 rs945379 0.9718
+Lcn8 LCN8 rs884519 1.1314
+Lcn9 LCN9 rs13292721 1.1111
+Lcor LCOR rs2790466 1.1068
+Lcorl LCORL rs17526499 1.3344
+Lcp1 LCP1 rs9316186 2.1096
+Lcp2 LCP2 rs12652307 1.4073
+Lct LCT rs4988235 2.9151
+Lctl LCTL rs997116 2.1938
+Ldb1 LDB1 rs4917960 0.9735
+Ldb2 LDB2 rs158272 2.1068
+Ldb3 LDB3 rs2354360 1.8184
+Ldha LDHA rs4757645 1.815
+Ldhal6b LDHAL6B rs11631462 3.8368
+EG433229 LDHB rs1677073 1.2802
+Ldhd LDHD rs1423877 2.9363
+Ldlr LDLR rs4804147 3.3337
+Ldlrad1 LDLRAD1 rs7515322 1.0976
+Ldlrad2 LDLRAD2 rs10917042 1.1869
+Ldlrad3 LDLRAD3 rs11033502 1.7775
+Ldlrap1 LDLRAP1 rs11806326 1.3892
+Leap2 LEAP2 rs39830 0.9108
+Lect1 LECT1 rs17541412 1.1265
+Lect2 LECT2 rs2520439 1.205
+Lef1 LEF1 rs41498346 1.0497
+Lekr1 LEKR1 rs17442066 2.2549
+1700012F11Rik LELP1 rs12757933 1.9927
+Lemd1 LEMD1 rs7531012 1.1506
+Lemd2 LEMD2 rs3828783 3.9094
+Leng1 LENG1 rs2576452 2.9531
+Leng8 LENG8 rs1035452 1.2796
+Leng9 LENG9 rs1035452 1.2796
+Leo1 LEO1 rs2470617 2.584
+Lep LEP rs11772985 1.3109
+Lepr LEPR rs10889550 2.3966
+Lepre1 LEPRE1 rs4660657 1.0818
+Leprel1 LEPREL1 rs9845602 2.4206
+Leprel2 LEPREL2 rs12302904 1.2145
+Leprel4 LEPREL4 rs13412 2.1228
+Leprot LEPROT rs10889550 2.3966
+Leprotl1 LEPROTL1 rs11777028 0.8128
+Letm1 LETM1 rs11936140 1.3092
+Letmd1 LETMD1 rs7722 1.3831
+Lfng LFNG rs6979501 1.4836
+Lgals2 LGALS2 rs8137687 1.2763
+Lgals3 LGALS3 rs8004787 1.0041
+Lgals3bp LGALS3BP rs9916673 1.7402
+Lgals4 LGALS4 rs8100321 1.2167
+Lgals7 LGALS7 rs8100321 1.2167
+Lgals8 LGALS8 rs653737 1.1938
+Lgals9 LGALS9 rs4523974 2.6637
+1110067D22Rik LGALSL rs13391261 1.6728
+Lgi1 LGI1 rs4322326 1.4151
+Lgi2 LGI2 rs12642011 1.9876
+Lgi3 LGI3 rs2070687 1.1863
+Lgi4 LGI4 rs2073948 1.6556
+Lgmn LGMN rs1885750 2.9176
+Lgr4 LGR4 rs7935021 2.3407
+Lgr5 LGR5 rs724201 1.7455
+Lgr6 LGR6 rs788793 0.9996
+Lgsn LGSN rs13212744 1.1301
+Lhb LHB rs3764621 1.2762
+Lhcgr LHCGR rs4519576 1.3208
+Lhfp LHFP rs9566460 1.6123
+Lhfpl2 LHFPL2 rs10942859 1.691
+Lhfpl3 LHFPL3 rs7776632 2.5829
+Lhfpl4 LHFPL4 rs17050371 1.2469
+Lhfpl5 LHFPL5 rs2766595 1.2328
+Lhpp LHPP rs748129 2.67
+Lhx1 LHX1 rs9897257 2.1655
+Lhx2 LHX2 rs7867942 2.1025
+Lhx3 LHX3 rs12551289 1.4248
+Lhx4 LHX4 rs357047 2.8133
+Lhx5 LHX5 rs12815824 1.1683
+Lhx6 LHX6 rs3793617 1.7533
+Lhx8 LHX8 rs17622436 1.4397
+Lhx9 LHX9 rs6671069 1.4779
+Lias LIAS rs2687975 2.9112
+Lif LIF rs2078856 1.9527
+Lifr LIFR rs1562134 2
+Lig1 LIG1 rs1019785 2.3565
+Lig3 LIG3 rs1482103 1.1642
+Lig4 LIG4 rs9514825 2.5979
+Pira4 LILRA2 rs17670440 0.8595
+Lilra5 LILRA5 rs386000 15.368
+Lilrb3 LILRB3 rs6509859 4.6117
+Gm15448 LILRB5 rs443501 9.2486
+Lim2 LIM2 rs11671525 1.5666
+Lima1 LIMA1 rs7972465 1.8011
+Limch1 LIMCH1 rs13142413 2.135
+Limd1 LIMD1 rs17262464 1.0524
+Limd2 LIMD2 rs721575 1.0399
+Lime1 LIME1 rs6011066 2.1665
+Limk1 LIMK1 rs1091813 1.4938
+Limk2 LIMK2 rs9621212 1.9431
+Lims1 LIMS1 rs6716426 1.4923
+Lims2 LIMS2 rs6732660 1.3527
+Lin28a LIN28A rs11247954 2.6887
+Lin28b LIN28B rs314279 0.971
+Lin52 LIN52 rs8015012 1.3654
+Lin54 LIN54 rs3214024 1.2199
+Lin7a LIN7A rs17731827 1.2937
+Lin7b LIN7B rs1009214 0.9194
+Lin7c LIN7C rs17309825 2.7873
+Lin9 LIN9 rs12122761 1.1363
+Ncrna00085 LINC00085 rs11666027 1.4706
+D2Bwg1423e LINC00094 rs7031860 0.8996
+1500011K16Rik LINC00116 rs3789726 1.0534
+Gm8154 LINC00167 rs12795082 1.8576
+1700017D01Rik LINC00301 rs586571 1.4981
+2810008D09Rik LINC00338 rs9896525 2.1002
+C130071C03Rik LINC00461 rs6894196 1.6544
+C030010C08Rik LINC00476 rs10124062 1.5252
+2810055G20Rik LINC00478 rs11909717 1.9355
+Gm561 LINC00493 rs6132084 1.7612
+1520401A03Rik LINC00514 rs12935188 1.6282
+A930011O12Rik LINC00599 rs12541159 1.6759
+Mug4-ps LINC00612 rs1805661 1.0128
+2810011L19Rik LINC00617 rs11848308 1.3018
+Lingo1 LINGO1 rs11072661 1.2776
+Lingo2 LINGO2 rs874183 2.3821
+Lingo3 LINGO3 rs8100329 1.5406
+Lingo4 LINGO4 rs12045886 0.8801
+Lins LINS rs8042302 1.7233
+Lip1 LIPA rs10887930 2.5667
+Lipc LIPC rs1532085 95.535
+Lipf LIPF rs303514 1.4693
+Lipg LIPG rs8090363 23.129
+Liph LIPH rs3849569 1.1585
+D930038D03Rik LIPI rs7283986 2.2907
+Lipk LIPK rs2839909 1.6216
+Lipm LIPM rs1441733 1.2124
+Lipn LIPN rs2839909 1.6216
+Litaf LITAF rs9923564 2.0412
+Lix1 LIX1 rs6556945 1.3579
+Lix1l LIX1L rs9424932 2.1283
+Llgl1 LLGL1 rs9899249 1.7943
+Llgl2 LLGL2 rs4789219 1.5355
+Llph LLPH rs11176081 0.9678
+Lman1 LMAN1 rs10503024 0.863
+Lman1l LMAN1L rs872263 1.7523
+Lman2 LMAN2 rs6860069 0.8228
+Lman2l LMAN2L rs3731940 2.3096
+Lmbr1 LMBR1 rs10247927 2.4708
+Lmbr1l LMBR1L rs7969967 1.1321
+Lmbrd1 LMBRD1 rs17765037 1.9129
+Lmbrd2 LMBRD2 rs2270909 1.0466
+Lmcd1 LMCD1 rs165177 1.0769
+Lmf1 LMF1 rs402605 2.819
+Lmf2 LMF2 rs6010122 1.1581
+Lmna LMNA rs6686943 1.8768
+Lmnb1 LMNB1 rs12515582 1.2823
+Lmnb2 LMNB2 rs11084940 1.8297
+Lmo1 LMO1 rs11041831 1.1643
+Lmo2 LMO2 rs6484674 1.8993
+Lmo3 LMO3 rs16912067 1.6388
+Lmo4 LMO4 rs3766023 0.9504
+Lmo7 LMO7 rs7988966 1.8114
+Lmod1 LMOD1 rs1043823 2.008
+Lmod2 LMOD2 rs1073682 0.934
+Lmod3 LMOD3 rs937862 0.9194
+Lmtk2 LMTK2 rs6969321 2.1576
+Lmtk3 LMTK3 rs1799257 3.3125
+Lmx1a LMX1A rs12742667 1.9918
+Lmx1b LMX1B rs2235055 1.9855
+Gm19797 LNP1 rs2316701 1.3512
+Lnpep LNPEP rs316211 1.4779
+Lnx1 LNX1 rs2117600 2.1832
+Lnx2 LNX2 rs11838661 2.1317
+Gm11837 LOC100127983 rs17721948 2.4013
+4833439F03Rik LOC100128531 rs6004417 1.1805
+Gm5547 LOC100129269 rs11102320 1.5025
+4933406K04Rik LOC100129345 rs234147 1.1792
+2700089E24Rik LOC100129361 rs17817150 1.2598
+4930538K18Rik LOC100129924 rs11809083 1.0207
+Gm9047 LOC100130705 rs4731523 1.5608
+BC046401 LOC100131496 rs6018397 2.4166
+Gm6422 LOC100288842 rs12376644 1.0934
+Gm20337 LOC100289511 rs2332166 1.3282
+E230008N13Rik LOC100499484-C9ORF174 rs10981280 0.8726
+1700018C11Rik LOC100505478 rs10759726 1.4135
+1700034E13Rik LOC100505841 rs11241629 1.0893
+1110001A16Rik LOC100505876 rs3845783 1.7011
+BC030870 LOC100505989 rs6832922 2.0761
+C630016N16Rik LOC100506469 rs12460932 1.828
+4930429F24Rik LOC100506804 rs654428 1.1704
+AI428898 LOC148413 rs2649588 1.2546
+Gm853 LOC149086 rs10798867 0.9248
+4933434I20Rik LOC152586 rs17005970 0.994
+Zfp783 LOC155060 rs11760996 1.8477
+Gm20365 LOC200726 rs16838403 1.9205
+D730039F16Rik LOC253039 rs10818476 1.0929
+AU018091 LOC284379 rs10403457 1.0925
+Gm5468 LOC285696 rs9885035 1.5435
+Clec2f-ps LOC374443 rs4763738 1.5688
+Gm10830 LOC400043 rs1872636 1.7981
+4930451C15Rik LOC400891 rs450741 1.9855
+2610001J05Rik LOC401397 rs2253183 2.6212
+Gm5480 LOC440335 rs12444397 1.5901
+Cd163l1 LOC619207 rs7077457 1.994
+Gm16464 LOC641367 rs17701305 1.2559
+1700012B09Rik LOC643037 rs13447608 1.4692
+1700026D11Rik LOC643406 rs6085179 2.2805
+Gm6537 LOC643669 rs10405150 2.3972
+Gm7271 LOC644145 rs895615 1.2145
+1100001G20Rik LOC645638 rs3863505 0.983
+BC048562 LOC646498 rs4499638 2.1233
+2310014L17Rik LOC646862 rs2289143 0.8462
+Krtap6-2 LOC647264 rs3922925 1.3306
+4933424G06Rik LOC728537 rs7425883 1.3094
+4930404A10Rik LOC728637 rs17616702 1.5047
+4930549C01Rik LOC729059 rs4654831 0.9747
+Gm16039 LOC729852 rs12702653 2.3549
+4930469G21Rik LOC730159 rs12081220 1.757
+2610020H08Rik LOC81691 rs12597620 1.3437
+Loh12cr1 LOH12CR1 rs12297085 1.9197
+Lonrf1 LONRF1 rs6995647 1.1315
+Lonrf2 LONRF2 rs11674945 1.4786
+Lor LOR rs1865233 1.0566
+Lox LOX rs17148773 1.0959
+Loxhd1 LOXHD1 rs328144 1.9348
+Loxl1 LOXL1 rs1530168 1.158
+Loxl2 LOXL2 rs10101089 2.0993
+Loxl4 LOXL4 rs12778335 1.5061
+Lpar1 LPAR1 rs2192591 1.8735
+Lpar2 LPAR2 rs2304129 1.1126
+Lpar3 LPAR3 rs11161482 1.3531
+Lpar5 LPAR5 rs11064303 1.8283
+Lpar6 LPAR6 rs17071686 1.8066
+Lpcat1 LPCAT1 rs456774 2.1221
+Lpcat2 LPCAT2 rs9934904 1.2256
+Lpcat3 LPCAT3 rs17788859 0.8775
+Lpcat4 LPCAT4 rs11631776 0.9602
+Lpgat1 LPGAT1 rs1663171 0.8453
+Lphn1 LPHN1 rs10417504 0.9646
+Lphn2 LPHN2 rs7524907 2.4171
+Lphn3 LPHN3 rs10008278 3.6985
+Lpin1 LPIN1 rs13001573 1.4881
+Lpin2 LPIN2 rs1790994 1.6323
+Lpin3 LPIN3 rs8118245 2.2438
+Lpl LPL rs12678919 97.013
+Lpo LPO rs2240262 1.0285
+Lpp LPP rs6771908 2.5393
+1110054M08Rik LPP-AS2 rs344947 1.2557
+E130309F12Rik LPPR1 rs719284 1.5984
+BC018242 LPPR2 rs3816864 1.3527
+BC005764 LPPR3 rs4536588 1.8794
+D3Bwg0562e LPPR4 rs4330940 1.5626
+4833424O15Rik LPPR5 rs1482144 1.9658
+Lpxn LPXN rs12808433 1.2398
+Lrat LRAT rs12507608 1.5704
+Lrba LRBA rs17027158 1.5039
+Lrch1 LRCH1 rs7330387 1.8881
+Lrch3 LRCH3 rs552427 1.7737
+Gm1679 LRCOL1 rs7397677 1.4143
+Lrfn1 LRFN1 rs7247086 1.0439
+Lrfn2 LRFN2 rs11969936 1.6098
+Lrfn5 LRFN5 rs17112270 1.5213
+Lrguk LRGUK rs11971451 1.7397
+4933421E11Rik LRIF1 rs12567924 1.21
+Lrig1 LRIG1 rs332339 3.6238
+Lrig2 LRIG2 rs1216781 1.1141
+Lrig3 LRIG3 rs17533647 1.233
+Lrit1 LRIT1 rs11595934 1.6052
+Lrit2 LRIT2 rs11595934 1.6052
+Lrit3 LRIT3 rs1002989 0.8739
+Lrmp LRMP rs7973136 1.7508
+Lrp1 LRP1 rs7301155 4.0166
+Lrp10 LRP10 rs2295904 1.234
+Lrp11 LRP11 rs10428740 1.3018
+Lrp12 LRP12 rs3018438 2.2816
+Lrp1b LRP1B rs16855058 2.0413
+Lrp2 LRP2 rs12470146 2.2333
+Lrp2bp LRP2BP rs2293367 1.9678
+Lrp3 LRP3 rs8111018 1.5372
+Lrp4 LRP4 rs11039024 15.606
+Lrp5 LRP5 rs450112 1.1531
+Lrp6 LRP6 rs1012672 1.2574
+Lrp8 LRP8 rs11808280 2.7235
+Lrpap1 LRPAP1 rs3468 1.9602
+Lrpprc LRPPRC rs7580868 1.2075
+Lrr1 LRR1 rs17701604 1.2842
+Lrrc1 LRRC1 rs6919015 1.0822
+Lrrc10 LRRC10 rs710764 2.3619
+Lrrc10b LRRC10B rs3802952 0.9066
+Lrrc14b LRRC14B rs3810870 2.5642
+Lrrc15 LRRC15 rs923931 2.465
+Lrrc16a LRRC16A rs9379772 3.059
+Lrrc16b LRRC16B rs8005834 1.0509
+Lrrc17 LRRC17 rs12113586 0.8811
+Lrrc18 LRRC18 rs1904595 1.4414
+Lrrc2 LRRC2 rs4683243 2.5255
+Lrrc20 LRRC20 rs12780221 1.6722
+Lrrc21 LRRC21 rs11595934 1.6052
+Lrrc23 LRRC23 rs11064445 0.8184
+Lrrc25 LRRC25 rs1054564 1.5943
+Lrrc27 LRRC27 rs2492643 1.1329
+Lrrc28 LRRC28 rs8030479 2.813
+Lrrc29 LRRC29 rs2233455 8.8128
+Lrrc3 LRRC3 rs2838564 1.2233
+Lrrc31 LRRC31 rs7635067 1.0818
+AI426318 LRRC32 rs2155223 1.6253
+Lrrc33 LRRC33 rs7629845 1.5361
+Lrrc36 LRRC36 rs9922615 6.7781
+Lrrc37a LRRC37A3 rs2684623 1.1205
+Lrrc38 LRRC38 rs3845607 1.2559
+Lrrc3b LRRC3B rs4973738 1.9303
+Lrrc4 LRRC4 rs6467157 1.1963
+Lrrc40 LRRC40 rs17131224 2.6059
+Lrrc41 LRRC41 rs3766248 1.1321
+Lrrc42 LRRC42 rs12092723 1.2105
+Lrrc43 LRRC43 rs11060167 2.3563
+Lrrc45 LRRC45 rs6502048 1.1675
+Lrrc46 LRRC46 rs11654648 3.8005
+Lrrc47 LRRC47 rs1181872 3.4342
+Lrrc49 LRRC49 rs11072239 1.2843
+Lrrc4b LRRC4B rs1862479 1.4725
+Lrrc4c LRRC4C rs11035994 3.0242
+Lrrc52 LRRC52 rs6700280 1.1226
+Lrrc55 LRRC55 rs11228933 1.4195
+Lrrc56 LRRC56 rs3740651 2.1787
+Lrrc57 LRRC57 rs10454692 1.3805
+Lrrc58 LRRC58 rs3914498 2.4559
+Lrrc59 LRRC59 rs7221450 1.5198
+Lrrc6 LRRC6 rs17663290 1.1391
+Lrrc61 LRRC61 rs11974980 0.8167
+Lrrc63 LRRC63 rs2209091 1.5884
+Lrrc66 LRRC66 rs225162 0.9735
+Lrrc7 LRRC7 rs17131224 2.6059
+Lrrc70 LRRC70 rs4526061 0.9378
+4933430H15Rik LRRC71 rs12566888 1.0995
+1700108M19Rik LRRC72 rs9639262 1.7133
+Gm88 LRRC73 rs1150798 2.7967
+Lrrc8a LRRC8A rs9031 1.0474
+Lrrc8b LRRC8B rs359906 1.0336
+Lrrc8c LRRC8C rs11585569 1.0592
+Lrrc8d LRRC8D rs7517253 5.3463
+Lrrc8e LRRC8E rs4804833 3.2673
+Lrrcc1 LRRCC1 rs11777917 1.2139
+Lrrfip1 LRRFIP1 rs1151800 1.3346
+Lrrfip2 LRRFIP2 rs9820888 1.8483
+Lrriq1 LRRIQ1 rs1973816 2.0761
+Lrriq3 LRRIQ3 rs10493535 1.1007
+Lrrk1 LRRK1 rs11631397 1.9465
+Lrrk2 LRRK2 rs11831092 1.514
+Lrrn1 LRRN1 rs17037524 1.9435
+Lrrn2 LRRN2 rs11240237 0.8303
+Lrrn3 LRRN3 rs214893 1.1016
+Lrrn4 LRRN4 rs6076910 1.075
+Lrrn4cl LRRN4CL rs7386 2.2897
+Lrrtm1 LRRTM1 rs9808084 4.0779
+Lrrtm2 LRRTM2 rs288027 0.8125
+Lrrtm3 LRRTM3 rs7897388 1.8274
+Lrrtm4 LRRTM4 rs7580390 2.7062
+Lrsam1 LRSAM1 rs2243464 1.2376
+AI841794 LRTM2 rs11062008 1.4878
+Lrwd1 LRWD1 rs3823656 1.0313
+Lsamp LSAMP rs1993733 3.1237
+Lsg1 LSG1 rs1705971 1.4927
+Lsm1 LSM1 rs2517390 1.1004
+Lsm10 LSM10 rs6688171 1.6786
+Lsm11 LSM11 rs10040565 2.4196
+2600001B17Rik LSM12 rs2523166 1.5691
+Lsm14a LSM14A rs10520211 1.1717
+Lsm14b LSM14B rs13043478 1.35
+Lsm2 LSM2 rs707938 2.3215
+Lsm3 LSM3 rs3731087 1.2156
+Lsm4 LSM4 rs12608504 2.3857
+Lsm5 LSM5 rs1817047 1.1553
+Lsm6 LSM6 rs4835289 1.4935
+Lsm7 LSM7 rs8100329 1.5406
+Lsp1 LSP1 rs4264135 1.5376
+Lss LSS rs9637215 1.6449
+Lst1 LST1 rs9469027 3.0645
+Lta LTA rs9469027 3.0645
+Lta4h LTA4H rs2540471 1.8713
+Ltb LTB rs9469027 3.0645
+Ltb4r1 LTB4R rs3940231 1.7162
+Ltb4r2 LTB4R2 rs3940231 1.7162
+Ltbp1 LTBP1 rs7423562 1.9083
+Ltbp2 LTBP2 rs8017691 1.1233
+Ltbp3 LTBP3 rs10896016 1.2016
+Ltbp4 LTBP4 rs890935 1.2218
+Ltbr LTBR rs2041375 1.6202
+Ltc4s LTC4S rs166624 1.5109
+Ltf LTF rs4234463 2.3551
+Ltk LTK rs3743031 4.2148
+Ltn1 LTN1 rs2274800 1.248
+Ltv1 LTV1 rs4896673 1.0157
+Luc7l LUC7L rs3918352 2.4299
+Luc7l2 LUC7L2 rs6965212 0.8557
+Luc7l3 LUC7L3 rs12452333 1.6817
+1520402A15Rik LURAP1 rs3845301 2.7537
+D4Bwg0951e LURAP1L rs1326795 1.7523
+Luzp1 LUZP1 rs763310 0.9129
+Luzp2 LUZP2 rs4604884 2.6519
+Ly6e LY6E rs4424237 1.3555
+Ly6g5b LY6G5B rs3117583 4.5605
+Ly6g5c LY6G5C rs3117583 4.5605
+Ly6g6c LY6G6C rs400547 2.5546
+Ly6g6d LY6G6D rs400547 2.5546
+Ly6g6e LY6G6E rs9267547 2.5296
+Ly6g6f LY6G6F rs400547 2.5546
+Ly6h LY6H rs12680172 2.5223
+Ly75 LY75 rs12469306 1.7737
+Ly86 LY86 rs11964772 2.4868
+Ly9 LY9 rs497490 1.0824
+Ly96 LY96 rs6988849 0.8847
+Lyar LYAR rs10937817 3.4235
+Lyg1 LYG1 rs4851200 1.0555
+Lyg2 LYG2 rs6542873 0.8213
+Lyn LYN rs6985703 1.5258
+Lynx1 LYNX1 rs2585135 0.9473
+Lypd1 LYPD1 rs10178695 2.191
+Lypd2 LYPD2 rs2585135 0.9473
+Lypd3 LYPD3 rs11668793 1.708
+Lypd4 LYPD4 rs3752172 1.6449
+Lypd5 LYPD5 rs10421887 1.4602
+Lypd6 LYPD6 rs16827053 1.3389
+Lypd6b LYPD6B rs11889139 1.8489
+Lyplal1 LYPLAL1 rs11118229 3.5832
+Lyrm1 LYRM1 rs382339 1.1857
+Lyrm2 LYRM2 rs9359859 1.833
+Lyrm4 LYRM4 rs12660749 3.3705
+Lysmd1 LYSMD1 rs2067606 1.1387
+Lysmd2 LYSMD2 rs2414116 1.4126
+Lysmd3 LYSMD3 rs10942595 0.9654
+Lysmd4 LYSMD4 rs897074 1.5589
+Lyst LYST rs7543128 2.3266
+Lyve1 LYVE1 rs10840433 1.6639
+Lyzs LYZ rs654721 1.2406
+Lyzl1 LYZL1 rs16930100 0.9255
+Lyzl4 LYZL4 rs6795743 2.2265
+Lztfl1 LZTFL1 rs2531747 0.9747
+Lztr1 LZTR1 rs178274 1.8713
+Lzts1 LZTS1 rs4436141 2.5022
+Lzts2 LZTS2 rs735137 1.5547
+M6pr M6PR rs4883201 2.762
+Mab21l3 MAB21L3 rs12410137 1.2253
+Macc1 MACC1 rs2110328 1.7224
+Macf1 MACF1 rs4660214 9.0702
+Macrod1 MACROD1 rs11231698 3.6358
+Macrod2 MACROD2 rs6034087 2.7375
+Mad1l1 MAD1L1 rs6944188 2.0033
+Mad2l1 MAD2L1 rs10446730 1.848
+Mad2l2 MAD2L2 rs12760507 1.3172
+Madcam1 MADCAM1 rs3745924 1.1985
+Madd MADD rs3824866 15.777
+Mael MAEL rs16858259 1.0696
+Maf MAF rs2287974 1.7768
+Mafa MAFA rs7005838 1.6349
+Mafb MAFB rs2425418 1.1118
+Maff MAFF rs132985 2.9714
+Mafk MAFK rs3735660 1.2261
+Mag MAG rs41407744 1.3203
+4933402E13Rik MAGEB6 rs11749757 0.8392
+6430590I03Rik MAGEF1 rs4299462 1.5839
+Magel2 MAGEL2 rs4480754 2.0457
+Magi1 MAGI1 rs17074209 2.263
+Magi2 MAGI2 rs491394 2.257
+Magi3 MAGI3 rs11102621 1.1487
+Magoh MAGOH rs7554022 1.6326
+Magohb MAGOHB rs4763527 1.5361
+Mak MAK rs560869 1.3996
+Mak16 MAK16 rs2581897 1.3069
+Mal MAL rs3113002 1.7455
+Mal2 MAL2 rs6997548 1.6115
+2410003K15Rik MALSU1 rs3087888 1.902
+Malt1 MALT1 rs7230211 1.2559
+Mamdc2 MAMDC2 rs17088691 1.3856
+Mamdc4 MAMDC4 rs2811749 1.5594
+Maml1 MAML1 rs166624 1.5109
+Maml2 MAML2 rs948164 1.7167
+Maml3 MAML3 rs17050970 1.7342
+Mamstr MAMSTR rs281384 2.5312
+Man1a MAN1A1 rs3819726 1.5432
+Man1a2 MAN1A2 rs3767797 3.6105
+Man1b1 MAN1B1 rs7466635 1.0547
+Man1c1 MAN1C1 rs12068771 1.823
+Man2a1 MAN2A1 rs6594405 1.2859
+Man2a2 MAN2A2 rs2677737 0.8492
+Man2b2 MAN2B2 rs4302525 1.3547
+Manba MANBA rs13113605 3.857
+Manbal MANBAL rs10470023 1.3849
+Manea MANEA rs13192906 1.731
+Gm50 MANEAL rs582811 1.2912
+Manf MANF rs10510757 2.5248
+Mansc1 MANSC1 rs12297085 1.9197
+Gm5887 MANSC4 rs4931412 1.3064
+Mtap1a MAP1A rs2439850 1.1699
+Mtap1b MAP1B rs10037731 2.369
+Map1lc3a MAP1LC3A rs6088515 2.0755
+Map1lc3b MAP1LC3B rs4240793 1.823
+Mtap1s MAP1S rs8106359 2.4874
+Mtap2 MAP2 rs6435526 1.95
+Map2k1 MAP2K1 rs9920038 1.0187
+Map2k2 MAP2K2 rs2890215 1.9066
+Map2k3 MAP2K3 rs9905187 1.8996
+Map2k4 MAP2K4 rs17613899 0.8887
+Map2k5 MAP2K5 rs938875 4.3759
+Map2k6 MAP2K6 rs756942 1.1263
+Map2k7 MAP2K7 rs4804833 3.2673
+Map3k1 MAP3K1 rs40497 1.8392
+Map3k10 MAP3K10 rs874269 1.2912
+Map3k11 MAP3K11 rs12801636 5.1298
+Map3k12 MAP3K12 rs1716966 2.6828
+Map3k13 MAP3K13 rs10937217 1.5003
+Map3k14 MAP3K14 rs4792849 1.7467
+Map3k2 MAP3K2 rs4150478 1.284
+Map3k3 MAP3K3 rs12940055 1.5456
+Map3k4 MAP3K4 rs1246809 1.3849
+Map3k5 MAP3K5 rs12529555 2.6794
+Map3k7 MAP3K7 rs10485120 1.6576
+Map3k8 MAP3K8 rs1754239 2.0575
+Map3k9 MAP3K9 rs12586825 1.2988
+Mtap4 MAP4 rs7638388 2.6857
+Map4k1 MAP4K1 rs1004795 1.5542
+Map4k2 MAP4K2 rs524386 2.4167
+Map4k3 MAP4K3 rs11688352 0.9751
+Map4k4 MAP4K4 rs6761085 1.3005
+Map4k5 MAP4K5 rs7154254 1.4204
+Mtap6 MAP6 rs4016408 1.8742
+Mtap7 MAP7 rs6934308 1.3233
+Mtap7d1 MAP7D1 rs562929 2.0178
+Mtap9 MAP9 rs7673215 1.8133
+Mapk1 MAPK1 rs7292106 1.2674
+Mapk10 MAPK10 rs17417758 4.9176
+Mapk11 MAPK11 rs17248301 2.184
+Mapk12 MAPK12 rs17248301 2.184
+Mapk13 MAPK13 rs916344 1.9223
+Mapk14 MAPK14 rs916344 1.9223
+Mapk15 MAPK15 rs3204170 1.044
+Mapk1ip1l MAPK1IP1L rs17128145 0.9101
+Mapk4 MAPK4 rs12959952 1.7637
+Mapk6 MAPK6 rs11070879 1.3801
+Mapk7 MAPK7 rs3866958 1.3811
+Mapk8 MAPK8 rs12269533 1.6998
+Mapk8ip1 MAPK8IP1 rs2292910 3.3325
+Mapk8ip2 MAPK8IP2 rs9628228 1.2908
+Mapk8ip3 MAPK8IP3 rs8059926 1.3165
+Mapk9 MAPK9 rs6601097 1.5158
+Mapkap1 MAPKAP1 rs2173485 0.9809
+Mapkapk2 MAPKAPK2 rs4845133 1.8791
+Mapkapk5 MAPKAPK5 rs11066056 0.8108
+Mapkbp1 MAPKBP1 rs4923917 3.9318
+Mapre1 MAPRE1 rs910085 1.9412
+Mapre2 MAPRE2 rs589787 2.1372
+Mapre3 MAPRE3 rs11678188 2.1821
+Mapt MAPT rs962885 1.4825
+Mosc1 MARC1 rs2642438 4.871
+Mosc2 MARC2 rs2642438 4.871
+March1 MARCH1 rs41469952 1.3512
+March10 MARCH10 rs8067624 1.0408
+March11 MARCH11 rs7736853 1.6295
+March2 MARCH2 rs1808536 6.738
+March3 MARCH3 rs7723656 1.1353
+March4 MARCH4 rs4674074 2.0008
+March6 MARCH6 rs16884614 2.5735
+March8 MARCH8 rs10900223 6.862
+March9 MARCH9 rs8176345 1.3226
+Marcks MARCKS rs688338 3.8935
+Marcksl1 MARCKSL1 rs8176781 1.3647
+Marco MARCO rs11685286 1.4693
+Mark1 MARK1 rs6656145 2.5558
+Mark2 MARK2 rs10736731 1.1851
+Mark3 MARK3 rs6575984 1.7555
+Mark4 MARK4 rs7260359 2.2874
+Mars MARS rs3741414 7.7857
+Mars2 MARS2 rs13015772 2.0241
+Marveld1 MARVELD1 rs12770904 1.1089
+Marveld2 MARVELD2 rs10077413 1.4527
+Marveld3 MARVELD3 rs1345868 2.9059
+Mas1 MAS1 rs6918897 1.6925
+Masp1 MASP1 rs6785391 1.4152
+Masp2 MASP2 rs9430163 1.6038
+Mast2 MAST2 rs2275426 3.0606
+Mast3 MAST3 rs11673460 2.0003
+Mast4 MAST4 rs10041441 2.1266
+Mastl MASTL rs3802526 1.0312
+Mat1a MAT1A rs2789668 1.1612
+Mat2b MAT2B rs6869277 1.6726
+Matn1 MATN1 rs1188357 0.8517
+Matn2 MATN2 rs2255464 1.5931
+Matn3 MATN3 rs10197385 1.0487
+Matn4 MATN4 rs6130803 1.7865
+Matr3 MATR3 rs17131975 2.1299
+Mavs MAVS rs914294 1.4766
+Max MAX rs7143495 1.7113
+Mb MB rs1894468 1.757
+E330016A19Rik MB21D1 rs7763873 1.5272
+1600021P15Rik MB21D2 rs12485900 1.764
+Mbd1 MBD1 rs1562004 1.4932
+Mbd2 MBD2 rs17462973 1.7737
+Mbd3 MBD3 rs10407104 1.7423
+Mbd3l1 MBD3L1 rs2972594 1.8567
+Mbd4 MBD4 rs13326661 1.5825
+Mbd5 MBD5 rs2123616 2.8386
+Mbl2 MBL2 rs11599622 1.4559
+Mblac1 MBLAC1 rs4134895 1.1281
+Mblac2 MBLAC2 rs3763070 1.6668
+Mbnl1 MBNL1 rs7633673 2.27
+Mbnl2 MBNL2 rs9584567 1.634
+Mboat1 MBOAT1 rs2472763 1.8821
+Mboat2 MBOAT2 rs13396431 0.9314
+Mboat7 MBOAT7 rs2576452 2.9531
+Mbp MBP rs7232502 2.3318
+Mbtps1 MBTPS1 rs9935658 1.3033
+Mc1r MC1R rs3212361 1.5267
+Mc2r MC2R rs9949192 0.9492
+Mc3r MC3R rs1326024 1.8407
+Mc4r MC4R rs1943226 4.9961
+Mc5r MC5R rs12606372 1.2569
+Mcam MCAM rs2511837 1.4466
+Mcat MCAT rs5759165 1.5955
+Mcc MCC rs6882155 2.7587
+Mccc1 MCCC1 rs7635560 1.4019
+Mccc2 MCCC2 rs41393950 1.6619
+Mcee MCEE rs11126317 2.3739
+Mcf2l MCF2L rs1475931 1.9259
+Mcfd2 MCFD2 rs6544933 1.4385
+Mchr1 MCHR1 rs133051 0.8438
+Mcm10 MCM10 rs12257536 1.0397
+Mcm2 MCM2 rs13082666 1.9531
+Mcm3 MCM3 rs10807440 1.7233
+Mcm3ap MCM3AP rs2839162 1.3922
+Mcm5 MCM5 rs735267 1.6946
+Mcm6 MCM6 rs4988235 2.9151
+Mcm7 MCM7 rs4134895 1.1281
+Mcm8 MCM8 rs6053819 0.8645
+Mcm9 MCM9 rs7775275 3.1757
+6030422M02Rik MCMDC2 rs4737815 1.232
+Mcoln1 MCOLN1 rs583984 1.459
+Mcoln2 MCOLN2 rs11161482 1.3531
+Mcoln3 MCOLN3 rs17371282 1.0526
+Mcph1 MCPH1 rs11997958 1.9686
+Mcrs1 MCRS1 rs7131915 1.0174
+Mctp1 MCTP1 rs26999 2.0067
+Gm489 MCTP2 rs8040258 2.4383
+Ccdc109a MCU rs2166336 0.95
+Mdc1 MDC1 rs2285320 1.5852
+Mdfi MDFI rs2746179 2.0595
+Mdfic MDFIC rs12673776 1.5786
+Mdga1 MDGA1 rs9689490 2.0344
+Mdga2 MDGA2 rs17562911 2.4105
+Mdh1 MDH1 rs2121350 2.8969
+Mdh1b MDH1B rs12993580 1.7846
+Mdh2 MDH2 rs1639617 0.87
+Mdk MDK rs11038871 2.3187
+Mdm1 MDM1 rs1038164 4.7673
+Mdm2 MDM2 rs11177368 1.3368
+Mdm4 MDM4 rs7518788 1.997
+Mdn1 MDN1 rs9359859 1.833
+Mdp1 MDP1 rs1950496 1.0338
+Me1 ME1 rs1180243 1.757
+Me2 ME2 rs8085716 1.0906
+Me3 ME3 rs3824953 2.4237
+Mea1 MEA1 rs2274517 3.8642
+Meaf6 MEAF6 rs11589893 1.9674
+Mecom MECOM rs16853270 1.6899
+Mecr MECR rs3103882 3.3572
+Med1 MED1 rs11655550 2.2712
+D13Wsu50e MED10 rs271402 1.0737
+Med11 MED11 rs4790224 1.7959
+Med12l MED12L rs1231520 1.7027
+Med13l MED13L rs2392972 2.3514
+Med16 MED16 rs12460890 1.2608
+Med21 MED21 rs10842825 1.1274
+Med22 MED22 rs11244086 2.4186
+Med23 MED23 rs2781659 2.3522
+Med24 MED24 rs2302776 3.6582
+Med25 MED25 rs17370523 1.6349
+Med26 MED26 rs17796594 2.0286
+Med27 MED27 rs10121309 1.4549
+Med29 MED29 rs36265 1.852
+Med30 MED30 rs1115177 1.5029
+Med31 MED31 rs4796519 2.1858
+Med4 MED4 rs9567986 1.0881
+Med6 MED6 rs2153113 1.217
+Med7 MED7 rs3734021 1.2107
+Med8 MED8 rs839756 1.3902
+Med9 MED9 rs16961756 1.584
+Mef2a MEF2A rs7173943 1.8058
+Mef2b MEF2B rs4808921 1.3946
+2310045N01Rik MEF2BNB rs2238663 0.8595
+Mef2b MEF2BNB-MEF2B rs4808921 1.3946
+Mef2c MEF2C rs6894196 1.6544
+Mef2d MEF2D rs16837415 1.0642
+Mefv MEFV rs224207 2.6156
+Meg3 MEG3 rs10147988 1.5223
+Megf10 MEGF10 rs10519939 2.4721
+Megf11 MEGF11 rs12904203 3.2952
+Megf6 MEGF6 rs4648505 1.2943
+Megf9 MEGF9 rs10818474 1.5394
+Meis1 MEIS1 rs2049019 2.372
+Meis2 MEIS2 rs7161782 2.1432
+Meis3 MEIS3 rs4802349 2.3168
+Memo1 MEMO1 rs17817912 2.7625
+Men1 MEN1 rs524386 2.4167
+Meox1 MEOX1 rs8081084 1.3111
+Meox2 MEOX2 rs41343244 2.4678
+Mep1a MEP1A rs6921815 1.0503
+Mep1b MEP1B rs233223 1.5566
+Mepce MEPCE rs17317214 0.8428
+Mepe MEPE rs2732167 2.1022
+Mertk MERTK rs12989536 1.8871
+Mesdc1 MESDC1 rs12148207 1.2774
+Mesp1 MESP1 rs4932248 1.87
+Mesp2 MESP2 rs4932248 1.87
+Mest MEST rs12706933 1.0979
+Met MET rs38851 3.5611
+Metap1 METAP1 rs2004994 1.6714
+Metap1d METAP1D rs10930502 1.5619
+Metap2 METAP2 rs301007 1.212
+Metrn METRN rs2071951 2.7371
+Metrnl METRNL rs7501672 1.1679
+Mettl1 METTL1 rs8176345 1.3226
+Mettl10 METTL10 rs4962696 1.1447
+Mettl11b METTL11B rs542077 1.1773
+Mettl13 METTL13 rs11800547 2.7778
+Mettl15 METTL15 rs11606414 0.9747
+Mettl16 METTL16 rs9989477 1.4815
+Mettl17 METTL17 rs943997 2.5029
+2810422O20Rik METTL18 rs12025581 1.67
+Mettl21a METTL21A rs2464978 1.6048
+Fam119b METTL21B rs8176345 1.3226
+A530098C11Rik METTL21C rs7990562 2.9337
+Mettl21d METTL21D rs10150798 0.8327
+Mettl22 METTL22 rs2312948 1.3231
+1110005A03Rik METTL23 rs1014390 1.0973
+9030224M15Rik METTL24 rs9374163 1.6293
+Mettl2 METTL2A rs11871268 1.185
+Mettl3 METTL3 rs7148594 1.1305
+Mettl4 METTL4 rs10502302 1.315
+Mettl5 METTL5 rs17624628 1.1119
+Mettl6 METTL6 rs6807885 2.808
+Mettl7a METTL7A rs7306330 0.8844
+Mettl7b METTL7B rs10876842 1.636
+Mettl8 METTL8 rs16859371 1.4267
+Mettl9 METTL9 rs41468646 1.3992
+Mex3b MEX3B rs715926 1.3862
+Mex3c MEX3C rs1893376 1.0023
+Mex3d MEX3D rs10407104 1.7423
+Mfap1 MFAP1 rs2228368 2.0663
+Mfap2 MFAP2 rs2281376 1.4255
+Mfap3 MFAP3 rs478020 3.2884
+Mfap3l MFAP3L rs2928818 1.0382
+Mfap4 MFAP4 rs3866958 1.3811
+Mfap5 MFAP5 rs10128818 1.0803
+Mff MFF rs7603414 1.4111
+Mfge8 MFGE8 rs8024779 0.919
+Mfhas1 MFHAS1 rs11775821 3.178
+Mfi2 MFI2 rs2568871 1.3344
+Mfn1 MFN1 rs13088298 1.582
+Mfn2 MFN2 rs3766742 1.0921
+Mfng MFNG rs133705 1.4322
+Mfrp MFRP rs2511837 1.4466
+Mfsd1 MFSD1 rs7651532 1.0544
+Mfsd10 MFSD10 rs7665452 1.1622
+Mfsd11 MFSD11 rs16969262 1.4925
+Mfsd12 MFSD12 rs7246261 1.0939
+Mfsd2a MFSD2A rs3103778 2.2201
+Mfsd2b MFSD2B rs11125371 1.808
+A930031D07Rik MFSD4 rs7529077 3.2073
+Mfsd6 MFSD6 rs3811610 1.2814
+Mfsd6l MFSD6L rs1553062 1.2683
+Mfsd7 MFSD7 rs3895658 1.2084
+Mfsd9 MFSD9 rs17776702 2.0242
+Mga MGA rs1819454 3.556
+Mgam MGAM rs7810984 1.1498
+Mgarp MGARP rs7660661 0.8156
+Mgat1 MGAT1 rs1872183 1.7011
+Mgat2 MGAT2 rs1011373 0.852
+Mgat3 MGAT3 rs5750824 1.2535
+Mgat4b MGAT4B rs166624 1.5109
+Mgat4c MGAT4C rs4583046 2.2063
+Mgat5 MGAT5 rs11904278 5.122
+Mgat5b MGAT5B rs4289018 2.4215
+Gm10109 MGC45922 rs2247216 1.988
+Mgea5 MGEA5 rs12221307 1.1332
+Mgll MGLL rs782448 2.2447
+Mgmt MGMT rs4751118 2.3039
+Mgp MGP rs1800802 1.1026
+Mgrn1 MGRN1 rs7186713 1.9698
+Mgst1 MGST1 rs1042669 0.9462
+Mgst2 MGST2 rs8192010 1.5016
+Mgst3 MGST3 rs4147596 1.4073
+Mia1 MIA rs2233152 1.3671
+Mia2 MIA2 rs10162550 1.2225
+Mia3 MIA3 rs3008634 1.9792
+Miat MIAT rs8139337 2.786
+Mib1 MIB1 rs11660303 1.9458
+Mical1 MICAL1 rs12200586 1.0957
+Mical2 MICAL2 rs12804449 2.7627
+Mical3 MICAL3 rs390495 1.9694
+Micalcl MICALCL rs10831805 1.9172
+Micall2 MICALL2 rs10231967 0.8173
+Mill2 MICB rs2844513 5.0311
+Micu1 MICU1 rs12770570 1.5249
+1810046J19Rik MIEN1 rs2952155 5.1719
+Mier2 MIER2 rs895343 1.2384
+Mier3 MIER3 rs1876588 2.8422
+Mif MIF rs915591 2.9307
+Mif4gd MIF4GD rs4789161 1.0457
+Miip MIIP rs3766742 1.0921
+Mina MINA rs1492037 0.963
+Mink1 MINK1 rs11654952 1.2197
+Minos1 MINOS1 rs7517326 2.5558
+Minpp1 MINPP1 rs7900733 2.1427
+Mios MIOS rs4720741 1.679
+Miox MIOX rs6010122 1.1581
+Mip MIP rs2657883 0.8133
+Mipep MIPEP rs7323049 1.5441
+AU021838 MIPOL1 rs41394648 5.203
+2210403K04Rik MIR22HG rs11078597 0.9333
+Mir96 MIR96 rs6948697 1.251
+Mis12 MIS12 rs16954705 1.1183
+2610039C10Rik MIS18A rs7281700 1.1787
+Mitf MITF rs9839600 3.0702
+Mixl1 MIXL1 rs12122761 1.1363
+Mki67 MKI67 rs12249657 1.3944
+Mki67ip MKI67IP rs7575623 1.601
+Mkks MKKS rs6032858 0.9834
+Mkl1 MKL1 rs5757948 1.7637
+Mkl2 MKL2 rs246159 1.9348
+Mkln1 MKLN1 rs10254984 2.8993
+Mknk1 MKNK1 rs6671527 1.9855
+Mknk2 MKNK2 rs2395891 1.1158
+Mkrn1 MKRN1 rs7781853 2.9038
+Mkrn2 MKRN2 rs2442807 1.3702
+Mkrn3 MKRN3 rs7164717 1.4218
+Mks1 MKS1 rs11079339 1.0232
+Mkx MKX rs4405204 1.1237
+Mlana MLANA rs999922 2.473
+Mlc1 MLC1 rs6010168 2.1747
+Mlec MLEC rs696338 1.8788
+Mlf1 MLF1 rs1656370 1.1588
+Mlf1ip MLF1IP rs871253 1.63
+Mlf2 MLF2 rs2301333 2.0057
+Mlh1 MLH1 rs7633154 1.0325
+2310046A06Rik MLIP rs7761919 2.303
+Mlkl MLKL rs7197388 1.8419
+Mllt1 MLLT1 rs4807860 1.385
+Mllt10 MLLT10 rs1243184 1.006
+Mllt11 MLLT11 rs7532312 1.1035
+Mllt3 MLLT3 rs7048991 1.6245
+Mllt4 MLLT4 rs2300084 2.5995
+Mllt6 MLLT6 rs968113 0.9439
+Mlph MLPH rs7569427 1.353
+Mlst8 MLST8 rs8062006 1.1593
+Mlx MLX rs676387 2.3565
+Mlxip MLXIP rs1043771 1.4532
+Mlxipl MLXIPL rs17145738 8.9252
+Mlycd MLYCD rs423344 1.4286
+Mmaa MMAA rs950333 1.7326
+Mmab MMAB rs7134594 14.162
+Mmachc MMACHC rs11211115 0.9927
+Mmadhc MMADHC rs4618014 1.7981
+Mmd MMD rs4334353 2.465
+Mmd2 MMD2 rs372454 1.3017
+Mme MME rs13326335 0.9179
+Mmel1 MMEL1 rs11590198 1.1254
+Mmp1a MMP1 rs1939008 1.3655
+Mmp10 MMP10 rs10791591 1.7953
+Mmp11 MMP11 rs6003870 1.5067
+Mmp12 MMP12 rs632009 2.0486
+Mmp13 MMP13 rs1942524 2.4559
+Mmp14 MMP14 rs17123091 0.9187
+Mmp15 MMP15 rs12600304 1.4535
+Mmp16 MMP16 rs2616506 1.4944
+Mmp17 MMP17 rs11613757 1.6887
+Mmp2 MMP2 rs17240737 1.9796
+Mmp20 MMP20 rs11225340 3.5016
+Mmp21 MMP21 rs4962485 1.0662
+Mmp24 MMP24 rs6058202 3.8327
+Mmp25 MMP25 rs2257295 1.637
+Mmp27 MMP27 rs11821755 2.4645
+Mmp28 MMP28 rs6505497 1.0631
+Mmp3 MMP3 rs673163 1.644
+Mmp7 MMP7 rs7127524 2.6245
+Mmp8 MMP8 rs11821755 2.4645
+Mmp9 MMP9 rs6065912 6.9682
+Mmrn1 MMRN1 rs6532200 1.9469
+Mmrn2 MMRN2 rs17335915 1.669
+Mms19 MMS19 rs7915501 1.8142
+Mms22l MMS22L rs13199204 1.1091
+Mn1 MN1 rs5752640 1.8719
+Mnat1 MNAT1 rs17834412 1.2088
+Mnd1 MND1 rs6838284 3.2402
+Ifi204 MNDA rs857859 1.2318
+2900010M23Rik MNF1 rs10947433 2.4178
+Mns1 MNS1 rs7175084 1.4031
+Mnt MNT rs2429909 2.7585
+Mnx1 MNX1 rs12671438 1.1566
+Moap1 MOAP1 rs1268371 0.8617
+Mob1a MOB1A rs828887 1.2324
+Mob1b MOB1B rs6819580 1.4344
+Mob2 MOB2 rs11605284 1.6592
+Mob3a MOB3A rs2395891 1.1158
+Mob3b MOB3B rs725804 2.4849
+Mob3c MOB3C rs6671527 1.9855
+Mobp MOBP rs816508 0.87
+Mocos MOCOS rs17672989 1.4353
+Mocs1 MOCS1 rs11754450 1.7657
+Mocs2 MOCS2 rs6868966 1.0572
+Mocs3 MOCS3 rs2426229 1.6196
+Mog MOG rs9501414 1.9706
+Mogat1 MOGAT1 rs1447112 1.7894
+Mogat2 MOGAT2 rs10793126 4.8486
+MMT00056823 MOGAT3 rs3735337 1.7133
+Mogs MOGS rs13396033 2.5805
+Stk30 MOK rs4906191 1.2982
+Mon1a MON1A rs7613875 5.8536
+Mon1b MON1B rs12933649 2.1348
+Mon2 MON2 rs11174557 2.1637
+Morc1 MORC1 rs16855064 1.3202
+Morc2a MORC2 rs5749202 1.2162
+Morf4l1 MORF4L1 rs11072813 0.8342
+Morn1 MORN1 rs903920 2.4536
+4930438O03Rik MORN3 rs11043310 2.1957
+Morn4 MORN4 rs933057 2.3049
+Morn5 MORN5 rs1556215 1.7749
+Mov10 MOV10 rs6679 1.4232
+Mov10l1 MOV10L1 rs6010168 2.1747
+Moxd1 MOXD1 rs1569524 1.3313
+Mpc1 MPC1 rs4305699 1.0768
+Mpc2 MPC2 rs203835 1.2513
+Mpdu1 MPDU1 rs8068222 2.1304
+Mpdz MPDZ rs10960942 1.1345
+Mpg MPG rs2562154 0.9374
+Mphosph10 MPHOSPH10 rs11126317 2.3739
+Mphosph6 MPHOSPH6 rs2059276 1.4188
+Mphosph8 MPHOSPH8 rs9578171 1.7667
+Mphosph9 MPHOSPH9 rs10773000 2.845
+Mpi MPI rs16973172 1.9183
+Mpl MPL rs3120122 1.0467
+2810021B07Rik MPLKIP rs6974493 1.2433
+Mpo MPO rs2240262 1.0285
+Mpp2 MPP2 rs11867362 4.1635
+Mpp3 MPP3 rs1234612 1.5889
+Mpp4 MPP4 rs10194353 1.2506
+Mpp6 MPP6 rs17149687 1.6712
+Mpp7 MPP7 rs12357385 2.4604
+Mppe1 MPPE1 rs8087997 3.3144
+Mpped1 MPPED1 rs135058 1.4292
+Mpped2 MPPED2 rs11031125 1.716
+Mprip MPRIP rs4399589 1.5948
+Mpst MPST rs5756477 0.9477
+Mpv17 MPV17 rs11689803 1.1015
+Mpv17l MPV17L rs153000 1.6312
+Mpz MPZ rs2501879 1.3304
+Mpzl1 MPZL1 rs13374598 1.3206
+Mpzl2 MPZL2 rs1715450 1.0452
+Mpzl3 MPZL3 rs1715450 1.0452
+Mr1 MR1 rs17374514 1.272
+Mrap MRAP rs7281700 1.1787
+Mrap2 MRAP2 rs9449775 1.4153
+Mras MRAS rs4678259 0.8142
+Mrc1 MRC1 rs10508547 0.9197
+Mrc2 MRC2 rs2072353 0.8972
+Mre11a MRE11A rs13447731 1.7144
+Mreg MREG rs13034681 2.6949
+Mrfap1 MRFAP1 rs4689495 1.3527
+Mrgprd MRGPRD rs2096777 1.5382
+Mrgpre MRGPRE rs4403792 1.3556
+Mrgprf MRGPRF rs2096777 1.5382
+Mrgprg MRGPRG rs4403792 1.3556
+Mrgpra6 MRGPRX1 rs4757738 1.0791
+Mrgprx2 MRGPRX2 rs11024956 1.3009
+Mrgpra1 MRGPRX3 rs11024500 0.914
+Mrgprx1 MRGPRX4 rs7936382 1.137
+Mri1 MRI1 rs378189 1.1026
+Mrm1 MRM1 rs17693183 1.4965
+Mro MRO rs17742992 1.4458
+Mrpl1 MRPL1 rs7692075 1.423
+Mrpl10 MRPL10 rs11654648 3.8005
+Mrpl11 MRPL11 rs2282529 1.3003
+Mrpl12 MRPL12 rs3088016 1.0063
+Mrpl13 MRPL13 rs11776121 1.7438
+Mrpl14 MRPL14 rs11967633 2.6672
+Mrpl15 MRPL15 rs11984645 1.4325
+Mrpl17 MRPL17 rs12291044 1.0652
+Mrpl18 MRPL18 rs4235925 0.8153
+Mrpl19 MRPL19 rs17011635 2.8804
+Mrpl2 MRPL2 rs3793021 1.2604
+Mrpl20 MRPL20 rs2649588 1.2546
+Mrpl21 MRPL21 rs513359 4.1376
+Mrpl22 MRPL22 rs17562321 1.5827
+Mrpl23 MRPL23 rs757409 1.0167
+Mrpl24 MRPL24 rs3806415 3.2961
+Mrpl27 MRPL27 rs2526524 1.3872
+Mrpl28 MRPL28 rs11866815 2.104
+Mrpl3 MRPL3 rs12637525 1.5642
+Mrpl30 MRPL30 rs4851200 1.0555
+Mrpl32 MRPL32 rs617821 1.2297
+Mrpl33 MRPL33 rs1317580 1.1978
+Mrpl34 MRPL34 rs7250266 2.0722
+Mrpl35 MRPL35 rs10201457 1.4755
+Mrpl36 MRPL36 rs4147775 1.9889
+Mrpl37 MRPL37 rs946447 1.3144
+Mrpl38 MRPL38 rs3760128 1.5632
+Mrpl39 MRPL39 rs926962 1.358
+Mrpl4 MRPL4 rs5030340 1.2874
+Mrpl40 MRPL40 rs2106144 1.5923
+Mrpl42 MRPL42 rs1972451 1.6413
+Mrpl43 MRPL43 rs735137 1.5547
+Mrpl44 MRPL44 rs893360 1.3467
+Mrpl46 MRPL46 rs7173327 1.1976
+Mrpl48 MRPL48 rs10898948 1.0463
+Mrpl49 MRPL49 rs769442 2.4264
+Mrpl50 MRPL50 rs16920291 1.5615
+Mrpl51 MRPL51 rs2532484 0.8611
+Mrpl52 MRPL52 rs17123091 0.9187
+Mrpl53 MRPL53 rs13396033 2.5805
+Mrpl55 MRPL55 rs3768331 1.4325
+Mrps10 MRPS10 rs9381152 1.0658
+Mrps11 MRPS11 rs7173327 1.1976
+Mrps12 MRPS12 rs10405150 2.3972
+Mrps14 MRPS14 rs10489330 1.4265
+Mrps15 MRPS15 rs7065 0.9307
+Mrps17 MRPS17 rs12671091 2.0054
+Mrps18a MRPS18A rs16869037 1.7183
+Mrps18b MRPS18B rs13195066 1.636
+Mrps2 MRPS2 rs535 1.7354
+Mrps21 MRPS21 rs7511649 0.8604
+Mrps22 MRPS22 rs6439849 3.3996
+Mrps23 MRPS23 rs9895822 1.7217
+Mrps24 MRPS24 rs670573 2.2218
+Mrps25 MRPS25 rs2164358 1.1606
+Mrps26 MRPS26 rs6115763 0.9263
+Mrps27 MRPS27 rs10037731 2.369
+Mrps28 MRPS28 rs11781815 1.7406
+Mrps30 MRPS30 rs1048758 1.1594
+Mrps31 MRPS31 rs9577109 0.8655
+Mrps33 MRPS33 rs10240085 0.9714
+Mrps34 MRPS34 rs1065663 1.2005
+Mrps35 MRPS35 rs11049091 1.871
+Mrps36 MRPS36 rs3966728 0.927
+Mrps5 MRPS5 rs3112989 1.3711
+Mrps6 MRPS6 rs1573464 1.5053
+Mrps7 MRPS7 rs4789161 1.0457
+Mrps9 MRPS9 rs11889143 1.1452
+Mrs2 MRS2 rs793676 1.3794
+Mrvi1 MRVI1 rs17320588 1.4403
+Ms4a1 MS4A1 rs10501385 1.4676
+Ms4a10 MS4A10 rs10792297 2.6891
+Ms4a12 MS4A12 rs4939387 1.5043
+Ms4a13 MS4A13 rs4939387 1.5043
+Ms4a14 MS4A14 rs12281658 1.9813
+Ms4a15 MS4A15 rs7925933 1.5613
+Ms4a2 MS4A2 rs12576889 1.2291
+Ms4a3 MS4A3 rs2276044 1.2304
+MMT00035477 MS4A4A rs12279983 0.9931
+Ms4a5 MS4A5 rs12281658 1.9813
+Ms4a6b MS4A6A rs587634 0.8395
+Ms4a7 MS4A7 rs12281658 1.9813
+A930005I04Rik MSANTD1 rs362322 1.3381
+BC024479 MSANTD2 rs12792040 1.2469
+5730528L13Rik MSANTD3 rs13302253 1.081
+8430410K20Rik MSANTD4 rs1939820 0.8342
+Msc MSC rs7460383 1.1379
+Msgn1 MSGN1 rs13026352 0.8887
+Msh2 MSH2 rs6729891 1.9682
+Msh3 MSH3 rs6151935 1.2194
+Msh4 MSH4 rs2347963 2.1875
+Msh5 MSH5 rs400547 2.5546
+Msh6 MSH6 rs3136259 1.4311
+Msi1 MSI1 rs12423303 1.3423
+Msi2 MSI2 rs9892791 2.9578
+Msl2 MSL2 rs9845457 3.6447
+Msln MSLN rs2071951 2.7371
+Msmb MSMB rs17720205 2.4927
+Sc4mol MSMO1 rs1530043 1.2099
+Msr1 MSR1 rs4831368 2.6071
+Msra MSRA rs6997710 2.3477
+Sepx1 MSRB1 rs1657137 1.6091
+Msrb2 MSRB2 rs7916969 1.0225
+Msrb3 MSRB3 rs7973013 1.2999
+Mst1 MST1 rs2131104 1.0302
+Mst1r MST1R rs10865959 2.3433
+Mstn MSTN rs6749852 1.0062
+Msx1 MSX1 rs2933580 1.3395
+Msx2 MSX2 rs6891331 1.3115
+Mt1 MT1A rs1604122 4.0227
+Mt2 MT2A rs1604122 4.0227
+Mt3 MT3 rs9938618 3.9842
+Mt4 MT4 rs176060 2.8529
+Mta1 MTA1 rs11849001 1.8841
+Mta2 MTA2 rs3888173 1.9543
+Mta3 MTA3 rs41515446 2.1315
+Mtap MTAP rs3731217 2.755
+Mtbp MTBP rs7818026 1.5815
+Mtch1 MTCH1 rs2293381 1.087
+Mtch2 MTCH2 rs11600974 1.3141
+Mtdh MTDH rs2516278 1.131
+Mterf MTERF rs11611373 2.0264
+Mterfd1 MTERFD1 rs10104165 1.3696
+Mterfd2 MTERFD2 rs2067987 1.5187
+Mterfd3 MTERFD3 rs4640029 1.6556
+Mtf1 MTF1 rs582811 1.2912
+Mtf2 MTF2 rs2391228 3.6919
+Mtfmt MTFMT rs12906662 0.9535
+Mtfp1 MTFP1 rs9608905 3.9038
+Mtfr1 MTFR1 rs2048648 1.971
+Mtg1 MTG1 rs2987803 1.5331
+Mthfd1 MTHFD1 rs3783728 2.2132
+Mthfd1l MTHFD1L rs12665298 1.7484
+Mthfd2 MTHFD2 rs7571842 1.2381
+1110019K23Rik MTHFD2L rs477975 1.4896
+Mthfr MTHFR rs2076003 1.7943
+Mthfs MTHFS rs12906758 2.704
+Mthfsd MTHFSD rs4843390 1.791
+Mtif2 MTIF2 rs2589071 1.2688
+Mtif3 MTIF3 rs9512681 1.8265
+Mtl5 MTL5 rs7103254 1.0546
+Mtmr10 MTMR10 rs11633433 2.0827
+Mtmr11 MTMR11 rs626785 1.9481
+Mtmr12 MTMR12 rs11745268 2.0035
+Mtmr14 MTMR14 rs9879089 1.1631
+Mtmr2 MTMR2 rs41496451 1.2885
+Mtmr3 MTMR3 rs17646919 1.3479
+Mtmr6 MTMR6 rs4453339 1.318
+Mtmr7 MTMR7 rs1989759 1.4304
+Mtmr9 MTMR9 rs11786313 1.5358
+Mtnr1a MTNR1A rs10021137 1.2276
+Mtnr1b MTNR1B rs12274325 1.7625
+Mto1 MTO1 rs7763873 1.5272
+Mtor MTOR rs11583740 0.9248
+Mtpap MTPAP rs567030 1.0931
+Mtr MTR rs3938327 2.1312
+Mtrf1l MTRF1L rs13199739 1.0046
+Mtrr MTRR rs327585 1.776
+Mtss1 MTSS1 rs4871519 1.8407
+Mtss1l MTSS1L rs11864186 1.8959
+Mttp MTTP rs4699383 2.277
+Mtus1 MTUS1 rs6986061 2.6381
+Mtus2 MTUS2 rs7319196 2.4623
+Mtx1 MTX1 rs12137850 1.0366
+Mtx3 MTX3 rs2434307 0.9622
+Muc1 MUC1 rs12137850 1.0366
+Muc13 MUC13 rs1909580 2.1923
+Muc15 MUC15 rs12295638 1.4338
+Muc3 MUC17 rs7806980 1.2794
+Muc19 MUC19 rs2638867 2.5784
+Muc2 MUC2 rs11245935 2.4464
+Muc20 MUC20 rs6795171 2.5707
+Gm9573 MUC21 rs6928738 1.5484
+Gm7534 MUC3A rs12705099 2.093
+Muc4 MUC4 rs9825756 2.6192
+Muc5ac MUC5AC rs35705491 1.0017
+Muc5b MUC5B rs885454 1.8798
+MMT00015254 MUC7 rs988180 1.5261
+Mul1 MUL1 rs17412543 1.0539
+Mum1 MUM1 rs12052097 1.1036
+Murc MURC rs10989170 1.4803
+Mus81 MUS81 rs679147 3.3254
+Musk MUSK rs11792334 2.4706
+Mustn1 MUSTN1 rs17331151 4.4108
+Mutyh MUTYH rs10890324 0.9241
+Mvd MVD rs899729 1.5645
+Mvk MVK rs7134594 14.162
+Mx2 MX1 rs2070792 1.4397
+Mx1 MX2 rs364525 1.6696
+Mxd1 MXD1 rs10196546 0.9901
+Mxd3 MXD3 rs6860069 0.8228
+Mxd4 MXD4 rs3762942 2.273
+Mxi1 MXI1 rs1133195 1.3467
+1810057P16Rik MXRA7 rs11867820 1.3601
+Mxra8 MXRA8 rs2649588 1.2546
+Myadm MYADM rs7247357 0.9634
+Myb MYB rs6936301 1.309
+Mybbp1a MYBBP1A rs3760194 1.1889
+Mybl1 MYBL1 rs16932958 0.8959
+Mybl2 MYBL2 rs285207 1.0328
+8030451F13Rik MYBPC1 rs17031683 1.7742
+Mybpc2 MYBPC2 rs3745519 1.2074
+Mybpc3 MYBPC3 rs2697920 12.997
+Mybph MYBPH rs10920569 2.2215
+Mybphl MYBPHL rs629301 7.2084
+Myc MYC rs10112382 2.107
+Mycbp MYCBP rs6690335 1.3136
+Mycbp2 MYCBP2 rs17750294 1.6755
+Mycbpap MYCBPAP rs8070491 0.819
+Myct1 MYCT1 rs9371267 1.7147
+Myd88 MYD88 rs172111 1.1772
+Myeov2 MYEOV2 rs12618001 0.9863
+Myf5 MYF5 rs12582674 0.9285
+Myf6 MYF6 rs12582674 0.9285
+Myh1 MYH1 rs3764851 1.3943
+Myh10 MYH10 rs3809739 1.5662
+Myh11 MYH11 rs17284809 1.1822
+Myh13 MYH13 rs11655678 2.6801
+Myh14 MYH14 rs788332 2.5567
+MMT00000076 MYH15 rs12186052 1.7678
+Myh2 MYH2 rs3764851 1.3943
+Myh3 MYH3 rs876659 2.1041
+Myh4 MYH4 rs2041126 1.0129
+Myh6 MYH6 rs12147533 1.833
+Myh7 MYH7 rs12147533 1.833
+Myh7b MYH7B rs3736802 2.8768
+Myh8 MYH8 rs11655678 2.6801
+Myh9 MYH9 rs762964 1.9889
+Myl1 MYL1 rs7563494 1.8897
+Myl10 MYL10 rs17135656 1.383
+Myl12a MYL12A rs1791085 1.5406
+Myl12b MYL12B rs12607409 1.1253
+Myl2 MYL2 rs12828640 1.3218
+Myl3 MYL3 rs12106917 2.1749
+Myl4 MYL4 rs4968255 3.3099
+Myl6 MYL6 rs2292237 1.0172
+Myl6b MYL6B rs2292237 1.0172
+Myl7 MYL7 rs882020 0.8392
+Myl9 MYL9 rs2076692 1.7476
+Mylip MYLIP rs9370867 1.3889
+Mylk MYLK rs820355 1.6627
+Mylk2 MYLK2 rs17340555 2.1077
+Mylk3 MYLK3 rs36469 1.0895
+Mylk4 MYLK4 rs17135340 2.0146
+Myo10 MYO10 rs17707735 1.6739
+Myo15 MYO15A rs854790 1.2
+Myo15b MYO15B rs4789219 1.5355
+Myo16 MYO16 rs2391677 2.2799
+Myo18a MYO18A rs12601994 1.657
+Myo18b MYO18B rs2285202 2.1637
+Myo19 MYO19 rs4796217 1.6887
+Myo1a MYO1A rs3803018 1.4226
+Myo1b MYO1B rs7608396 1.4464
+Myo1c MYO1C rs17833825 1.4947
+Myo1d MYO1D rs225205 1.471
+Myo1e MYO1E rs11631462 3.8368
+Myo1f MYO1F rs4804311 4.4901
+Myo1g MYO1G rs7799222 1.7978
+Myo1h MYO1H rs1558804 12.3
+Myo3a MYO3A rs17665973 1.2028
+Myo3b MYO3B rs1861289 2.0624
+Myo5a MYO5A rs11853954 1.4628
+Myo5b MYO5B rs1787328 16.744
+Myo5c MYO5C rs10220890 2.6809
+Myo7a MYO7A rs948968 1.0858
+Myo7b MYO7B rs6732660 1.3527
+Myo9a MYO9A rs7180325 1.0259
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+Myod1 MYOD1 rs4757584 1.2629
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+Myom1 MYOM1 rs7236788 1.8156
+Myom2 MYOM2 rs7463722 2.1997
+Myom3 MYOM3 rs7537874 1.4825
+Myot MYOT rs4288 1.3778
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+Nat15 NAA60 rs3859147 1.5346
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+Nat6 NAT6 rs13100173 1.7749
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+Nckap1l NCKAP1L rs1874308 2.1002
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+Ndufs6 NDUFS6 rs4147775 1.9889
+Ndufs7 NDUFS7 rs11670805 0.8374
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+Nedd4l NEDD4L rs4149593 2.9062
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+Nedd9 NEDD9 rs16871174 1.6421
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+Nek10 NEK10 rs13091109 1.963
+Nek11 NEK11 rs13066072 1.6169
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+Nek3 NEK3 rs2296351 1.4242
+Nek4 NEK4 rs2710322 1.7891
+Nek5 NEK5 rs2296351 1.4242
+Nek6 NEK6 rs6478659 1.7567
+Nek7 NEK7 rs2226273 1.5792
+Nek8 NEK8 rs11657641 1.7959
+Nek9 NEK9 rs17493927 1.289
+Nell1 NELL1 rs7950230 2.5722
+Nell2 NELL2 rs10880655 1.5889
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+Nenf NENF rs479564 1.7796
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+Neto1 NETO1 rs11151807 1.1532
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+Neu2 NEU2 rs11680313 1.0298
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+Neurl NEURL rs17737062 2.4593
+Neurl1b NEURL1B rs883150 1.8083
+Neurl2 NEURL2 rs378114 9.8748
+Neurl4 NEURL4 rs3809822 1.3322
+Neurod1 NEUROD1 rs7602326 1.0857
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+Neurod4 NEUROD4 rs17116813 1.7768
+Neurod6 NEUROD6 rs10487718 1.4116
+Neurog1 NEUROG1 rs2880872 1.5774
+Neurog2 NEUROG2 rs7659503 1.5875
+Neurog3 NEUROG3 rs10998867 1.4466
+Nf1 NF1 rs10853138 2.4343
+Nf2 NF2 rs715595 2.7989
+Nfam1 NFAM1 rs11912584 1.0597
+Nfasc NFASC rs7543425 1.5935
+Nfat5 NFAT5 rs244422 1.9133
+Nfatc1 NFATC1 rs838050 1.6091
+Nfatc2 NFATC2 rs6021262 1.2738
+Nfatc2ip NFATC2IP rs3922668 4.181
+Nfatc3 NFATC3 rs2301814 22.623
+Nfatc4 NFATC4 rs2228233 0.9333
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+Nfe2l1 NFE2L1 rs10491183 4.4033
+Nfe2l2 NFE2L2 rs17647588 1.1838
+Nfe2l3 NFE2L3 rs10270359 3.4094
+Nfia NFIA rs2806425 3.9219
+Nfib NFIB rs575660 2.9165
+Nfic NFIC rs4807470 2.716
+Nfil3 NFIL3 rs7024443 1.1942
+Nfix NFIX rs4926176 0.8687
+Nfkb1 NFKB1 rs230489 4.5667
+Nfkb2 NFKB2 rs7076748 1.3418
+Nfkbia NFKBIA rs7155561 1.5896
+Nfkbib NFKBIB rs10405150 2.3972
+Nfkbid NFKBID rs3814995 3.1925
+Nfkbie NFKBIE rs2233434 2.6832
+Nfkbil1 NFKBIL1 rs6929796 2.5474
+Nfkbiz NFKBIZ rs2625288 1.3191
+Nfrkb NFRKB rs692325 2.4509
+Nfs1 NFS1 rs6060558 3.539
+Nfu1 NFU1 rs13418928 1.0475
+Nfxl1 NFXL1 rs16860723 1.7488
+Nfya NFYA rs10456499 1.8204
+Nfyb NFYB rs41449349 1.5157
+Nfyc NFYC rs4660450 1.5194
+Ngb NGB rs11547793 2.0755
+Ngef NGEF rs895432 1.0508
+Ngf NGF rs12145726 1.0072
+Ngfr NGFR rs534561 1.5629
+Ngly1 NGLY1 rs6550990 1.1103
+Ngrn NGRN rs16944099 0.819
+Nhej1 NHEJ1 rs16859497 0.8681
+Nhlh1 NHLH1 rs12127831 1.8626
+Nhlh2 NHLH2 rs7555256 1.2779
+Nhlrc1 NHLRC1 rs2842936 1.4947
+Nhlrc2 NHLRC2 rs239862 2.0599
+Nhlrc3 NHLRC3 rs12431010 1.3922
+Nhp2 NHP2 rs7710186 1.304
+Nhsl1 NHSL1 rs7753434 1.9076
+Nicn1 NICN1 rs8897 1.2095
+Nid1 NID1 rs10737895 3.8401
+Nid2 NID2 rs12432630 3.2592
+Nif3l1 NIF3L1 rs17467700 1.1239
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+Nin NIN rs2883951 2.1632
+Ninj1 NINJ1 rs2398827 1.7342
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+Nipa1 NIPA1 rs17841100 1.3299
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+Nipal3 NIPAL3 rs10489442 3.0511
+Nipal4 NIPAL4 rs1559145 2.0704
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+Nipsnap3a NIPSNAP3A rs2740485 4.9634
+Nipsnap3b NIPSNAP3B rs2777800 6.6453
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+Nkain1 NKAIN1 rs12033154 2.758
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+Nkain3 NKAIN3 rs7015211 2.1834
+Nkain4 NKAIN4 rs6122403 1.6751
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+Nkd2 NKD2 rs326046 1.1866
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+Nkiras2 NKIRAS2 rs4796756 1.4436
+Nkpd1 NKPD1 rs6509196 2.4991
+Nktr NKTR rs339720 1.1576
+Nkx1-2 NKX1-2 rs11244995 0.9397
+Nkx2-1 NKX2-1 rs17765618 1.4697
+Nkx2-2 NKX2-2 rs13042739 3.1008
+Nkx2-3 NKX2-3 rs4313504 1.2638
+Nkx2-4 NKX2-4 rs1409818 1.1974
+Nkx2-5 NKX2-5 rs13176674 2.223
+Nkx2-6 NKX2-6 rs920300 1.0214
+Nkx2-9 NKX2-8 rs17104686 1.3322
+Nkx3-1 NKX3-1 rs920300 1.0214
+Nkx3-2 NKX3-2 rs13105197 1.4253
+Nkx6-1 NKX6-1 rs10516724 0.9355
+Nkx6-3 NKX6-3 rs1565124 1.7867
+Nle1 NLE1 rs1471615 2.4342
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+Nln NLN rs34986 1.6192
+Nlrc3 NLRC3 rs9930893 1.6674
+Nlrc4 NLRC4 rs407451 1.3443
+Nlrc5 NLRC5 rs3764261 323.01
+Nlrp1a NLRP1 rs4577136 0.8533
+Nlrp10 NLRP10 rs10839957 0.8811
+Nlrp12 NLRP12 rs11880440 1.2434
+Nlrp14 NLRP14 rs7118454 1.1161
+Nlrp2 NLRP2 rs703474 1.6568
+Nlrp3 NLRP3 rs4925671 2.7345
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+Nlrp5 NLRP5 rs636728 1.7064
+Nlrp6 NLRP6 rs577357 1.4242
+Nlrx1 NLRX1 rs12417928 1.9094
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+Nmbr NMBR rs10240302 0.9658
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+Gm5566 NME1-NME2 rs1558253 1.1979
+Nme2 NME2 rs1558253 1.1979
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+Nme7 NME7 rs1011638 2.2803
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+Nmi NMI rs9287955 2.3794
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+Nmnat3 NMNAT3 rs11920622 2.5003
+Nmral1 NMRAL1 rs2270363 2.0391
+Nmrk1 NMRK1 rs13294415 0.9104
+Nmrk2 NMRK2 rs3810373 1.368
+Nmt1 NMT1 rs2285427 2.1238
+Nmt2 NMT2 rs11259535 1.6805
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+Nnmt NNMT rs573264 1.0398
+Nnt NNT rs3828696 1.2297
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+Nodal NODAL rs999551 1.1994
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+Nol11 NOL11 rs2706669 1.7462
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+Nol6 NOL6 rs7022355 1.1655
+Nol7 NOL7 rs3757261 2.0403
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+Nos1 NOS1 rs2293054 2.0564
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+Nos3 NOS3 rs1799983 4.0004
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+Nov NOV rs11778277 1.4549
+Nova1 NOVA1 rs8009255 1.4985
+Nova2 NOVA2 rs11672738 2.9278
+Nox3 NOX3 rs231943 1.0519
+Nox4 NOX4 rs3897708 2.0306
+Noxo1 NOXO1 rs8055439 1.1127
+4933437F05Rik NOXRED1 rs13379432 1.7111
+Npas1 NPAS1 rs1862499 1.3106
+Npas2 NPAS2 rs10181743 1.7525
+Npas3 NPAS3 rs8004665 3.2437
+Npas4 NPAS4 rs2282529 1.3003
+Npat NPAT rs2280332 1.7005
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+Npr1 NPR1 rs6674929 2.8582
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+Nprl3 NPRL3 rs11865131 1.504
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+Npy2r NPY2R rs1317400 2.1945
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+Npy6r NPY6R rs17740890 1
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+Nr1i3 NR1I3 rs1136224 1.5842
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+Nr2c2 NR2C2 rs2164358 1.1606
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+Perp PERP rs480631 1.2234
+Pes1 PES1 rs4149488 2.3397
+2900053A13Rik PET100 rs1674033 1.0259
+Gm20571 PET117 rs3828013 1.3973
+Pex1 PEX1 rs2111200 0.95
+Pex10 PEX10 rs2840536 1.9718
+Pex11a PEX11A rs7178463 1.7102
+Pex11b PEX11B rs4471211 1.1824
+Pex11c PEX11G rs2287916 2.1734
+Pex12 PEX12 rs4516277 1.2567
+Pex13 PEX13 rs1177279 2.0901
+Pex14 PEX14 rs12129512 0.9073
+Pex16 PEX16 rs2292910 3.3325
+Pex19 PEX19 rs10159339 1.0948
+Pex2 PEX2 rs9643719 1.033
+Pex26 PEX26 rs450703 1.5884
+Pex3 PEX3 rs223235 0.9859
+Pex5 PEX5 rs3816424 1.5962
+Pex2 PEX5L rs11711626 1.6782
+Pex6 PEX6 rs2274517 3.8642
+Pex7 PEX7 rs11755484 2.2056
+Cxcl4 PF4 rs2367442 0.8097
+Pfas PFAS rs11650309 2.8193
+Pfdn2 PFDN2 rs11265554 1.3805
+Pfdn4 PFDN4 rs423338 2.413
+H2-Ke2 PFDN6 rs6906691 1.1757
+Pfkfb2 PFKFB2 rs6673109 1.4848
+Pfkfb3 PFKFB3 rs11257103 2.1189
+Pfkfb4 PFKFB4 rs7615170 1.218
+Pfkl PFKL rs2155724 1.4012
+Pfkm PFKM rs2408955 1.1238
+Pfkp PFKP rs7074733 1.1693
+Pfn1 PFN1 rs11654952 1.2197
+Pfn2 PFN2 rs41448248 1.6517
+Pfn3 PFN3 rs1801020 1.5835
+Pfn4 PFN4 rs10495746 0.9897
+Pgam2 PGAM2 rs4640970 1.0725
+Pgap2 PGAP2 rs7934650 1.2415
+Pgap3 PGAP3 rs881844 13.546
+4921509E05Rik PGBD1 rs6901575 2.5753
+Pgbd5 PGBD5 rs6680658 4.1096
+Pgc PGC rs4711690 2.1268
+Pgd PGD rs17034849 1.1994
+Pgf PGF rs759059 1.034
+Pggt1b PGGT1B rs267296 0.9344
+Pgk2 PGK2 rs511454 1.2239
+Pgls PGLS rs3827011 1.0825
+Pglyrp1 PGLYRP1 rs8108561 1.2286
+Pglyrp2 PGLYRP2 rs10854145 0.9527
+Pglyrp3 PGLYRP3 rs1865233 1.0566
+Pglyrp4 PGLYRP4 rs1560833 1.5943
+Pgm2 PGM1 rs4382759 1.1871
+Pgm1 PGM2 rs2911892 1.9259
+Pgm2l1 PGM2L1 rs12416880 1.6232
+Pgm3 PGM3 rs686786 0.9263
+Pgm5 PGM5 rs4744594 1.2756
+Pgp PGP rs13333044 0.8401
+Pgpep1 PGPEP1 rs8110195 1.9055
+Pgpep1l PGPEP1L rs1115505 1.4766
+Pgr PGR rs572483 1.1812
+Pgrmc2 PGRMC2 rs4975200 1.0948
+Pgs1 PGS1 rs4082919 8.3032
+Phactr1 PHACTR1 rs17679107 1.8877
+Phactr2 PHACTR2 rs7739094 1.4934
+Phactr3 PHACTR3 rs17455968 2.0548
+Phactr4 PHACTR4 rs10158229 1.1321
+Phax PHAX rs12658710 1.9045
+Phb PHB rs935129 2.3152
+Phc1 PHC1 rs4883201 2.762
+Phc2 PHC2 rs4575049 1.1189
+Phc3 PHC3 rs879161 1.6981
+Phf1 PHF1 rs2747479 1.041
+Phf10 PHF10 rs3823465 2.1051
+AW476263 PHF11 rs7324615 1.2969
+Phf12 PHF12 rs8081659 1.2189
+Phf13 PHF13 rs11892 1.3938
+Phf14 PHF14 rs12669876 2.7172
+Phf15 PHF15 rs329119 1.3009
+Phf17 PHF17 rs11722830 1.1801
+Phf2 PHF2 rs12237139 1.9492
+Phf20 PHF20 rs6060685 3.0778
+Phf20l1 PHF20L1 rs7839989 1.3939
+Phf21a PHF21A rs7128184 3.9784
+Phf21b PHF21B rs12166569 2.4626
+Phf23 PHF23 rs222850 0.9263
+Phf5a PHF5A rs11704973 1.9454
+Phf7 PHF7 rs648514 1.5889
+Phgdh PHGDH rs539426 1.9307
+Phgr1 PHGR1 rs28602975 1.1929
+Phkb PHKB rs12930708 1.7378
+Phlda1 PHLDA1 rs10880028 1.5249
+Phlda2 PHLDA2 rs400643 1.6428
+Phlda3 PHLDA3 rs10920200 1.6686
+Phldb1 PHLDB1 rs601318 1.4983
+Phldb2 PHLDB2 rs10804494 1.0919
+Phldb3 PHLDB3 rs11668793 1.708
+Phlpp1 PHLPP1 rs508702 2.1085
+Phlpp2 PHLPP2 rs1345868 2.9059
+Phospho1 PHOSPHO1 rs7222840 2.3984
+Phospho2 PHOSPHO2 rs7607103 1.6003
+Phox2a PHOX2A rs7110451 1.4204
+Phpt1 PHPT1 rs2811749 1.5594
+Phrf1 PHRF1 rs12419694 2.7284
+Phtf2 PHTF2 rs17234466 1.4068
+Phyh PHYH rs11258312 1.7135
+Phyhd1 PHYHD1 rs9031 1.0474
+Phyhip PHYHIP rs1126677 1.0879
+Phyhipl PHYHIPL rs11006406 1.1613
+Pi15 PI15 rs2570183 1.6678
+Pi16 PI16 rs2293381 1.087
+Pi4k2a PI4K2A rs933057 2.3049
+Pi4k2b PI4K2B rs3822221 0.8834
+Pi4ka PI4KA rs165794 1.373
+Pi4kb PI4KB rs2233850 1.4505
+Pias1 PIAS1 rs12898722 3.393
+Pias2 PIAS2 rs3826586 1.1151
+Pias3 PIAS3 rs17352281 2.2366
+Pias4 PIAS4 rs737204 4.1622
+Pibf1 PIBF1 rs2147807 0.9646
+Picalm PICALM rs41489645 2.3866
+Pick1 PICK1 rs4373009 1.2079
+Pid1 PID1 rs17614019 1.8289
+Fam38a PIEZO1 rs565211 1.6148
+Fam38b PIEZO2 rs12959566 2.1022
+Pigb PIGB rs3203152 1.3779
+Pigc PIGC rs2227198 2.7113
+Pigf PIGF rs896213 1.4339
+Pigh PIGH rs4899214 1.4156
+Pigk PIGK rs4949770 1.5879
+Pigl PIGL rs11078338 0.8986
+Pign PIGN rs17712434 1.6613
+Pigo PIGO rs944700 0.9809
+Pigr PIGR rs2883215 1.815
+Pigt PIGT rs3761195 2.6087
+Pigu PIGU rs2295443 2.4881
+Pigx PIGX rs13061191 1.2064
+Pigz PIGZ rs6785916 1.5107
+Pih1d1 PIH1D1 rs1043558 0.9201
+Pih1d2 PIH1D2 rs7935331 1.4073
+Pik3ap1 PIK3AP1 rs3748236 1.2027
+Pik3c2a PIK3C2A rs214940 0.9219
+Pik3c2b PIK3C2B rs2942150 2.2011
+Pik3c2g PIK3C2G rs12099555 1.6853
+Pik3c3 PIK3C3 rs12456084 1.5317
+Pik3ca PIK3CA rs7645550 1.7251
+Pik3cg PIK3CG rs1724262 2.082
+Pik3ip1 PIK3IP1 rs2040532 1.3818
+Pik3r1 PIK3R1 rs831122 2.0776
+Pik3r2 PIK3R2 rs273476 0.8142
+Pik3r3 PIK3R3 rs2275426 3.0606
+Pik3r5 PIK3R5 rs17206162 2.8265
+Pik3r6 PIK3R6 rs17206162 2.8265
+Pikfyve PIKFYVE rs12694110 1.702
+Pilra PILRA rs13228694 1.3364
+Pilrb1 PILRB rs13228694 1.3364
+Pim1 PIM1 rs10223418 1.8044
+mCT21996 PIN1 rs4804467 1.2224
+Pink1 PINK1 rs10916827 2.1743
+2310033E01Rik PINLYP rs2682587 1.0153
+Pinx1 PINX1 rs7014168 4.991
+Pip PIP rs17366934 0.8611
+Pip4k2a PIP4K2A rs11013071 0.8655
+Pip4k2b PIP4K2B rs228293 1.763
+Pip4k2c PIP4K2C rs11613457 3.7375
+Pip5k1a PIP5K1A rs6587557 1.057
+Pip5k1c PIP5K1C rs1005170 1.4045
+Pip5kl1 PIP5KL1 rs7997 1.2138
+Pipox PIPOX rs7225915 2.8972
+Pirt PIRT rs4791426 1.12
+Pisd PISD rs1018490 1.0251
+Pitpna PITPNA rs17833825 1.4947
+Pitpnb PITPNB rs6519730 3.0705
+Pitpnc1 PITPNC1 rs11652752 2.6351
+Pitpnm1 PITPNM1 rs2276118 1.1834
+Pitpnm2 PITPNM2 rs7312673 2.1836
+A330068P14Rik PITPNM3 rs12449580 1.7031
+Pitrm1 PITRM1 rs9423502 1.1911
+Pitx1 PITX1 rs254550 1.0468
+Pitx2 PITX2 rs1900829 2.2634
+Pitx3 PITX3 rs17094517 0.9352
+Piwil1 PIWIL1 rs11832858 1.3827
+Piwil2 PIWIL2 rs4872465 1.4648
+Piwil4 PIWIL4 rs595127 1.1789
+Pja2 PJA2 rs6594370 1.1842
+Pkd1 PKD1 rs17134783 1.1355
+Pkd1l1 PKD1L1 rs17627534 2.9598
+Pkd1l2 PKD1L2 rs2317117 2.4291
+Pkd1l3 PKD1L3 rs17358402 2.1436
+Pkd2 PKD2 rs2725234 2.8804
+Pkd2l1 PKD2L1 rs2305384 2.7675
+Pkd2l2 PKD2L2 rs17647439 1.3818
+Pkdrej PKDREJ rs6007662 1.0959
+Pkhd1 PKHD1 rs6922220 1.4681
+Pkhd1l1 PKHD1L1 rs7834070 2.8706
+Pkia PKIA rs6997757 2.3451
+Pkib PKIB rs9385252 2.5355
+Pkig PKIG rs17829335 1.2407
+Pklr PKLR rs8177970 0.8677
+Pkmyt1 PKMYT1 rs12935188 1.6282
+Pkn1 PKN1 rs3810255 1.5825
+Pkn2 PKN2 rs1361594 2.6202
+Pkn3 PKN3 rs9697210 1.6747
+Pknox1 PKNOX1 rs11702511 1.5547
+Pknox2 PKNOX2 rs7950499 2.4443
+Pkp1 PKP1 rs16848172 1.1342
+Pkp2 PKP2 rs4931655 1.5887
+Pkp3 PKP3 rs6598025 1.8128
+Pkp4 PKP4 rs2108213 1.5829
+Pla2g12a PLA2G12A rs2301717 2.3451
+Pla2g12b PLA2G12B rs4745707 1.803
+Pla2g15 PLA2G15 rs12446418 8.4541
+Pla2g16 PLA2G16 rs11231525 1.6676
+Pla2g1b PLA2G1B rs7311566 1.5599
+Pla2g2a PLA2G2A rs10916685 1.6904
+Pla2g2c PLA2G2C rs3179690 2.5459
+Pla2g2d PLA2G2D rs569039 1.0099
+Pla2g2e PLA2G2E rs11799685 1.8392
+Pla2g2f PLA2G2F rs12065685 1.2233
+Pla2g3 PLA2G3 rs2074739 3.5815
+Pla2g4a PLA2G4A rs4651345 1.9215
+Pla2g4b PLA2G4B rs4923917 3.9318
+Pla2g4c PLA2G4C rs1019785 2.3565
+Pla2g4d PLA2G4D rs639227 1.9115
+Pla2g4e PLA2G4E rs8031547 1.6047
+Pla2g4f PLA2G4F rs8040255 3.3772
+Pla2g5 PLA2G5 rs681784 1.8921
+Pla2g6 PLA2G6 rs132985 2.9714
+Pla2g7 PLA2G7 rs7766818 0.8948
+Pla2r1 PLA2R1 rs17828734 3.7875
+Plaa PLAA rs10283643 1.2413
+Plac1l PLAC1L rs2276044 1.2304
+Plac8 PLAC8 rs41462844 1.644
+Plag1 PLAG1 rs1128143 1.2259
+Plagl1 PLAGL1 rs6570589 1.3579
+Plagl2 PLAGL2 rs6061195 0.8633
+Plaur PLAUR rs400058 2.2827
+Plb1 PLB1 rs2280733 1.5085
+Plbd1 PLBD1 rs16909912 2.5348
+Plbd2 PLBD2 rs3812826 0.8983
+Plcb1 PLCB1 rs12481262 2.9825
+Plcb2 PLCB2 rs11638662 1.6574
+Plcb3 PLCB3 rs2302263 2.1145
+Plcb4 PLCB4 rs6039389 2.0534
+Plcd1 PLCD1 rs11922130 1.0919
+Plcd3 PLCD3 rs2285427 2.1238
+Plce1 PLCE1 rs12771776 1.3256
+Plcg2 PLCG2 rs4586425 3.0503
+Plch1 PLCH1 rs1835212 2.9374
+Plch2 PLCH2 rs10910079 1.4743
+Plcl1 PLCL1 rs16825181 1.7585
+Plcl2 PLCL2 rs11716021 2.2917
+Plcxd2 PLCXD2 rs6807632 1.238
+Plcxd3 PLCXD3 rs581905 1.794
+Plcz1 PLCZ1 rs588066 1.5792
+Pld1 PLD1 rs7635003 0.983
+Pld2 PLD2 rs2279961 1.2692
+Pld3 PLD3 rs8108272 1.527
+Pld4 PLD4 rs2841247 0.8884
+Pld5 PLD5 rs3933049 1.6047
+Pld6 PLD6 rs4399589 1.5948
+Plec PLEC rs11136331 0.8713
+Plek PLEK rs6546383 1.7916
+Plek2 PLEK2 rs17248930 0.9957
+Plekha1 PLEKHA1 rs2281673 3.2144
+Plekha2 PLEKHA2 rs7008738 1.656
+Plekha3 PLEKHA3 rs12618265 1.2828
+Plekha4 PLEKHA4 rs17206686 1.6979
+Plekha5 PLEKHA5 rs1399471 2.081
+Plekha6 PLEKHA6 rs12141465 1.2647
+Plekha7 PLEKHA7 rs4757457 1.0901
+Plekha8 PLEKHA8 rs6973967 1.2184
+Plekhb1 PLEKHB1 rs7948050 1.3913
+Plekhb2 PLEKHB2 rs34052165 1.3377
+3830431G21Rik PLEKHD1 rs17107058 0.852
+Plekhf1 PLEKHF1 rs4805462 0.823
+Plekhf2 PLEKHF2 rs11778956 1.3465
+Plekhg1 PLEKHG1 rs6929935 1.4168
+Plekhg2 PLEKHG2 rs519575 1.1362
+Plekhg3 PLEKHG3 rs17179984 1.1609
+Plekhg4 PLEKHG4 rs9940665 8.185
+Plekhg5 PLEKHG5 rs1010583 0.8283
+Plekhg6 PLEKHG6 rs2228576 1.6035
+Plekhh1 PLEKHH1 rs4899214 1.4156
+Plekhh2 PLEKHH2 rs13418853 1.5706
+Plekhh3 PLEKHH3 rs8079242 2.9987
+Plekhm1 PLEKHM1 rs2684623 1.1205
+Plekhm2 PLEKHM2 rs2280115 1.1524
+Plekhm3 PLEKHM3 rs7582078 1.5575
+9930023K05Rik PLEKHS1 rs3124737 1.1017
+Plg PLG rs1084651 7.5268
+5033414D02Rik PLGRKT rs6476976 2.6519
+Plin1 PLIN1 rs7178463 1.7102
+Plin2 PLIN2 rs10963961 2.2844
+Plin3 PLIN3 rs3760949 1.1239
+Plin4 PLIN4 rs16992410 1.6334
+Plin5 PLIN5 rs16992410 1.6334
+Plk1 PLK1 rs4967959 1.5669
+Plk1s1 PLK1S1 rs16982500 1.5802
+Plk2 PLK2 rs32617 1.7133
+Plk5 PLK5 rs2292455 1.3293
+Pllp PLLP rs4293366 2.4019
+AK031320 PLN rs17424845 1.1456
+Plod1 PLOD1 rs7529452 0.971
+Plod2 PLOD2 rs7642145 0.997
+Plod3 PLOD3 rs3735337 1.7133
+Plrg1 PLRG1 rs7690851 2.4288
+AI427122 PLS1 rs953239 1.1337
+Plscr1 PLSCR1 rs343340 1.3769
+Plscr3 PLSCR3 rs2292068 1.4675
+Plscr4 PLSCR4 rs9815240 1.607
+Plscr5 PLSCR5 rs17432215 1.4239
+Pltp PLTP rs6065906 21.721
+Plvap PLVAP rs1529482 1.8681
+Plxdc1 PLXDC1 rs8068922 2.7701
+Plxdc2 PLXDC2 rs1412562 3.1568
+Plxna1 PLXNA1 rs1318548 1.6231
+Plxna2 PLXNA2 rs12035884 2.2481
+Plxnb2 PLXNB2 rs7290465 0.9208
+Plxnc1 PLXNC1 rs11837444 2.2935
+Plxnd1 PLXND1 rs2625978 1.1142
+Pm20d1 PM20D1 rs1361754 2.5116
+Pm20d2 PM20D2 rs13202074 2.9805
+Pmaip1 PMAIP1 rs1788870 1.6139
+Pmel PMEL rs11171710 1.4055
+Pmepa1 PMEPA1 rs6025743 1.2204
+Pmf1 PMF1 rs12722815 0.9923
+Pmfbp1 PMFBP1 rs217181 1.8392
+Pml PML rs5742915 1.5707
+Pmm1 PMM1 rs5751124 1.7233
+Pmm2 PMM2 rs12445515 1.9481
+Pmp2 PMP2 rs10097139 2.3766
+Pmp22 PMP22 rs8065610 1.9318
+Pmpca PMPCA rs4487900 0.908
+Pmpcb PMPCB rs3757507 0.8626
+Pms1 PMS1 rs5743175 1.2995
+Pms2 PMS2 rs3815383 1.5421
+Pnkd PNKD rs7587403 1.157
+Pnkp PNKP rs1290655 0.9412
+Pnldc1 PNLDC1 rs4235925 0.8153
+Pnliprp1 PNLIPRP1 rs1867991 0.9179
+Pnliprp2 PNLIPRP2 rs1867991 0.9179
+Pnma1 PNMA1 rs2075025 2.3894
+Pnmal1 PNMAL1 rs3745787 1.6586
+Pnmal2 PNMAL2 rs3745787 1.6586
+Pnmt PNMT rs881844 13.546
+Pnn PNN rs9322995 1.0466
+Pno1 PNO1 rs4078978 1.5977
+Pnoc PNOC rs2614067 1.16
+Pnp PNP rs7785 1.4396
+Pnpla1 PNPLA1 rs12189786 2.2712
+Pnpla3 PNPLA3 rs2072905 2.2749
+Pnpla5 PNPLA5 rs12160050 1.2578
+Pnpla6 PNPLA6 rs583984 1.459
+Pnpla8 PNPLA8 rs2041001 1.3036
+Pnpo PNPO rs12944587 1.6755
+Pnpt1 PNPT1 rs12993839 1.2427
+Pnrc1 PNRC1 rs958328 1.2832
+Pnrc2 PNRC2 rs1655201 0.8111
+Poc1b POC1B rs7305095 1.1295
+Poc5 POC5 rs2112347 3.5759
+Podn PODN rs12095038 1.6289
+Podnl1 PODNL1 rs10409785 1.5467
+Podxl PODXL rs1030146 1.2091
+Podxl2 PODXL2 rs782448 2.2447
+Pofut1 POFUT1 rs6087844 1.0078
+Pofut2 POFUT2 rs7282557 1.7086
+Pogk POGK rs10918585 1.5802
+Poglut1 POGLUT1 rs11922005 1.93
+Pogz POGZ rs4246527 1.07
+Pola2 POLA2 rs11227169 1.1935
+Polb POLB rs10096210 0.9263
+Pold1 POLD1 rs2546549 1.1547
+Pold2 POLD2 rs4640970 1.0725
+Pold3 POLD3 rs7110993 0.8877
+Pold4 POLD4 rs7123483 1.5311
+Poldip2 POLDIP2 rs7221462 1.4002
+Poldip3 POLDIP3 rs4822171 1.7833
+Pole POLE rs7397677 1.4143
+Pole2 POLE2 rs1011373 0.852
+Pole3 POLE3 rs818710 1.7368
+Pole4 POLE4 rs7586613 1.1357
+Polg POLG rs3176201 1.0982
+Polg2 POLG2 rs2872315 1.0169
+Polh POLH rs17287964 0.8791
+Poli POLI rs3730718 1.3136
+Poll POLL rs11597334 0.8097
+Polm POLM rs4640970 1.0725
+Poln POLN rs3762942 2.273
+Polr1a POLR1A rs1469982 2.1447
+Polr1b POLR1B rs10197847 1.0725
+Polr1c POLR1C rs1150798 2.7967
+Polr1d POLR1D rs1218959 1.8854
+Polr1e POLR1E rs913279 2.0041
+Polr2a POLR2A rs8069501 1.8088
+Polr2b POLR2B rs2271806 1.7555
+Polr2c POLR2C rs223839 2.6216
+Polr2d POLR2D rs17340503 1.0857
+Polr2e POLR2E rs2074452 1.8142
+Polr2f POLR2F rs139896 1.2234
+Polr2g POLR2G rs11603028 0.9223
+Polr2h POLR2H rs9944 1.3203
+Polr2i POLR2I rs17777328 1.0453
+Polr2m POLR2M rs4564513 1.902
+Polr3a POLR3A rs12764690 1.5308
+Polr3b POLR3B rs4964463 1.2817
+Polr3c POLR3C rs17352281 2.2366
+Polr3d POLR3D rs1126677 1.0879
+Polr3e POLR3E rs2290829 1.0239
+Polr3f POLR3F rs6035051 2.6094
+Polr3g POLR3G rs3763070 1.6668
+Polr3gl POLR3GL rs9424932 2.1283
+Polr3h POLR3H rs5751124 1.7233
+Polr3k POLR3K rs2562132 0.8722
+Polrmt POLRMT rs10426061 2.0133
+Pom121 POM121 rs2530492 0.9055
+Pom121l12 POM121L12 rs4947777 1.7368
+Gm24 POM121L2 rs6917419 0.9201
+Pomc POMC rs2304572 2.0323
+Pomgnt1 POMGNT1 rs3845301 2.7537
+Pomp POMP rs9579214 1.6334
+Pomt1 POMT1 rs7854462 1.4161
+Pomt2 POMT2 rs3759733 3.2024
+Pon1 PON1 rs705378 1.8771
+Pon2 PON2 rs740264 0.9698
+Pon3 PON3 rs2237584 1.2874
+Pop1 POP1 rs10504985 2.0799
+Pop4 POP4 rs10411749 2.5264
+Pop5 POP5 rs637799 2.001
+Pop7 POP7 rs221786 1.0272
+Popdc2 POPDC2 rs6773852 1.1045
+Popdc3 POPDC3 rs1190291 1.1364
+Por POR rs10280802 1.8821
+Postn POSTN rs12870273 1.3717
+Pot1a POT1 rs4728006 0.969
+Gm12715 POTEF rs11685415 1.3815
+Pou1f1 POU1F1 rs300982 1.1677
+Pou2af1 POU2AF1 rs7931570 1.5986
+Pou2f1 POU2F1 rs17344504 1.6216
+Pou2f2 POU2F2 rs10414815 1.518
+Pou2f3 POU2F3 rs717052 2.1072
+Pou3f1 POU3F1 rs12121956 1.669
+Pou3f3 POU3F3 rs4851726 2.3882
+Pou4f3 POU4F3 rs2277016 1.1924
+Pou5f1 POU5F1 rs3823417 3.5312
+Pou5f2 POU5F2 rs17371926 1.1252
+Pou6f1 POU6F1 rs11169724 1.2397
+Pou6f2 POU6F2 rs12535553 2.8658
+4921523A10Rik PP2D1 rs11128927 1.0604
+Ppa1 PPA1 rs10999211 2.8256
+Ppa2 PPA2 rs2726456 2.0797
+Ppan PPAN rs1979262 1.1248
+Ppap2a PPAP2A rs6450316 2.8359
+Ppap2b PPAP2B rs17114046 1.8489
+Ppap2c PPAP2C rs895343 1.2384
+Ppapdc1a PPAPDC1A rs10788085 2.8864
+Ppapdc2 PPAPDC2 rs1385454 1.9727
+Ppapdc3 PPAPDC3 rs10901363 1.1828
+Ppara PPARA rs6007662 1.0959
+Ppard PPARD rs9470001 2.2906
+Pparg PPARG rs4135280 4.03
+Ppargc1a PPARGC1A rs938067 2.3994
+Ppargc1b PPARGC1B rs245082 1.8801
+Ppat PPAT rs9684143 1.9574
+Ppbp PPBP rs2367442 0.8097
+Ppcdc PPCDC rs1822324 0.9539
+Ppdpf PPDPF rs10485825 1.3704
+Ppef2 PPEF2 rs17001192 1.3151
+Ppfia1 PPFIA1 rs670012 1.1777
+Ppfia2 PPFIA2 rs11114954 3.4312
+2410127E16Rik PPFIA3 rs11667393 1.3431
+Ppfia4 PPFIA4 rs1046529 1.5626
+Ppfibp1 PPFIBP1 rs11049091 1.871
+Ppfibp2 PPFIBP2 rs4500477 2.2237
+Pphln1 PPHLN1 rs7968314 1.3694
+Ppia PPIA rs17134462 1.4447
+Ppic PPIC rs10455038 1.8979
+Ppid PPID rs12640862 1.5113
+Ppie PPIE rs12136257 1.6419
+Ppif PPIF rs3824718 1.382
+Ppig PPIG rs7571407 1.0752
+Ppil1 PPIL1 rs41408445 0.9326
+Ppil2 PPIL2 rs108816 3.9586
+Ppil3 PPIL3 rs17467700 1.1239
+Ppil4 PPIL4 rs9791197 1.1213
+Ppil6 PPIL6 rs4946968 1.3925
+Ppip5k1 PPIP5K1 rs11635852 1.2377
+Ppip5k2 PPIP5K2 rs34774 1.3884
+Ppl PPL rs2251666 2.5764
+Ppm1a PPM1A rs721445 0.8874
+Ppm1b PPM1B rs6740758 1.7036
+Ppm1f PPM1F rs6000051 1.0868
+Ppm1g PPM1G rs11675428 1.5809
+Ppm1h PPM1H rs7979878 3.6914
+Ppm1j PPM1J rs6679 1.4232
+Ppm1k PPM1K rs11932727 1.751
+Ppm1l PPM1L rs1403046 0.9519
+Ppm1m PPM1M rs164637 3.8508
+Ppm1n PPM1N rs758761 1.2099
+Ppme1 PPME1 rs7944140 1.7605
+Ppox PPOX rs3813619 1.3813
+Ppp1ca PPP1CA rs1064876 1.339
+Ppp1cb PPP1CB rs17743079 1.633
+Ppp1cc PPP1CC rs7974047 2.0166
+Ppp1r10 PPP1R10 rs13195066 1.636
+Ppp1r11 PPP1R11 rs1245371 1.8312
+Ppp1r12b PPP1R12B rs3817222 3.5756
+Ppp1r12c PPP1R12C rs11672111 2.1797
+Ppp1r13b PPP1R13B rs10140452 1.0105
+Ppp1r13l PPP1R13L rs12610134 1.6209
+Ppp1r14a PPP1R14A rs2304174 1.8441
+Ppp1r14b PPP1R14B rs2302263 2.1145
+Ppp1r14c PPP1R14C rs9371818 1.62
+Ppp1r14d PPP1R14D rs2010300 1.6786
+Myd116 PPP1R15A rs17206686 1.6979
+Ppp1r15b PPP1R15B rs7527664 1.4694
+Ppp1r16a PPP1R16A rs2721166 2.5933
+Ppp1r16b PPP1R16B rs1535431 0.8611
+Ppp1r17 PPP1R17 rs12701133 1.4947
+Ppp1r18 PPP1R18 rs2285320 1.5852
+Ppp1r1a PPP1R1A rs1874308 2.1002
+Ppp1r1b PPP1R1B rs881844 13.546
+Ppp1r1c PPP1R1C rs1153744 1.3774
+Ppp1r2 PPP1R2 rs862063 1.5588
+Klraq1 PPP1R21 rs7566996 1.5855
+Ppp1r26 PPP1R26 rs535 1.7354
+Ppp1r2-ps3 PPP1R2P3 rs11948317 2.5662
+Ppp1r32 PPP1R32 rs3802952 0.9066
+Ppp1r36 PPP1R36 rs2045022 1.2043
+Ppp1r37 PPP1R37 rs6509196 2.4991
+Ppp1r3a PPP1R3A rs12705917 1.3481
+Ppp1r3b PPP1R3B rs330938 2.1653
+Ppp1r3c PPP1R3C rs11598720 1.8184
+Ppp1r3e PPP1R3E rs10144278 1.5275
+Ppp1r3g PPP1R3G rs200854 0.8601
+Ppp1r42 PPP1R42 rs11785782 1.4514
+Ppp1r7 PPP1R7 rs7568781 1.115
+Ppp1r8 PPP1R8 rs11247696 0.9722
+Ppp1r9a PPP1R9A rs2188477 2.3044
+Ppp1r9b PPP1R9B rs847698 1.3058
+Ppp2cb PPP2CB rs1116003 1.506
+Ppp2r1a PPP2R1A rs7246914 1.7525
+Ppp2r1b PPP2R1B rs10891290 2.7595
+Ppp2r2a PPP2R2A rs2467728 3.1334
+Ppp2r2b PPP2R2B rs12514241 2.7395
+Ppp2r2c PPP2R2C rs6856061 3.2841
+Ppp2r2d PPP2R2D rs10430614 0.9378
+Ppp2r3a PPP2R3A rs17198295 2.9066
+Ppp2r3c PPP2R3C rs7155406 2.1445
+Ppp2r4 PPP2R4 rs10988229 2.1685
+Ppp2r5a PPP2R5A rs11589817 2.6759
+Ppp2r5b PPP2R5B rs11606204 2.3351
+Ppp2r5d PPP2R5D rs2274517 3.8642
+Ppp2r5e PPP2R5E rs9944005 0.8652
+Ppp3ca PPP3CA rs1031034 2.4117
+Ppp3cb PPP3CB rs11000698 1.264
+Ppp3cc PPP3CC rs13271192 1.7433
+Ppp3r1 PPP3R1 rs13003580 1.3451
+Ppp3r2 PPP3R2 rs17774987 2.1649
+Ppp4r1 PPP4R1 rs11872991 1.2963
+Ppp4r4 PPP4R4 rs8015929 1.0744
+Ppp5c PPP5C rs7250724 1.3098
+Ppp6c PPP6C rs10986569 1.0861
+Ppp6r1 PPP6R1 rs12983684 1.7421
+Ppp6r3 PPP6R3 rs11228270 2.0742
+Pprc1 PPRC1 rs4917960 0.9735
+Ppt1 PPT1 rs2247 1.6404
+Ppt2 PPT2 rs17493811 1.0915
+Ppwd1 PPWD1 rs6895641 1.3329
+Ppy PPY rs1662750 5.1262
+Pqlc2 PQLC2 rs1535037 1.1613
+Pqlc3 PQLC3 rs11892684 1.8145
+1700040I03Rik PRADC1 rs909065 0.8173
+mCT168149 PRAM1 rs1054623 2.9401
+mCT118583 PRAME rs8405 1.6444
+Pramef6 PRAMEF12 rs4072841 1.4246
+Gm13102 PRAMEF2 rs11121996 1.5151
+Pramef8 PRAMEF8 rs2884616 1.0274
+Prap1 PRAP1 rs11101683 2.0436
+MMT00014721 PRB1 rs2900149 1.8658
+Prp15 PRB4 rs2923234 1.404
+Prc1 PRC1 rs1867219 1.6415
+Prcc PRCC rs3806415 3.2961
+Prcp PRCP rs7924546 1.2906
+Prdm1 PRDM1 rs10499050 1.721
+Prdm10 PRDM10 rs1783919 2.3202
+Prdm11 PRDM11 rs4755942 1.7693
+Prdm12 PRDM12 rs11244096 2.8857
+Prdm13 PRDM13 rs330838 0.9014
+Prdm14 PRDM14 rs10216606 2.1221
+Prdm15 PRDM15 rs2277793 2.8739
+Prdm16 PRDM16 rs946758 2.7545
+Prdm2 PRDM2 rs2744679 2.0698
+Prdm4 PRDM4 rs1816009 1.9122
+Prdm5 PRDM5 rs4833173 1.7031
+Prdm6 PRDM6 rs459638 1.8502
+Prdm8 PRDM8 rs41527946 1.1961
+Prdm9 PRDM9 rs2914276 0.9473
+Prdx1 PRDX1 rs12137389 0.8781
+Prdx3 PRDX3 rs11819388 1.2607
+Prdx5 PRDX5 rs11231749 2.0274
+Preb PREB rs7588926 1.3223
+Prep PREP rs17065778 1.1739
+Prepl PREPL rs2033951 2.1732
+Prex1 PREX1 rs13043461 2.8441
+Prex2 PREX2 rs10100326 1.452
+Prg1 PRG1 rs11671911 1.5433
+Prg2 PRG2 rs2584860 1.9813
+Prg3 PRG3 rs2584860 1.9813
+Prg4 PRG4 rs630472 1.3542
+Prhoxnb PRHOXNB rs2481952 1.4793
+Prickle1 PRICKLE1 rs17091364 2.0228
+Prickle2 PRICKLE2 rs12493866 1.6012
+Prickle4 PRICKLE4 rs4711690 2.1268
+Prima1 PRIMA1 rs8014232 1.4567
+Prkaa1 PRKAA1 rs6868112 1.5346
+Prkaa2 PRKAA2 rs2796528 1.0895
+Prkacb PRKACB rs2792975 2.6271
+Prkag1 PRKAG1 rs7311091 1.1649
+Prkag2 PRKAG2 rs2241053 2.7203
+Prkag3 PRKAG3 rs4674345 1.4153
+Prkar1a PRKAR1A rs11653751 2.7711
+Prkar2b PRKAR2B rs3779500 3.5429
+Prkca PRKCA rs6504459 2.6668
+Prkcb PRKCB rs886116 1.848
+Prkcd PRKCD rs7615501 2.473
+Prkcdbp PRKCDBP rs10769684 1.2827
+Prkce PRKCE rs13024806 2.1736
+Prkcc PRKCG rs11672982 1.317
+Prkch PRKCH rs7148876 1.4613
+Prkci PRKCI rs6797896 1.0973
+Prkcq PRKCQ rs4750470 2.6726
+Prkcsh PRKCSH rs167479 1.1759
+Prkcz PRKCZ rs2292857 2.0711
+Prkcm PRKD1 rs1953205 2.9003
+Prkd2 PRKD2 rs931647 1.0094
+Prkcn PRKD3 rs4670678 1.4756
+Prkdc PRKDC rs8178086 1.5
+Prkg1 PRKG1 rs10999872 2.6169
+Prkg2 PRKG2 rs2685462 1.9205
+Prkrip1 PRKRIP1 rs3988090 1.3066
+Prkrir PRKRIR rs750801 1.4482
+Prl PRL rs849872 1.3497
+MMT00044992 PRLH rs6431547 1.0853
+Prlhr PRLHR rs10886272 1.8274
+Prlr PRLR rs344507 1.8938
+Prm1 PRM1 rs243315 2.4877
+Prm2 PRM2 rs243315 2.4877
+Prm3 PRM3 rs243315 2.4877
+Prmt1 PRMT1 rs7251 1.1906
+Prmt10 PRMT10 rs17475359 1.203
+Prmt2 PRMT2 rs34722617 1.3044
+Prmt3 PRMT3 rs17232646 1.5938
+Prmt5 PRMT5 rs2295904 1.234
+Prmt6 PRMT6 rs3108680 1.9897
+Prmt7 PRMT7 rs11350 18.283
+Prmt8 PRMT8 rs2109193 1.8626
+Prnd PRND rs2422937 1.0803
+Prnp PRNP rs16989992 1.2629
+Proc PROC rs2069934 1.0014
+Procr PROCR rs6058202 3.8327
+Prodh PRODH rs5993426 2.26
+Prodh2 PRODH2 rs17777002 1.6533
+Prok1 PROK1 rs11102114 1.0122
+Prok2 PROK2 rs1343839 2.4725
+Prokr1 PROKR1 rs7572135 1.8972
+Prokr2 PROKR2 rs6038134 1.9618
+MMT00040149 PROL1 rs988180 1.5261
+Prom1 PROM1 rs6849167 1.6047
+Prom2 PROM2 rs2278067 1.4132
+Prop1 PROP1 rs4604209 2.0464
+Prorsd1 PRORSD1P rs2589071 1.2688
+Pros1 PROS1 rs6771536 1.0114
+Prosc PROSC rs7831942 0.9654
+2810046L04Rik PROSER1 rs9566386 0.9539
+Prox1 PROX1 rs2075424 3.0432
+Proz PROZ rs2026160 1.018
+Prpf18 PRPF18 rs7095654 1.9859
+Prpf19 PRPF19 rs7925540 1.0838
+Prpf3 PRPF3 rs7511649 0.8604
+Prpf31 PRPF31 rs36640 2.9274
+Prpf38a PRPF38A rs3087476 1.0535
+Prpf38b PRPF38B rs12122967 1.4102
+Prpf39 PRPF39 rs3783700 1.1095
+Prpf4 PRPF4 rs10733604 1.7875
+Prpf40a PRPF40A rs10181036 2.0693
+Prpf40b PRPF40B rs11169088 1.2997
+Prpf4b PRPF4B rs6939049 1.9586
+Prpf6 PRPF6 rs817318 0.8948
+Prpf8 PRPF8 rs7503397 0.8139
+Prph PRPH rs11837234 3.4465
+Prph2 PRPH2 rs6928781 3.0764
+Prps1l1 PRPS1L1 rs4535616 3.0045
+Prpsap1 PRPSAP1 rs346789 1.5031
+Prpsap2 PRPSAP2 rs11656953 1.7765
+Prr11 PRR11 rs9909925 1.1508
+Prr13 PRR13 rs17695156 1.1567
+Prr14 PRR14 rs1036696 1.6492
+C330019G07Rik PRR14L rs5753767 2.2556
+Prr15 PRR15 rs17810763 1.6192
+Prr15l PRR15L rs12944587 1.6755
+Prr16 PRR16 rs1608670 2.1669
+Prr18 PRR18 rs6934656 0.8703
+Prr22 PRR22 rs7257191 1.2541
+Prr24 PRR24 rs10406676 1.7335
+Prr3 PRR3 rs2524222 0.9333
+Arhgap8 PRR5 rs136904 1.2424
+Prr5l PRR5L rs4756302 2.5867
+Prr7 PRR7 rs1801020 1.5835
+Prr9 PRR9 rs12757933 1.9927
+Prrc1 PRRC1 rs10491249 1.2699
+Prrc2a PRRC2A rs3117583 4.5605
+Prrc2b PRRC2B rs7854462 1.4161
+Prrc2c PRRC2C rs11589825 0.8173
+Prrg4 PRRG4 rs198270 1.9352
+Prrt1 PRRT1 rs2269426 1.555
+Prrt3 PRRT3 rs13097123 2.0881
+Prrt4 PRRT4 rs962294 1.2704
+Prrx1 PRRX1 rs10919449 1.7711
+Prrx2 PRRX2 rs12004168 2.1431
+Prss1 PRSS1 rs4726584 0.9884
+Prss16 PRSS16 rs6917419 0.9201
+Prss23 PRSS23 rs2511880 1.9197
+Prss27 PRSS27 rs11646634 1.58
+mCT171504 PRSS3 rs855552 1.6451
+Prss35 PRSS35 rs1171106 1.137
+Prss36 PRSS36 rs11865499 1.1896
+Prss38 PRSS38 rs11590137 1.5957
+Prss42 PRSS42 rs12106917 2.1749
+Prss45 PRSS45 rs6768252 1.9205
+Prss46 PRSS46 rs7642783 1.4938
+Prss48 PRSS48 rs17027480 1.636
+Prss50 PRSS50 rs6768252 1.9205
+Prss54 PRSS54 rs4784030 1.0426
+Prss55 PRSS55 rs4496927 1.5325
+Prss56 PRSS56 rs2117770 1.783
+Prss57 PRSS57 rs10426061 2.0133
+Prss8 PRSS8 rs11865499 1.1896
+MMT00038006 PRTFDC1 rs12246114 1.5424
+Prtg PRTG rs9972557 1.9169
+Prtn3 PRTN3 rs12460890 1.2608
+Prune PRUNE rs267733 1.2416
+Prune2 PRUNE2 rs870923 2.5358
+Prx PRX rs744389 1.4602
+Psap PSAP rs2593163 1.6196
+Psapl1 PSAPL1 rs7693497 1.1383
+Psat1 PSAT1 rs7018865 2.2743
+Psca PSCA rs4545075 2.8182
+Psd PSD rs7076748 1.3418
+Psd2 PSD2 rs165185 2.2655
+Psd3 PSD3 rs7824687 2.8551
+Psd4 PSD4 rs6751201 1.2456
+Psen1 PSEN1 rs7523 0.9893
+Psen2 PSEN2 rs2313007 1.7967
+mCT3269 PSG4 rs2355416 0.8351
+Psip1 PSIP1 rs2665515 2.4101
+Pskh1 PSKH1 rs16942887 32.076
+Psma1 PSMA1 rs7121538 3.9763
+Psma2 PSMA2 rs582214 1.8083
+Psma4 PSMA4 rs8031948 1.4755
+Psma5 PSMA5 rs3768492 1.8002
+Psma6 PSMA6 rs3742370 0.969
+Psma7 PSMA7 rs13043478 1.35
+Psma8 PSMA8 rs8087585 1.081
+Psmb1 PSMB1 rs7772282 1.5653
+Psmb10 PSMB10 rs16942887 32.076
+Psmb11 PSMB11 rs2295689 3.0717
+Psmb3 PSMB3 rs228293 1.763
+Psmb4 PSMB4 rs6704126 0.9519
+Psmb5 PSMB5 rs2295689 3.0717
+Psmb6 PSMB6 rs2279961 1.2692
+Psmb7 PSMB7 rs6478659 1.7567
+Psmb8 PSMB8 rs2621366 3.0859
+Psmb9 PSMB9 rs7383287 2.9201
+Psmc1 PSMC1 rs6575121 1.9059
+Psmc2 PSMC2 rs4498476 1.1823
+Psmc3 PSMC3 rs10838708 5.6476
+Psmc3ip PSMC3IP rs676387 2.3565
+Psmc4 PSMC4 rs234368 1.1269
+Psmc5 PSMC5 rs2727280 1.291
+Psmc6 PSMC6 rs7142734 0.9991
+Psmd1 PSMD1 rs11692681 1.3195
+Psmd11 PSMD11 rs1047208 1.0017
+Psmd12 PSMD12 rs11652752 2.6351
+Psmd13 PSMD13 rs4980319 1.4276
+Psmd14 PSMD14 rs3769957 1.983
+Psmd2 PSMD2 rs1687230 1.0777
+Psmd3 PSMD3 rs3894194 6.433
+Psmd4 PSMD4 rs16833107 0.9658
+Psmd5 PSMD5 rs10818476 1.0929
+Psmd7 PSMD7 rs1424083 1.8817
+Psmd8 PSMD8 rs2074979 0.8108
+Psmd9 PSMD9 rs3847694 3.5758
+Psme1 PSME1 rs11574503 1.442
+Psme2 PSME2 rs11574503 1.442
+Psme4 PSME4 rs17189589 2.7577
+Psmf1 PSMF1 rs2317428 1.1936
+Psmg1 PSMG1 rs2836935 1.289
+Psmg2 PSMG2 rs4797700 1.5349
+Psmg3 PSMG3 rs4725235 1.1946
+Psmg4 PSMG4 rs3813485 1.3028
+Psors1c2 PSORS1C2 rs3823417 3.5312
+Pspc1 PSPC1 rs17086486 1.0494
+Psph PSPH rs4947534 1.1967
+Pspn PSPN rs7540 1.4981
+Psrc1 PSRC1 rs629301 7.2084
+Pstk PSTK rs7085513 2.6119
+Pstpip1 PSTPIP1 rs1567670 0.9348
+Pstpip2 PSTPIP2 rs7227347 1.0795
+Ptafr PTAFR rs11247757 1.3302
+Ptar1 PTAR1 rs10511969 1.0339
+Ptbp1 PTBP1 rs4536588 1.8794
+Ptbp2 PTBP2 rs490376 2.1045
+Rod1 PTBP3 rs6477902 1.7711
+Ptcd2 PTCD2 rs2879132 1.5678
+Ptcd3 PTCD3 rs17618119 1.6582
+Ptch1 PTCH1 rs16909877 1.4761
+Ptchd2 PTCHD2 rs11585441 1.9965
+Ptchd3 PTCHD3 rs16927368 2.1898
+3110082D06Rik PTCHD4 rs12213871 2.7109
+Ptcra PTCRA rs4711731 1.927
+Ptdss1 PTDSS1 rs6468501 1.1713
+Ptdss2 PTDSS2 rs11823647 1.8914
+Pten PTEN rs11202575 1.0065
+Pter PTER rs7896320 1.7135
+AK053418 PTF1A rs1408987 0.8336
+Ptgdr PTGDR rs1495788 1.2935
+Gpr44 PTGDR2 rs10792297 2.6891
+Ptgds PTGDS rs11790360 2.3242
+Ptger1 PTGER1 rs3810255 1.5825
+Ptger2 PTGER2 rs1495788 1.2935
+Ptger3 PTGER3 rs3000466 1.8167
+Ptger4 PTGER4 rs7729520 1.5131
+Ptges PTGES rs12004168 2.1431
+Ptges2 PTGES2 rs7047254 1.3447
+Ptgfr PTGFR rs520171 1.9606
+Ptgfrn PTGFRN rs12144703 1.9446
+Ptgir PTGIR rs931647 1.0094
+Ptgis PTGIS rs556731 1.2411
+Ptgr1 PTGR1 rs4979009 1.9248
+Ptgr2 PTGR2 rs8022626 1.0976
+Ptgs1 PTGS1 rs10306194 1.7293
+Ptgs2 PTGS2 rs12042763 0.8798
+Pth PTH rs4356206 3.0345
+Pth1r PTH1R rs12106917 2.1749
+Pth2 PTH2 rs7260611 1.6631
+Pth2r PTH2R rs12694110 1.702
+Pthlh PTHLH rs7309492 1.3599
+Ptk2 PTK2 rs7816620 0.9618
+Ptk2b PTK2B rs4733047 1.7978
+Ptk6 PTK6 rs10485825 1.3704
+Ptk7 PTK7 rs9472004 1.9241
+Ptma PTMA rs4973469 1.8136
+Ptms PTMS rs2301333 2.0057
+Ptn PTN rs1431097 1.8804
+Ptov1 PTOV1 rs1290655 0.9412
+Ptp4a1 PTP4A1 rs1322416 0.9385
+Ptp4a3 PTP4A3 rs7459842 1.4671
+AW456874 PTPDC1 rs10993052 2.6139
+Ptplad1 PTPLAD1 rs352480 0.957
+Ptplad2 PTPLAD2 rs10757172 2.0844
+Ptplb PTPLB rs12665 1.2145
+Ptpmt1 PTPMT1 rs12269874 0.9359
+Ptpn1 PTPN1 rs6067462 2.1163
+Ptpn11 PTPN11 rs11066320 2.7027
+Ptpn12 PTPN12 rs17467135 1.8164
+Ptpn13 PTPN13 rs2869619 2.9666
+Ptpn14 PTPN14 rs10465696 1.9796
+Ptpn18 PTPN18 rs11542415 1.1487
+Ptpn2 PTPN2 rs8090599 1.1589
+Ptpn20 PTPN20B rs812740 1.7279
+Ptpn21 PTPN21 rs2991286 1.9326
+Ptpn22 PTPN22 rs12064048 1.0875
+Ptpn3 PTPN3 rs10979839 1.5715
+Ptpn4 PTPN4 rs6542553 1.0884
+Ptpn5 PTPN5 rs4757718 1.7347
+Ptpra PTPRA rs1016753 1.7203
+Ptprb PTPRB rs2717440 2.5303
+Ptprc PTPRC rs3767748 1.5553
+Ptprcap PTPRCAP rs1064876 1.339
+Ptprd PTPRD rs7873146 2.8222
+Ptpre PTPRE rs11016021 2.2039
+Ptprf PTPRF rs11580074 1.4716
+Ptprg PTPRG rs1317639 3.0925
+Ptprh PTPRH rs2288527 1.4681
+Ptprj PTPRJ rs2270993 11.309
+Ptprk PTPRK rs13215503 1.9582
+Ptprm PTPRM rs9966297 2.0507
+Ptprn2 PTPRN2 rs11760246 2.1137
+Ptpro PTPRO rs1121613 2.5667
+Ptprq PTPRQ rs17007033 1.6449
+Ptprr PTPRR rs9971806 3.4564
+Ptprs PTPRS rs7246182 2.1424
+Ptprt PTPRT rs6513775 2.3966
+Ptpru PTPRU rs12032476 2.202
+Ptprv PTPRVP rs10920357 0.9496
+Ptrf PTRF rs7222941 1.0373
+Ptrh1 PTRH1 rs564754 1.4466
+Ptrh2 PTRH2 rs7213568 1.2073
+2410017P09Rik PTRHD1 rs1550116 3.2937
+Pts PTS rs4084132 1.3535
+Pttg1 PTTG1 rs12515175 1.207
+Pttg1ip PTTG1IP rs235314 3.4064
+Ptx3 PTX3 rs1456099 1.2389
+Ptx4 PTX4 rs1040499 1.0024
+Puf60 PUF60 rs4875051 1.2205
+Pum1 PUM1 rs11586148 1.5561
+Pum2 PUM2 rs7585590 3.4746
+Purb PURB rs1006231 2.7572
+Purg PURG rs17648656 2.3335
+Pus1 PUS1 rs9652059 1.1153
+Pus10 PUS10 rs12988616 1.3743
+Pus7 PUS7 rs6972417 1.198
+Pus7l PUS7L rs1461567 1.2935
+Pvalb PVALB rs6519034 1.3334
+Pvr PVR rs6509170 3.392
+EG620147 PVRIG rs11762932 1.3452
+Pvrl1 PVRL1 rs884395 1.7162
+Pvrl2 PVRL2 rs6857 17.258
+Pvrl4 PVRL4 rs1556260 2.1183
+Pvt1 PVT1 rs2608056 3.2036
+Pwp1 PWP1 rs12322163 2.1367
+Pwwp2a PWWP2A rs2546962 0.9678
+Pwwp2b PWWP2B rs2474339 0.9485
+1300014I06Rik PXDC1 rs2296260 1.3672
+Pxdn PXDN rs9712143 1.5467
+Pxk PXK rs1427 1.6077
+Pxmp4 PXMP4 rs2377925 1.3451
+Pxn PXN rs3759397 1.3506
+Pxt1 PXT1 rs7747099 1.8013
+Pygb PYGB rs3787081 1.8019
+Pygl PYGL rs883699 2.3329
+Pygm PYGM rs551890 1.4752
+Pygo1 PYGO1 rs9972557 1.9169
+Pyroxd1 PYROXD1 rs10161132 0.9017
+Pyroxd2 PYROXD2 rs7096654 0.8327
+Pyy PYY rs1642599 5.4369
+Pzp PZP rs10771386 1.6488
+Qdpr QDPR rs13138275 1.9014
+Qk QKI rs6939187 2.0887
+Qpct QPCT rs3815555 1.486
+Qpctl QPCTL rs10423928 3.2446
+Qprt QPRT rs7198549 1.2985
+Qrfp QRFP rs3857984 1.3796
+Qrfpr QRFPR rs6827473 1.8377
+2610028H07Rik QRICH1 rs4974081 0.9416
+Qrich2 QRICH2 rs346789 1.5031
+Qrsl1 QRSL1 rs9386570 0.9535
+Qser1 QSER1 rs2297781 0.9241
+Qsox1 QSOX1 rs357104 1.8225
+Qsox2 QSOX2 rs7038554 1.0494
+Qtrt1 QTRT1 rs7251693 2.2035
+Qtrtd1 QTRTD1 rs2306730 1.0985
+R3hcc1 R3HCC1 rs10101089 2.0993
+D19Ertd386e R3HCC1L rs1325494 1.7481
+R3hdm1 R3HDM1 rs12465802 2.9215
+R3hdm2 R3HDM2 rs3741414 7.7857
+C030046I01Rik R3HDM4 rs350134 1.1903
+MMT00009592 R3HDML rs1884613 1.08
+Rab11a RAB11A rs12904203 3.2952
+Rab11b RAB11B rs7255436 7.4888
+Rab11fip1 RAB11FIP1 rs7016233 1.8742
+Rab11fip2 RAB11FIP2 rs11198240 1.3758
+Rab11fip4 RAB11FIP4 rs8065496 1.8539
+Rab11fip5 RAB11FIP5 rs6710755 1.1154
+Rab12 RAB12 rs11081400 1.1521
+Rab13 RAB13 rs6413529 0.9842
+Rab17 RAB17 rs6755617 1.1763
+Rab18 RAB18 rs9804355 1.2147
+Rab19 RAB19 rs12539339 1.4795
+Rab1 RAB1A rs1009358 3.4916
+Rab1b RAB1B rs3814739 1.1383
+Rab20 RAB20 rs2479426 1.6103
+Rab21 RAB21 rs12368844 1.4105
+Rab22a RAB22A rs522045 1.9435
+Rab23 RAB23 rs7774488 2.0308
+Rab26 RAB26 rs8062006 1.1593
+Rab27a RAB27A rs10851585 1.1828
+Rab27b RAB27B rs12604321 0.9504
+Rab28 RAB28 rs10023750 1.9389
+Rab2a RAB2A rs10957148 1.4944
+Rab30 RAB30 rs7113358 1.5733
+Rab31 RAB31 rs8092431 2.5464
+Rab32 RAB32 rs1407373 1.1117
+Rab35 RAB35 rs17499109 2.1521
+Rab36 RAB36 rs5759612 1.219
+Rab37 RAB37 rs11657185 1.3441
+Rab38 RAB38 rs2386579 3.0578
+Rab39 RAB39A rs17107565 2.2668
+Rab3b RAB3B rs2792593 0.9348
+Rab3c RAB3C rs17359529 1.6761
+Rab3d RAB3D rs3816864 1.3527
+Rab3gap1 RAB3GAP1 rs7570971 2.4861
+Rab3gap2 RAB3GAP2 rs12129903 0.9574
+Rab3il1 RAB3IL1 rs174449 13.885
+Rab3ip RAB3IP rs7297056 1.436
+Rab40b RAB40B rs2252314 1.791
+Rab40c RAB40C rs9929621 1.1517
+Rab42 RAB42 rs9426420 2.4662
+1810048P08Rik RAB43 rs9841893 1.1266
+Rab44 RAB44 rs3176344 2.1918
+Rab4a RAB4A rs237775 1.5131
+Rab4b RAB4B rs4803372 1.4827
+Rab5a RAB5A rs11128927 1.0604
+Rab5b RAB5B rs11171710 1.4055
+Rab5c RAB5C rs12600570 2.6598
+Rab6 RAB6A rs7126049 0.8742
+Rab6b RAB6B rs41453446 2.2258
+Rab7 RAB7A rs9853945 1.3999
+Rab7l1 RAB7L1 rs1832775 2.7201
+Rab8a RAB8A rs2032882 1.6488
+Rab8b RAB8B rs1867479 4.4827
+Rabac1 RABAC1 rs1075728 1.2096
+Rabep1 RABEP1 rs7215336 1.3719
+Rabep2 RABEP2 rs6565259 2.3576
+Rabgap1 RABGAP1 rs12685577 1.4254
+Rabgap1l RABGAP1L rs6666169 2.37
+Rabgef1 RABGEF1 rs3764902 1.7352
+Rabggta RABGGTA rs3940231 1.7162
+Rabggtb RABGGTB rs721204 0.9281
+Rabif RABIF rs1131905 1.4249
+Rabl2a RABL2B rs3865766 0.9543
+Rabl3 RABL3 rs6785511 1.5018
+Rabl6 RABL6 rs2811749 1.5594
+Rac1 RAC1 rs702485 5.862
+Rac2 RAC2 rs6000644 1.4379
+Rac3 RAC3 rs4969483 1.5018
+Racgap1 RACGAP1 rs457487 1.6334
+Rad17 RAD17 rs10077413 1.4527
+Rad18 RAD18 rs658339 0.8199
+Rad21 RAD21 rs10098598 1.4925
+Rad21l RAD21L1 rs6074272 1.4157
+Rad23b RAD23B rs1323808 4.0818
+Rad50 RAD50 rs17772565 2.0478
+Rad51 RAD51 rs11855560 2.3641
+Rad51ap1 RAD51AP1 rs12831479 1.358
+Rad51ap2 RAD51AP2 rs11689379 1.1183
+Rad51l1 RAD51B rs8008240 3.3687
+Rad51l3 RAD51D rs7215209 1.2481
+Rad52 RAD52 rs7954858 0.9594
+E130016E03Rik RAD54B rs3133637 1.1488
+Rad54l RAD54L rs12738055 1.5699
+Rad54l2 RAD54L2 rs4494918 1.3792
+Rad9 RAD9A rs1064876 1.339
+Radil RADIL rs3750016 1.7765
+Rae1 RAE1 rs6025518 1.1734
+Raet1e RAET1E rs10428740 1.3018
+Raf1 RAF1 rs2442807 1.3702
+Rag1 RAG1 rs11033690 1.3708
+Rag2 RAG2 rs1498336 1.5312
+Rai1 RAI1 rs11653424 3.6035
+Rai14 RAI14 rs11950200 1.7284
+Rala RALA rs10266149 1.1242
+Ralb RALB rs17050080 1.3866
+Ralbp1 RALBP1 rs10468797 1.624
+Ralgapa1 RALGAPA1 rs10129498 1.2439
+Ralgapa2 RALGAPA2 rs1555361 2.4012
+Ralgapb RALGAPB rs805532 1.902
+Ralgds RALGDS rs6597598 1.9566
+Ralgps1 RALGPS1 rs1556889 2.7943
+Ralgps2 RALGPS2 rs913857 2.5136
+Raly RALY rs6142078 2.8292
+Ralyl RALYL rs6473532 1.5563
+Ramp1 RAMP1 rs4349294 2.501
+Ramp2 RAMP2 rs4792953 2.1614
+Ramp3 RAMP3 rs4724363 1.8861
+Ran RAN rs7954011 2.0659
+Ranbp1 RANBP1 rs2286928 1.3489
+Ranbp10 RANBP10 rs8057184 6.8894
+Ranbp17 RANBP17 rs1445796 3.5834
+Ranbp2 RANBP2 rs6716426 1.4923
+Ranbp3 RANBP3 rs11882789 1.9352
+Ranbp3l RANBP3L rs2133766 1.05
+Ranbp6 RANBP6 rs10758734 1.1852
+Ranbp9 RANBP9 rs3757261 2.0403
+Rangap1 RANGAP1 rs139477 0.9014
+Rangrf RANGRF rs1132554 2.6055
+Rap1a RAP1A rs565522 1.7491
+Rap1b RAP1B rs3970938 1.01
+Rap1gap RAP1GAP rs12128325 1.9551
+Rap1gap2 RAP1GAP2 rs178557 2.0745
+Rap1gds1 RAP1GDS1 rs6834829 2.4299
+Rap2a RAP2A rs9584567 1.634
+Rapgef1 RAPGEF1 rs2183021 1.5372
+Rapgef2 RAPGEF2 rs10022399 0.901
+Rapgef3 RAPGEF3 rs11168236 1.0219
+Rapgef4 RAPGEF4 rs2290378 3.1542
+Rapgef5 RAPGEF5 rs16873135 1.6007
+Rapgef6 RAPGEF6 rs13360460 1.869
+Rapgefl1 RAPGEFL1 rs16965666 1.4633
+Rapsn RAPSN rs10838708 5.6476
+Rara RARA rs13695 1.4533
+Rarb RARB rs1560633 1.9259
+Rarg RARG rs1465056 2.3702
+Rarres1 RARRES1 rs4680458 1.797
+Rarres2 RARRES2 rs11974980 0.8167
+Rars RARS rs2305734 1.8005
+Rars2 RARS2 rs17504727 1.5447
+Rasa1 RASA1 rs1062035 1.1925
+Rasa2 RASA2 rs9844564 1.4802
+Rasa3 RASA3 rs2274712 1.4156
+Rasa4 RASA4 rs2908625 0.9574
+Rasal1 RASAL1 rs2701626 2.2793
+Rasal2 RASAL2 rs6688185 1.7867
+Rasal3 RASAL3 rs10854145 0.9527
+Rasd1 RASD1 rs16961756 1.584
+Rasd2 RASD2 rs926337 1.3367
+Rasef RASEF rs13298758 1.1001
+Rasgef1a RASGEF1A rs2460545 1.143
+Rasgef1b RASGEF1B rs17005145 1.7683
+Rasgef1c RASGEF1C rs1867447 2.0335
+Rasgrf1 RASGRF1 rs11632649 1.7217
+Rasgrf2 RASGRF2 rs571653 2.6851
+Rasgrp1 RASGRP1 rs12912109 1.83
+Rasgrp2 RASGRP2 rs3862373 2.6876
+Rasgrp3 RASGRP3 rs41369046 1.5243
+Rasgrp4 RASGRP4 rs3745962 0.9318
+Rasip1 RASIP1 rs281384 2.5312
+Rasl10b RASL10B rs2240903 0.9052
+Rasl11b RASL11B rs7667301 2.3905
+Rasl12 RASL12 rs4238399 1.2723
+Rassf1 RASSF1 rs709210 2.4806
+Rassf10 RASSF10 rs11022588 1.0017
+Rassf2 RASSF2 rs968432 1.6286
+Rassf3 RASSF3 rs12820666 0.8389
+Rassf4 RASSF4 rs17157321 1.7778
+Rassf5 RASSF5 rs17433804 3.4739
+Rassf6 RASSF6 rs13149172 1.137
+Rassf7 RASSF7 rs3740651 2.1787
+Rassf8 RASSF8 rs10842654 1.5902
+Rassf9 RASSF9 rs7397266 0.9027
+Raver1 RAVER1 rs281423 1.459
+Raver2 RAVER2 rs17127091 1.4448
+Rax RAX rs6567071 0.8665
+Rb1 RB1 rs17071686 1.8066
+Rbak RBAK rs11979787 1.0543
+Rbbp4 RBBP4 rs12022735 2.3001
+Rbbp5 RBBP5 rs2369525 1.2155
+Rbbp8 RBBP8 rs12962334 1.6465
+Rbbp9 RBBP9 rs6035051 2.6094
+Rbck1 RBCK1 rs6051517 1.751
+Rbfa RBFA rs612913 1.8948
+Rbfox1 RBFOX1 rs17139608 2.3894
+Rbfox2 RBFOX2 rs12167443 1.6828
+Rbfox3 RBFOX3 rs4790002 2.0856
+Rbks RBKS rs6547811 2.3354
+Rbm11 RBM11 rs454065 1.3282
+Rbm12 RBM12 rs6060558 3.539
+Rbm14 RBM14 rs1110707 1.6731
+Rbm15 RBM15 rs12024264 1.2237
+Rbm15b RBM15B rs10510757 2.5248
+Rbm17 RBM17 rs10795763 1.5656
+Rbm18 RBM18 rs3793617 1.7533
+Rbm19 RBM19 rs731745 2.5901
+Rbm20 RBM20 rs12413827 1.9901
+Rbm22 RBM22 rs28298 0.927
+MMS010091173 RBM24 rs1175988 1.607
+Rbm27 RBM27 rs10515566 0.9821
+Rbm28 RBM28 rs3750035 1.5388
+Rbm33 RBM33 rs11769364 1.2444
+Rbm38 RBM38 rs6025518 1.1734
+Rbm39 RBM39 rs6060558 3.539
+Rbm42 RBM42 rs3814983 1.1949
+Rbm43 RBM43 rs387297 2.3741
+Rbm44 RBM44 rs4349294 2.501
+Rbm45 RBM45 rs11674372 1.6828
+Rbm46 RBM46 rs12507608 1.5704
+Rbm47 RBM47 rs10517543 2.4998
+C030048B08Rik RBM48 rs2111200 0.95
+Rbm5 RBM5 rs2014830 4.8697
+Rbm6 RBM6 rs7613875 5.8536
+Rbm7 RBM7 rs7113178 1.2088
+Rbm8a RBM8A rs4471211 1.1824
+Rbms1 RBMS1 rs12692596 2.8703
+Rbms2 RBMS2 rs10747768 1.4262
+Rbms3 RBMS3 rs9310901 2.023
+Rbmxl1 RBMXL1 rs13376225 1.8891
+Rbmxl2 RBMXL2 rs6578828 1.1218
+Rbp1 RBP1 rs3772871 1.3785
+Rbp4 RBP4 rs940390 3.8592
+Rbpj RBPJ rs2871198 1.6891
+Rbpjl RBPJL rs6130803 1.7865
+Rbpms RBPMS rs7017014 1.2884
+Rbpms2 RBPMS2 rs7402386 1.2793
+Rc3h1 RC3H1 rs12122061 0.8851
+Rc3h2 RC3H2 rs16912287 2.0755
+Rcan1 RCAN1 rs7279771 1.8693
+Rcan2 RCAN2 rs2396640 1.9893
+Rcan3 RCAN3 rs195725 1.1874
+Rcbtb1 RCBTB1 rs4430650 1.2366
+Rcbtb2 RCBTB2 rs198589 1.4126
+Rcc1 RCC1 rs10158229 1.1321
+Rcc2 RCC2 rs2883272 2.3734
+Rccd1 RCCD1 rs1867219 1.6415
+Rchy1 RCHY1 rs6851699 1.0185
+Rcl1 RCL1 rs10815099 1.4676
+Rcn1 RCN1 rs224618 2.5033
+Rcn2 RCN2 rs17380507 0.8462
+Rcn3 RCN3 rs7507588 1.3332
+Rcor1 RCOR1 rs8014003 1.286
+Rcor2 RCOR2 rs10736731 1.1851
+Rcor3 RCOR3 rs3125829 1.4915
+Rcsd1 RCSD1 rs10918734 1.5674
+Rcvrn RCVRN rs4791906 2.0982
+Rd3 RD3 rs12040334 4.3253
+Rdh10 RDH10 rs12681551 1.25
+Rdh12 RDH12 rs910315 1.0985
+Rdh13 RDH13 rs11672111 2.1797
+Rdh14 RDH14 rs10210151 1.4855
+Rdh16 RDH16 rs901069 1.2108
+Rdh5 RDH5 rs2293414 1.1448
+Rdh8 RDH8 rs3745592 1.2463
+Rdx RDX rs595379 2.937
+Rec8 REC8 rs11574503 1.442
+Reck RECK rs10814336 0.9731
+Recql RECQL rs1061627 1.2946
+Recql5 RECQL5 rs4789219 1.5355
+Reep1 REEP1 rs13421310 2.3451
+Reep2 REEP2 rs2269947 1.5943
+Reep3 REEP3 rs7075302 2.7637
+Reep4 REEP4 rs6557841 0.9927
+Reep5 REEP5 rs10046063 1.145
+Reg1 REG1A rs3819316 1.1809
+Reg2 REG1B rs369760 2.6164
+Pap REG3A rs3819316 1.1809
+Reg3a REG3G rs426233 1.5583
+Reg4 REG4 rs3790693 2.1279
+Rel REL rs1053862 1.0209
+Rela RELA rs12801636 5.1298
+Relb RELB rs874743 1.766
+Rell1 RELL1 rs705099 2.0073
+Rell2 RELL2 rs702379 1.0669
+Reln RELN rs6954870 2.3945
+Relt RELT rs11235736 1.6054
+Rem1 REM1 rs6119442 2.424
+Rem2 REM2 rs2295904 1.234
+Ren1 REN rs1525051 1.5672
+Rep15 REP15 rs11049091 1.871
+Repin1 REPIN1 rs7795021 2.5456
+Reps1 REPS1 rs9403023 1.4798
+Rer1 RER1 rs2840536 1.9718
+Rere RERE rs13374834 1.8292
+Rerg RERG rs1544671 0.8386
+Rest REST rs1713985 1.5452
+Ret RET rs3026713 1.4648
+Retn RETN rs1862513 0.9606
+Retnlb RETNLB rs9809154 1.9289
+Retsat RETSAT rs1053560 6.9974
+Rev1 REV1 rs17763718 0.9622
+Rev3l REV3L rs4276544 1.53
+Rexo1 REXO1 rs12981256 3.3698
+Rexo2 REXO2 rs12573949 1.63
+Rexo4 REXO4 rs7040821 1.4449
+Rfc1 RFC1 rs2123028 2.7022
+Rfc2 RFC2 rs17146094 1.1099
+Rfc3 RFC3 rs17676901 1.5047
+Rfc4 RFC4 rs17299991 1.9329
+Rfc5 RFC5 rs10774975 1.2028
+Rfesd RFESD rs17211585 1.1057
+Rffl RFFL rs3135962 0.8814
+Rfk RFK rs7038009 1.687
+Rfng RFNG rs4969483 1.5018
+Rfpl4 RFPL4A rs2547254 1.7632
+Rft1 RFT1 rs13072457 4.2856
+Rftn1 RFTN1 rs17042365 1.9151
+Rftn2 RFTN2 rs13032881 1.5814
+Rfwd2 RFWD2 rs12738115 1.1568
+Rfwd3 RFWD3 rs4072450 1.4067
+Rfx1 RFX1 rs10409785 1.5467
+Rfx2 RFX2 rs628271 1.1561
+Rfx3 RFX3 rs10971120 1.2706
+Rfx4 RFX4 rs10861632 2.7783
+Rfx5 RFX5 rs2233850 1.4505
+Rfx6 RFX6 rs1321370 1.1617
+Rfx7 RFX7 rs1553737 1.3614
+Rfx8 RFX8 rs2037082 2.2103
+Rfxank RFXANK rs12459686 0.8245
+1190002H23Rik RGCC rs7328265 1.3218
+Rgl1 RGL1 rs12129684 2.3683
+Rgl2 RGL2 rs6906691 1.1757
+Rgl3 RGL3 rs167479 1.1759
+Rgma RGMA rs7164173 1.8447
+Rgr RGR rs11595934 1.6052
+Rgs1 RGS1 rs2224733 2.2952
+Rgs10 RGS10 rs7076994 1.9278
+Rgs11 RGS11 rs8051485 2.1812
+Rgs12 RGS12 rs4690099 1.5626
+Rgs13 RGS13 rs4568808 1.1799
+Rgs14 RGS14 rs6860069 0.8228
+Rgs16 RGS16 rs602988 1.4289
+Rgs17 RGS17 rs1442744 2.5936
+Rgs18 RGS18 rs10801098 1.4955
+Rgs19 RGS19 rs6011280 1.6592
+Rgs2 RGS2 rs710143 2.0919
+Rgs20 RGS20 rs12708158 1.4056
+MMT00048679 RGS21 rs1936711 1.1163
+Rgs22 RGS22 rs7830936 1.5574
+Rgs3 RGS3 rs785099 1.6919
+Rgs4 RGS4 rs14665 0.9834
+Rgs5 RGS5 rs2457185 1.991
+Rgs6 RGS6 rs847256 2.3845
+Rgs7 RGS7 rs17698283 2.4653
+Rgs7bp RGS7BP rs10080155 1.2363
+Rgs8 RGS8 rs680277 1.4521
+Rgs9 RGS9 rs938299 1.2367
+Rgs9bp RGS9BP rs259256 1.0759
+Rgsl1 RGSL1 rs2584820 2.1355
+Rhag RHAG rs12176464 1.1041
+Rhbdd1 RHBDD1 rs12053125 1.1305
+Rhbdd2 RHBDD2 rs10280802 1.8821
+Rhbdd3 RHBDD3 rs909795 1.2241
+Rhbdf1 RHBDF1 rs2562154 0.9374
+Rhbdf2 RHBDF2 rs3826287 1.8219
+Rhbdl1 RHBDL1 rs3752496 0.8176
+MMT00002427 RHBDL2 rs11810357 1.8959
+Rhbdl3 RHBDL3 rs1039402 1.6396
+Rhbg RHBG rs2245623 0.8533
+Rhd RHCE rs2982324 1.4444
+Rhcg RHCG rs2072693 2.0038
+Rheb RHEB rs4368893 1.6949
+Rhebl1 RHEBL1 rs11168830 0.9763
+Rhoa RHOA rs13078949 1.4594
+Rhob RHOB rs17653822 1.9594
+Rhobtb1 RHOBTB1 rs10994594 1.7921
+Rhobtb2 RHOBTB2 rs2466196 1.6105
+Rhobtb3 RHOBTB3 rs7736948 1.6975
+Rhoc RHOC rs6679 1.4232
+Rhod RHOD rs7943216 1.4174
+Rhof RHOF rs895953 4.2226
+Rhog RHOG rs4910858 1.4476
+Rhoh RHOH rs17512296 1.511
+Rhoj RHOJ rs17824335 0.8834
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+Rhot1 RHOT1 rs9892952 0.9838
+Rhot2 RHOT2 rs9929621 1.1517
+Rhou RHOU rs10916361 1.4441
+Rhpn1 RHPN1 rs7005838 1.6349
+Rhpn2 RHPN2 rs4805021 2.8639
+Ribc2 RIBC2 rs2350929 1.1415
+Ric3 RIC3 rs4758284 3.0766
+Ric8 RIC8A rs4980319 1.4276
+Ric8b RIC8B rs2589266 4.3156
+Rictor RICTOR rs357248 1.545
+Rif1 RIF1 rs2290368 2.3341
+Riiad1 RIIAD1 rs1196466 1.1341
+Rilpl1 RILPL1 rs1057879 2.1695
+Rimbp2 RIMBP2 rs11060910 1.7397
+Rimbp3 RIMBP3C rs8139142 7.4358
+Rimkla RIMKLA rs11210622 1.4864
+Rimklb RIMKLB rs2969090 1.5547
+Rims1 RIMS1 rs7751563 2.3219
+Rims2 RIMS2 rs7008904 1.9747
+Rims3 RIMS3 rs16827399 1.6349
+Rims4 RIMS4 rs11698203 0.869
+Rin1 RIN1 rs479315 2.3097
+Rin2 RIN2 rs16981233 2.2417
+Rin3 RIN3 rs943659 2.1744
+Ring1 RING1 rs2854028 1.4432
+Rinl RINL rs10405150 2.3972
+Rint1 RINT1 rs6943132 1.0574
+Riok1 RIOK1 rs1324502 1.3855
+Riok2 RIOK2 rs30997 0.9144
+Riok3 RIOK3 rs7241000 2.403
+Ripk1 RIPK1 rs7762140 2.0591
+Ripk2 RIPK2 rs10113478 1.1521
+Ripk3 RIPK3 rs4280164 1.2101
+Ripk4 RIPK4 rs6586239 1.595
+Ripply2 RIPPLY2 rs1925462 1.0345
+Rit1 RIT1 rs822503 1.3063
+Rit2 RIT2 rs12966528 3.1663
+Rlbp1 RLBP1 rs1005398 1.4711
+Rlf RLF rs11589811 1.0285
+Rln1 RLN1 rs1575279 0.8952
+Rln3 RLN3 rs7249436 1.2081
+Rmi1 RMI1 rs3737134 4.1881
+A630055G03Rik RMI2 rs1646070 2.5481
+Rmnd1 RMND1 rs9397054 1.3431
+Rmnd5a RMND5A rs13004867 1.5137
+Rmnd5b RMND5B rs7710186 1.304
+Rmrp RMRP rs10972546 1.3806
+Rnase1 RNASE1 rs10131121 1.2112
+Rnase10 RNASE10 rs12431933 2.1808
+Rnase11 RNASE11 rs3827902 2.0707
+Rnase12 RNASE12 rs3827902 2.0707
+Rnase13 RNASE13 rs943997 2.5029
+Ear11 RNASE2 rs1980657 0.9333
+Ear1 RNASE3 rs1980657 0.9333
+Rnase4 RNASE4 rs17242783 3.2028
+Rnase6 RNASE6 rs17211691 1.9714
+Rnase9 RNASE9 rs1243645 1.3494
+Rnaseh1 RNASEH1 rs4583476 2.1019
+Rnaseh2b RNASEH2B rs1870839 1.0646
+Rnaseh2c RNASEH2C rs11227261 3.1577
+Rnasel RNASEL rs579067 1.1179
+Rnaset2a RNASET2 rs13216122 1.9504
+Rnd1 RND1 rs1057908 0.9101
+Rnd2 RND2 rs8076790 1.1148
+Rnd3 RND3 rs6711507 1.2978
+Rnf10 RNF10 rs580016 1.6968
+Rnf103 RNF103 rs3754998 0.9024
+Rnf111 RNF111 rs1044673 2.0619
+Rnf112 RNF112 rs3866958 1.3811
+Rnf114 RNF114 rs492702 1.1884
+Rnf115 RNF115 rs2040086 2.9618
+Rnf121 RNF121 rs10501408 0.9834
+Rnf122 RNF122 rs2581897 1.3069
+Rnf123 RNF123 rs2291542 3.556
+Rnf125 RNF125 rs8086283 1.5675
+Rnf126 RNF126 rs10426061 2.0133
+Rnf13 RNF13 rs41448248 1.6517
+Rnf130 RNF130 rs1867447 2.0335
+Rnf133 RNF133 rs6466836 1.3778
+Rnf135 RNF135 rs17826255 1.2198
+Rnf138 RNF138 rs11081741 1.5061
+Rnf139 RNF139 rs11986208 1.7696
+Rnf14 RNF14 rs164077 1.5923
+Rnf141 RNF141 rs11601268 3.5482
+Rnf144a RNF144A rs10198816 1.7342
+Rnf144b RNF144B rs6924327 1.8162
+Rnf145 RNF145 rs2043268 1.2911
+Rnf146 RNF146 rs4371861 1.8952
+Rnf148 RNF148 rs7797250 1.7242
+Rnf149 RNF149 rs1192805 0.9851
+Rnf150 RNF150 rs12509440 1.8604
+Rnf151 RNF151 rs8055439 1.1127
+Rnf152 RNF152 rs9962603 1.8297
+Rnf157 RNF157 rs7217334 2.8911
+Gm96 RNF165 rs12456260 1.2387
+Rnf166 RNF166 rs4782395 1.5171
+Rnf167 RNF167 rs11654952 1.2197
+Rnf168 RNF168 rs6583309 0.9069
+2900057K09Rik RNF169 rs572656 1.1523
+MMT00048370 RNF175 rs12504447 0.9508
+Rnf181 RNF181 rs10206961 0.8573
+Rnf182 RNF182 rs17769281 2.2005
+Rnf183 RNF183 rs10733604 1.7875
+Rnf185 RNF185 rs2074739 3.5815
+Rnf186 RNF186 rs12076332 0.8395
+Rnf187 RNF187 rs10733022 2.3463
+Rnf19a RNF19A rs1788183 1.1797
+Rnf19b RNF19B rs4412566 2.1253
+Rnf2 RNF2 rs41529047 0.8969
+Rnf20 RNF20 rs17774987 2.1649
+Rnf207 RNF207 rs11121407 1.461
+Rnf212 RNF212 rs11941528 1.2135
+Rnf213 RNF213 rs9898470 1.7501
+Rnf214 RNF214 rs11216321 7.0402
+Rnf215 RNF215 rs5994293 1.9427
+Rnf216 RNF216 rs852397 1.9767
+Rnf217 RNF217 rs11154288 2.109
+Rnf220 RNF220 rs16832028 2.5738
+Rnf222 RNF222 rs3930740 2.0427
+9930005F22Rik RNF223 rs3121561 1.475
+Rnf24 RNF24 rs6052162 1.0796
+Rnf25 RNF25 rs33946522 0.965
+Rnf26 RNF26 rs2511837 1.4466
+Rnf31 RNF31 rs11574503 1.442
+Rnf32 RNF32 rs10277429 1.373
+Rnf34 RNF34 rs10849885 1.0875
+Rnf39 RNF39 rs1245371 1.8312
+Rnf4 RNF4 rs11734497 1.9285
+Rnf43 RNF43 rs1985749 1.7953
+Rnf44 RNF44 rs890835 0.9355
+Rnf5 RNF5 rs17493811 1.0915
+Rnf6 RNF6 rs1830 1.0532
+Rnf7 RNF7 rs9818297 0.9322
+Rnf8 RNF8 rs369329 1.3521
+Rnft1 RNFT1 rs180531 0.8333
+Rnft2 RNFT2 rs2172953 2.0229
+Rngtt RNGTT rs12215608 1.2952
+Rnh1 RNH1 rs11823647 1.8914
+Rnls RNLS rs12360149 2.0553
+Rnmt RNMT rs9965741 1.2284
+Rnmtl1 RNMTL1 rs592294 1.2516
+Rnpc3 RNPC3 rs10037423 1.1295
+Rnpep RNPEP rs6689634 1.2343
+Rnpepl1 RNPEPL1 rs4494742 1.3342
+MMS010007186 RNPS1 rs27382 0.8395
+Rnu11 RNU11 rs6673481 1.2791
+Robo1 ROBO1 rs17376012 2.0646
+Robo2 ROBO2 rs6548507 2.8008
+Robo3 ROBO3 rs12277811 1.5289
+Robo4 ROBO4 rs12277811 1.5289
+Rock1 ROCK1 rs7227454 1.9935
+Rock2 ROCK2 rs4669715 3.2763
+Rogdi ROGDI rs6500635 2.9923
+Rom1 ROM1 rs3888173 1.9543
+Romo1 ROMO1 rs6060558 3.539
+Ropn1 ROPN1 rs12638706 1.3373
+Ropn1 ROPN1B rs6770959 1.294
+Ropn1l ROPN1L rs1814856 1.522
+Ror1 ROR1 rs10493354 1.9017
+Ror2 ROR2 rs16907979 2.0966
+Rora RORA rs17270188 2.3022
+Rorb RORB rs7024262 1.5294
+Rorc RORC rs12045886 0.8801
+Ros1 ROS1 rs35134954 1.8652
+Rp1 RP1 rs7824820 1.348
+Rp1hl1 RP1L1 rs4841382 1.4542
+Rp9 RP9 rs7802256 0.8179
+Rpa1 RPA1 rs12150513 2.1329
+Rpa2 RPA2 rs1467465 1.0211
+Rpa3 RPA3 rs4140936 1.163
+Rpain RPAIN rs3026143 1.3339
+Rpap1 RPAP1 rs721772 4.7683
+Rpap2 RPAP2 rs41344152 1.2457
+Rpap3 RPAP3 rs2239187 0.8969
+Rpe65 RPE65 rs12564647 1.5249
+Rpf1 RPF1 rs4512665 1.6117
+Rpf2 RPF2 rs9386979 1.6774
+Rpgrip1 RPGRIP1 rs3748359 1.3976
+Rpgrip1l RPGRIP1L rs7404301 1.2219
+Rph3a RPH3A rs10774659 3.7812
+Rph3al RPH3AL rs12451101 1.8184
+Rpia RPIA rs335115 1.2337
+Rpl10a RPL10A rs11966736 2.2326
+Rpl10l RPL10L rs17675363 0.8642
+Rpl11 RPL11 rs4649110 0.9594
+Rpl12 RPL12 rs10987640 1.1617
+Rpl13 RPL13 rs455527 1.4861
+Rpl14 RPL14 rs41485644 1.9136
+Rpl17 RPL17 rs12963212 8.3966
+Rpl18 RPL18 rs2292342 1.8551
+Rpl18a RPL18A rs3212798 1.3594
+Rpl19 RPL19 rs16530 2.9041
+Rpl21 RPL21 rs4769547 1.7007
+Rpl22 RPL22 rs11121407 1.461
+Rpl23 RPL23 rs8076343 1.0752
+MMS010061863 RPL26 rs3930740 2.0427
+Rpl27 RPL27 rs8076790 1.1148
+Rpl27a RPL27A rs3365 1.1167
+Rpl28 RPL28 rs4252552 1.5524
+Rpl29 RPL29 rs323874 1.1723
+Rpl3 RPL3 rs137618 3.6499
+Rpl30 RPL30 rs2255464 1.5931
+Rpl31 RPL31 rs4851391 1.5629
+Rpl32 RPL32 rs906671 1.5306
+Rpl35 RPL35 rs913233 0.9179
+Rpl36al RPL36AL rs1011373 0.852
+Rpl37 RPL37 rs6868112 1.5346
+Rpl38 RPL38 rs11651406 1.3202
+Rpl3l RPL3L rs1657137 1.6091
+Rpl4 RPL4 rs8033122 0.9226
+Rpl41 RPL41 rs10783779 1.1852
+Rpl5 RPL5 rs10782945 1.2909
+Rpl6 RPL6 rs16942044 1.3163
+Rpl7 RPL7 rs12681551 1.25
+Rpl7a RPL7A rs11244086 2.4186
+Rpl7l1 RPL7L1 rs9471932 1.0323
+Rpl8 RPL8 rs11784860 0.8592
+Rpl9 RPL9 rs2687975 2.9112
+Rplp1 RPLP1 rs11852675 0.8687
+Rpn1 RPN1 rs1806722 2.4465
+Rpn2 RPN2 rs11698899 0.8817
+Rpp14 RPP14 rs7626140 2.2162
+Rpp21 RPP21 rs3094064 3.4589
+Rpp25 RPP25 rs6495131 1.515
+Rpp30 RPP30 rs11186333 1.041
+Rpp38 RPP38 rs7099479 1.186
+Rpp40 RPP40 rs1246939 0.8841
+Rpph1 RPPH1 rs1713432 1.1129
+Rprd1a RPRD1A rs6507158 1.0878
+Rprd1b RPRD1B rs6022537 1.2975
+Rprd2 RPRD2 rs12027008 0.8545
+Rprm RPRM rs4294952 1.5262
+Rprml RPRML rs9911967 4.0492
+Rps10 RPS10 rs3798564 1.7865
+Rps12 RPS12 rs7773338 0.9996
+Rps14 RPS14 rs2569093 1.1103
+Rps15 RPS15 rs4807927 1.348
+Rps15a RPS15A rs2606464 0.8213
+Rps16 RPS16 rs8107194 1.8159
+Rps18 RPS18 rs457565 1.555
+Rps19 RPS19 rs2075751 2.1193
+Rps19bp1 RPS19BP1 rs17400681 1.1886
+Rps2 RPS2 rs8055439 1.1127
+Rps20 RPS20 rs3914183 1.5305
+Rps21 RPS21 rs6089219 1.5938
+Rps23 RPS23 rs226200 1.2635
+Rps24 RPS24 rs12764690 1.5308
+Rps25 RPS25 rs4456264 3.2666
+Rps27 RPS27 rs6413529 0.9842
+Rps27a RPS27A rs11125559 1.0717
+Rps27l RPS27L rs6494394 5.2321
+MMT00000788 RPS28 rs2241590 2.7312
+Rps29 RPS29 rs11622841 1.7491
+Rps3 RPS3 rs17310905 1.2713
+Rps3a RPS3A rs6535763 1.2738
+Rps5 RPS5 rs2289143 0.8462
+Rps6-ps4 RPS6 rs12340464 0.9465
+Rps6ka1 RPS6KA1 rs6666757 6.4219
+Rps6ka2 RPS6KA2 rs4710051 2.8948
+Rps6ka4 RPS6KA4 rs11231776 2.6234
+Rps6ka5 RPS6KA5 rs1957386 1.1389
+Rps6ka6 RPS6KA6 rs13356831 1.143
+Rps6kb1 RPS6KB1 rs180531 0.8333
+Rps6kb2 RPS6KB2 rs1064876 1.339
+Rps6kc1 RPS6KC1 rs12136981 2.183
+Rps7 RPS7 rs13004318 2.8633
+Rpsa RPSA rs3821374 1.2142
+Rpsa-ps9 RPSAP52 rs7977238 0.8814
+Rptn RPTN rs11588885 0.9792
+Rptor RPTOR rs9899178 1.4737
+Rpusd3 RPUSD3 rs203563 2.2186
+Rpusd4 RPUSD4 rs580145 2.2752
+Rrad RRAD rs8045523 2.0879
+Rraga RRAGA rs7022576 3.9318
+Rragc RRAGC rs6690335 1.3136
+Rragd RRAGD rs1358852 1.3005
+Rras RRAS rs7251 1.1906
+Rras2 RRAS2 rs11023190 3.8649
+Rrbp1 RRBP1 rs6080767 2.2824
+Rreb1 RREB1 rs9328401 1.7902
+Rrh RRH rs10024583 1.56
+Rrm1 RRM1 rs17196385 1.9119
+Rrm2 RRM2 rs729917 1.3421
+Rrm2b RRM2B rs11784534 1.1757
+Rrn3 RRN3 rs3198697 2.3031
+Rrnad1 RRNAD1 rs3806415 3.2961
+Rrp1 RRP1 rs9978643 1.3118
+Rrp12 RRP12 rs11599228 1.309
+Rrp15 RRP15 rs6604604 1.2294
+Rrp1b RRP1B rs2070533 2.2664
+Rrp36 RRP36 rs2273918 0.9516
+Rrp7a RRP7A rs5758746 1.421
+Rrp8 RRP8 rs11040933 1.8716
+Rnu3ip2 RRP9 rs323874 1.1723
+Rrs1 RRS1 rs4737798 1.1836
+Rsad1 RSAD1 rs8070491 0.819
+Rsad2 RSAD2 rs10198816 1.7342
+Rsbn1 RSBN1 rs12064048 1.0875
+Rsbn1l RSBN1L rs2903929 1.3811
+Rsf1 RSF1 rs7482006 1.6012
+6330545A04Rik RSG1 rs1008529 0.9158
+Rsl1d1 RSL1D1 rs8056344 1.7245
+Rsl24d1 RSL24D1 rs10851585 1.1828
+Rsph1 RSPH1 rs12483506 0.9401
+Rsph10b2 RSPH10B2 rs6463580 0.8573
+Rsph3a RSPH3 rs9355256 1.1737
+Rsph4a RSPH4A rs6924915 0.8567
+Rsph6a RSPH6A rs8100208 1.4514
+Rsph9 RSPH9 rs2277125 1.4616
+Rspo2 RSPO2 rs988886 1.7552
+Rspo3 RSPO3 rs2800703 6.2793
+Rspo4 RSPO4 rs504507 1.1016
+Rspry1 RSPRY1 rs933716 2.2042
+Rsrc1 RSRC1 rs16828998 1.5654
+Rsu1 RSU1 rs7085659 1.8952
+Rtcd1 RTCA rs17454070 1.2826
+Rtdr1 RTDR1 rs5751579 1.3115
+Rtel1 RTEL1 rs2738770 2.0248
+Rtf1 RTF1 rs316607 1.0274
+Rtkn RTKN rs13396033 2.5805
+Rtkn2 RTKN2 rs1432413 1.2269
+Rtl1 RTL1 rs6575805 1.6347
+Rtn1 RTN1 rs17804446 2.2687
+Rtn2 RTN2 rs758761 1.2099
+Rtn3 RTN3 rs551466 1.3416
+Rtn4 RTN4 rs13394790 1.6273
+Rtn4ip1 RTN4IP1 rs9386570 0.9535
+Rtn4r RTN4R rs701431 1.3203
+Rtn4rl1 RTN4RL1 rs882495 1.561
+Rtn4rl2 RTN4RL2 rs7106223 1.2594
+Rtp1 RTP1 rs6785391 1.4152
+Rtp2 RTP2 rs9873218 2.257
+Rtp3 RTP3 rs4234463 2.3551
+Rtp4 RTP4 rs9817629 0.8877
+Rttn RTTN rs4891822 1.5057
+Rufy1 RUFY1 rs4700812 3.4441
+Rufy2 RUFY2 rs10998105 2.1187
+Rufy4 RUFY4 rs13389420 1.1838
+MMT00006239 RUNDC1 rs8076790 1.1148
+Rundc3a RUNDC3A rs9916116 1.0242
+Rundc3b RUNDC3B rs10486999 1.0066
+Runx1 RUNX1 rs2834669 4.2313
+Runx2 RUNX2 rs675613 1.0866
+Runx3 RUNX3 rs6686594 2.2211
+Rusc1 RUSC1 rs8177970 0.8677
+Rusc2 RUSC2 rs717384 4.3852
+Ruvbl1 RUVBL1 rs11714256 1.0043
+Ruvbl2 RUVBL2 rs3764621 1.2762
+Rwdd1 RWDD1 rs7762318 1.7678
+Rwdd2a RWDD2A rs1180243 1.757
+Rwdd3 RWDD3 rs12408499 1.2126
+Rxfp1 RXFP1 rs7695391 2.346
+Rxfp2 RXFP2 rs9576911 1.019
+Rxfp3 RXFP3 rs1549283 0.8948
+Rxfp4 RXFP4 rs2364403 1.1925
+Rxra RXRA rs12004589 2.2025
+Rxrb RXRB rs2854028 1.4432
+Rxrg RXRG rs7536062 1.7989
+Rybp RYBP rs2118592 2.0793
+Ryk RYK rs9283588 1.2346
+Ryr1 RYR1 rs2229147 1.5847
+Ryr2 RYR2 rs4393145 2.4246
+Ryr3 RYR3 rs713202 2.9052
+S100a11 S100A11 rs3007672 1.4866
+S100a2 S100A2 rs16835455 1.0462
+S100a3 S100A3 rs16835455 1.0462
+S100a4 S100A4 rs16835455 1.0462
+S100a5 S100A5 rs1005436 0.8425
+S100a6 S100A6 rs1005436 0.8425
+S100a7a S100A7A rs3014824 1.3531
+S100a8 S100A8 rs1560833 1.5943
+S100a9 S100A9 rs1560833 1.5943
+S100b S100B rs34722617 1.3044
+S100pbp S100PBP rs12760548 1.2125
+S100z S100Z rs9885484 2.5057
+S1pr1 S1PR1 rs33967958 1.8047
+S1pr2 S1PR2 rs11085721 0.8105
+S1pr3 S1PR3 rs10867149 1.6562
+S1pr4 S1PR4 rs12461827 1.604
+S1pr5 S1PR5 rs7246953 1.6304
+Saa1 SAA1 rs10832911 1.758
+Saa3 SAA2 rs10832911 1.758
+Saa4 SAA4 rs10832911 1.758
+Sac3d1 SAC3D1 rs1300845 2.5488
+Sacm1l SACM1L rs17262464 1.0524
+Sacs SACS rs1326506 1.4052
+Sae1 SAE1 rs309193 3.3639
+Safb SAFB rs2285963 1.1615
+Safb2 SAFB2 rs2285963 1.1615
+Sag SAG rs3828305 1.7395
+Sall1 SALL1 rs7205341 1.0498
+Sall2 SALL2 rs10134091 1.497
+Sall3 SALL3 rs11659407 2.5528
+Sall4 SALL4 rs6512748 2.743
+Samd10 SAMD10 rs817318 0.8948
+Samd12 SAMD12 rs7000702 1.8105
+1700013A07Rik SAMD13 rs17535959 3.1528
+Samd14 SAMD14 rs847698 1.3058
+Samd15 SAMD15 rs13379432 1.7111
+Gm623 SAMD3 rs4897370 2.821
+Samd4 SAMD4A rs8012266 2.4874
+Samd4b SAMD4B rs36265 1.852
+Samd5 SAMD5 rs325119 1.5361
+Samd7 SAMD7 rs17217328 1.0934
+Samd8 SAMD8 rs879155 2.5686
+Samd9l SAMD9L rs4729072 1.2671
+Samhd1 SAMHD1 rs1891643 1.0181
+Samm50 SAMM50 rs2072905 2.2749
+Samsn1 SAMSN1 rs9976798 1.1605
+Sap130 SAP130 rs7560383 2.13
+Sap18 SAP18 rs4770116 1.6377
+Sap30 SAP30 rs1383629 1.2323
+Sap30bp SAP30BP rs9896073 1.0472
+Sap30l SAP30L rs17116163 1.5029
+2010317E24Rik SAPCD2 rs7466635 1.0547
+Sar1a SAR1A rs10762345 2.0293
+Sardh SARDH rs129886 1.6959
+A830091I15Rik SARM1 rs6505081 1.9846
+Sars SARS rs688386 2.5897
+Sars2 SARS2 rs10405150 2.3972
+Sart1 SART1 rs586921 1.3842
+Sart3 SART3 rs1121639 1.721
+Sash1 SASH1 rs2057013 2.0701
+Sat2 SAT2 rs2955617 3.1015
+Satb1 SATB1 rs11921478 1.7783
+Satb2 SATB2 rs17250955 1.5261
+Satl1 SATL1 rs11722555 0.8732
+Sav1 SAV1 rs2883951 2.1632
+1810063B07Rik SAYSD1 rs9296292 3.1999
+Sbds SBDS rs17680744 1.0251
+Sbf2 SBF2 rs11042689 3.9052
+Sbk2 SBK2 rs12461779 1.4511
+Sbno1 SBNO1 rs4759375 8.125
+Sbno2 SBNO2 rs2024093 2.4686
+Sbsn SBSN rs12460932 1.828
+Gm106 SBSPON rs11992993 1.3448
+Scaf1 SCAF1 rs7251 1.1906
+Scaf11 SCAF11 rs11574960 1.1882
+Scaf4 SCAF4 rs2173962 1.7286
+Scaf8 SCAF8 rs12191294 2.2999
+Scai SCAI rs10986569 1.0861
+Scamp1 SCAMP1 rs9293742 1.8102
+Scamp2 SCAMP2 rs16973172 1.9183
+Scamp4 SCAMP4 rs12981256 3.3698
+Scand1 SCAND1 rs6060685 3.0778
+Scap SCAP rs12490383 1.1516
+Scaper SCAPER rs12441433 1.7097
+Scara3 SCARA3 rs485609 1.6645
+Scara5 SCARA5 rs11783717 2.2089
+Scarb1 SCARB1 rs838880 13.589
+Scarb2 SCARB2 rs3821982 1.8055
+Scarf2 SCARF2 rs362075 1.9918
+Scarna10 SCARNA10 rs1043262 1.8283
+Scarna17 SCARNA17 rs11659231 3.3605
+Scarna2 SCARNA2 rs17838321 0.8294
+Scarna3a SCARNA3 rs10798425 0.9905
+Scarna6 SCARNA6 rs6758317 2.8752
+Scarna8 SCARNA8 rs7022576 3.9318
+Sccpdh SCCPDH rs6426329 2.1794
+Scd2 SCD rs10883466 2.6737
+Scel SCEL rs2274016 1.3122
+Scfd1 SCFD1 rs8010048 2.1044
+Scfd2 SCFD2 rs301130 2.4339
+Scg3 SCG3 rs17525472 1.647
+Scg5 SCG5 rs16966853 1.9931
+Scgb1a1 SCGB1A1 rs7113294 1.1906
+MMT00033265 SCGB1C1 rs4980319 1.4276
+Scgb3a1 SCGB3A1 rs307803 2.3263
+Scgb3a2 SCGB3A2 rs17703430 1.0396
+Scgn SCGN rs9379780 2.1562
+Schip1 SCHIP1 rs16830334 1.6269
+A430084P05Rik SCIMP rs3844577 1.624
+Scin SCIN rs10262356 3.1026
+Sclt1 SCLT1 rs17014190 1.537
+Scly SCLY rs11682046 0.8831
+Scmh1 SCMH1 rs17358038 1.071
+Scml4 SCML4 rs9400131 1.528
+Scn10a SCN10A rs7430439 1.4737
+Scn11a SCN11A rs6599273 2.1357
+Scn1a SCN1A rs7606233 2.0438
+Scn1b SCN1B rs35879596 2.6444
+Scn2b SCN2B rs648389 2.0156
+Scn3a SCN3A rs16850088 0.9512
+Scn3b SCN3B rs41497645 0.8119
+Scn4a SCN4A rs3744287 1.6019
+Scn4b SCN4B rs648389 2.0156
+Scn5a SCN5A rs7428882 1.7185
+Scn7a SCN7A rs17818608 1.6682
+Scn8a SCN8A rs303807 1.4963
+Scn9a SCN9A rs10165613 1.9333
+Scnm1 SCNM1 rs2067606 1.1387
+Scnn1a SCNN1A rs2041375 1.6202
+Scnn1b SCNN1B rs585589 1.4475
+Scnn1g SCNN1G rs4073931 1.3111
+Sco1 SCO1 rs876659 2.1041
+Sco2 SCO2 rs6010122 1.1581
+Scoc SCOC rs17005670 1.8474
+Scp2 SCP2 rs1580356 1.0902
+Scpep1 SCPEP1 rs2240215 1.582
+Scrg1 SCRG1 rs4696072 1.2343
+Scrib SCRIB rs4875051 1.2205
+Scrn1 SCRN1 rs6979942 1.2881
+Scrn2 SCRN2 rs11654648 3.8005
+Scrn3 SCRN3 rs12612947 0.9359
+Scrt2 SCRT2 rs6038187 2.3124
+Sct SCT rs12419694 2.7284
+Sctr SCTR rs17016143 1.6799
+Scube1 SCUBE1 rs138963 2.3697
+Scube2 SCUBE2 rs7127387 1.9255
+Scube3 SCUBE3 rs11755266 3.8582
+Scx SCXB rs4557742 1.8887
+Scyl1 SCYL1 rs947791 0.9578
+Scyl2 SCYL2 rs6538960 2.4708
+Scyl3 SCYL3 rs10919267 1.4872
+Sdad1 SDAD1 rs17001192 1.3151
+Sdc1 SDC1 rs12478327 3.6733
+Sdc2 SDC2 rs1347607 1.2761
+Sdc3 SDC3 rs3820088 1.3934
+Sdc4 SDC4 rs6130803 1.7865
+Sdcbp SDCBP rs10102709 1.6934
+Sdcbp2 SDCBP2 rs3764713 1.4465
+Sdccag3 SDCCAG3 rs10870201 1.6269
+Sdccag8 SDCCAG8 rs6429416 2.0779
+Sdf2l1 SDF2L1 rs447001 2.7637
+Sdha SDHA rs3822759 1.4041
+Sdhaf1 SDHAF1 rs7254830 3.473
+Sdhb SDHB rs15786 0.9901
+Sdhc SDHC rs16832872 1.9462
+Sdhd SDHD rs7935331 1.4073
+Sdk1 SDK1 rs17134389 2.9948
+Sdk2 SDK2 rs9896718 2.0661
+Sdpr SDPR rs4366844 2.2126
+Sdr16c5 SDR16C5 rs4075155 2.2754
+Sdr39u1 SDR39U1 rs1956894 1.4218
+Sdr42e1 SDR42E1 rs9938835 1.1959
+Sdr9c7 SDR9C7 rs901069 1.2108
+Sds SDS rs7295347 1.0873
+Sdsl SDSL rs7295347 1.0873
+Sebox SEBOX rs7221462 1.4002
+Sec11a SEC11A rs7169629 1.1174
+Sec11c SEC11C rs17768262 1.7438
+Sec13 SEC13 rs1642974 1.8085
+Sec14l1 SEC14L1 rs8078674 3.6358
+Sec14l2 SEC14L2 rs9608905 3.9038
+Sec14l3 SEC14L3 rs9606727 2.9559
+Sec14l4 SEC14L4 rs8136992 1.2301
+Sec14l5 SEC14L5 rs2908654 1.5627
+Sec16a SEC16A rs4487900 0.908
+Sec16b SEC16B rs630372 1.582
+Sec22a SEC22A rs9846935 1.6031
+Sec22c SEC22C rs407931 1.1882
+Sec23a SEC23A rs17108815 1.3298
+Sec23b SEC23B rs6081173 2.1503
+Sec23ip SEC23IP rs12259729 1.1202
+Sec24a SEC24A rs3924084 1.3635
+Sec24b SEC24B rs13126627 1.9382
+Sec24c SEC24C rs4746143 2.7433
+Sec24d SEC24D rs10518326 1.5588
+Sec31a SEC31A rs6830527 2.7129
+Sec31b SEC31B rs9420790 1.1145
+Sec61a2 SEC61A2 rs876089 1.9686
+Sec61b SEC61B rs12005715 1.2431
+Gm10177 SEC61G rs10253804 1.4803
+Sec62 SEC62 rs3772180 1.414
+Sec63 SEC63 rs11755614 1.4276
+Secisbp2 SECISBP2 rs3211681 1.0705
+Secisbp2l SECISBP2L rs4575482 1.9041
+Sectm1a SECTM1 rs7212093 1.2822
+Seh1l SEH1L rs11080616 1.9197
+Sel1l SEL1L rs11499034 2.0088
+Sel1l2 SEL1L2 rs6033864 2.0994
+Sel1l3 SEL1L3 rs4692119 2.15
+Sele SELE rs4987346 1.2656
+Selenbp1 SELENBP1 rs2233850 1.4505
+Selk SELK rs2289204 1.2409
+Sell SELL rs4987346 1.2656
+Selm SELM rs4820938 2.0444
+1300018J18Rik SELO rs17248301 2.184
+Selp SELP rs3917788 1.0411
+Selpl SELPLG rs8179137 2.3738
+2010305A19Rik SELRC1 rs448788 2.6979
+2810407C02Rik SELT rs9830228 1.7981
+BC089491 SELV rs3212275 1.0661
+Sema3a SEMA3A rs6955283 2.0432
+Sema3b SEMA3B rs13100173 1.7749
+Sema3c SEMA3C rs7793555 1.537
+Sema3d SEMA3D rs1528619 1.1456
+Sema3e SEMA3E rs302116 1.5686
+Sema3f SEMA3F rs2014830 4.8697
+Sema3g SEMA3G rs648514 1.5889
+Sema4a SEMA4A rs6686943 1.8768
+Sema4b SEMA4B rs11853835 1.234
+Sema4c SEMA4C rs4907254 1.2289
+Sema4d SEMA4D rs11265783 1.9531
+Sema4f SEMA4F rs17723774 1.0966
+Sema4g SEMA4G rs735137 1.5547
+Sema5a SEMA5A rs13176952 3.316
+Sema5b SEMA5B rs9838948 2.3717
+Sema6a SEMA6A rs6885885 1.9731
+Sema6c SEMA6C rs2067606 1.1387
+Sema6d SEMA6D rs8026338 2.8517
+Sema7a SEMA7A rs8026319 1.2778
+Svs2 SEMG1 rs7270676 1.3978
+Senp1 SENP1 rs7134203 1.6484
+Senp2 SENP2 rs6774871 1.3435
+Senp3 SENP3 rs9902027 2.7459
+Senp5 SENP5 rs4916561 2.3105
+Senp6 SENP6 rs2313544 1.6262
+Senp7 SENP7 rs9814840 2.9337
+Senp8 SENP8 rs4777489 0.8353
+Sephs1 SEPHS1 rs11258312 1.7135
+Sepn1 SEPN1 rs807247 1.1098
+Sepsecs SEPSECS rs3822221 0.8834
+Sept11 SEPT11 rs6850387 1.3096
+Sept12 SEPT12 rs12926158 1.9314
+Sept14 SEPT14 rs10277978 1.4111
+Sept2 SEPT2 rs12619647 3.1661
+Sept3 SEPT3 rs5751188 1.1444
+Sept4 SEPT4 rs6503869 1.1109
+Sept7 SEPT7 rs17397155 0.9038
+Sept8 SEPT8 rs30508 1.6737
+Sept9 SEPT9 rs385419 2.3042
+Sepw1 SEPW1 rs17703870 1.0197
+Serbp1 SERBP1 rs3762313 1.327
+Serf2 SERF2 rs2228368 2.0663
+Sergef SERGEF rs7107811 0.997
+Serhl SERHL2 rs4822171 1.7833
+Serinc1 SERINC1 rs12662883 1.4588
+Serinc2 SERINC2 rs12131153 1.8817
+Serinc4 SERINC4 rs2228368 2.0663
+Serinc5 SERINC5 rs1470025 1.7345
+Serp1 SERP1 rs9830228 1.7981
+Serp2 SERP2 rs9567416 1.8775
+Serpina1a SERPINA1 rs2896268 1.0219
+Serpina10 SERPINA10 rs2281519 1.3324
+Serpina11 SERPINA11 rs2208430 1.5498
+Serpina12 SERPINA12 rs10134781 1.7542
+Serpina3n SERPINA3 rs10142884 1.3005
+Serpina4-ps1 SERPINA4 rs7156329 1.2167
+Serpina5 SERPINA5 rs10142884 1.3005
+Serpina6 SERPINA6 rs2281519 1.3324
+Serpina9 SERPINA9 rs10134781 1.7542
+Serpinb1a SERPINB1 rs10900929 1.0553
+Serpinb10 SERPINB10 rs1014952 1.2948
+Serpinb12 SERPINB12 rs997466 1.2338
+Serpinb13 SERPINB13 rs9966805 0.8768
+Serpinb2 SERPINB2 rs1014952 1.2948
+Serpinb3d SERPINB3 rs3180227 0.9115
+Serpinb3c SERPINB4 rs3180227 0.9115
+Serpinb5 SERPINB5 rs9953252 1.8854
+Serpinb6a SERPINB6 rs6907803 1.3866
+Serpinb7 SERPINB7 rs9952185 0.9397
+Serpinb8 SERPINB8 rs17244903 0.9961
+Serpinb9 SERPINB9 rs7757819 1.0554
+Serpinc1 SERPINC1 rs35571466 1.6162
+Serpine1 SERPINE1 rs1050955 1.8413
+Serpine2 SERPINE2 rs861442 1.5563
+Serpine3 SERPINE3 rs7990847 1.6424
+Serpinf1 SERPINF1 rs7215532 1.1472
+Serpinf2 SERPINF2 rs8075977 1.0895
+Serping1 SERPING1 rs1846568 2.0365
+Serpinh1 SERPINH1 rs1557473 1.5456
+Serpini1 SERPINI1 rs41403449 1.2865
+Serpini2 SERPINI2 rs1104570 0.8207
+Sertad1 SERTAD1 rs744389 1.4602
+Sertad2 SERTAD2 rs6750065 1.5256
+Sertad3 SERTAD3 rs444241 1.6407
+Sertad4 SERTAD4 rs11119445 2.7366
+6030405A18Rik SERTM1 rs637675 1.2823
+Sesn1 SESN1 rs2806357 3.2255
+Sesn2 SESN2 rs7516655 1.8817
+Sesn3 SESN3 rs12803674 0.8351
+Sestd1 SESTD1 rs1490997 1.2674
+Set SET rs9697210 1.6747
+Setbp1 SETBP1 rs11661549 3.0381
+Setd1a SETD1A rs4889606 2.7307
+Setd1b SETD1B rs895953 4.2226
+Setd2 SETD2 rs2290547 2.9446
+Setd3 SETD3 rs3918048 1.3697
+Setd4 SETD4 rs2845727 2.2328
+Setd5 SETD5 rs3928602 1.5991
+0610039J04Rik SETD6 rs4784046 2.0448
+Setd7 SETD7 rs1995103 1.232
+Setdb1 SETDB1 rs267738 2.5605
+Setdb2 SETDB2 rs7324615 1.2969
+Setx SETX rs3780825 1.4973
+Sez6 SEZ6 rs7225915 2.8972
+Sez6l SEZ6L rs650276 2.9094
+Sez6l2 SEZ6L2 rs12917712 0.8097
+Sf3a1 SF3A1 rs5994293 1.9427
+Sf3a3 SF3A3 rs17531077 1.5842
+Sf3b1 SF3B1 rs7603828 1.228
+0610009D07Rik SF3B14 rs11125371 1.808
+Sf3b2 SF3B2 rs10791838 1.4466
+Sf3b3 SF3B3 rs11075766 0.967
+Sf3b4 SF3B4 rs626785 1.9481
+Sf3b5 SF3B5 rs12527693 0.8162
+Sfi1 SFI1 rs5998031 1.3064
+Sfmbt1 SFMBT1 rs11712910 2.8083
+Sfmbt2 SFMBT2 rs12218175 1.4496
+Sfn SFN rs737524 3.3318
+Sfpq SFPQ rs10908387 2.5799
+Sfr1 SFR1 rs1320448 1.0047
+Sfrp1 SFRP1 rs968428 1.8139
+Sfrp2 SFRP2 rs41320045 1.1637
+Sfrp5 SFRP5 rs12770904 1.1089
+Sfswap SFSWAP rs4964990 1.2399
+Sft2d1 SFT2D1 rs6934656 0.8703
+Sft2d2 SFT2D2 rs12410719 0.9727
+Sfta2 SFTA2 rs2844651 2.8011
+Sftpb SFTPB rs4832179 1.4073
+Sftpc SFTPC rs2070687 1.1863
+Sftpd SFTPD rs1885553 1.58
+Sfxn1 SFXN1 rs267386 1.8735
+Sfxn2 SFXN2 rs2778037 1.2429
+Sfxn3 SFXN3 rs2275382 1.0838
+Sfxn4 SFXN4 rs11198782 1.4284
+Sfxn5 SFXN5 rs11682000 1.8173
+Sgca SGCA rs2696297 0.8928
+Sgcb SGCB rs225162 0.9735
+Sgcd SGCD rs1368327 2.0171
+Sgcg SGCG rs4769249 2.1044
+Sgcz SGCZ rs17118845 2.295
+Sgip1 SGIP1 rs17129315 1.178
+Sgk1 SGK1 rs7453135 2.2205
+Gm1078 SGK110 rs12461779 1.4511
+Sgk2 SGK2 rs2867725 1.2164
+D8Ertd82e SGK223 rs724266 1.67
+Sgk3 SGK3 rs16933080 1.1321
+Sgms1 SGMS1 rs10763590 2.2168
+Sgms2 SGMS2 rs6826111 1.1605
+Sgol1 SGOL1 rs6792672 1.2302
+Sgol2 SGOL2 rs17630975 1.3384
+Sgpl1 SGPL1 rs12221473 1.5638
+Sgpp1 SGPP1 rs7155351 1.0421
+Sgpp2 SGPP2 rs13385240 2.2635
+Sgsh SGSH rs7207000 1.5571
+Sgsm1 SGSM1 rs5752024 1.6488
+Sgsm2 SGSM2 rs2429909 2.7585
+Sgsm3 SGSM3 rs5757948 1.7637
+Sgta SGTA rs4807325 1.7183
+Sgtb SGTB rs34986 1.6192
+Sh2b1 SH2B1 rs6565259 2.3576
+Sh2b2 SH2B2 rs2021739 1.3037
+Sh2b3 SH2B3 rs2239196 2.1025
+Sh2d2a SH2D2A rs2768764 2.4583
+MMT00065646 SH2D3A rs340141 1.1946
+Sh2d3c SH2D3C rs3217739 1.5566
+Sh2d4a SH2D4A rs17128235 1.5521
+Sh2d4b SH2D4B rs873258 2.2416
+Sh2d5 SH2D5 rs10916853 1.7345
+4933424C13Rik SH2D6 rs11674612 3.0878
+Sh2d7 SH2D7 rs2289525 0.9851
+Sh3bgr SH3BGR rs3787926 1.4761
+Sh3bgrl2 SH3BGRL2 rs2814381 1.6236
+Sh3bgrl3 SH3BGRL3 rs6598955 1.2391
+Sh3bp1 SH3BP1 rs7290615 0.9809
+Sh3bp2 SH3BP2 rs370470 2.2915
+Sh3bp4 SH3BP4 rs4663526 2.413
+Sh3bp5 SH3BP5 rs17041034 3.1019
+Sh3bp5l SH3BP5L rs11205402 1.0141
+Sh3d19 SH3D19 rs4696237 1.4787
+Sh3gl2 SH3GL2 rs3808719 2.458
+Sh3gl3 SH3GL3 rs2244838 1.3487
+Sh3glb1 SH3GLB1 rs470310 0.9129
+Sh3glb2 SH3GLB2 rs17455482 2.5648
+Sh3pxd2a SH3PXD2A rs2073341 1.7471
+Sh3pxd2b SH3PXD2B rs2569218 1.6341
+Sh3rf1 SH3RF1 rs17054719 1.4931
+Sh3rf2 SH3RF2 rs13436709 1.549
+Sh3rf3 SH3RF3 rs10496428 1.3754
+Sh3tc1 SH3TC1 rs4696773 1.8111
+Sh3tc2 SH3TC2 rs10491337 2.7373
+Sh3yl1 SH3YL1 rs17713879 2.4944
+Shank1 SHANK1 rs11665802 2.9759
+Shank2 SHANK2 rs11604023 1.764
+Shank3 SHANK3 rs134774 1.084
+Shb SHB rs735740 1.7126
+Shbg SHBG rs2955617 3.1015
+Shc2 SHC2 rs1115033 1.721
+Shc3 SHC3 rs10867149 1.6562
+Shc4 SHC4 rs11638748 3.0986
+1700012A16Rik SHCBP1L rs10911167 2.2209
+Shd SHD rs8107226 1.991
+Shf SHF rs12438755 1.6031
+Shh SHH rs12113009 0.9473
+Shisa2 SHISA2 rs4770904 3.4429
+Shisa3 SHISA3 rs11939228 1.776
+Shisa4 SHISA4 rs1043823 2.008
+Shisa5 SHISA5 rs6762888 1.0415
+Shisa6 SHISA6 rs12325823 2.2122
+Shisa7 SHISA7 rs11672075 1.0317
+Shisa9 SHISA9 rs11075184 3.0781
+Shkbp1 SHKBP1 rs890935 1.2218
+Shmt1 SHMT1 rs8075915 1.3082
+Shmt2 SHMT2 rs7301155 4.0166
+Shoc2 SHOC2 rs10787289 2.1302
+Shox2 SHOX2 rs1608100 0.9154
+Shpk SHPK rs456253 1.3991
+Shprh SHPRH rs9485028 1.1642
+Shq1 SHQ1 rs6764729 1.6353
+Shroom3 SHROOM3 rs344109 1.5013
+Sis SI rs7641586 0.8119
+Siae SIAE rs12226648 1.5133
+Siah1a SIAH1 rs7197521 1.3637
+Siah2 SIAH2 rs2014279 1.8139
+Siah3 SIAH3 rs1325647 2.218
+Sidt1 SIDT1 rs4331647 1.9274
+Sidt2 SIDT2 rs508487 5.9731
+Sigirr SIGIRR rs6598025 1.8128
+Siglec1 SIGLEC1 rs511898 1.2513
+Siglecg SIGLEC10 rs11671525 1.5666
+Siglec5 SIGLEC5 rs4356587 2.1124
+Siglech SIGLEC6 rs28460194 1.0727
+Siglece SIGLEC8 rs11672925 1.3868
+Sigmar1 SIGMAR1 rs3739882 0.9303
+Sik1 SIK1 rs17004541 1.9531
+Sik2 SIK2 rs1940528 3.3078
+Sik3 SIK3 rs625145 15.808
+Sike1 SIKE1 rs2207701 1.314
+Sil1 SIL1 rs10044736 2.2773
+Sim1 SIM1 rs1857859 3.3677
+Sim2 SIM2 rs2073601 1.226
+Sin3b SIN3B rs773869 1.286
+Sipa1 SIPA1 rs12801636 5.1298
+Sipa1l1 SIPA1L1 rs11628525 1.3187
+Sipa1l2 SIPA1L2 rs6424231 1.7625
+2610511M17Rik SIPA1L3 rs11671853 2.7049
+Sirpa SIRPA rs4814734 2.2674
+Sirpb1a SIRPB1 rs2244133 1.3433
+Gm5150 SIRPB2 rs6042507 0.9226
+Sirt2 SIRT2 rs10405150 2.3972
+Sirt3 SIRT3 rs4980319 1.4276
+Sirt4 SIRT4 rs7311566 1.5599
+Sirt5 SIRT5 rs3757261 2.0403
+Sirt6 SIRT6 rs352494 2.7627
+Sit1 SIT1 rs10972546 1.3806
+Siva1 SIVA1 rs3001371 1.4132
+Six1 SIX1 rs3783820 1.6033
+Six4 SIX4 rs17834412 1.2088
+Six5 SIX5 rs8100208 1.4514
+Six6 SIX6 rs11949787 0.8706
+Ska1 SKA1 rs6507992 1.3036
+Ska2 SKA2 rs9909925 1.1508
+Ska3 SKA3 rs4770116 1.6377
+Skap1 SKAP1 rs11079810 4.3003
+Skap2 SKAP2 rs7810127 1.9674
+Ski SKI rs903920 2.4536
+2810030E01Rik SKIDA1 rs10828247 0.838
+Skil SKIL rs6763533 2.0281
+Skiv2l SKIV2L rs9332739 2.0606
+Skiv2l2 SKIV2L2 rs230758 1.0956
+Skor1 SKOR1 rs2241423 2.493
+Skor2 SKOR2 rs8096219 0.9901
+Skp2 SKP2 rs7719357 1.0552
+Sla SLA rs2958674 1.7258
+Sla2 SLA2 rs4812405 1.0088
+Slain1 SLAIN1 rs9544597 1.8969
+Slain2 SLAIN2 rs17655765 1.9034
+Slamf1 SLAMF1 rs2134704 1.6472
+Slamf6 SLAMF6 rs12401994 1.1331
+Slamf7 SLAMF7 rs11265472 1.422
+Slamf8 SLAMF8 rs11584887 1.4927
+Slbp SLBP rs6827250 1.023
+Slc10a1 SLC10A1 rs2332166 1.3282
+Slc10a2 SLC10A2 rs183965 1.3364
+Slc10a4 SLC10A4 rs6447641 1.3013
+Slc10a5 SLC10A5 rs2955003 1.3286
+Slc10a6 SLC10A6 rs8180256 2.9792
+Slc10a7 SLC10A7 rs12186135 1.5151
+Slc11a1 SLC11A1 rs2276631 1.1314
+Slc12a1 SLC12A1 rs12907018 1.6424
+Slc12a2 SLC12A2 rs13175996 2.1569
+Slc12a3 SLC12A3 rs1366544 43.564
+Slc12a4 SLC12A4 rs4986970 7.1735
+Slc12a5 SLC12A5 rs6065912 6.9682
+Slc12a6 SLC12A6 rs10519916 1.4623
+Slc12a7 SLC12A7 rs6894574 1.3201
+Slc12a8 SLC12A8 rs1068926 1.8099
+Slc12a9 SLC12A9 rs314376 2.3639
+Slc13a1 SLC13A1 rs1343904 1.3732
+Slc13a2 SLC13A2 rs12939182 1.4393
+Slc13a3 SLC13A3 rs202385 2.1749
+Slc13a4 SLC13A4 rs7798256 3.4484
+Slc13a5 SLC13A5 rs218685 1.2355
+Slc14a1 SLC14A1 rs1484877 1.4823
+Slc14a2 SLC14A2 rs7228782 2.2658
+Slc15a1 SLC15A1 rs1626391 2.1605
+Slc15a2 SLC15A2 rs17203299 0.9634
+Slc15a4 SLC15A4 rs4760570 2.8277
+Slc15a5 SLC15A5 rs7965574 1.5408
+Slc16a1 SLC16A1 rs11587120 1.8327
+Slc16a10 SLC16A10 rs9386999 1.4193
+Slc16a11 SLC16A11 rs312456 1.9101
+Slc16a12 SLC16A12 rs17121329 1.6676
+Slc16a13 SLC16A13 rs312456 1.9101
+Slc16a14 SLC16A14 rs4973251 0.9469
+Slc16a3 SLC16A3 rs12601558 1.0715
+Slc16a4 SLC16A4 rs3768458 0.9129
+Slc16a5 SLC16A5 rs4789128 1.161
+Slc16a6 SLC16A6 rs9909207 1.1779
+Slc16a7 SLC16A7 rs10506400 2.3051
+Slc16a8 SLC16A8 rs4373009 1.2079
+Slc16a9 SLC16A9 rs11006644 8.9801
+Slc17a1 SLC17A1 rs9467604 4.2957
+Slc17a2 SLC17A2 rs17586553 6.0374
+Slc17a3 SLC17A3 rs504576 1.7812
+9130214H05Rik SLC17A4 rs9379789 1.213
+Slc17a6 SLC17A6 rs1579020 1.0159
+Slc17a7 SLC17A7 rs8110789 1.1123
+Slc17a8 SLC17A8 rs6538960 2.4708
+Slc17a9 SLC17A9 rs4809443 1.2484
+Slc18a1 SLC18A1 rs16842 7.6882
+Slc18a2 SLC18A2 rs363333 1.3831
+Slc18a3 SLC18A3 rs12217567 1.2531
+1110021L09Rik SLC18B1 rs12530317 1.0113
+Slc19a1 SLC19A1 rs2274808 1.8422
+Slc19a2 SLC19A2 rs6696810 1.914
+Slc19a3 SLC19A3 rs7585777 1.4506
+Slc1a1 SLC1A1 rs1330687 1.4369
+Slc1a2 SLC1A2 rs41352148 2.3137
+Slc1a3 SLC1A3 rs3776585 1.4965
+Slc1a4 SLC1A4 rs6546119 2.8523
+Slc1a5 SLC1A5 rs10406432 1.0348
+Slc1a6 SLC1A6 rs17377848 1.9698
+Slc1a7 SLC1A7 rs1288371 4.0608
+Slc20a1 SLC20A1 rs17647908 1.8271
+Slc20a2 SLC20A2 rs7832529 1.1527
+Slc22a1 SLC22A1 rs4646283 1.6373
+Slc22a19 SLC22A10 rs12803485 2.0387
+Slc22a12 SLC22A12 rs693591 3.0635
+Slc22a13 SLC22A13 rs2298421 1.0457
+Slc22a14 SLC22A14 rs6799940 1.1506
+Slc22a15 SLC22A15 rs4046190 1.7342
+Slc22a16 SLC22A16 rs2494543 1.0162
+Slc22a18 SLC22A18 rs400643 1.6428
+Slc22a2 SLC22A2 rs533452 1.9485
+Slc22a23 SLC22A23 rs7771439 2.2356
+Slc22a30 SLC22A24 rs12807647 3.1368
+Slc22a3 SLC22A3 rs16891445 3.8623
+Slc22a4 SLC22A4 rs272885 3.1008
+Slc22a5 SLC22A5 rs272885 3.1008
+Slc22a6 SLC22A6 rs10897310 1.3716
+Slc22a7 SLC22A7 rs4714674 0.9901
+Slc22a8 SLC22A8 rs10897310 1.3716
+Slc22a19 SLC22A9 rs4121881 1.4339
+Slc23a1 SLC23A1 rs6880443 1.5226
+Slc23a2 SLC23A2 rs6052956 1.6988
+Slc24a1 SLC24A1 rs16948873 1.4383
+Slc24a2 SLC24A2 rs957910 1.7757
+Slc24a3 SLC24A3 rs808686 2.9859
+Slc24a4 SLC24A4 rs943659 2.1744
+Slc24a6 SLC24A6 rs2232514 1.7894
+Slc25a1 SLC25A1 rs807665 3.0845
+Slc25a10 SLC25A10 rs3088016 1.0063
+Slc25a11 SLC25A11 rs11654952 1.2197
+Slc25a12 SLC25A12 rs12622385 1.0126
+Slc25a13 SLC25A13 rs42078 1.7368
+Slc25a16 SLC25A16 rs10998222 0.9961
+Slc25a17 SLC25A17 rs7349026 1.0653
+Slc25a18 SLC25A18 rs1003861 1.0005
+Slc25a19 SLC25A19 rs4789161 1.0457
+Slc25a2 SLC25A2 rs10044936 1.8857
+Slc25a21 SLC25A21 rs17178003 2.2765
+Slc25a22 SLC25A22 rs7945912 0.8271
+Slc25a23 SLC25A23 rs1663803 2.1806
+Slc25a24 SLC25A24 rs476769 2.5885
+Slc25a25 SLC25A25 rs10987845 2.2528
+Slc25a26 SLC25A26 rs332339 3.6238
+Slc25a27 SLC25A27 rs9472808 1.2117
+Slc25a28 SLC25A28 rs9783252 1.5209
+Slc25a29 SLC25A29 rs2146103 2.8623
+Slc25a3 SLC25A3 rs7299837 0.8196
+Slc25a30 SLC25A30 rs7323850 1.2935
+Slc25a31 SLC25A31 rs17012568 1.2399
+Slc25a32 SLC25A32 rs17803441 1.7474
+Slc25a33 SLC25A33 rs4073710 0.9285
+Slc25a34 SLC25A34 rs2280115 1.1524
+Slc25a35 SLC25A35 rs1132554 2.6055
+Slc25a37 SLC25A37 rs4871875 1.4549
+Slc25a38 SLC25A38 rs3821374 1.2142
+Slc25a39 SLC25A39 rs2074105 0.8216
+Slc25a4 SLC25A4 rs12506141 1.0963
+Slc25a40 SLC25A40 rs11982729 2.0429
+Slc25a41 SLC25A41 rs2860185 1.2479
+Slc25a42 SLC25A42 rs4808921 1.3946
+Slc25a44 SLC25A44 rs12722815 0.9923
+AW491445 SLC25A45 rs4647575 1.0858
+Slc25a46 SLC25A46 rs3843243 0.8817
+Slc25a47 SLC25A47 rs9453 4.0217
+Slc25a48 SLC25A48 rs4246801 1.4132
+Mcart1 SLC25A51 rs6476664 1.4343
+Slc26a1 SLC26A1 rs11724804 1.0655
+C78409 SLC26A10 rs11613457 3.7375
+Slc26a11 SLC26A11 rs7207000 1.5571
+Slc26a2 SLC26A2 rs30832 1.0993
+Slc26a3 SLC26A3 rs1476673 1.5825
+Slc26a4 SLC26A4 rs16872356 1.8545
+Slc26a5 SLC26A5 rs9918642 1.6739
+Slc26a6 SLC26A6 rs2255532 1.1337
+Slc26a7 SLC26A7 rs4639475 1.5706
+Slc26a8 SLC26A8 rs2237094 1.2317
+Slc26a9 SLC26A9 rs11240595 1.9626
+Slc27a1 SLC27A1 rs6512194 2.3273
+Slc27a2 SLC27A2 rs4775860 1.6194
+Slc27a3 SLC27A3 rs10908475 4.1259
+Slc27a4 SLC27A4 rs6478827 1.2808
+Slc27a5 SLC27A5 rs1077420 1.1312
+Slc27a6 SLC27A6 rs1148740 2.7307
+Slc28a1 SLC28A1 rs12910991 1.5605
+Slc28a3 SLC28A3 rs11793319 1.1505
+Slc29a1 SLC29A1 rs9381299 1.0457
+Slc29a2 SLC29A2 rs2276106 1.1389
+Slc29a3 SLC29A3 rs2487067 2.4759
+Slc29a4 SLC29A4 rs4720572 1.2684
+Slc2a1 SLC2A1 rs841860 1.3496
+Slc2a10 SLC2A10 rs7267790 1.2351
+Slc2a12 SLC2A12 rs9493790 1.9183
+Slc2a13 SLC2A13 rs567779 2.6992
+Slc2a2 SLC2A2 rs11711206 1.1946
+Slc2a3 SLC2A3 rs6488789 1.8019
+Slc2a4 SLC2A4 rs17805277 0.9492
+BF453932 SLC2A4RG rs6011066 2.1665
+Slc2a5 SLC2A5 rs7556128 1.4256
+Slc2a6 SLC2A6 rs2073933 2.2327
+MMT00078621 SLC2A7 rs12564569 2.0498
+Slc2a8 SLC2A8 rs3824415 1.9594
+Slc2a9 SLC2A9 rs13435427 1.8604
+Slc30a1 SLC30A1 rs12727395 1.6623
+Slc30a10 SLC30A10 rs2378283 1.2177
+Slc30a2 SLC30A2 rs17163470 1.838
+Slc30a3 SLC30A3 rs2083363 0.8891
+Slc30a4 SLC30A4 rs6493154 0.9834
+Slc30a5 SLC30A5 rs435395 2.1874
+Slc30a6 SLC30A6 rs17011828 1.1674
+Slc30a7 SLC30A7 rs7543296 0.923
+Slc30a8 SLC30A8 rs1578978 1.9559
+Slc30a9 SLC30A9 rs13142165 1.4471
+Slc31a1 SLC31A1 rs4342685 1.8499
+Slc31a2 SLC31A2 rs4352917 2.4337
+Slc32a1 SLC32A1 rs2250446 1.5262
+Slc33a1 SLC33A1 rs382534 1.7491
+Slc34a1 SLC34A1 rs1801020 1.5835
+Slc34a2 SLC34A2 rs1052898 1.0544
+Slc35a1 SLC35A1 rs17504727 1.5447
+Slc35a3 SLC35A3 rs12146139 1.0703
+Slc35a5 SLC35A5 rs2705514 0.9031
+Slc35b1 SLC35B1 rs17712721 1.2803
+Slc35b2 SLC35B2 rs2233434 2.6832
+Slc35b3 SLC35B3 rs3799255 2.0441
+Slc35b4 SLC35B4 rs1450890 1.5499
+Slc35c1 SLC35C1 rs10838519 5.6925
+Slc35c2 SLC35C2 rs2425863 1.3875
+Slc35d1 SLC35D1 rs12724834 0.9658
+Slc35d2 SLC35D2 rs10120552 1.8274
+Slc35d3 SLC35D3 rs10872463 2.2622
+ENSMUST00000043238 SLC35E1 rs12461442 1.4711
+Slc35e3 SLC35E3 rs11177368 1.3368
+Slc35e4 SLC35E4 rs4820020 1.9927
+Slc35f1 SLC35F1 rs1006932 1.8422
+Slc35f2 SLC35F2 rs553663 1.6047
+Slc35f3 SLC35F3 rs1563693 1.289
+Slc35f4 SLC35F4 rs10140914 1.7605
+Slc35f5 SLC35F5 rs4372913 1.4262
+Tmem20 SLC35G1 rs7913668 1.6878
+Tmem22 SLC35G2 rs13075615 3.0342
+Slc36a1 SLC36A1 rs7723079 1.4184
+Slc36a2 SLC36A2 rs6579853 1.9555
+Slc36a3 SLC36A3 rs9324686 1.5296
+Slc36a4 SLC36A4 rs11020171 1.6544
+Slc37a1 SLC37A1 rs401809 1.6185
+Slc37a2 SLC37A2 rs12418284 1.8456
+Slc37a4 SLC37A4 rs3759012 3.0154
+Slc38a1 SLC38A1 rs11183370 1.7102
+Slc38a10 SLC38A10 rs11652842 1.114
+Slc38a11 SLC38A11 rs717339 2.2884
+Slc38a2 SLC38A2 rs1948425 1.0024
+Slc38a3 SLC38A3 rs2236942 2.7662
+Slc38a4 SLC38A4 rs180422 1.0498
+Slc38a6 SLC38A6 rs12893450 1.1246
+Slc38a7 SLC38A7 rs17241189 1.2831
+Slc38a8 SLC38A8 rs9935658 1.3033
+Slc39a1 SLC39A1 rs6413529 0.9842
+Slc39a10 SLC39A10 rs1371122 1.1986
+Slc39a11 SLC39A11 rs2714001 2.9129
+Slc39a12 SLC39A12 rs11593092 1.3271
+Slc39a13 SLC39A13 rs2697920 12.997
+Slc39a14 SLC39A14 rs13271192 1.7433
+Slc39a2 SLC39A2 rs943997 2.5029
+Slc39a3 SLC39A3 rs1001328 1.6678
+Slc39a6 SLC39A6 rs3819174 0.8228
+Slc39a7 SLC39A7 rs2854028 1.4432
+Slc39a8 SLC39A8 rs13107325 10.143
+Slc39a9 SLC39A9 rs1275673 0.957
+Slc3a1 SLC3A1 rs2033951 2.1732
+Slc3a2 SLC3A2 rs12794763 1.1124
+Slc40a1 SLC40A1 rs10202029 2.453
+Slc41a1 SLC41A1 rs1832775 2.7201
+Slc41a2 SLC41A2 rs4375513 1.3049
+Slc41a3 SLC41A3 rs3772423 1.2218
+Slc43a1 SLC43A1 rs6591429 1.0656
+Slc43a2 SLC43A2 rs2302460 1.111
+Slc43a3 SLC43A3 rs2584860 1.9813
+Slc44a1 SLC44A1 rs327992 2.074
+Slc44a2 SLC44A2 rs8106664 2.2957
+Slc44a3 SLC44A3 rs11581979 3.1312
+Slc44a4 SLC44A4 rs3869145 2.4438
+Slc44a5 SLC44A5 rs6686290 1.9772
+Slc45a1 SLC45A1 rs1058790 1.5931
+Slc45a2 SLC45A2 rs3776553 2.2777
+Slc45a3 SLC45A3 rs7529037 5.7089
+Slc45a4 SLC45A4 rs3739239 3.1856
+Slc46a1 SLC46A1 rs6505081 1.9846
+Slc46a2 SLC46A2 rs912670 1.4284
+Slc46a3 SLC46A3 rs9579214 1.6334
+Slc47a1 SLC47A1 rs2453594 1.8262
+Slc48a1 SLC48A1 rs11168236 1.0219
+Slc4a1 SLC4A1 rs1476512 3.0285
+Slc4a10 SLC4A10 rs10497224 1.4815
+Slc4a11 SLC4A11 rs2422862 1.5193
+Slc4a1ap SLC4A1AP rs3749147 1.1586
+Slc4a2 SLC4A2 rs6464127 3.2599
+Slc4a3 SLC4A3 rs635311 1.3689
+Slc4a4 SLC4A4 rs1377527 2.1987
+C330016K18Rik SLC4A5 rs7571842 1.2381
+Slc4a7 SLC4A7 rs1389771 3.344
+Slc4a8 SLC4A8 rs12581530 0.9531
+Slc4a9 SLC4A9 rs4912711 0.8242
+Slc50a1 SLC50A1 rs7517158 2.0327
+Slc52a3 SLC52A3 rs6140144 1.4078
+Slc5a1 SLC5A1 rs9609429 1.2629
+Slc5a10 SLC5A10 rs11656953 1.7765
+Slc5a11 SLC5A11 rs13338491 2.3079
+Slc5a12 SLC5A12 rs4553331 2.314
+Slc5a2 SLC5A2 rs11646911 1.5201
+Slc5a3 SLC5A3 rs1573464 1.5053
+Slc5a4a SLC5A4 rs5753905 0.8465
+Slc5a5 SLC5A5 rs7245612 1.4584
+Slc5a6 SLC5A6 rs1275521 1.2551
+Slc5a7 SLC5A7 rs10184700 0.9318
+Slc5a8 SLC5A8 rs4764786 2.2398
+Slc5a9 SLC5A9 rs520189 1.3754
+Slc6a1 SLC6A1 rs1710897 1.1975
+Slc6a11 SLC6A11 rs1881354 1.2316
+Slc6a12 SLC6A12 rs2284328 1.1707
+Slc6a13 SLC6A13 rs2284328 1.1707
+Slc6a15 SLC6A15 rs11116628 1.6455
+AV280171 SLC6A16 rs419174 1.8309
+Slc6a17 SLC6A17 rs4839225 0.9876
+Slc6a18 SLC6A18 rs6554691 1.7162
+Slc6a19 SLC6A19 rs6554677 1.669
+Slc6a2 SLC6A2 rs35915 1.471
+MMT00027251 SLC6A20 rs2252547 1.51
+Slc6a3 SLC6A3 rs456774 2.1221
+Slc6a4 SLC6A4 rs12449783 1.4651
+Slc6a5 SLC6A5 rs1036927 1.3094
+Slc6a6 SLC6A6 rs12494428 1.9622
+Slc6a9 SLC6A9 rs1859728 1.9031
+Slc7a1 SLC7A1 rs17604461 1.0309
+Slc7a10 SLC7A10 rs8111018 1.5372
+Slc7a11 SLC7A11 rs6832143 1.1998
+Slc7a13 SLC7A13 rs13254996 2.2952
+Slc7a14 SLC7A14 rs1978399 1.6265
+Slc7a2 SLC7A2 rs17124742 2.4987
+Slc7a4 SLC7A4 rs450741 1.9855
+Slc7a5 SLC7A5 rs6540092 1.3319
+Slc7a6 SLC7A6 rs11350 18.283
+Slc7a6os SLC7A6OS rs11350 18.283
+Slc7a7 SLC7A7 rs6572754 0.9813
+Slc7a8 SLC7A8 rs11625112 2.6225
+Slc7a9 SLC7A9 rs10164326 1.6496
+Slc8a1 SLC8A1 rs7567178 1.759
+Slc8a2 SLC8A2 rs10406234 1.0787
+Slc8a3 SLC8A3 rs10136718 1.8254
+Slc9a1 SLC9A1 rs12410656 4.2075
+Slc9a2 SLC9A2 rs17776702 2.0242
+Slc9a3 SLC9A3 rs6864158 1.8245
+Slc9a3r2 SLC9A3R2 rs3211994 1.2234
+Slc9a5 SLC9A5 rs9940665 8.185
+Slc9a8 SLC9A8 rs492702 1.1884
+Slc9a9 SLC9A9 rs9289666 2.8147
+Nhedc1 SLC9B1 rs6848961 3.95
+Nhedc2 SLC9B2 rs6848961 3.95
+Slc9a10 SLC9C1 rs6787547 2.8297
+Gm6185 SLC9C2 rs9425728 2.7528
+Slco1a5 SLCO1A2 rs11513226 1.2237
+Slco1b2 SLCO1B3 rs11045577 1.2479
+Slco1c1 SLCO1C1 rs10841596 2.6247
+Slco2a1 SLCO2A1 rs41453446 2.2258
+Slco2b1 SLCO2B1 rs2851085 1.3494
+Slco3a1 SLCO3A1 rs17644254 1.909
+Slco4a1 SLCO4A1 rs4239643 1.0791
+Slco4c1 SLCO4C1 rs3096196 1.9208
+Slco5a1 SLCO5A1 rs9987292 1.3505
+Slco6b1 SLCO6A1 rs4386713 1.5977
+Slfn1 SLFN12 rs2376263 1.4267
+Slfn2 SLFN12L rs2376263 1.4267
+Slfn10 SLFN13 rs2376263 1.4267
+Slfn14-ps SLFN14 rs4516277 1.2567
+Slfn5 SLFN5 rs2291189 1.7303
+Slfnl1 SLFNL1 rs10889792 0.9473
+1810035L17Rik SLIRP rs17752640 1.2653
+Slit1 SLIT1 rs4919079 2.7247
+Slit2 SLIT2 rs556816 1.8655
+Slit3 SLIT3 rs17071071 2.5797
+Slitrk1 SLITRK1 rs9318986 1.0997
+Slitrk3 SLITRK3 rs13082820 1.3082
+Slitrk6 SLITRK6 rs17080128 1.1039
+Slk SLK rs10883953 1.3408
+Slmap SLMAP rs6445935 1.4653
+Slmo1 SLMO1 rs16977205 0.9682
+Slmo2 SLMO2 rs151348 1.9158
+Sln SLN rs17107346 0.963
+Slpi SLPI rs6130789 1.297
+Sltm SLTM rs12903823 1.2895
+Slu7 SLU7 rs12515175 1.207
+Slurp1 SLURP1 rs2585135 0.9473
+Slx4 SLX4 rs9930893 1.6674
+Smad1 SMAD1 rs2036138 1.5412
+Smad2 SMAD2 rs1847173 1.6786
+Smad3 SMAD3 rs12916244 1.6621
+Smad4 SMAD4 rs8085716 1.0906
+Smad5 SMAD5 rs4645328 1.9266
+Smad6 SMAD6 rs8024238 1.218
+Smad7 SMAD7 rs9946510 2.6538
+Smad9 SMAD9 rs678037 1.5174
+Smagp SMAGP rs4761995 1.1896
+Smap1 SMAP1 rs2691483 2.1064
+Smap2 SMAP2 rs909969 0.8788
+Smarca2 SMARCA2 rs16937792 2.6388
+Smarca4 SMARCA4 rs11879293 3.047
+Smarca5 SMARCA5 rs10008867 1.6455
+Smarcad1 SMARCAD1 rs1386531 1.7929
+Smarcal1 SMARCAL1 rs2738280 1.1759
+Smarcb1 SMARCB1 rs6003870 1.5067
+Smarcc2 SMARCC2 rs2292237 1.0172
+Smarcd1 SMARCD1 rs836177 2.5291
+Smarcd2 SMARCD2 rs2727280 1.291
+Smarcd3 SMARCD3 rs1122979 1.2444
+Smc1b SMC1B rs2350929 1.1415
+Smc2 SMC2 rs41337947 1.9531
+Smc3 SMC3 rs11815960 1.366
+Smc4 SMC4 rs1141023 1.7865
+Smc5 SMC5 rs10868811 1.4122
+Smc6 SMC6 rs7556983 2.0659
+Smchd1 SMCHD1 rs12958035 1.7924
+Smcp SMCP rs7519200 1.5414
+Smcr7 SMCR7 rs2746028 1.5484
+Smcr7l SMCR7L rs17400681 1.1886
+Smcr8 SMCR8 rs8075915 1.3082
+1110034C04Rik SMEK1 rs4904785 0.8492
+Smek2 SMEK2 rs8080 1.4171
+Smg1 SMG1 rs17731755 0.9404
+Smg6 SMG6 rs170040 2.9747
+Smg7 SMG7 rs1044879 2.393
+1500002O20Rik SMG9 rs12610476 1.8881
+1190007F08Rik SMIM1 rs1181872 3.4342
+Smndc1 SMNDC1 rs1133195 1.3467
+Smo SMO rs3734969 1.7192
+Smoc1 SMOC1 rs10136718 1.8254
+Smoc2 SMOC2 rs7764852 1.9314
+Smox SMOX rs1741297 1.9055
+Smpd1 SMPD1 rs7951904 1.2118
+Smpd2 SMPD2 rs12200586 1.0957
+Smpd3 SMPD3 rs8050499 10.236
+Smpd4 SMPD4 rs11685415 1.3815
+Smpdl3a SMPDL3A rs7752838 1.0235
+Smpdl3b SMPDL3B rs3813804 1.0248
+Smtn SMTN rs4820938 2.0444
+Smtnl1 SMTNL1 rs2649649 1.533
+D130058I21Rik SMTNL2 rs4790210 1.425
+Smu1 SMU1 rs3818644 1.3096
+Smug1 SMUG1 rs1872636 1.7981
+Smurf1 SMURF1 rs2138088 2.8184
+Smurf2 SMURF2 rs6504248 2.2115
+Smyd1 SMYD1 rs7588239 0.8775
+Smyd2 SMYD2 rs9429914 2.0277
+Smyd3 SMYD3 rs12123089 3.0866
+Smyd4 SMYD4 rs7215532 1.1472
+Snai1 SNAI1 rs4647957 1.0796
+Snai2 SNAI2 rs13279837 1.0309
+Snai3 SNAI3 rs4782395 1.5171
+Snap23 SNAP23 rs8027207 1.1156
+Snap25 SNAP25 rs6032829 1.6295
+Snap29 SNAP29 rs165794 1.373
+Snap47 SNAP47 rs11590137 1.5957
+Snap91 SNAP91 rs522115 1.1617
+Snapc2 SNAPC2 rs4804833 3.2673
+Snapc3 SNAPC3 rs2665515 2.4101
+Snapc4 SNAPC4 rs10870201 1.6269
+Snapin SNAPIN rs6674929 2.8582
+Snca SNCA rs17286290 1.5842
+Sncaip SNCAIP rs967170 1.6112
+Sncb SNCB rs11134988 0.8153
+Sncg SNCG rs17335915 1.669
+Snd1 SND1 rs10250463 1.3943
+Sned1 SNED1 rs2067987 1.5187
+Snf8 SNF8 rs1058018 1.5036
+Snhg10 SNHG10 rs10484055 2.5701
+Snhg11 SNHG11 rs805532 1.902
+Snhg12 SNHG12 rs9426420 2.4662
+Snhg3 SNHG3 rs10158229 1.1321
+Snhg6 SNHG6 rs4737815 1.232
+Snhg7 SNHG7 rs1832149 0.8239
+Snhg9 SNHG9 rs8055439 1.1127
+Snip1 SNIP1 rs4653295 0.8242
+Snn SNN rs11641208 0.8921
+Snora17 SNORA17 rs1832149 0.8239
+Snora19 SNORA19 rs10886368 1.4878
+Snora20 SNORA20 rs763561 1.6229
+Snora21 SNORA21 rs8076343 1.0752
+Snora24 SNORA24 rs2811502 0.8589
+Snora26 SNORA26 rs10517266 1.5936
+Snora2b SNORA2B rs6580689 3.3264
+Snora3 SNORA3 rs3365 1.1167
+Snora30 SNORA30 rs1036696 1.6492
+Snora31 SNORA31 rs6052956 1.6988
+Snora33 SNORA33 rs7773338 0.9996
+Snora34 SNORA34 rs6580689 3.3264
+Snora36b SNORA36B rs12129903 0.9574
+Snora43 SNORA43 rs41379249 1.2594
+Snora44 SNORA44 rs9426420 2.4662
+Snora47 SNORA47 rs9293702 1.0466
+Snora5c SNORA5C rs11977608 1.7804
+Snora61 SNORA61 rs9426420 2.4662
+Snora62 SNORA62 rs463922 2.0061
+Snora64 SNORA64 rs8055439 1.1127
+Snora65 SNORA65 rs10987640 1.1617
+Snora68 SNORA68 rs3212798 1.3594
+Snora70 SNORA70 rs1882022 1.8735
+Snora75 SNORA75 rs4031375 1.9682
+Snora78 SNORA78 rs8055439 1.1127
+Snora81 SNORA81 rs17299991 1.9329
+Snord104 SNORD104 rs2291676 1.0915
+Snord11 SNORD11 rs7572505 1.218
+Snord110 SNORD110 rs5026920 0.8798
+Snord111 SNORD111 rs11075766 0.967
+Snord12 SNORD12 rs6122779 0.8428
+Snord15a SNORD15A rs17310905 1.2713
+Snord15b SNORD15B rs17310905 1.2713
+Snord1a SNORD1A rs9890158 1.459
+Snord1b SNORD1B rs9890158 1.459
+Snord2 SNORD2 rs17299991 1.9329
+Snord21 SNORD21 rs10782945 1.2909
+Snord22 SNORD22 rs12794763 1.1124
+Snord23 SNORD23 rs17703870 1.0197
+Snord37 SNORD37 rs737204 4.1622
+Snord43 SNORD43 rs137618 3.6499
+Snord45b SNORD45B rs721204 0.9281
+Snord45c SNORD45C rs721204 0.9281
+Snord47 SNORD47 rs35571466 1.6162
+Snord49a SNORD49A rs9910014 1.6949
+Snord49b SNORD49B rs9910014 1.6949
+Snord52 SNORD52 rs9368699 1.3581
+Snord53 SNORD53 rs7580240 1.0841
+Snord58b SNORD58B rs12963212 8.3966
+Snord65 SNORD65 rs274019 1.7368
+Snord66 SNORD66 rs9357696 1.5799
+Snord67 SNORD67 rs3136441 17.458
+Snord68 SNORD68 rs455527 1.4861
+Snord7 SNORD7 rs4516277 1.2567
+Snord70 SNORD70 rs7572505 1.218
+Snord71 SNORD71 rs7202288 1.5098
+Snord72 SNORD72 rs6868112 1.5346
+Snord82 SNORD82 rs7598759 1.2099
+Snord85 SNORD85 rs11586148 1.5561
+Snord87 SNORD87 rs4737815 1.232
+Snord88a SNORD88A rs2247216 1.988
+Snord88c SNORD88C rs2247216 1.988
+Snord89 SNORD89 rs1192805 0.9851
+Snord90 SNORD90 rs16912287 2.0755
+Snord91a SNORD91A rs413016 2.3978
+Snord92 SNORD92 rs7580240 1.0841
+Snord93 SNORD93 rs2270105 1.6245
+Snord95 SNORD95 rs10210296 2.506
+Snord96a SNORD96A rs254455 0.9412
+Snord99 SNORD99 rs9426420 2.4662
+Snph SNPH rs3764713 1.4465
+Snrk SNRK rs6791641 1.6258
+Snrnp25 SNRNP25 rs2562154 0.9374
+Snrnp40 SNRNP40 rs6699372 1.6651
+Snrnp48 SNRNP48 rs2744373 1.8857
+Snrnp70 SNRNP70 rs4802546 1.0414
+Snrpa SNRPA rs2604913 2.4279
+Snrpa1 SNRPA1 rs12438654 1.2077
+Snrpb SNRPB rs13044564 3.4636
+Snrpb2 SNRPB2 rs6080387 1.4277
+Snrpc SNRPC rs7755822 3.8474
+Snrpd2 SNRPD2 rs10423928 3.2446
+Snrpd3 SNRPD3 rs5760492 0.8935
+Snrpf SNRPF rs11108305 0.8404
+Snrpg SNRPG rs17500213 1.6619
+Snrpn SNRPN rs17114852 1.4488
+Sntb1 SNTB1 rs10094256 1.6308
+Sntb2 SNTB2 rs11075710 2.1122
+Sntg1 SNTG1 rs10957894 1.4988
+Sntg2 SNTG2 rs6706944 1.4375
+Sntn SNTN rs6445370 1.2484
+Snupn SNUPN rs11634402 0.8145
+Snrpn SNURF rs17114852 1.4488
+Snw1 SNW1 rs17752640 1.2653
+Snx10 SNX10 rs1534696 4.1119
+Snx11 SNX11 rs6847 2.2253
+Snx13 SNX13 rs10282707 5.8425
+Snx16 SNX16 rs4637773 1.2798
+Snx17 SNX17 rs11689803 1.1015
+Snx18 SNX18 rs13153389 2.4974
+Snx19 SNX19 rs3751035 1.3836
+Snx2 SNX2 rs2407400 1.3492
+Snx21 SNX21 rs12480887 5.8453
+Snx24 SNX24 rs30038 2.7902
+Snx25 SNX25 rs2293367 1.9678
+Snx27 SNX27 rs3748608 1.7397
+Snx29 SNX29 rs12930375 2.2977
+Snx3 SNX3 rs217520 0.945
+Snx30 SNX30 rs10981526 1.0939
+Snx31 SNX31 rs10955237 1.447
+Snx32 SNX32 rs10896050 2.7357
+Snx33 SNX33 rs8031394 1.3194
+Snx4 SNX4 rs9850995 2.12
+Snx5 SNX5 rs6080895 1.281
+Snx6 SNX6 rs12433475 2.0669
+Snx7 SNX7 rs4341382 0.9446
+Snx8 SNX8 rs7803189 2.0509
+Snx9 SNX9 rs7742187 2.0149
+Soat1 SOAT1 rs12749666 1.7775
+Soat2 SOAT2 rs10876407 1.8356
+Sobp SOBP rs12661385 1.4647
+Socs1 SOCS1 rs1646070 2.5481
+Socs2 SOCS2 rs11107116 1.759
+Socs3 SOCS3 rs4969168 2.6964
+Socs5 SOCS5 rs6544913 1.4561
+Socs6 SOCS6 rs10037423 1.1295
+Sod1 SOD1 rs2173962 1.7286
+Sod2 SOD2 rs2842960 1.0904
+Sod3 SOD3 rs17552548 1.9698
+9830001H06Rik SOGA1 rs6102336 1.0668
+1110012J17Rik SOGA2 rs12604208 2.0824
+Sohlh2 SOHLH2 rs4297570 0.9682
+Son SON rs2834233 1.1594
+Sorbs1 SORBS1 rs7916334 2.4977
+Sorbs2 SORBS2 rs830829 2.5049
+Sorbs3 SORBS3 rs3758038 1.8256
+Sorcs1 SORCS1 rs9630080 3.3731
+Sorcs2 SORCS2 rs4423876 2.4377
+Sorcs3 SORCS3 rs2297500 2.7156
+Sord SORD rs11636774 2.0743
+Sorl1 SORL1 rs7946888 2.5017
+Sort1 SORT1 rs629301 7.2084
+Sos1 SOS1 rs297153 0.9393
+Sos2 SOS2 rs10150798 0.8327
+Sost SOST rs1877632 3.0391
+Sostdc1 SOSTDC1 rs17169488 1.7402
+Sowahb SOWAHB rs979175 1.3951
+Sox1 SOX1 rs7993373 1.0776
+Sox10 SOX10 rs139896 1.2234
+Sox11 SOX11 rs1008001 1.4441
+Sox12 SOX12 rs6049002 1.4965
+Sox13 SOX13 rs6700051 1.7605
+Sox14 SOX14 rs11717337 1.4567
+Sox15 SOX15 rs8068222 2.1304
+Sox17 SOX17 rs12543193 1.2306
+Sox18 SOX18 rs816953 1.1708
+Sox2 SOX2 rs4129750 1.2126
+Sox2ot SOX2-OT rs41330845 1.4636
+Sox21 SOX21 rs1417733 0.9492
+Sox30 SOX30 rs10035961 2.0067
+Sox5 SOX5 rs2970422 2.8345
+Sox6 SOX6 rs966933 2.4789
+Sox7 SOX7 rs7014168 4.991
+Sox8 SOX8 rs402605 2.819
+Sp1 SP1 rs3741651 2.2289
+Sp100 SP100 rs6755211 1.5667
+5830484A20Rik SP110 rs16826917 1.4281
+Sp140 SP140 rs6755211 1.5667
+Sp2 SP2 rs12944587 1.6755
+Sp3 SP3 rs6721460 2.4291
+Sp4 SP4 rs17144546 1.1183
+Sp5 SP5 rs10754975 2.7357
+Sp6 SP6 rs11654648 3.8005
+Sp7 SP7 rs10876431 2.9923
+Sp8 SP8 rs10231520 0.9408
+Spa17 SPA17 rs483946 1.0307
+Spaca1 SPACA1 rs651123 1.4052
+Spaca3 SPACA3 rs17734411 1.1762
+Spaca4 SPACA4 rs2292342 1.8551
+Spag1 SPAG1 rs1788188 0.9551
+Spag16 SPAG16 rs4673825 1.9739
+Spag17 SPAG17 rs17037868 2.3478
+Spag4 SPAG4 rs6120996 1.9427
+Spag7 SPAG7 rs7212695 1.0472
+Spag9 SPAG9 rs9910597 1.4676
+Hyal5 SPAM1 rs10240170 1.4437
+Sparc SPARC rs4958485 2.3869
+Sparcl1 SPARCL1 rs10028788 3.1098
+Spast SPAST rs17011828 1.1674
+Spata1 SPATA1 rs4906999 2.3561
+Spata13 SPATA13 rs9553143 2.8407
+Spata16 SPATA16 rs981996 2.5724
+Spata17 SPATA17 rs3790735 2.4936
+Spata18 SPATA18 rs7678370 1.8948
+Spata2 SPATA2 rs492702 1.1884
+Spata20 SPATA20 rs198542 1.2642
+Spata21 SPATA21 rs12064256 1.9784
+Spata22 SPATA22 rs2318036 2.4012
+Spata24 SPATA24 rs6880443 1.5226
+Spata25 SPATA25 rs11086985 7.1822
+Spata3 SPATA3 rs11692681 1.3195
+Spata4 SPATA4 rs17062623 1.5496
+Spata5 SPATA5 rs9918042 1.2914
+Spata6 SPATA6 rs11587270 1.5577
+Spata7 SPATA7 rs2991286 1.9326
+Spata9 SPATA9 rs17211585 1.1057
+Spats1 SPATS1 rs2233434 2.6832
+Spats2 SPATS2 rs10506285 1.3191
+Spats2l SPATS2L rs6747833 2.6857
+Spc24 SPC24 rs4804147 3.3337
+Spcs2 SPCS2 rs3829935 1.2946
+Spcs3 SPCS3 rs11730950 2.6745
+Spdef SPDEF rs11754548 6.1834
+Spdya SPDYA rs17743079 1.633
+Spdyb SPDYE4 rs4309437 1.3291
+Specc1 SPECC1 rs7207459 2.14
+4932439K10Rik SPECC1L rs5760361 2.2812
+Spef1 SPEF1 rs6139201 1.8993
+Spef2 SPEF2 rs10071847 1.0764
+Speg SPEG rs6724274 2.9821
+Spem1 SPEM1 rs11652328 2.8827
+Spen SPEN rs471828 1.4132
+Spert SPERT rs17067033 1.1326
+Spg11 SPG11 rs7179390 1.1552
+Spg20 SPG20 rs9315411 1.443
+Spg21 SPG21 rs4776654 0.8887
+Spg7 SPG7 rs455527 1.4861
+Sphk1 SPHK1 rs9894366 0.9722
+Sphk2 SPHK2 rs2292342 1.8551
+Sphkap SPHKAP rs6436766 2.3561
+Sfpi1 SPI1 rs2697920 12.997
+Spib SPIB rs2546549 1.1547
+Spic SPIC rs7309447 2.9169
+Spice1 SPICE1 rs1552433 1.1137
+Spin1 SPIN1 rs11142321 1.1415
+Spink3 SPINK1 rs7716258 1.442
+Spink14 SPINK14 rs41435445 1.3622
+Spink2 SPINK2 rs1399002 1.4248
+Spink4 SPINK4 rs4879671 1.0687
+Spink5 SPINK5 rs6891643 1.6592
+EG433180 SPINK6 rs17637955 0.9788
+Spink12 SPINK9 rs17719822 1.3364
+Spint2 SPINT2 rs8102476 1.3485
+Spint3 SPINT3 rs7272535 1.0759
+Spint4 SPINT4 rs736317 2.0755
+Spire1 SPIRE1 rs4797700 1.5349
+Spire2 SPIRE2 rs8047581 1.7203
+Spn SPN rs7198549 1.2985
+Spns1 SPNS1 rs3922668 4.181
+Spns2 SPNS2 rs3760194 1.1889
+Spns3 SPNS3 rs8068577 1.3144
+Spo11 SPO11 rs6127999 1.1322
+MMT00072944 SPOCD1 rs4949222 1.783
+Spock1 SPOCK1 rs4976349 2.3263
+Spock2 SPOCK2 rs1269600 1.7183
+Spock3 SPOCK3 rs17521607 2.6482
+Spon1 SPON1 rs11607668 2.458
+Spop SPOP rs17712721 1.2803
+Spp1 SPP1 rs17013707 2.2623
+Spp2 SPP2 rs683719 1.5928
+2010106G01Rik SPPL2A rs12903915 0.9187
+3110056O03Rik SPPL2B rs8100329 1.5406
+4933407P14Rik SPPL2C rs242942 1.7967
+Sppl3 SPPL3 rs11065270 1.7657
+Spr SPR rs1561245 0.9859
+Spred1 SPRED1 rs17648059 4.277
+Spred2 SPRED2 rs11695503 1.6592
+Spred3 SPRED3 rs3745962 0.9318
+Sprn SPRN rs2987803 1.5331
+Sprr1a SPRR1A rs7546648 1.1029
+Sprr2a1 SPRR2A rs1415966 1.3888
+Sprr3 SPRR3 rs2339494 0.8539
+Sprr4 SPRR4 rs7546648 1.1029
+Gm505 SPRTN rs16854120 1.0697
+Spry1 SPRY1 rs12505913 1.9462
+Spry2 SPRY2 rs9545394 1.2632
+Spry4 SPRY4 rs11743306 1.6073
+Spryd4 SPRYD4 rs2657883 0.8133
+6330409N04Rik SPRYD7 rs1609571 1.0566
+Spsb1 SPSB1 rs553161 1.6659
+Spsb2 SPSB2 rs12302904 1.2145
+Spsb3 SPSB3 rs1065663 1.2005
+Spsb4 SPSB4 rs7623980 1.8085
+Spna1 SPTA1 rs703127 1.8608
+Spna2 SPTAN1 rs10513497 1.3354
+Spnb1 SPTB rs2285003 1.2208
+Spnb2 SPTBN1 rs1052788 1.5488
+Spnb4 SPTBN4 rs444241 1.6407
+MMT00039625 SPTBN5 rs4923917 3.9318
+Sptlc1 SPTLC1 rs12156501 1.3075
+Sptlc2 SPTLC2 rs2284229 1.5589
+Sptlc3 SPTLC3 rs2876323 2.15
+1110002B05Rik SPTSSA rs4982176 0.9961
+1110032A04Rik SPTSSB rs9809257 0.8938
+Spty2d1 SPTY2D1 rs872872 1.8722
+Gm9999 SPTY2D1-AS1 rs17494675 1.4599
+Sqle SQLE rs4347009 1.5645
+Sqrdl SQRDL rs2028589 1.5146
+Sqstm1 SQSTM1 rs166624 1.5109
+Srbd1 SRBD1 rs3770268 1.296
+Src SRC rs10470023 1.3849
+Srcap SRCAP rs1036696 1.6492
+Srcin1 SRCIN1 rs7214959 3.074
+BC068293 SRCRB4D rs7777294 2.2234
+Srd5a1 SRD5A1 rs521361 1.7975
+Srd5a2 SRD5A2 rs2300700 0.967
+Srd5a3 SRD5A3 rs7692806 1.3404
+Srebf1 SREBF1 rs1006656 2.6731
+Srebf2 SREBF2 rs12165579 1.4135
+Srek1 SREK1 rs154845 1.3919
+Srf SRF rs6905948 1.3261
+Srfbp1 SRFBP1 rs13360946 1.4035
+Srgap1 SRGAP1 rs2647910 2.7409
+Srgap2 SRGAP2 rs9242 4.3716
+Srgap3 SRGAP3 rs487269 2.2803
+Srgn SRGN rs10998538 1.1011
+Sri SRI rs1063964 1.2661
+Srl SRL rs153037 3.5294
+Srm SRM rs11583740 0.9248
+Srms SRMS rs10485825 1.3704
+Srp14 SRP14 rs4432245 0.8713
+Srp19 SRP19 rs10046063 1.145
+Srp54 SRP54 rs1955437 1.4622
+Srp68 SRP68 rs2665964 1.7542
+Srp72 SRP72 rs12505749 1.2403
+Srp9 SRP9 rs3738037 3.0597
+Srpk1 SRPK1 rs17654547 1.2523
+Srpk2 SRPK2 rs3801999 1.1478
+Srpr SRPR rs3824924 1.9245
+Srprb SRPRB rs2715628 1.3785
+Srr SRR rs413016 2.3978
+Srrd SRRD rs129845 1.1556
+Srrm1 SRRM1 rs4649013 1.5467
+Srrm3 SRRM3 rs2097947 1.5979
+Srrm4 SRRM4 rs11612607 2.7051
+Srrt SRRT rs314376 2.3639
+Srsf1 SRSF1 rs2233911 1.6033
+Srsf10 SRSF10 rs1655201 0.8111
+Srsf11 SRSF11 rs12139863 1.408
+Srsf12 SRSF12 rs958328 1.2832
+Srsf2 SRSF2 rs1014390 1.0973
+Srsf3 SRSF3 rs13202984 0.9462
+Srsf4 SRSF4 rs3103882 3.3572
+Srsf5 SRSF5 rs2332166 1.3282
+Srsf6 SRSF6 rs6103315 1.2362
+Srsf7 SRSF7 rs4670265 1.2522
+Srsf9 SRSF9 rs540520 1.6192
+Srxn1 SRXN1 rs6038187 2.3124
+Ss18 SS18 rs12326584 1.5232
+Ss18l1 SS18L1 rs2427258 1.2901
+Ssb SSB rs10803839 1.5693
+Ssbp2 SSBP2 rs447574 1.9863
+Ssbp3 SSBP3 rs4927080 1.4289
+Ssbp4 SSBP4 rs1054564 1.5943
+A430110N23Rik SSC5D rs12461779 1.4511
+Ssfa2 SSFA2 rs17456481 1.0413
+Ssh2 SSH2 rs2289629 2.3484
+Ssh3 SSH3 rs7123483 1.5311
+Sspn SSPN rs11048425 2.7118
+Sspo SSPO rs855653 1.335
+Ssr1 SSR1 rs6929509 1.5478
+Ssr2 SSR2 rs1889532 1.5589
+Ssr3 SSR3 rs12107942 2.185
+Ssrp1 SSRP1 rs12787728 2.4139
+Sssca1 SSSCA1 rs10896016 1.2016
+Sst SST rs9873218 2.257
+Sstr1 SSTR1 rs4143959 0.8877
+Sstr3 SSTR3 rs229565 1.2594
+Sstr4 SSTR4 rs6113896 1.934
+Sstr5 SSTR5 rs2076422 2.702
+Ssu72 SSU72 rs3128342 1.187
+Ssx2ip SSX2IP rs1340523 2.1376
+St13 ST13 rs5758106 1.2289
+St14 ST14 rs619606 1.522
+St18 ST18 rs16917565 1.9401
+St3gal1 ST3GAL1 rs2736874 3.3839
+St3gal2 ST3GAL2 rs775212 1.0222
+St3gal3 ST3GAL3 rs3011216 2.3695
+St3gal4 ST3GAL4 rs17135399 2.7881
+St3gal5 ST3GAL5 rs4832219 1.413
+St3gal6 ST3GAL6 rs828596 2.2626
+St5 ST5 rs7110224 3.1338
+St6gal1 ST6GAL1 rs4686821 1.4983
+St6gal2 ST6GAL2 rs4676310 1.2817
+St6galnac1 ST6GALNAC1 rs11867820 1.3601
+St6galnac2 ST6GALNAC2 rs9890158 1.459
+St6galnac3 ST6GALNAC3 rs6696780 2.7178
+St6galnac4 ST6GALNAC4 rs913984 1.7528
+St6galnac5 ST6GALNAC5 rs13375961 1.4143
+St6galnac6 ST6GALNAC6 rs913984 1.7528
+St7 ST7 rs3864652 2.4447
+St7l ST7L rs7516521 1.6442
+St8sia1 ST8SIA1 rs2012722 2.0254
+St8sia2 ST8SIA2 rs7170647 1.8111
+St8sia3 ST8SIA3 rs9953561 1.159
+St8sia4 ST8SIA4 rs17721790 1.7226
+St8sia5 ST8SIA5 rs11082552 1.0614
+St8sia6 ST8SIA6 rs11254592 2.5594
+Stab1 STAB1 rs13326165 6.2503
+Stab2 STAB2 rs4015379 2.166
+Stac STAC rs1155010 1.7542
+Stac2 STAC2 rs9897185 7.991
+Stac3 STAC3 rs7301155 4.0166
+Stag1 STAG1 rs699165 4.5877
+Stag3 STAG3 rs11762932 1.3452
+Stam STAM rs10904986 1.3962
+Stam2 STAM2 rs10190828 1.6265
+Stambp STAMBP rs7594485 1.4535
+Stambpl1 STAMBPL1 rs1388 2.5099
+Stap1 STAP1 rs7699834 1.4715
+Stap2 STAP2 rs8107226 1.991
+Star STAR rs2517390 1.1004
+Stard10 STARD10 rs660043 1.1939
+Stard13 STARD13 rs12871317 2.2923
+Stard3 STARD3 rs881844 13.546
+Stard3nl STARD3NL rs6973458 3.1532
+Stard4 STARD4 rs10058699 0.8404
+Stard5 STARD5 rs4778640 1.1522
+ENSMUST00000062156 STARD6 rs4525561 1.0985
+Stard9 STARD9 rs10454692 1.3805
+Stat1 STAT1 rs12469810 1.1381
+Stat3 STAT3 rs7222941 1.0373
+Stat4 STAT4 rs7595886 1.6529
+Stat5b STAT5B rs939881 1.9158
+Stat6 STAT6 rs324019 1.3953
+Stau1 STAU1 rs348291 1.2557
+Stau2 STAU2 rs716009 2.1473
+Stbd1 STBD1 rs1530295 1.3624
+Stc1 STC1 rs4872195 1.9097
+Stc2 STC2 rs17075791 1.6415
+Steap1 STEAP1 rs17865274 1.6315
+Steap2 STEAP2 rs17863957 1.6507
+Steap3 STEAP3 rs11685085 1.7005
+Steap4 STEAP4 rs10249055 1.0719
+Stil STIL rs2845 2.1438
+Stim1 STIM1 rs10742189 2.6423
+Stim2 STIM2 rs12649969 2.6983
+Stip1 STIP1 rs4980525 2.1599
+Stk10 STK10 rs13436568 2.1888
+Stk11 STK11 rs873289 1.5283
+Stk11ip STK11IP rs3731920 1.4403
+Stk17b STK17B rs16845970 1.0719
+Stk19 STK19 rs9332739 2.0606
+Stk24 STK24 rs9300484 1.83
+Stk25 STK25 rs4675812 2.6949
+Stk3 STK3 rs4629834 2.0118
+Stk31 STK31 rs17148487 0.9126
+Stk32a STK32A rs3805536 1.507
+Stk32b STK32B rs195140 2.0072
+Stk32c STK32C rs7094598 1.6538
+Stk33 STK33 rs7942490 0.8576
+Stk35 STK35 rs947436 1.7144
+Stk36 STK36 rs33946522 0.965
+Stk38 STK38 rs2239807 1.2865
+Stk38l STK38L rs7304482 1.3803
+Stk39 STK39 rs1850437 3.1828
+Stk4 STK4 rs2180291 1.6988
+Stk40 STK40 rs6688171 1.6786
+Stmn2 STMN2 rs10504711 1.9307
+Stmn3 STMN3 rs2738770 2.0248
+Stmn4 STMN4 rs7844646 1.249
+Stom STOM rs10818527 1.6533
+Stoml1 STOML1 rs7171694 1.2177
+Stoml2 STOML2 rs944700 0.9809
+Stoml3 STOML3 rs10220044 1.902
+4921524J06Rik STON1 rs6738233 2.3698
+Ston2 STON2 rs7146488 1.9439
+Stox1 STOX1 rs3740596 1.6576
+Stox2 STOX2 rs2309944 1.6383
+4930555I21Rik STPG1 rs10489442 3.0511
+B930007M17Rik STPG2 rs1980058 1.8465
+Stra13 STRA13 rs9903371 1.1444
+Stra6 STRA6 rs16967059 1.4499
+Stra8 STRA8 rs292578 2.3632
+Strada STRADA rs721575 1.0399
+Stradb STRADB rs7573536 1.2496
+Strap STRAP rs11056702 1.3577
+Strc STRC rs4923964 1.3621
+Strn STRN rs10182741 2.1509
+Strn3 STRN3 rs1124181 2.9606
+Strn4 STRN4 rs10406432 1.0348
+Stt3b STT3B rs11915458 1.624
+Stub1 STUB1 rs3752496 0.8176
+Stx11 STX11 rs12215407 2.0744
+Stx16 STX16 rs9680039 1.0737
+Stx17 STX17 rs7850043 0.9662
+Stx18 STX18 rs7670059 2.4246
+Stx19 STX19 rs13071953 0.8788
+Stx1a STX1A rs875342 0.9618
+Stx1b STX1B rs4889606 2.7307
+Stx2 STX2 rs7954011 2.0659
+Stx3 STX3 rs11230067 1.727
+Stx4a STX4 rs4889606 2.7307
+Stx5a STX5 rs2241908 1.2476
+Stx6 STX6 rs3795504 0.9547
+Stx7 STX7 rs6908213 1.0058
+Stx8 STX8 rs12942488 1.6824
+Stxbp1 STXBP1 rs2417053 0.8153
+Stxbp2 STXBP2 rs8111036 0.8851
+Stxbp3a STXBP3 rs11102503 3.0847
+Stxbp5 STXBP5 rs1496032 1.7607
+Stxbp5l STXBP5L rs9827836 2.2269
+Stxbp6 STXBP6 rs7153942 2.0168
+Styk1 STYK1 rs4763527 1.5361
+Styx STYX rs7142734 0.9991
+Sub1 SUB1 rs11744250 1.002
+Sucla2 SUCLA2 rs9534858 1.5654
+Suclg1 SUCLG1 rs2085189 0.8283
+Suclg2 SUCLG2 rs6798228 2.3342
+Sucnr1 SUCNR1 rs12152407 1.5493
+Suco SUCO rs12068145 1.426
+2400003N08Rik SUDS3 rs17440893 1.9378
+Sufu SUFU rs2298281 1.3605
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+Sugp2 SUGP2 rs2013813 1.5842
+Sugt1 SUGT1 rs17541412 1.1265
+Sulf1 SULF1 rs17646998 1.8133
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+Sult1a1 SULT1A1 rs7193402 1.063
+Sult1b1 SULT1B1 rs2221366 2.16
+Sult1e1 SULT1E1 rs4149535 1.7224
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+Sult2b1 SULT2B1 rs2292342 1.8551
+Sult4a1 SULT4A1 rs12160050 1.2578
+Sult6b1 SULT6B1 rs3845783 1.7011
+Sumf1 SUMF1 rs17040083 2.3473
+Sumo1 SUMO1 rs16838917 1.9914
+Sumo3 SUMO3 rs235293 2.3827
+Sun1 SUN1 rs11769388 1.6258
+Sun2 SUN2 rs5750678 1.1939
+Sun3 SUN3 rs3176493 2.7773
+Sun5 SUN5 rs1133358 1.9
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+Supt7l SUPT7L rs2068834 2.1142
+Supv3l1 SUPV3L1 rs5030933 1.5394
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+Surf2 SURF2 rs11244086 2.4186
+Surf4 SURF4 rs11244086 2.4186
+Surf6 SURF6 rs11244086 2.4186
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+Susd5 SUSD5 rs4678713 0.9935
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+1200016B10Rik SWT1 rs1544052 0.8831
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+Syde1 SYDE1 rs7251788 3.194
+Syde2 SYDE2 rs6576756 3.3126
+Syk SYK rs1675317 3.2708
+Sympk SYMPK rs10416943 1.6267
+Syn2 SYN2 rs3931883 3.934
+Syn3 SYN3 rs137548 2.8196
+Sync SYNC rs12022735 2.3001
+Tmem90b SYNDIG1 rs6114812 2.4051
+Tmem90a SYNDIG1L rs11159080 1.9523
+Syne1 SYNE1 rs2306914 2.2543
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+4831426I19Rik SYNE3 rs17092559 2.2125
+AI428936 SYNE4 rs7254830 3.473
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+Syngr3 SYNGR3 rs8055439 1.1127
+Synj1 SYNJ1 rs607020 1.4327
+Synj2 SYNJ2 rs9458974 1.5928
+Synj2bp SYNJ2BP rs1044527 3.808
+Synm SYNM rs1703761 1.9851
+Synpo SYNPO rs12188485 0.9481
+Synpo2 SYNPO2 rs2162399 1.9867
+Synpr SYNPR rs12492837 1.9796
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+Sypl SYPL1 rs2079313 1.1851
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+Syt13 SYT13 rs4755942 1.7693
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+Syt17 SYT17 rs916768 2.5123
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+Syt5 SYT5 rs2288527 1.4681
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+Syt8 SYT8 rs4752761 0.8422
+Syt9 SYT9 rs4757993 2.5546
+Sytl2 SYTL2 rs7938015 1.273
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+Syvn1 SYVN1 rs769442 2.4264
+D4Ertd22e SZRD1 rs11260735 1.1651
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+T T rs3099280 0.9378
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+Taar2 TAAR2 rs9385621 1.3361
+Taar3 TAAR3 rs17061476 1.2541
+Taar5 TAAR5 rs17061433 1.2739
+Taar6 TAAR6 rs17061433 1.2739
+Taar8a TAAR8 rs17061433 1.2739
+Taar9 TAAR9 rs2840839 0.8196
+Tab1 TAB1 rs909685 3.037
+Tab2 TAB2 rs4460229 1.5829
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+Tac4 TAC4 rs755736 1.0757
+Tacc1 TACC1 rs13252677 1.6801
+Tacc2 TACC2 rs7091348 1.7605
+Tacc3 TACC3 rs6827250 1.023
+Tacr1 TACR1 rs7563461 1.7706
+Tacr2 TACR2 rs10128330 0.9108
+Tacr3 TACR3 rs17196160 1.706
+Tacstd2 TACSTD2 rs6587830 1.0305
+Tada1 TADA1 rs10918585 1.5802
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+Taf11 TAF11 rs17703197 1.011
+Taf12 TAF12 rs9426420 2.4662
+Taf13 TAF13 rs17838321 0.8294
+Taf15 TAF15 rs6505497 1.0631
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+Taf2 TAF2 rs4871528 1.4526
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+Taf4a TAF4 rs744777 1.7607
+Taf4b TAF4B rs1786252 1.5842
+Taf5 TAF5 rs3740383 1.2749
+Taf5l TAF5L rs348334 1.3104
+Taf6 TAF6 rs4134895 1.1281
+Taf6l TAF6L rs2241908 1.2476
+Taf7 TAF7 rs17097187 1.6678
+Taf8 TAF8 rs6901077 2.479
+Taf9 TAF9 rs10077413 1.4527
+Tagap TAGAP rs11753630 1.9535
+Tagln TAGLN rs11216321 7.0402
+Tagln2 TAGLN2 rs2501318 0.8199
+Tagln3 TAGLN3 rs812149 0.8645
+Tal1 TAL1 rs2845 2.1438
+Tal2 TAL2 rs6477450 2.6519
+Taldo1 TALDO1 rs7395822 1.3033
+1500001M20Rik TAMM41 rs10510406 2.502
+Tanc1 TANC1 rs2288111 1.3094
+Tanc2 TANC2 rs17687734 1.3198
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+Taok1 TAOK1 rs7208363 2.4509
+Taok3 TAOK3 rs17440893 1.9378
+Tap1 TAP1 rs7383287 2.9201
+Tap2 TAP2 rs4947258 4.0967
+Tapbp TAPBP rs6906691 1.1757
+Tapbpl TAPBPL rs2364488 1.1099
+Tapt1 TAPT1 rs6853114 2.0236
+Gm17296 TARBP1 rs538237 1.5754
+Tarbp2 TARBP2 rs1716966 2.6828
+Tardbp TARDBP rs9430163 1.6038
+Tarm1 TARM1 rs2114650 2.978
+Tars2 TARS2 rs4970972 0.8441
+Tarsl2 TARSL2 rs9635431 1.5918
+Tas1r1 TAS1R1 rs10864624 1.2551
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+Tas2r119 TAS2R1 rs218049 1.3425
+Tas2r105 TAS2R10 rs612456 1.086
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+Tas2r135 TAS2R60 rs11771145 1.095
+Tas2r130 TAS2R7 rs612456 1.086
+Tasp1 TASP1 rs8124686 2.1073
+Tat TAT rs7187638 1.234
+Tatdn1 TATDN1 rs11986208 1.7696
+Tatdn2 TATDN2 rs3844283 2.2548
+Tatdn3 TATDN3 rs11120027 2.3513
+Tax1bp1 TAX1BP1 rs6958646 1.0973
+Tax1bp3 TAX1BP3 rs222757 1.8884
+Tbata TBATA rs12221473 1.5638
+Tbc1d1 TBC1D1 rs10517460 1.7219
+Tbc1d10a TBC1D10A rs10854598 1.6428
+Tbc1d10c TBC1D10C rs1064876 1.339
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+Tbc1d22a TBC1D22A rs4823572 1.9586
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+MMT00021886 TBC1D3B rs4796168 2.2643
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+Tbc1d5 TBC1D5 rs11606958 1.2837
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+Tbcel TBCEL rs11604663 1.0907
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+Tbl2 TBL2 rs17145738 8.9252
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+Tbpl1 TBPL1 rs6910014 2.8851
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+Tbr1 TBR1 rs3769957 1.983
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+Tecr TECR rs8104167 3.2958
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+Tecta TECTA rs2282565 1.8204
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+Teddm1 TEDDM1 rs6684297 2.1162
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+Tespa1 TESPA1 rs1532048 2.3536
+Tet1 TET1 rs10762231 2.2297
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+Tet3 TET3 rs6738709 1.561
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+Tex14 TEX14 rs8066492 1.3293
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+Tex2 TEX2 rs12939850 1.2453
+Tex21 TEX21P rs3020443 1.5119
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+4930588N13Rik TEX26 rs2313004 1.3879
+Tex261 TEX261 rs1006049 1.545
+Tex264 TEX264 rs4494918 1.3792
+1700018L24Rik TEX29 rs9588398 1.7481
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+1700061J05Rik TEX33 rs7288361 2.0447
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+Tfb2m TFB2M rs3007410 1.857
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+Tcfcp2l1 TFCP2L1 rs17006161 1.4669
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+Tfdp2 TFDP2 rs4683653 1.7183
+Tcfeb TFEB rs9462734 2.5536
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+Tff2 TFF2 rs225417 1.5712
+Tff3 TFF3 rs7276176 1.8755
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+Tg TG rs2958674 1.7258
+Tgds TGDS rs9556401 1.1273
+Tgfa TGFA rs3771475 0.9412
+Tgfb1i1 TGFB1I1 rs11646911 1.5201
+Tgfb2 TGFB2 rs17047740 1.8419
+Tgfb3 TGFB3 rs4252330 1.9465
+Tgfbi TGFBI rs1054124 2.628
+Tgfbr1 TGFBR1 rs6478973 1.4204
+Tgfbr2 TGFBR2 rs304839 1.8598
+Tgfbr3 TGFBR3 rs284877 1.608
+Tgfbrap1 TGFBRAP1 rs6758290 1.7802
+Tgif1 TGIF1 rs12959911 1.9698
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+Tgm7 TGM7 rs9652444 1.5615
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+Thbs3 THBS3 rs12137850 1.0366
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+Theg THEG rs7508262 0.9252
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+Themis THEMIS rs722916 1.8069
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+Thop1 THOP1 rs4807325 1.7183
+Thpo THPO rs9944 1.3203
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+Thrap3 THRAP3 rs272822 1.182
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+Thrsp THRSP rs669206 1.4211
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+Tiaf1 TIAF1 rs7220662 1.58
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+Tiam1 TIAM1 rs550522 2.7047
+Tiam2 TIAM2 rs9480006 2.2437
+Ticam1 TICAM1 rs12459389 1.1588
+Ticam2 TICAM2 rs11957931 0.8496
+5730590G19Rik TICRR rs16943389 1.071
+Tie1 TIE1 rs34783763 1.3189
+Tifab TIFAB rs874158 1.2638
+Tigd3 TIGD3 rs4149841 1.6643
+Tigd4 TIGD4 rs10009931 0.8456
+Tigd5 TIGD5 rs10099003 0.8989
+mCT156517 TIGD7 rs220377 0.9702
+Tigit TIGIT rs17607135 1.4572
+Timeless TIMELESS rs2047229 1.0098
+Timm10 TIMM10 rs6591429 1.0656
+Timm13 TIMM13 rs11084940 1.8297
+Timm17a TIMM17A rs2790903 1.5792
+1700034H14Rik TIMM21 rs3813110 2.0213
+Timm22 TIMM22 rs3263 1.0933
+Timm23 TIMM23 rs17720205 2.4927
+Timm44 TIMM44 rs4804833 3.2673
+Timm50 TIMM50 rs8107194 1.8159
+Timm8b TIMM8B rs7935331 1.4073
+Timm9 TIMM9 rs12433823 0.9805
+4930455C21Rik TIMMDC1 rs11922005 1.93
+Timp2 TIMP2 rs4789939 2.6598
+Timp3 TIMP3 rs130547 2.4397
+Timp4 TIMP4 rs3931883 3.934
+Tinag TINAG rs1934792 1.8179
+Tinagl TINAGL1 rs4949448 0.9458
+Tinf2 TINF2 rs7151201 1.1572
+Tiparp TIPARP rs2136690 1.3889
+4931440P22Rik TIPARP-AS1 rs2136690 1.3889
+Tiprl TIPRL rs12097383 1.5283
+Tirap TIRAP rs652534 2.3584
+Tjap1 TJAP1 rs1150798 2.7967
+Tjp1 TJP1 rs11638797 3.1162
+Tjp2 TJP2 rs7854933 1.346
+Tjp3 TJP3 rs1005170 1.4045
+Tk1 TK1 rs11651741 0.8735
+Tk2 TK2 rs37167 3.1727
+Tkt TKT rs12632671 1.8847
+Tktl2 TKTL2 rs10005483 0.9859
+Tlcd2 TLCD2 rs11078597 0.9333
+Tle1 TLE1 rs7854019 2.4881
+Tle2 TLE2 rs7249809 1.5907
+Tle3 TLE3 rs7176098 2.8419
+Tle4 TLE4 rs11138346 1.1526
+Tle6 TLE6 rs11150 1.0162
+Tlk1 TLK1 rs1019710 1.762
+Tlk2 TLK2 rs733025 1.0849
+Tll1 TLL1 rs16999565 1.7488
+Tll2 TLL2 rs12764493 1.4968
+Tln1 TLN1 rs10972557 1.4008
+Tln2 TLN2 rs8043290 1.2638
+Tlr1 TLR1 rs7658893 1.8844
+Tlr2 TLR2 rs13123368 1.9638
+Tlr3 TLR3 rs9312342 2.3413
+Tlr4 TLR4 rs10983754 1.745
+Tlr5 TLR5 rs9839269 1.4125
+Tlr1 TLR6 rs4833103 1.2286
+Tlr9 TLR9 rs164637 3.8508
+Tlx1 TLX1 rs2788739 1.4482
+Tlx3 TLX3 rs2914340 3.2183
+Tm2d1 TM2D1 rs6694023 2.6517
+Tm2d2 TM2D2 rs11776561 1.1122
+Tm2d3 TM2D3 rs9635431 1.5918
+Tm4sf1 TM4SF1 rs4681171 1.5704
+Tm4sf20 TM4SF20 rs7603414 1.4111
+Tm4sf4 TM4SF4 rs12488877 2.9706
+Tm4sf5 TM4SF5 rs754814 2.6297
+Tm6sf2 TM6SF2 rs11555053 0.9614
+Tm7sf2 TM7SF2 rs769442 2.4264
+Tm7sf3 TM7SF3 rs17408108 1.3057
+Tm9sf2 TM9SF2 rs17656454 1.8557
+Tm9sf3 TM9SF3 rs6584079 1.1266
+Tm9sf4 TM9SF4 rs6058526 1.7675
+1810029B16Rik TMA16 rs11730351 0.994
+Tmbim4 TMBIM4 rs11176081 0.9678
+Tmbim6 TMBIM6 rs17123943 1.5459
+Tmc1 TMC1 rs1663743 3.4613
+Tmc2 TMC2 rs6114605 1.3659
+Tmc3 TMC3 rs4778640 1.1522
+Tmc4 TMC4 rs2576452 2.9531
+Tmc5 TMC5 rs7200119 1.1822
+Tmc6 TMC6 rs450474 1.5748
+Tmc7 TMC7 rs4072393 0.9555
+Tmc8 TMC8 rs450474 1.5748
+Tmcc1 TMCC1 rs2625978 1.1142
+Tmcc2 TMCC2 rs17417002 1.3967
+Tmcc3 TMCC3 rs2651962 1.8216
+Tmco1 TMCO1 rs6663203 1.2226
+Tmco2 TMCO2 rs6682701 1.4528
+Tmco3 TMCO3 rs2265147 1.4697
+Tmco4 TMCO4 rs3748749 1.4041
+Tmco5 TMCO5A rs8027391 1.2877
+Tmco5b TMCO5B rs8025470 1.3872
+Tmed1 TMED1 rs2278444 5.1405
+Tmed11 TMED11P rs10002248 0.8807
+Tmed2 TMED2 rs4456350 1.9179
+Tmed3 TMED3 rs12708526 1.767
+Tmed5 TMED5 rs12118262 6.3873
+Tmed6 TMED6 rs3852689 1.5064
+Tmed7 TMED7 rs11960574 1.3235
+Tmed8 TMED8 rs3759733 3.2024
+Tmeff1 TMEFF1 rs11794817 3.2269
+Tmeff2 TMEFF2 rs7558259 2.1439
+Tmem100 TMEM100 rs9915349 2.6678
+Tmem101 TMEM101 rs2523166 1.5691
+Tmem102 TMEM102 rs11652328 2.8827
+Tmem104 TMEM104 rs10512600 1.1864
+Tmem106b TMEM106B rs11980425 0.9867
+Tmem106c TMEM106C rs7972027 1.8755
+1110004B13Rik TMEM107 rs8064449 3.2797
+Tmem108 TMEM108 rs1521088 1.9194
+Tmem11 TMEM11 rs2279164 1.7815
+Tmem110 TMEM110 rs17331151 4.4108
+Cldn26 TMEM114 rs12103064 1.1882
+Tmem115 TMEM115 rs709210 2.4806
+Tmem116 TMEM116 rs7135514 1.0687
+Tmem117 TMEM117 rs11182474 1.9367
+Tmem119 TMEM119 rs10861954 1.4772
+Tmem120b TMEM120B rs9282 1.0599
+Tmem121 TMEM121 rs4983425 1.1851
+Tmem123 TMEM123 rs655139 2.0752
+6330530A05Rik TMEM125 rs34783763 1.3189
+Tmem126a TMEM126A rs17743826 0.8941
+Tmem128 TMEM128 rs10937817 3.4235
+Tmem129 TMEM129 rs6827250 1.023
+Tmem131 TMEM131 rs1446326 1.6194
+Tmem132b TMEM132B rs7135568 1.64
+4632425D07Rik TMEM132C rs4882785 2.786
+Tmem132d TMEM132D rs12579060 3.2706
+Tmem132e TMEM132E rs4636954 1.3241
+Tmem134 TMEM134 rs17501521 1.0215
+Tmem135 TMEM135 rs2513219 1.856
+Tmem136 TMEM136 rs717052 2.1072
+Tmem139 TMEM139 rs3212147 1.0615
+1110007F12Rik TMEM140 rs12707218 2.8576
+Tmem141 TMEM141 rs884519 1.1314
+Tmem144 TMEM144 rs13102503 1.1556
+Tmem147 TMEM147 rs12460932 1.828
+Tmem14a TMEM14A rs767311 1.6607
+Tmem14c TMEM14C rs9358774 0.8289
+Tmem150a TMEM150A rs10206961 0.8573
+Tmem150b TMEM150B rs8113016 2.4566
+Tmem150c TMEM150C rs4693030 1.3552
+Tmem151a TMEM151A rs479315 2.3097
+Tmem151b TMEM151B rs2233434 2.6832
+Tmem154 TMEM154 rs7670831 1.9602
+Tmem156 TMEM156 rs1473578 2.1006
+Tmem158 TMEM158 rs1541786 1.2598
+Tmem159 TMEM159 rs16971140 1.0699
+Tmem160 TMEM160 rs1862499 1.3106
+Tmem161a TMEM161A rs4808921 1.3946
+Tmem161b TMEM161B rs17468356 1.1353
+Gm17750 TMEM161B-AS1 rs13163266 1.2461
+Tmem163 TMEM163 rs11904278 5.122
+Tmem165 TMEM165 rs10462028 2.1144
+Tmem167 TMEM167A rs1478487 1.8353
+Tmem167b TMEM167B rs17838321 0.8294
+Tmem168 TMEM168 rs1227174 1.4234
+Tmem169 TMEM169 rs16855357 1.3389
+Tmem17 TMEM17 rs11125924 1.4051
+Tmem170b TMEM170B rs16871482 1.0238
+Tmem171 TMEM171 rs13160226 2.9867
+Tmem174 TMEM174 rs13160226 2.9867
+Tmem175 TMEM175 rs11724804 1.0655
+Tmem176a TMEM176A rs10235739 4.1172
+Tmem176b TMEM176B rs10238091 3.667
+Tmem178 TMEM178A rs2540207 1.7742
+Gm5567 TMEM178B rs1181763 2.3497
+Tmem179b TMEM179B rs2241908 1.2476
+Tmem18 TMEM18 rs12999373 2.4821
+Tmem180 TMEM180 rs11191293 1.0654
+Tmem181a TMEM181 rs9457410 1.7637
+Tmem182 TMEM182 rs17776702 2.0242
+Tmem183a TMEM183A rs1046529 1.5626
+Tmem184b TMEM184B rs7290309 1.9408
+Tmem184c TMEM184C rs7660882 2.0136
+Tmem185b TMEM185B rs34520190 0.8804
+Tmem186 TMEM186 rs12445515 1.9481
+Tmem19 TMEM19 rs12368844 1.4105
+Tmem190 TMEM190 rs4252552 1.5524
+Tmem192 TMEM192 rs6847921 1.3497
+Tmem194 TMEM194A rs324019 1.3953
+Tmem194b TMEM194B rs3811610 1.2814
+Tmem196 TMEM196 rs17141796 1.7675
+Tmem198 TMEM198 rs3731920 1.4403
+Tmem199 TMEM199 rs7221462 1.4002
+Tmem2 TMEM2 rs17475375 1.2534
+Tmem200a TMEM200A rs6569679 1.5814
+Tmem200b TMEM200B rs12117132 0.9838
+Tmem200c TMEM200C rs11876129 1.5615
+Tmem201 TMEM201 rs7364899 1.3276
+Tmem202 TMEM202 rs16956826 0.8368
+Tmem204 TMEM204 rs2235637 0.8199
+Tmem205 TMEM205 rs3816864 1.3527
+Tmem206 TMEM206 rs11577005 1.0573
+Tmem207 TMEM207 rs11715898 2.8159
+Tmem208 TMEM208 rs3730405 0.8798
+Tmem209 TMEM209 rs17647134 1.0373
+Tmem211 TMEM211 rs5752024 1.6488
+Tmem212 TMEM212 rs4894776 1.1171
+Tmem213 TMEM213 rs17160564 1.4195
+Tmem214 TMEM214 rs11678188 2.1821
+Tmem215 TMEM215 rs12551778 1.6405
+Tmem217 TMEM217 rs10223418 1.8044
+Tmem218 TMEM218 rs12418284 1.8456
+Tmem220 TMEM220 rs201621 1.8348
+Tmem221 TMEM221 rs6512194 2.3273
+Tmem222 TMEM222 rs10902656 0.8595
+Tmem223 TMEM223 rs2241908 1.2476
+Tmem229b TMEM229B rs11851741 1.5336
+5730494N06Rik TMEM230 rs371581 2.1034
+Tmem231 TMEM231 rs2453141 1.494
+Tmem232 TMEM232 rs7733338 2.1344
+Tmem233 TMEM233 rs804454 0.8119
+Tmem234 TMEM234 rs1358823 0.857
+Tmem235 TMEM235 rs8064645 0.9122
+Tmem236 TMEM236 rs10508547 0.9197
+Tmem237 TMEM237 rs10194353 1.2506
+Tmem238 TMEM238 rs4252552 1.5524
+Tmem239 TMEM239 rs6051416 1.5427
+Tmem240 TMEM240 rs3128342 1.187
+Tmem241 TMEM241 rs750071 5.2372
+Tmem242 TMEM242 rs9355774 1.3489
+Tmem245 TMEM245 rs17552439 1.8758
+Tmem246 TMEM246 rs10819945 2.2754
+Tmem247 TMEM247 rs9812717 1.9492
+0610007L01Rik TMEM248 rs17680744 1.0251
+Tmem25 TMEM25 rs573971 1.1182
+D230037D09Rik TMEM251 rs1268371 0.8617
+E030010A14Rik TMEM252 rs10123848 1.174
+Tmem26 TMEM26 rs11593515 1.0706
+Tmem30a TMEM30A rs240421 1.9718
+Tmem30b TMEM30B rs9788493 1.0412
+Tmem30c TMEM30C rs2178397 2.4331
+Tmem33 TMEM33 rs13111879 1.1968
+Tmem37 TMEM37 rs2278565 1.403
+Tmem38a TMEM38A rs2306843 2.0649
+Tmem38b TMEM38B rs6477450 2.6519
+Tmem39a TMEM39A rs7653469 2.1934
+Tmem40 TMEM40 rs11915269 1.4111
+Tmem41a TMEM41A rs2276738 0.9165
+Tmem41b TMEM41B rs7123342 1.3131
+Tmem42 TMEM42 rs12632450 1.4488
+Tmem44 TMEM44 rs7372014 1.8645
+Tmem45a TMEM45A rs7611136 1.0307
+Tmem45b TMEM45B rs692325 2.4509
+Tmem5 TMEM5 rs1986622 2.1502
+Tmem50b TMEM50B rs2012075 1.0582
+Tmem51 TMEM51 rs7540957 1.0055
+Tmem52 TMEM52 rs1884454 1.3862
+Tmem53 TMEM53 rs7538419 1.9776
+Tmem54 TMEM54 rs7527330 1.3002
+Tmem55a TMEM55A rs17721948 2.4013
+Tmem55b TMEM55B rs7785 1.4396
+Tmem56 TMEM56 rs4950077 1.3211
+Tmem57 TMEM57 rs2982324 1.4444
+Tmem59 TMEM59 rs41331348 1.4186
+Tmem59l TMEM59L rs3170474 3.1899
+Tmem60 TMEM60 rs2903929 1.3811
+Tmem61 TMEM61 rs4339899 1.1655
+Tmem63a TMEM63A rs868966 2.0809
+Tmem63b TMEM63B rs11967633 2.6672
+Tmem63c TMEM63C rs8016340 2.2856
+Tmem64 TMEM64 rs4556055 1.4876
+Tmem65 TMEM65 rs10956170 0.9851
+Tmem66 TMEM66 rs7011477 0.9219
+Tmem68 TMEM68 rs10099417 0.9996
+Tmem69 TMEM69 rs12757451 1.857
+Tmem71 TMEM71 rs2472230 1.0755
+Tmem72 TMEM72 rs4375391 1.7428
+Tmem74 TMEM74 rs7816073 0.9158
+5430405G05Rik TMEM74B rs6074272 1.4157
+Tmem79 TMEM79 rs2230194 0.8492
+Tmem80 TMEM80 rs7123677 2.492
+Tmem81 TMEM81 rs11240340 1.2426
+Tmem82 TMEM82 rs2280115 1.1524
+Tmem86a TMEM86A rs2289965 0.9851
+Tmem86b TMEM86B rs12983684 1.7421
+Tmem87a TMEM87A rs10518756 3.4344
+2610301K12Rik TMEM87B rs4848993 1.1627
+Tmem89 TMEM89 rs2255532 1.1337
+Tmem8 TMEM8A rs11866815 2.104
+Tmem8b TMEM8B rs10758328 0.9747
+Tmem8c TMEM8C rs3124758 1.2854
+Tmem9 TMEM9 rs6699862 2.1427
+Tmem91 TMEM91 rs4803462 0.9215
+A430060F13Rik TMEM92 rs11652322 1.8844
+Tmem95 TMEM95 rs2292068 1.4675
+Tmem97 TMEM97 rs658591 2.5093
+Tmem98 TMEM98 rs28992 1.2013
+Tmf1 TMF1 rs41375844 1.0633
+Tmie TMIE rs7642783 1.4938
+Tmigd1 TMIGD1 rs17767328 3.2266
+Tmod1 TMOD1 rs10817736 2.0799
+Tmod2 TMOD2 rs17526337 2.057
+Tmod3 TMOD3 rs2554328 2.2906
+Tmod4 TMOD4 rs2067606 1.1387
+Tmprss11a TMPRSS11A rs12642405 1.0746
+Tmprss11c TMPRSS11B rs11943911 2.1337
+Tmprss11bnl TMPRSS11BNL rs11943911 2.1337
+Tmprss11d TMPRSS11D rs12715789 2.7044
+Tmprss11e TMPRSS11E rs4860941 0.9792
+Tmprss11f TMPRSS11F rs7686436 1.5194
+Tmprss12 TMPRSS12 rs10876100 1.6621
+Tmprss13 TMPRSS13 rs12801144 1.4795
+Tmprss15 TMPRSS15 rs10482897 1.5759
+Tmprss2 TMPRSS2 rs8127674 1.9805
+Tmprss3 TMPRSS3 rs17114696 1.2787
+Tmprss4 TMPRSS4 rs675479 1.6586
+Tmprss6 TMPRSS6 rs1005478 1.5865
+Tmprss7 TMPRSS7 rs9811691 0.9638
+Tmprss9 TMPRSS9 rs11084940 1.8297
+Tmsb10 TMSB10 rs4404282 0.9087
+BC023818 TMTC1 rs16934752 3.053
+Tmtc2 TMTC2 rs17010061 2.923
+Tmtc3 TMTC3 rs7488121 2.76
+Tmtc4 TMTC4 rs9513779 1.4844
+Tmub1 TMUB1 rs6464127 3.2599
+2010008E23Rik TMUB2 rs7220138 1.2367
+Tmx1 TMX1 rs4442724 1.298
+Tmx2 TMX2 rs9420 1.0711
+Tmx3 TMX3 rs7238527 1.19
+Tmx4 TMX4 rs11906221 1.2177
+Tnc TNC rs6478127 2.7994
+Tnf TNF rs9469027 3.0645
+Tnfaip1 TNFAIP1 rs7221462 1.4002
+Tnfaip2 TNFAIP2 rs7150854 1.0593
+Tnfaip3 TNFAIP3 rs603904 1.6432
+Tnfaip6 TNFAIP6 rs17805045 2.8864
+Tnfaip8 TNFAIP8 rs6876958 2.5216
+Tnfaip8l1 TNFAIP8L1 rs8109860 1.1385
+Tnfaip8l2 TNFAIP8L2 rs2067606 1.1387
+Tnfaip8l3 TNFAIP8L3 rs17522169 1.041
+mCT4507 TNFRSF10A rs7830709 2.012
+Tnfrsf10b TNFRSF10B rs2241261 1.4385
+Tnfrsf11a TNFRSF11A rs12969851 1.154
+Tnfrsf11b TNFRSF11B rs11573871 1.6934
+Tnfrsf12a TNFRSF12A rs2257295 1.637
+Tnfrsf13b TNFRSF13B rs8067211 2.0154
+Tnfrsf14 TNFRSF14 rs10797432 1.032
+Tnfrsf17 TNFRSF17 rs13332630 0.9893
+Tnfrsf19 TNFRSF19 rs2149499 1.0281
+Tnfrsf1a TNFRSF1A rs2228576 1.6035
+Tnfrsf1b TNFRSF1B rs7552664 1.2761
+Tnfrsf21 TNFRSF21 rs2295267 1.5931
+Tnfrsf8 TNFRSF8 rs6690493 1.5535
+Tnfrsf9 TNFRSF9 rs226484 1.2306
+Tnfsf10 TNFSF10 rs6445062 2.2683
+Tnfsf11 TNFSF11 rs17063499 3.5268
+Tnfsf12 TNFSF12 rs9902027 2.7459
+BC096441 TNFSF12-TNFSF13 rs9902027 2.7459
+Tnfsf13 TNFSF13 rs9902027 2.7459
+Tnfsf13b TNFSF13B rs11069727 1.2896
+Tnfsf14 TNFSF14 rs2277984 0.9678
+Tnfsf15 TNFSF15 rs7848158 1.2163
+Tnfsf4 TNFSF4 rs3861951 1.1253
+Tnfsf8 TNFSF8 rs10982448 2.0749
+Tnfsf9 TNFSF9 rs3865466 1.2924
+Tnik TNIK rs12488990 2.5746
+Tnip1 TNIP1 rs3792780 1.3888
+Tnip2 TNIP2 rs6600766 2.2514
+9030611K07Rik TNIP3 rs10518385 1.0616
+Tnk1 TNK1 rs2292068 1.4675
+Tnk2 TNK2 rs733119 1.7828
+Tnks TNKS rs4841167 1.7711
+Tnks1bp1 TNKS1BP1 rs12787728 2.4139
+Tnks2 TNKS2 rs11594359 1.1647
+Tnn TNN rs7355181 1.4873
+Tnnc1 TNNC1 rs648514 1.5889
+Tnnc2 TNNC2 rs12480887 5.8453
+Tnni1 TNNI1 rs3738287 2.2195
+Tnni2 TNNI2 rs4752761 0.8422
+Tnni3 TNNI3 rs3810351 0.9187
+Tnni3k TNNI3K rs12037741 1.5918
+Tnnt1 TNNT1 rs3810351 0.9187
+Tnnt2 TNNT2 rs3923236 1.4359
+Tnnt3 TNNT3 rs4264135 1.5376
+Tnp1 TNP1 rs1179726 1.4896
+Tnp2 TNP2 rs243318 2.0872
+Tnpo1 TNPO1 rs266431 0.8122
+Tnpo2 TNPO2 rs2066489 0.821
+Tnpo3 TNPO3 rs1839600 1.3265
+Tnr TNR rs10489314 1.9311
+Tnrc18 TNRC18 rs6975264 1.8523
+Tnrc6a TNRC6A rs7186893 1.0205
+Tnrc6b TNRC6B rs5995842 1.3631
+Tnrc6c TNRC6C rs16970730 1.9311
+Tns1 TNS1 rs3088214 1.759
+Tns3 TNS3 rs6463399 1.3662
+Tns4 TNS4 rs10305319 1.7916
+Tnxa TNXA rs204883 1.3768
+Tnxb TNXB rs1150754 3.7924
+Tob1 TOB1 rs10853116 1.0733
+Tob2 TOB2 rs11704973 1.9454
+Toe1 TOE1 rs10890324 0.9241
+Tollip TOLLIP rs5743899 1.15
+Tom1 TOM1 rs11558473 1.2777
+Tom1l1 TOM1L1 rs2958933 2.8931
+Tom1l2 TOM1L2 rs3818717 1.7104
+Tomm20 TOMM20 rs9662936 0.9326
+Tomm20l TOMM20L rs2230098 1.2904
+Tomm22 TOMM22 rs3827358 1.3892
+Tomm34 TOMM34 rs16989514 1.3694
+Tomm40 TOMM40 rs4420638 20.357
+Tomm40l TOMM40L rs1136224 1.5842
+Tomm5 TOMM5 rs4621903 2.2327
+Tomm6 TOMM6 rs3747750 1.8677
+Tomm7 TOMM7 rs10256620 1.9158
+Tomm70a TOMM70A rs17279967 1.5733
+Top1 TOP1 rs1006325 1.1532
+Top1mt TOP1MT rs11544484 1.8897
+Top2a TOP2A rs13695 1.4533
+Top2b TOP2B rs1369601 1.442
+Top3a TOP3A rs2746028 1.5484
+Top3b TOP3B rs12159264 0.9813
+Topbp1 TOPBP1 rs3732574 1.1978
+Topors TOPORS rs11795343 2.6623
+Tor1a TOR1A rs13297359 1.6432
+Tor1aip2 TOR1AIP2 rs16854795 1.0975
+Tor1b TOR1B rs17418268 1.3468
+Tor2a TOR2A rs564754 1.4466
+Tor3a TOR3A rs16853612 2.0786
+Tox TOX rs2726587 1.8962
+Tox2 TOX2 rs4810403 3.3368
+Tox3 TOX3 rs4784221 1.7724
+Tox4 TOX4 rs7148594 1.1305
+Trp53 TP53 rs2955617 3.1015
+Trp53bp1 TP53BP1 rs1058298 1.3275
+Trp53bp2 TP53BP2 rs1222155 1.2211
+Trp53i11 TP53I11 rs12576326 2.2708
+Trp53i13 TP53I13 rs548731 1.558
+Trp53rk TP53RK rs13044870 1.0826
+Trp53tg5 TP53TG5 rs2284277 1.6645
+Trp63 TP63 rs6776687 2.1559
+Trp73 TP73 rs2760319 1.5842
+Tpbg TPBG rs5007914 1.7073
+Tpcn1 TPCN1 rs2232514 1.7894
+Tpcn2 TPCN2 rs10896397 1.3261
+Tpd52 TPD52 rs11781815 1.7406
+Tpd52l1 TPD52L1 rs9388411 2.1027
+Tpd52l2 TPD52L2 rs817377 1.334
+Trpd52l3 TPD52L3 rs2290913 1.2886
+Gm16517 TPGS1 rs3745924 1.1985
+5730494M16Rik TPGS2 rs528245 1.4126
+Tph1 TPH1 rs172424 0.9197
+Tph2 TPH2 rs12231356 1.8976
+Tpi1 TPI1 rs12302904 1.2145
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+Ucp3 UCP3 rs2632725 1.248
+Uevld UEVLD rs17494675 1.4599
+Ufc1 UFC1 rs3813619 1.3813
+Ufd1l UFD1L rs41455044 1.3621
+1810074P20Rik UFL1 rs9784852 0.8256
+Ufsp1 UFSP1 rs314376 2.3639
+Ufsp2 UFSP2 rs2293367 1.9678
+Ugcg UGCG rs7037877 2.2043
+Ugdh UGDH rs2687975 2.9112
+Uggt1 UGGT1 rs4662775 1.2351
+Uggt2 UGGT2 rs3099362 2.2182
+Ugp2 UGP2 rs10210482 1.267
+Ugt1a1 UGT1A1 rs17868337 2.0383
+Ugt1a2 UGT1A3 rs17868337 2.0383
+Ugt1a5 UGT1A4 rs17868337 2.0383
+Ugt1a6a UGT1A6 rs17868337 2.0383
+Ugt1a7c UGT1A8 rs17868337 2.0383
+Ugt1a9 UGT1A9 rs17868337 2.0383
+Ugt2a1 UGT2A1 rs1835828 2.8671
+Ugt2a2 UGT2A2 rs1835828 2.8671
+Ugt2a3 UGT2A3 rs10000598 2.0667
+Ugt2b34 UGT2B10 rs10000481 1.0064
+Ugt2b1 UGT2B28 rs4386668 0.8176
+Ugt2b35 UGT2B7 rs6600894 1.0662
+Ugt3a2 UGT3A1 rs2441088 1.5133
+Ugt8a UGT8 rs4240295 1.8162
+Uhmk1 UHMK1 rs10919539 0.9481
+Uhrf1 UHRF1 rs9676980 1.833
+Uhrf1bp1 UHRF1BP1 rs7755822 3.8474
+Uhrf1bp1l UHRF1BP1L rs11833159 1.3601
+Uhrf2 UHRF2 rs1547256 1.739
+Uimc1 UIMC1 rs2292256 1.3627
+Ulbp1 ULBP1 rs12527800 2.1995
+Ulk1 ULK1 rs9652059 1.1153
+Ulk2 ULK2 rs17794370 2.1591
+Ulk3 ULK3 rs872263 1.7523
+Ulk4 ULK4 rs17212658 1.6173
+Umod UMOD rs9935075 0.9285
+Umodl1 UMODL1 rs7276814 1.061
+Umps UMPS rs585021 1.6499
+Unc119b UNC119B rs696338 1.8788
+Unc13a UNC13A rs8106359 2.4874
+Unc13b UNC13B rs10758301 1.5144
+Unc13c UNC13C rs4644815 3.3964
+Unc13d UNC13D rs8069105 1.9658
+Unc45a UNC45A rs1867219 1.6415
+Unc45b UNC45B rs1471615 2.4342
+Unc5a UNC5A rs2962836 1.5646
+Unc5b UNC5B rs1105174 1.5569
+Unc5c UNC5C rs2865384 1.8204
+Unc5cl UNC5CL rs2395754 1.407
+Unc5d UNC5D rs13274515 2.0221
+Unc79 UNC79 rs12897179 2.1357
+Unc93a UNC93A rs2092090 2.4202
+Unc93b1 UNC93B1 rs11535787 2.3824
+Uncx UNCX rs9791389 1.2373
+Ung UNG rs1018782 1.6613
+Unk UNK rs8069105 1.9658
+Unkl UNKL rs3826226 1.0838
+Uox UOX rs12144715 1.3396
+Upb1 UPB1 rs17650937 1.1146
+Upf2 UPF2 rs17151048 1.2351
+Upf3a UPF3A rs3813131 1.6158
+Upk1a UPK1A rs2311871 1.2562
+Upk1b UPK1B rs6807110 2.4477
+Upk2 UPK2 rs10892313 1.1963
+Upk3a UPK3A rs58667 1.4116
+Upk3b UPK3B rs10952935 0.8972
+Upp1 UPP1 rs9639018 1.0335
+Upp2 UPP2 rs6745721 1.604
+Uprt UPRT rs3954247 1.8268
+Uqcc UQCC rs13037376 1.9367
+Uqcr10 UQCR10 rs140096 2.6467
+Uqcr11 UQCR11 rs10407104 1.7423
+Uqcrb UQCRB rs11776025 2.3325
+Uqcrc1 UQCRC1 rs2255532 1.1337
+Uqcrc2 UQCRC2 rs2945466 0.9355
+Uqcrfs1 UQCRFS1 rs1013424 0.8706
+Uqcrh UQCRH rs3766248 1.1321
+Uqcrq UQCRQ rs39830 0.9108
+Urb1 URB1 rs17616689 1.365
+Urb2 URB2 rs12142450 1.5131
+Urgcp URGCP rs670573 2.2218
+C80913 URI1 rs34707 1.5616
+Urm1 URM1 rs6478827 1.2808
+Uroc1 UROC1 rs17691504 1.547
+Uros UROS rs12259918 0.8719
+Use1 USE1 rs8113494 1.0675
+Usf1 USF1 rs6695707 2.1294
+Ush1c USH1C rs17775799 1.2271
+Ush1g USH1G rs7219093 1.6613
+Ush2a USH2A rs7548730 2.5891
+Ushbp1 USHBP1 rs7250266 2.0722
+Usmg5 USMG5 rs3740383 1.2749
+MMT00001293 USP1 rs12406266 1.64
+Usp10 USP10 rs16974453 1.7404
+Usp12 USP12 rs9553968 2.6135
+Usp13 USP13 rs12636924 2.0148
+Usp14 USP14 rs3889860 1.6396
+Usp15 USP15 rs7956714 1.7208
+Usp18 USP18 rs2252257 2.1011
+Usp2 USP2 rs2511834 1.1302
+Usp20 USP20 rs13297359 1.6432
+Usp21 USP21 rs3813619 1.3813
+Usp22 USP22 rs4985963 1.3945
+Usp24 USP24 rs11206514 2.3286
+Usp25 USP25 rs2823468 1.0612
+Usp28 USP28 rs1713675 1.2953
+Usp29 USP29 rs17305563 1.2176
+Usp3 USP3 rs2058914 1.9122
+Usp30 USP30 rs1018782 1.6613
+Usp31 USP31 rs11863274 0.8502
+Usp32 USP32 rs6503961 1.6582
+Usp33 USP33 rs17098297 1.1059
+Usp34 USP34 rs2290324 2.8321
+Usp35 USP35 rs476913 1.4549
+Usp36 USP36 rs4796817 2.4112
+Usp37 USP37 rs492400 1.6776
+Usp39 USP39 rs4832179 1.4073
+Usp4 USP4 rs13078949 1.4594
+Usp40 USP40 rs1545525 1.4382
+Usp42 USP42 rs6463529 1.5157
+Usp43 USP43 rs6503239 1.7997
+Usp45 USP45 rs7744845 1.1959
+Usp47 USP47 rs11022073 1.2077
+Usp48 USP48 rs12128325 1.9551
+Usp49 USP49 rs3747750 1.8677
+Usp5 USP5 rs12302904 1.2145
+Usp50 USP50 rs11070795 2.0559
+Usp53 USP53 rs12645965 4.9385
+C030002J06Rik USP54 rs16930700 1.3966
+Usp6nl USP6NL rs11257115 1.474
+Usp7 USP7 rs1058982 1.5289
+Usp8 USP8 rs3098169 1.6767
+Uspl1 USPL1 rs11842552 1.2336
+Ust UST rs1471677 1.7249
+Utf1 UTF1 rs1984262 1.4768
+Utp11l UTP11L rs11211263 1.3151
+Utp15 UTP15 rs819600 0.9307
+Utp20 UTP20 rs1514374 1.2851
+Utp23 UTP23 rs6992589 1.6293
+Utp6 UTP6 rs4795657 0.994
+Utrn UTRN rs4895659 3.0484
+Uts2 UTS2 rs515830 1.8422
+Uvrag UVRAG rs2371371 2.0915
+Uxs1 UXS1 rs13016988 1.1761
+Vac14 VAC14 rs11864186 1.8959
+Vamp1 VAMP1 rs2364488 1.1099
+Vamp2 VAMP2 rs8064449 3.2797
+Vamp3 VAMP3 rs12093175 1.0735
+Vamp4 VAMP4 rs11800547 2.7778
+Vamp5 VAMP5 rs10206961 0.8573
+Vamp8 VAMP8 rs10206961 0.8573
+Vangl1 VANGL1 rs4345806 1.6751
+Vangl2 VANGL2 rs2147474 2.2629
+Vapa VAPA rs29114 2.1059
+Vapb VAPB rs2294970 1.4776
+Vars VARS rs707938 2.3215
+Vars2 VARS2 rs2844651 2.8011
+Vash1 VASH1 rs12589778 1.582
+Vash2 VASH2 rs10735511 2.2323
+Vasn VASN rs3747576 2.3563
+Vasp VASP rs16979873 1.5536
+Vat1 VAT1 rs8076790 1.1148
+Vat1l VAT1L rs9934540 1.8877
+Vav1 VAV1 rs186295 1.8668
+Vav2 VAV2 rs7025939 2.5089
+Vav3 VAV3 rs1328210 1.838
+Vax1 VAX1 rs12255500 0.8153
+Vax2 VAX2 rs3771391 2.8318
+Vcam1 VCAM1 rs3176862 1.7138
+Vcan VCAN rs3096169 2.9393
+Vcp VCP rs3824552 0.9169
+Vcpip1 VCPIP1 rs2936696 1.2654
+Vdac1 VDAC1 rs152405 1.551
+Vdac2 VDAC2 rs879155 2.5686
+Vdac3 VDAC3 rs10096210 0.9263
+Vdr VDR rs4328262 1.4755
+Vegfa VEGFA rs998584 4.7647
+Vegfb VEGFB rs2302263 2.1145
+Vegfc VEGFC rs3775203 1.2994
+Veph1 VEPH1 rs10936073 2.7349
+Vezf1 VEZF1 rs2233911 1.6033
+Vezt VEZT rs11107962 1.2552
+Vgf VGF rs1050955 1.8413
+Vgll2 VGLL2 rs13201929 1.2188
+1700110N18Rik VGLL3 rs6788631 1.2045
+Vgll4 VGLL4 rs10510405 2.9586
+Vhl VHL rs457414 1.7178
+Vil1 VIL1 rs2276631 1.1314
+Vill VILL rs7372246 1.1832
+Vim VIM rs10904908 3.5638
+H47 VIMP rs12438654 1.2077
+Vip VIP rs9371267 1.7147
+Vipas39 VIPAS39 rs13379432 1.7111
+Vipr1 VIPR1 rs407931 1.1882
+Vipr2 VIPR2 rs2730260 1.6262
+Vis1 VIS1 rs10496967 1.2823
+Vit VIT rs11690186 1.6657
+Vkorc1l1 VKORC1L1 rs12698510 1.2075
+Vldlr VLDLR rs2242104 2.1697
+Vmac VMAC rs6510862 0.8824
+Vmo1 VMO1 rs754814 2.6297
+Vmp1 VMP1 rs7213568 1.2073
+V1rd12 VN1R1 rs726260 2.0471
+Vmn1r224 VN1R2 rs10404048 1.2299
+V1re8 VN1R4 rs10404048 1.2299
+Vnn1 VNN1 rs9385621 1.3361
+Vnn3 VNN3 rs12664574 1.2626
+Vopp1 VOPP1 rs6957301 1.0334
+B930007L02Rik VPRBP rs10510757 2.5248
+Vpreb1 VPREB1 rs6001525 1.03
+Vpreb3 VPREB3 rs6003870 1.5067
+Vps11 VPS11 rs1784460 3.2948
+Vps13a VPS13A rs1801258 2.1719
+Vps13b VPS13B rs10464840 1.5433
+Vps13c VPS13C rs7163441 1.5931
+Vps13d VPS13D rs12125606 1.6731
+Vps16 VPS16 rs6051416 1.5427
+Vps18 VPS18 rs4924514 1.988
+Vps25 VPS25 rs9892506 1.0934
+Vps26a VPS26A rs11552063 1.0357
+Vps26b VPS26B rs1031381 1.144
+Vps33a VPS33A rs11060167 2.3563
+Vps33b VPS33B rs1867219 1.6415
+Vps36 VPS36 rs9536055 1.7077
+Vps37a VPS37A rs17502404 1.5646
+Vps37b VPS37B rs3817094 6.8027
+Vps37c VPS37C rs175133 1.7328
+Vps37d VPS37D rs799168 5.5067
+Vps39 VPS39 rs8040255 3.3772
+Vps41 VPS41 rs7809020 2.2466
+Vps4a VPS4A rs3852689 1.5064
+Vps4b VPS4B rs489520 0.9838
+1110014N23Rik VPS51 rs769442 2.4264
+Vps52 VPS52 rs457565 1.555
+Vps53 VPS53 rs9907102 2.1351
+Vps54 VPS54 rs7579192 1.357
+Vps72 VPS72 rs2067606 1.1387
+Vps8 VPS8 rs13069941 1.6643
+Vrk1 VRK1 rs2224442 2.4548
+Vrk2 VRK2 rs17049381 1.9662
+Vrk3 VRK3 rs11883067 1.2699
+Vsig10 VSIG10 rs5745869 1.0691
+Vsig10l VSIG10L rs10414689 1.066
+Vsig2 VSIG2 rs12792040 1.2469
+Vsig8 VSIG8 rs7516146 0.8739
+Vsnl1 VSNL1 rs12478492 1.8851
+Vstm2a VSTM2A rs1220247 1.0413
+Vstm2b VSTM2B rs11668665 3.9274
+Vstm2l VSTM2L rs1569940 1.0379
+E130203B14Rik VSTM4 rs2175914 1.2612
+2200002K05Rik VSTM5 rs7115115 1.9435
+Vsx1 VSX1 rs6083731 1.0356
+Vsx2 VSX2 rs9671667 1.1851
+Vta1 VTA1 rs12206219 1.1299
+Vtcn1 VTCN1 rs10923236 2.1921
+Vti1a VTI1A rs3780955 2.6957
+Vti1b VTI1B rs2295643 0.8529
+Vtn VTN rs7221462 1.4002
+Vwa2 VWA2 rs12219192 1.6747
+Vwa3a VWA3A rs11862108 0.9666
+Vwa3b VWA3B rs7606591 1.2096
+Vwa5a VWA5A rs3741110 1.5522
+Vwa5b1 VWA5B1 rs743982 1.8881
+Vwa5b2 VWA5B2 rs13315366 1.1534
+D17H6S56E-3 VWA7 rs400547 2.5546
+1300010F03Rik VWA8 rs7332129 2.5664
+Vwc2 VWC2 rs7778226 2.1622
+Vwc2l VWC2L rs10048690 4.3483
+Vwde VWDE rs12533416 2.7319
+Vwf VWF rs2058223 1.639
+Wac WAC rs332123 2.8055
+Wapal WAPAL rs10887621 2.3411
+Wars WARS rs9453 4.0217
+Wars2 WARS2 rs7522839 1.073
+Wasf1 WASF1 rs6927697 1.2768
+Wasf2 WASF2 rs9438514 2.1951
+Wasf3 WASF3 rs793627 2.581
+Wasl WASL rs1073682 0.934
+Wbp1 WBP1 rs13396033 2.5805
+Wbp11 WBP11 rs17821751 1.4057
+D19Wsu162e WBP1L rs12356821 1.9516
+Wbp2 WBP2 rs1463485 1.3209
+Wbp2nl WBP2NL rs12167228 1.218
+Wbp4 WBP4 rs17532301 2.3697
+Wbscr17 WBSCR17 rs1202657 1.5941
+Wbscr22 WBSCR22 rs799160 1.4142
+Wbscr27 WBSCR27 rs9647713 1.0723
+Wbscr28 WBSCR28 rs1136647 0.9706
+Wdfy1 WDFY1 rs893360 1.3467
+Wdfy2 WDFY2 rs7333623 1.7953
+Wdfy3 WDFY3 rs16995923 0.9901
+Wdfy4 WDFY4 rs17771583 1.7632
+Wdhd1 WDHD1 rs8011358 0.8752
+Wdpcp WDPCP rs2121350 2.8969
+Wdr1 WDR1 rs6834555 1.2431
+Wdr11 WDR11 rs4751800 1.3666
+Wdr12 WDR12 rs13398418 1.3211
+Wdr17 WDR17 rs13110700 1.5484
+Wdr18 WDR18 rs6510986 1.9223
+Wdr19 WDR19 rs2123028 2.7022
+Wdr24 WDR24 rs4984685 1.1576
+Wdr25 WDR25 rs9453 4.0217
+Wdr26 WDR26 rs12129540 1.2677
+Wdr27 WDR27 rs3823465 2.1051
+Wdr3 WDR3 rs6683735 0.8914
+Wdr31 WDR31 rs1998503 2.1305
+Wdr35 WDR35 rs10197385 1.0487
+Wdr36 WDR36 rs10043985 1.9678
+Wdr37 WDR37 rs11250250 2.0031
+Wdr38 WDR38 rs913233 0.9179
+Wdr4 WDR4 rs9808758 0.9722
+Wdr41 WDR41 rs459366 1.0927
+Wdr43 WDR43 rs6707606 1.1
+Wdr46 WDR46 rs457565 1.555
+Wdr47 WDR47 rs550743 1.2297
+Wdr48 WDR48 rs6769051 1.6426
+MMT00007851 WDR49 rs11918205 1.0103
+Wdr5 WDR5 rs10761411 1.0675
+Wdr52 WDR52 rs6780604 1.7138
+Wdr54 WDR54 rs13396033 2.5805
+Wdr59 WDR59 rs11149775 3.2218
+Wdr5b WDR5B rs10511409 1.9948
+Wdr6 WDR6 rs4974078 1.2623
+D430033N04Rik WDR60 rs2527199 1.504
+Wdr61 WDR61 rs4887024 1.3032
+Wdr62 WDR62 rs17777328 1.0453
+Wdr63 WDR63 rs6576756 3.3126
+Wdr64 WDR64 rs6679632 2.5227
+Wdr65 WDR65 rs4660721 1.1089
+Wdr66 WDR66 rs3847694 3.5758
+Wdr67 WDR67 rs4871342 1.2441
+Wdr7 WDR7 rs3810083 2.3994
+Wdr70 WDR70 rs16903706 1.5106
+Wdr72 WDR72 rs1906433 2.5572
+Wdr73 WDR73 rs7169629 1.1174
+Wdr74 WDR74 rs12794763 1.1124
+Wdr75 WDR75 rs1356976 1.8435
+Wdr76 WDR76 rs9989313 2.3254
+Wdr81 WDR81 rs8075977 1.0895
+Wdr82 WDR82 rs164637 3.8508
+Wdr86 WDR86 rs404919 2.105
+Wdr88 WDR88 rs8111018 1.5372
+Wdr90 WDR90 rs9929621 1.1517
+Wdr91 WDR91 rs12707218 2.8576
+Wdr92 WDR92 rs4078978 1.5977
+Wdr93 WDR93 rs7178463 1.7102
+Wdsub1 WDSUB1 rs2288111 1.3094
+Wdtc1 WDTC1 rs7530701 3.0198
+Wdyhv1 WDYHV1 rs7005105 1.5207
+Wee1 WEE1 rs3763872 2.7053
+Wfdc1 WFDC1 rs1023628 1.8496
+Wfdc11 WFDC11 rs978779 1.0888
+Wfdc12 WFDC12 rs6130754 1.9412
+Wfdc13 WFDC13 rs736317 2.0755
+Wfdc2 WFDC2 rs973447 2.1706
+Wfdc3 WFDC3 rs12480887 5.8453
+Wfdc5 WFDC5 rs6130754 1.9412
+Wfdc6b WFDC6 rs6104219 1.4637
+Wfdc8 WFDC8 rs6104219 1.4637
+MMT00044435 WFDC9 rs978779 1.0888
+Wfikkn1 WFIKKN1 rs9929621 1.1517
+Wfikkn2 WFIKKN2 rs11079930 1.2418
+Wfs1 WFS1 rs6446490 1.9987
+Whamm WHAMM rs7179000 1.6139
+Whsc1 WHSC1 rs11725821 1.8196
+Wibg WIBG rs11171694 1.3512
+Wif1 WIF1 rs3782499 1.3501
+Waspip WIPF1 rs10186599 1.1679
+5730509C05Rik WIPF2 rs6503539 0.8271
+Wipf3 WIPF3 rs12531505 1.9094
+Wipi1 WIPI1 rs7209319 3.0166
+Wipi2 WIPI2 rs10280819 1.4461
+Wisp1 WISP1 rs2929965 1.129
+Wisp2 WISP2 rs8121830 2.6155
+Wisp3 WISP3 rs4947155 1.7007
+Wls WLS rs2986216 1.8176
+Wnk1 WNK1 rs12306737 0.9991
+Wnk2 WNK2 rs1110335 2.3249
+Wnk4 WNK4 rs9892506 1.0934
+Wnt1 WNT1 rs12122 1.1656
+Wnt10a WNT10A rs10197074 0.8576
+Wnt10b WNT10B rs12122 1.1656
+Wnt11 WNT11 rs11605549 1.4129
+Wnt16 WNT16 rs3757552 2.3023
+Wnt2 WNT2 rs39314 2.5933
+Wnt2b WNT2B rs7516521 1.6442
+Wnt3 WNT3 rs12325819 1.3923
+Wnt3a WNT3A rs708122 1.8674
+Wnt4 WNT4 rs909817 1.3629
+Wnt5a WNT5A rs7624718 1.8959
+Wnt5b WNT5B rs10848536 1.7455
+Wnt6 WNT6 rs10197074 0.8576
+Wnt7a WNT7A rs9828013 1.7612
+Wnt8b WNT8B rs9420790 1.1145
+Wnt9a WNT9A rs12725747 1.3811
+Wnt9b WNT9B rs1105127 1.8639
+Wrap53 WRAP53 rs1641523 2.7258
+Wdr8 WRAP73 rs2760319 1.5842
+Wrb WRB rs4816624 1.5758
+Wrn WRN rs17648656 2.3335
+Wrnip1 WRNIP1 rs4083550 1.0478
+Wsb1 WSB1 rs1047616 1.431
+Wsb2 WSB2 rs5745869 1.0691
+Wscd1 WSCD1 rs7216000 2.7167
+Wscd2 WSCD2 rs338166 2.2681
+Wt1 WT1 rs3858454 3.1099
+Wtap WTAP rs763561 1.6229
+Wwc1 WWC1 rs3822658 1.6434
+Wwc2 WWC2 rs907366 1.4645
+Wwox WWOX rs12448854 3.8371
+Wwp1 WWP1 rs10089781 2.3421
+Wwp2 WWP2 rs8048550 1.7296
+Wwtr1 WWTR1 rs11713975 2.3161
+Xab2 XAB2 rs1674033 1.0259
+Xaf1 XAF1 rs218685 1.2355
+Xbp1 XBP1 rs133441 2.8582
+Xcl1 XCL1 rs16860928 2.8768
+Xcr1 XCR1 rs13097556 1.2055
+Xdh XDH rs2043013 2.5296
+Xirp1 XIRP1 rs1274963 2.3268
+Xirp2 XIRP2 rs12105375 1.6045
+Xkr4 XKR4 rs2658931 2.5909
+Xkr5 XKR5 rs2702934 1.9602
+Xkr6 XKR6 rs17778223 3.0299
+Xkr7 XKR7 rs8119189 0.8908
+Xkr8 XKR8 rs12130736 1.4702
+Xkr9 XKR9 rs17760081 1.6801
+Xpa XPA rs3176658 1.9329
+Xpc XPC rs3731087 1.2156
+Xpnpep1 XPNPEP1 rs11194894 2.4604
+Xpnpep3 XPNPEP3 rs5758106 1.2289
+Xpo1 XPO1 rs1446532 0.9872
+Xpo4 XPO4 rs4769135 2.2009
+Xpo5 XPO5 rs17287964 0.8791
+Xpo6 XPO6 rs17590103 1.1975
+Xpo7 XPO7 rs11783414 3.178
+Xpot XPOT rs10128757 1.0188
+Xpr1 XPR1 rs10914067 1.8636
+Xrcc1 XRCC1 rs2599448 1.3556
+Xrcc2 XRCC2 rs12534240 1.2699
+Xrcc4 XRCC4 rs1478487 1.8353
+Xrcc5 XRCC5 rs12992693 1.9805
+Xrcc6bp1 XRCC6BP1 rs4760349 1.1552
+Xrn1 XRN1 rs4404464 0.9626
+Xrn2 XRN2 rs1409818 1.1974
+Xrra1 XRRA1 rs3829935 1.2946
+AI480653 XXYLT1 rs7616009 1.7069
+Xylb XYLB rs17037644 1.4825
+Xylt1 XYLT1 rs7187355 1.6426
+Xylt2 XYLT2 rs2526532 1.5571
+1600012F09Rik YAE1D1 rs17713790 1.5971
+Yaf2 YAF2 rs3990966 1.5528
+Yap1 YAP1 rs11225179 1.0999
+Yars YARS rs16866009 1.4372
+Yars2 YARS2 rs11830186 1.0323
+Ybey YBEY rs11700398 1.2021
+Ybx2 YBX2 rs17805277 0.9492
+Ydjc YDJC rs861857 2.6851
+Yeats4 YEATS4 rs11177645 1.6872
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diff --git a/inst/extdata/network.mouseliver.mouse.txt b/inst/extdata/network.mouseliver.mouse.txt
new file mode 100644
index 0000000..32c317e
--- /dev/null
+++ b/inst/extdata/network.mouseliver.mouse.txt
@@ -0,0 +1,156853 @@
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+Ccdc96 EG330819 1
+Ccdc96 mCT184614 1
+Ccdc96 MMT00033825 1
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+Ccdc96 XM_141432 1
+Ccdc96 XM_141432 1
+Ccdc96 XM_141432 1
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+Cct4 Hsp90aa1 1
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+Cd14 Osteopontin_RBM_log_sex_W10 1
+Cd14 Osteopontin_RBM_log_sex_W10 1
+Cd14 Osteopontin_RBM_normal_None_W10 1
+Cd14 Osteopontin_RBM_normal_sex_W10 1
+Cd14 Pten 1
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+Cd1d2 Fga 1
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+Cd1d2 Gucy1a3 1
+Cd1d2 Hist2h2aa1 1
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+Cd1d2 Hist2h2aa1 1
+Cd1d2 mCT51612 1
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+Cd209c Trpm1 1
+Cd209e Farsb 1
+Cd209e Ifng 1
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+Cd34 Boc 1
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+Cd36 1110028A07Rik 1
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+Cd52 Apol10b 1
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+Cd59a 0610012H03Rik 1
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+Cd5l Alox5ap 1
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+Cd5l Ptgs1 1
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+Cd63 2210013O21Rik 1
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+Cd68 Car13 1
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+Cd68 Pld3 1
+Cd68 Tbxas1 1
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+Cd69 2310026E23Rik 1
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+Cd70 EG408191 1
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+Cd72 9430031J16Rik 1
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+Cd72 Abpz 1
+Cd72 Alg9 1
+Cd72 BE950662 1
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+Cd74 Ccdc109b 1
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+Cd74 H2-Ab1 1
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+Cd74 H2-DMb1 1
+Cd74 H2-Eb1 1
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+Cd79a A530030E21Rik 1
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+Cd81 Ccnd1 1
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+Cd82 ENSMUSG00000075025 1
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+Cd82 Mpdu1 1
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+Cd84 Gp49a 1
+Cd84 Gp49a 1
+Cd84 Gpr137b-ps 1
+Cd84 Gusb 1
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+Cd84 Vsig4 1
+Cd86 Bcl2a1a 1
+Cd86 Cd55 1
+Cd86 Gpr65 1
+Cd86 Gpr65 1
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+Cdc14a Olfr53 1
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+Cdc25a EG545047 1
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+Cdc2a Ccdc99 1
+Cdc2a Cd52 1
+Cdc2a Timp1 1
+Cdc2a Usp18 1
+Cdc2l5 Ush1g 1
+Cdc2l6 4930488B01Rik 1
+Cdc2l6 4931407J08Rik 1
+Cdc2l6 5530402G07Rik 1
+Cdc2l6 AK038464 1
+Cdc2l6 AK038464 1
+Cdc2l6 Glra3 1
+Cdc2l6 Glra3 1
+Cdc2l6 Gm804 1
+Cdc2l6 Il1rapl1 1
+Cdc2l6 Il1rapl1 1
+Cdc2l6 Il1rapl1 1
+Cdc2l6 mCT167634 1
+Cdc2l6 mCT167634 1
+Cdc2l6 mCT52637 1
+Cdc2l6 MMT00003674 1
+Cdc2l6 Olfr1450 1
+Cdc2l6 Slc5a8 1
+Cdc2l6 Tas2r105 1
+Cdc2l6 Vmn2r7 1
+Cdc2l6 Wdr23 1
+Cdc2l6 Zfp109 1
+Cdc2l6 Zfp109 1
+Cdc2l6 Zfp109 1
+Cdc34 1700057A11Rik 1
+Cdc34 BB230421 1
+Cdc37l1 MMT00005644 1
+Cdc37l1 Pthr1 1
+Cdc37l1 Stoml1 1
+Cdc42 Alkbh6 1
+Cdc42bpa 2310031A18Rik 1
+Cdc42bpa 9330156P08Rik 1
+Cdc42bpa Dyrk3 1
+Cdc42bpa E030004H24Rik 1
+Cdc42bpa Ebf1 1
+Cdc42bpa Ephx1 1
+Cdc42bpa Hps4 1
+Cdc42bpa Lefty1 1
+Cdc42bpa Lipa 1
+Cdc42bpa MMT00064683 1
+Cdc42bpa Ppm1g 1
+Cdc42bpa Slc16a1 1
+Cdc42bpa Slc25a30 1
+Cdc42bpa Tlr5 1
+Cdc42bpa Trib1 1
+Cdc42 Cda 1
+Cdc42 Ddost 1
+Cdc42ep1 6030439D06Rik 1
+Cdc42ep1 6030439D06Rik 1
+Cdc42ep1 Arfgap3 1
+Cdc42ep1 BC005537 1
+Cdc42ep1 Card10 1
+Cdc42ep1 Card10 1
+Cdc42ep1 Cyp2d22 1
+Cdc42ep1 Fbxo6 1
+Cdc42ep1 Fscn1 1
+Cdc42ep1 Gpc1 1
+Cdc42ep1 Lgals1 1
+Cdc42ep1 mCT116030 1
+Cdc42ep1 MMT00015578 1
+Cdc42ep1 MMT00035096 1
+Cdc42ep1 MMT00035096 1
+Cdc42ep1 MMT00038895 1
+Cdc42ep1 MMT00075644 1
+Cdc42ep1 MMT00075644 1
+Cdc42ep1 MMT00080331 1
+Cdc42ep1 Mn1 1
+Cdc42ep1 Palmd 1
+Cdc42ep1 Pmm1 1
+Cdc42ep1 Rbm9 1
+Cdc42ep1 Rpl3 1
+Cdc42ep1 Shroom1 1
+Cdc42ep1 Smcr7l 1
+Cdc42ep1 Tenc1 1
+Cdc42ep1 Tenc1 1
+Cdc42ep1 Tenc1 1
+Cdc42ep1 Tomm34 1
+Cdc42ep1 Wisp1 1
+Cdc42ep1 XM_142323 1
+Cdc42ep1 XM_142485 1
+Cdc42ep1 XM_142485 1
+Cdc42ep1 XM_354772 1
+Cdc42ep1 XM_358035 1
+Cdc42ep1 XM_359338 1
+Cdc42ep3 Ccdc80 1
+Cdc42ep3 Cd34 1
+Cdc42ep3 Cxcr7 1
+Cdc42ep3 Fktn 1
+Cdc42ep3 Il17rd 1
+Cdc42ep3 Nfe2l2 1
+Cdc42ep3 Prkcdbp 1
+Cdc42ep3 Sparcl1 1
+Cdc42ep3 Wnt5a 1
+Cdc42ep3 Zfp503 1
+Cdc42ep4 Mn1 1
+Cdc42ep4 Slc9a3r1 1
+Cdc42ep4 Vasn 1
+Cdc42 Nol9 1
+Cdc42 Rps6ka1 1
+Cdc42 Rps6ka1 1
+Cdc42 Uros 1
+Cdc5l Ddx52 1
+Cdc73 5830433M19Rik 1
+Cdc73 Nrbf2 1
+Cdc73 Otud4 1
+Cdc7 Creb1 1
+Cdc7 Mthfd2 1
+Cdca2 Tubb6 1
+Cdca3 4930547N16Rik 1
+Cdca3 Birc5 1
+Cdca3 Birc5 1
+Cdca3 Ccna2 1
+Cdca3 Ccnb1-rs1 1
+Cdca3 Ccnb2 1
+Cdca3 Ccnb2 1
+Cdca3 Cdc20 1
+Cdca3 Cdc20 1
+Cdca3 Cenpa 1
+Cdca3 Cenpf 1
+Cdca3 D2Ertd750e 1
+Cdca3 D2Ertd750e 1
+Cdca3 EG434175 1
+Cdca3 EG434175 1
+Cdca3 Eomes 1
+Cdca3 Kif22 1
+Cdca3 Kif22 1
+Cdca3 MMT00004673 1
+Cdca3 MMT00004673 1
+Cdca3 N6amt2 1
+Cdca3 Racgap1 1
+Cdca3 Slc35f2 1
+Cdca3 Tpx2 1
+Cdca5 Apitd1 1
+Cdca5 Asf1b 1
+Cdca5 Cdca8 1
+Cdca5 Cep192 1
+Cdca5 Ercc6l 1
+Cdca5 Kif11 1
+Cdca5 Lig1 1
+Cdca5 Ltb4dh 1
+Cdca5 Mest 1
+Cdca5 Mest 1
+Cdca5 Ndc80 1
+Cdca5 Pbk 1
+Cdca5 Psrc1 1
+Cdca5 Racgap1 1
+Cdca5 Spink3 1
+Cdca5 Trip13 1
+Cdca5 Uhrf1 1
+Cdca7 1810046K07Rik 1
+Cdca7 AU040829 1
+Cdca7 Dmxl1 1
+Cdca7 MMT00018326 1
+Cdca7 MMT00018326 1
+Cdca7 MMT00018326 1
+Cdca8 1700037H04Rik 1
+Cdca8 2610039C10Rik 1
+Cdca8 3300001G02Rik 1
+Cdca8 5430432M24Rik 1
+Cdca8 6720460F02Rik 1
+Cdca8 Ccdc99 1
+Cdca8 Cdca2 1
+Cdca8 Cdca5 1
+Cdca8 Cdca5 1
+Cdca8 D2Ertd750e 1
+Cdca8 Dbf4 1
+Cdca8 F730047E07Rik 1
+Cdca8 Gpx7 1
+Cdca8 Gyg 1
+Cdca8 Hells 1
+Cdca8 Heyl 1
+Cdca8 Hmgb2 1
+Cdca8 Hmgb2 1
+Cdca8 Kif11 1
+Cdca8 Kif22 1
+Cdca8 Kif22 1
+Cdca8 Kif2c 1
+Cdca8 Lig1 1
+Cdca8 Lig1 1
+Cdca8 Mad2l1 1
+Cdca8 Mcm2 1
+Cdca8 Ndc80 1
+Cdca8 Nek2 1
+Cdca8 Obfc2a 1
+Cdca8 Olig2 1
+Cdca8 Pkmyt1 1
+Cdca8 Plk1 1
+Cdca8 RP23-480B19.10 1
+Cdca8 Smc2 1
+Cdca8 Smc2 1
+Cdca8 Smc2 1
+Cdca8 Tcf19 1
+Cdca8 Tcf19 1
+Cdca8 Trip13 1
+Cdcp2 Aak1 1
+Cdcp2 Abat 1
+Cdcp2 Cnga3 1
+Cdcp2 Olfr631 1
+Cdcp2 XM_357524 1
+Cdgap 1200009F10Rik 1
+Cdh10 1110005A03Rik 1
+Cdh10 1110005A03Rik 1
+Cdh10 1700047F07Rik 1
+Cdh10 5830432E09Rik 1
+Cdh10 Cdh9 1
+Cdh10 Defb21 1
+Cdh10 Gm75 1
+Cdh10 mCT148517 1
+Cdh10 MMT00051784 1
+Cdh10 Olfr640 1
+Cdh10 V1re13 1
+Cdh10 V1re13 1
+Cdh1 100*mesenteric_w/body_w_adj_s_s_sqrt_W16 1
+Cdh12 4930426I24Rik 1
+Cdh12 4933411G06Rik 1
+Cdh1 2700022O18Rik 1
+Cdh12 A730036I17Rik 1
+Cdh12 BC030307 1
+Cdh12 Defb8 1
+Cdh12 Ifi205 1
+Cdh12 Krt78 1
+Cdh12 Maml3 1
+Cdh12 Olfr1002 1
+Cdh12 Speer2 1
+Cdh1 3110082I17Rik 1
+Cdh15 Galns 1
+Cdh15 mCT17090 1
+Cdh18 1700021L22Rik 1
+Cdh18 6430519N07Rik 1
+Cdh18 MMS010200424 1
+Cdh18 MMS010200424 1
+Cdh18 MMT00054907 1
+Cdh1 9030619P08Rik 1
+Cdh19 AK050511 1
+Cdh19 MMT00006908 1
+Cdh1 AB056442 1
+Cdh1 Asgr1 1
+Cdh1 bw_chg_6_9_adj_s_s_sqrt_W16 1
+Cdh1 Cd276 1
+Cdh1 Cd276 1
+Cdh1 Cpne2 1
+Cdh1 Cryz 1
+Cdh1 Cyp2b9 1
+Cdh1 Defb1 1
+Cdh1 Fkbp11 1
+Cdh1 Galns 1
+Cdh1 Gsta2 1
+Cdh1 Gstt3 1
+Cdh1 H2-Q6 1
+Cdh1 Hamp2 1
+Cdh1 Hao3 1
+Cdh1 heart_weight_ratio_adj_s_slog_W16 1
+Cdh1 Itih5 1
+Cdh1 Itih5 1
+Cdh1 mCT165724 1
+Cdh1 MESENTERIC_adj_s_s_sqrt_W16 1
+Cdh1 Parva 1
+Cdh1 Rdh16 1
+Cdh1 Rrbp1 1
+Cdh1 Slc18a1 1
+Cdh1 Slc25a10 1
+Cdh1 Slc47a1 1
+Cdh1 Sort1 1
+Cdh1 SUBCUTANEOUS_adj_s_slog_W16 1
+Cdh1 Sult1e1 1
+Cdh1 Sv2c 1
+Cdh1 weight_adj_S_Slog_W16 1
+Cdh1 Wipi1 1
+Cdh1 Zcwpw1 1
+Cdh1 Zfp93 1
+Cdh20 Olfr488 1
+Cdh20 Olfr8 1
+Cdh20 Olfr8 1
+Cdh20 Tram1l1 1
+Cdh2 1810013L24Rik 1
+Cdh22 Atp6v0a4 1
+Cdh22 Gm106 1
+Cdh22 Ypel1 1
+Cdh26 1110020C03Rik 1
+Cdh26 1110020C03Rik 1
+Cdh26 Acsf2 1
+Cdh26 C130038G02Rik 1
+Cdh26 EG545936 1
+Cdh26 Lhx3 1
+Cdh26 Lrch4 1
+Cdh26 Map2k7 1
+Cdh26 Olfr446 1
+Cdh26 Thbs1 1
+Cdh26 Wdr31 1
+Cdh26 XM_358499 1
+Cdh2 E330037G11Rik 1
+Cdh2 Tmprss5 1
+Cdh2 Upf1 1
+Cdh3 Adamts4 1
+Cdh3 Ctsz 1
+Cdh8 4921505C17Rik 1
+Cdh8 C530043A13Rik 1
+Cdh8 Dnahc8 1
+Cdh8 Nhlh1 1
+Cdh8 Olfr183 1
+Cdh8 Sbp 1
+Cdh9 1500002J14Rik 1
+Cdh9 ENSMUSG00000073518 1
+Cdh9 MMT00067749 1
+Cdh9 Olfr837 1
+Cdh9 Tprg 1
+Cdh9 Tprg 1
+Cdh9 XM_358249 1
+Cdk2 Ap1g2 1
+Cdk2ap1 ORF9 1
+Cdk2ap2 2410014A08Rik 1
+Cdk2ap2 Tmed3 1
+Cdk2 Hsd17b10 1
+Cdk2 Rbm38 1
+Cdk2 Rbm38 1
+Cdk5r2 2810040C05Rik 1
+Cdk5r2 9230105E10Rik 1
+Cdk5r2 Abca7 1
+Cdk5r2 Abca7 1
+Cdk5r2 BC030307 1
+Cdk5r2 C230081A13Rik 1
+Cdk5r2 Chodl 1
+Cdk5r2 Erdr1 1
+Cdk5r2 Fbn2 1
+Cdk5r2 Fbn2 1
+Cdk5r2 Inpp4a 1
+Cdk5r2 MMT00079364 1
+Cdk5r2 Olfr1256 1
+Cdk5r2 Ppap2c 1
+Cdk5rap2 Mterfd2 1
+Cdk5rap3 Coil 1
+Cdk5rap3 Eftud2 1
+Cdk5rap3 Lrrc59 1
+Cdk5rap3 Mbtd1 1
+Cdk5rap3 Slc35b1 1
+Cdk5rap3 Slc35b1 1
+Cdk6 1700024F13Rik 1
+Cdk6 1810023F06Rik 1
+Cdk6 Bhlhb9 1
+Cdk6 Rhoq 1
+Cdk6 Wwtr1 1
+Cdk8 Ahnak 1
+Cdk8 Capns1 1
+Cdk8 Chac2 1
+Cdk8 Dsc2 1
+Cdk8 Fam13b 1
+Cdk8 Gls2 1
+Cdk8 MMT00056600 1
+Cdkal1 Cops7b 1
+Cdkl1 AV089949 1
+Cdkl1 Zfp418 1
+Cdkl2 Apbb2 1
+Cdkl2 Klhl5 1
+Cdkl2 Lrrc8b 1
+Cdkl2 Mtf2 1
+Cdkl2 Pclo 1
+Cdkl2 Pclo 1
+Cdkl2 Reep5 1
+Cdkl2 Sccpdh 1
+Cdkl3 ORF9 1
+Cdkl5 Cnbp2 1
+Cdkl5 Cnbp2 1
+Cdkl5 Dydc2 1
+Cdkl5 Sh3md4 1
+Cdkl5 Vmn2r68 1
+Cdkn1a 1190002H23Rik 1
+Cdkn1a 1810054D07Rik 1
+Cdkn1a 2310016C08Rik 1
+Cdkn1a 2310076L09Rik 1
+Cdkn1a 2310076L09Rik 1
+Cdkn1a 5430432M24Rik 1
+Cdkn1a 9530048O09Rik 1
+Cdkn1a Adcy1 1
+Cdkn1a Apbb3 1
+Cdkn1a Arl4a 1
+Cdkn1a Baiap2 1
+Cdkn1a C330011F01Rik 1
+Cdkn1a Chka 1
+Cdkn1a Dclk3 1
+Cdkn1a Ddit4 1
+Cdkn1a Ddit4 1
+Cdkn1a Dyrk3 1
+Cdkn1a Dyrk3 1
+Cdkn1a Dyrk3 1
+Cdkn1a Erbb3 1
+Cdkn1a Fa2h 1
+Cdkn1a Gas1 1
+Cdkn1a Grhl1 1
+Cdkn1a Irf2bp2 1
+Cdkn1a Kcnk10 1
+Cdkn1a Klhl25 1
+Cdkn1a Mbd1 1
+Cdkn1a Rabgef1 1
+Cdkn1a Rabgef1 1
+Cdkn1a Slc16a6 1
+Cdkn1a Slc45a3 1
+Cdkn1a St3gal5 1
+Cdkn1a Stard4 1
+Cdkn1a Tnk2 1
+Cdkn1a Tpst2 1
+Cdkn1a Tubb2a 1
+Cdkn1a Xrcc6bp1 1
+Cdkn1a Zmat3 1
+Cdkn1b C630028N24Rik 1
+Cdkn1b Cpeb4 1
+Cdkn1b Daam1 1
+Cdkn1b ENSMUSG00000073518 1
+Cdkn1b FFA_sqrt 1
+Cdkn1b Ifngr2 1
+Cdkn1b Jarid1a 1
+Cdkn1b MMT00036892 1
+Cdkn1b MMT00038301 1
+Cdkn1b Nedd4 1
+Cdkn1b Oog4 1
+Cdkn1b Oog4 1
+Cdkn1b Ppig 1
+Cdkn1b Ripk1 1
+Cdkn1b Socs2 1
+Cdkn1c 1700040L02Rik 1
+Cdkn1c A530088I07Rik 1
+Cdkn1c Dmxl2 1
+Cdkn1c Hsd17b7 1
+Cdkn1c M36660 1
+Cdkn1c Ppp1r16a 1
+Cdkn1c Ttl 1
+Cdkn2a Dmc1 1
+Cdkn2a Vmn2r122 1
+Cdkn2b Pdzk1ip1 1
+Cdkn2c 6030451C04Rik 1
+Cdkn2c 6030451C04Rik 1
+Cdkn2c ENSMUSG00000073100 1
+Cdkn2d A230106M15Rik 1
+Cdkn2d Gtlf3a 1
+Cdkn2d Mid1ip1 1
+Cdkn2d Xrcc6bp1 1
+Cdo1 Adamts8 1
+Cds1 Ripk3 1
+Cds2 6330439K17Rik 1
+Cds2 Cux2 1
+Cds2 Emp2 1
+Cds2 Ganc 1
+Cds2 Prkab1 1
+Cds2 Sh3yl1 1
+Cdt1 6720463M24Rik 1
+Cdt1 Abcc12 1
+Cdt1 Aprt 1
+Cdt1 Aprt 1
+Cdt1 Ccnd1 1
+Cdt1 Cyr61 1
+Cdt1 Hunk 1
+Cdt1 Ppp1r3b 1
+Cdt1 Rrm1 1
+Cdt1 Slc41a1 1
+Cdt1 Tcf19 1
+Cdv3 B830007D08Rik 1
+Cdv3 EG319225 1
+Cdx2 2810407A14Rik 1
+Cdx2 Cramp1l 1
+Cdx2 Mmel1 1
+Cdx2 Npsr1 1
+Cdx2 Npsr1 1
+Cdx2 Nr2e1 1
+Cdx2 Olfr710 1
+Cdx2 Vcan 1
+Cdx2 Vcan 1
+Cdx4 Abpb 1
+Cdx4 Chic1 1
+Cdx4 ENSMUSG00000073019 1
+Cdx4 Pcdha3 1
+Cdyl2 Cdyl2 1
+Cdyl2 Cpne2 1
+Cdyl2 Rin2 1
+Ceacam12 BC050777 1
+Ceacam12 Ctnnd2 1
+Ceacam18 6330527O06Rik 1
+Ceacam18 Kir3dl1 1
+Ceacam18 Trpv5 1
+Ceacam19 Pitx2 1
+Ceacam5 Rpp38 1
+Ceacam5 XM_358126 1
+Ceacam9 Pdgfrl 1
+Ceacam9 Rilpl1 1
+Cebpb 5830400J07Rik 1
+Cebpb Angptl4 1
+Cebpb Angptl4 1
+Cebpb Ankhd1 1
+Cebpb Cebpd 1
+Cebpb Cebpd 1
+Cebpb Cebpd 1
+Cebpb Cln8 1
+Cebpb Errfi1 1
+Cebpb Gja4 1
+Cebpb Pck1 1
+Cebpb Pfkfb3 1
+Cebpb Pim3 1
+Cebpb Pim3 1
+Cebpb Pim3 1
+Cebpb Plk3 1
+Cebpb Pnpla2 1
+Cebpb Rgs4 1
+Cebpb St3gal5 1
+Cebpb St3gal5 1
+Cebpb Trpm4 1
+Cebpb Vasn 1
+Cebpd 1810011O10Rik 1
+Cebpd 1810011O10Rik 1
+Cebpd 2010001M09Rik 1
+Cebpd 2410137F16Rik 1
+Cebpd 4930431B09Rik 1
+Cebpd 6030422H21Rik 1
+Cebpd 6030422H21Rik 1
+Cebpd 9530048O09Rik 1
+Cebpd Adra2b 1
+Cebpd Arid5b 1
+Cebpd Arid5b 1
+Cebpd Arrdc3 1
+Cebpd Ccrn4l 1
+Cebpd Cebpb 1
+Cebpd Cebpb 1
+Cebpd Cebpb 1
+Cebpd Chac1 1
+Cebpd Ctgf 1
+Cebpd Ctgf 1
+Cebpd Ctgf 1
+Cebpd D18Ertd232e 1
+Cebpd Dact2 1
+Cebpd Ddit4 1
+Cebpd Ddit4 1
+Cebpd Ddit4 1
+Cebpd EG624219 1
+Cebpd Egln3 1
+Cebpd Egln3 1
+Cebpd Errfi1 1
+Cebpd Errfi1 1
+Cebpd Errfi1 1
+Cebpd Fermt2 1
+Cebpd G0s2 1
+Cebpd Gadd45g 1
+Cebpd Gdap5 1
+Cebpd Gja4 1
+Cebpd Gmeb1 1
+Cebpd Gmeb1 1
+Cebpd Id2 1
+Cebpd Igfbp1 1
+Cebpd Irf2bp2 1
+Cebpd Lmod2 1
+Cebpd Mmp7 1
+Cebpd Nfkbia 1
+Cebpd Nfkbia 1
+Cebpd Pfkfb3 1
+Cebpd Pfkfb3 1
+Cebpd Rasd1 1
+Cebpd Rasd1 1
+Cebpd Rasd1 1
+Cebpd Rasgef1b 1
+Cebpd Rasgef1b 1
+Cebpd Rasgef1b 1
+Cebpd Rgs16 1
+Cebpd Rnf170 1
+Cebpd Slc10a1 1
+Cebpd Slc10a6 1
+Cebpd Slc38a2 1
+Cebpd Slc41a3 1
+Cebpd Slc45a3 1
+Cebpd Slc8a1 1
+Cebpd Speer4f 1
+Cebpd Speer4f 1
+Cebpd Tat 1
+Cebpd Tat 1
+Cebpd Tmem100 1
+Cebpd Tsc22d3 1
+Cebpd Tsc22d3 1
+Cebpd Tsc22d3 1
+Cebpd Vcam1 1
+Cebpd Zfp454 1
+Cebpe 1700011H14Rik 1
+Cebpe 2310014G06Rik 1
+Cebpe 2310014G06Rik 1
+Cebpe 2310014G06Rik 1
+Cebpe 4931414P19Rik 1
+Cebpe 4931414P19Rik 1
+Cebpe 6330409N04Rik 1
+Cebpe 6330409N04Rik 1
+Cebpe 6330409N04Rik 1
+Cebpe 6720456H20Rik 1
+Cebpe A930018M24Rik 1
+Cebpe A930018M24Rik 1
+Cebpe Adamdec1 1
+Cebpe Adcy4 1
+Cebpe Adcy4 1
+Cebpe AV336316 1
+Cebpe AV336316 1
+Cebpe C78692 1
+Cebpe Cbln3 1
+Cebpe Cbln3 1
+Cebpe Cbln3 1
+Cebpe D14Ertd449e 1
+Cebpe D14Ertd500e 1
+Cebpe D14Ertd500e 1
+Cebpe Dad1 1
+Cebpe Dad1 1
+Cebpe Dgkh 1
+Cebpe Dhrs1 1
+Cebpe Dock5 1
+Cebpe Dyrk1b 1
+Cebpe Hnrnpc 1
+Cebpe Jub 1
+Cebpe Lrtm1 1
+Cebpe Mapk6 1
+Cebpe Mett11d1 1
+Cebpe Mmp14 1
+Cebpe MMS010072212 1
+Cebpe MMT00021419 1
+Cebpe MMT00065262 1
+Cebpe MMT00080724 1
+Cebpe Ndrg2 1
+Cebpe Ngdn 1
+Cebpe Parp4 1
+Cebpe Pck2 1
+Cebpe Pck2 1
+Cebpe Pck2 1
+Cebpe Pir 1
+Cebpe Rnase4 1
+Cebpe Rnaseh2b 1
+Cebpe Rpp14 1
+Cebpe Sall2 1
+Cebpe Sall2 1
+Cebpe Sgcg 1
+Cebpe Slc7a8 1
+Cebpe Supt16h 1
+Cebpe Supt16h 1
+Cebpe Trim75 1
+Cebpe Ttc5 1
+Cebpe Ubtf 1
+Cebpe XM_357749 1
+Cebpe XM_357749 1
+Cebpg Fgfrl1 1
+Cecr2 Fbxo21 1
+Cecr2 Fzd6 1
+Cecr2 Olfm1 1
+Cecr2 Setd1b 1
+Cel 1810008K16Rik 1
+Cel Clps 1
+Cel Ctrb1 1
+Cel Ctrl 1
+Celsr1 2210021J22Rik 1
+Celsr1 2210021J22Rik 1
+Celsr1 2810001A02Rik 1
+Celsr1 3110048E14Rik 1
+Celsr1 6430520M22Rik 1
+Celsr1 Cdc42ep1 1
+Celsr1 Cpne8 1
+Celsr1 Csnk1e 1
+Celsr1 Fam118a 1
+Celsr1 Gramd4 1
+Celsr1 Gramd4 1
+Celsr1 L1td1 1
+Celsr1 Mkx 1
+Celsr1 MMT00038895 1
+Celsr1 Pla2g12a 1
+Celsr1 Ppara 1
+Celsr1 Ppara 1
+Celsr1 Rabl4 1
+Celsr1 Tspo 1
+Celsr2 AK045332 1
+Celsr2 Alb 1
+Celsr2 Lrriq1 1
+Cend1 ENSMUST00000052693 1
+Cend1 Hhatl 1
+Cenpa D5Wsu178e 1
+Cenpa Nol14 1
+Cenpa Nol14 1
+Cenpb Il17ra 1
+Cenpb Thop1 1
+Cenpc1 MMT00019454 1
+Cenpe Aurkb 1
+Cenpe Cdc2a 1
+Cenpe Cdca5 1
+Cenpe Cdca5 1
+Cenpe Cenpn 1
+Cenpe D2Ertd750e 1
+Cenpe Fanci 1
+Cenpe Kif22 1
+Cenpe Nuf2 1
+Cenpe Nuf2 1
+Cenpe Orm2 1
+Cenpe Plk1 1
+Cenpe Pycard 1
+Cenpe Racgap1 1
+Cenpe RP23-480B19.10 1
+Cenpe Slc35f2 1
+Cenpe Tacc3 1
+Cenpf Kif5c 1
+Cenpi 2900069M18Rik 1
+Cenpi Gng5 1
+Cenpi Gng5 1
+Cenpi Hist1h1b 1
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+Cidec Slco2a1 1
+Cidec Smpd3 1
+Cidec Sorbs1 1
+Cidec Sorbs1 1
+Cidec Sorbs1 1
+Cidec Sprr1a 1
+Cidec Srxn1 1
+Cidec Sulf2 1
+Cidec Synpo 1
+Cidec Tceal8 1
+Cidec Tlr12 1
+Cidec Tmem120a 1
+Cidec Tmem98 1
+Cidec Tprkb 1
+Cidec Treh 1
+Cidec Treh 1
+Cidec Treh 1
+Cidec Unc119 1
+Cidec Unc119 1
+Ciita H2-DMa 1
+Cilp BC009120 1
+Cilp Ccl21c 1
+Cirbp 1700056E22Rik 1
+Cirbp 2310035C23Rik 1
+Cirbp 2410018M08Rik 1
+Cirbp 4930458D05Rik 1
+Cirbp 4930535B03Rik 1
+Cirbp 5930430L01Rik 1
+Cirbp 9030616G12Rik 1
+Cirbp Chic2 1
+Cirbp Chordc1 1
+Cirbp Cyp2j9 1
+Cirbp Ddx5 1
+Cirbp E130304F04Rik 1
+Cirbp E2f8 1
+Cirbp Fem1b 1
+Cirbp Hist2h2aa1 1
+Cirbp Hspa8 1
+Cirbp Hsph1 1
+Cirbp l7Rn6 1
+Cirbp MMT00077535 1
+Cirbp Rbm3 1
+Cirbp Rbm3 1
+Cirbp Rbm3 1
+Cirbp Rprd2 1
+Cirbp Sfrs5 1
+Cirbp XM_355334 1
+Cirbp XM_355334 1
+Cirbp Zc3h6 1
+Cirbp Zc3hav1 1
+Cisd1 1700040L02Rik 1
+Cisd1 9530050K03Rik 1
+Cisd1 Hibadh 1
+Cisd1 Pah 1
+Cisd1 Suclg1 1
+Cisd2 2010015P12Rik 1
+Cisd2 Adh6-ps1 1
+Cisd2 Ccdc109b 1
+Cisd2 MMT00057975 1
+Cish Acvr2b 1
+Cish Arid5b 1
+Cish Arid5b 1
+Cish Bcl6 1
+Cish Bcl6 1
+Cish Bcl6 1
+Cish Chac1 1
+Cish E230015J15Rik 1
+Cish E230015J15Rik 1
+Cish EG634650 1
+Cish EG634650 1
+Cish EG634650 1
+Cish ENSMUST00000061991 1
+Cish ENSMUST00000061991 1
+Cish Foxa2 1
+Cish Gbp4 1
+Cish Hhex 1
+Cish Idi1 1
+Cish Irx3 1
+Cish Irx3 1
+Cish MMT00030978 1
+Cish MMT00030978 1
+Cish MMT00043905 1
+Cish MMT00079605 1
+Cish Myc 1
+Cish Onecut1 1
+Cish Onecut1 1
+Cish Phlda1 1
+Cish Phlda1 1
+Cish Phlda1 1
+Cish Rgs3 1
+Cish Rgs3 1
+Cish Socs2 1
+Cish Vasn 1
+Cish Wsb1 1
+Cit Cit 1
+Cit Cit 1
+Cit Rb1 1
+Cit Rims2 1
+Cit Scara3 1
+Cit Serpinb2 1
+Cit Tmed2 1
+Ciz1 2310015A16Rik 1
+Ciz1 A530057A03Rik 1
+Ciz1 Adrbk1 1
+Ciz1 C030046I01Rik 1
+Ciz1 Cnot3 1
+Ciz1 Cpne2 1
+Ciz1 D430042O09Rik 1
+Ciz1 Ehmt2 1
+Ciz1 Fat1 1
+Ciz1 Fubp1 1
+Ciz1 Gnmt 1
+Ciz1 H2-M3 1
+Ciz1 H2-Q7 1
+Ciz1 Leng8 1
+Ciz1 Lnx2 1
+Ciz1 Med12 1
+Ciz1 Med12 1
+Ciz1 Mink1 1
+Ciz1 MMT00004881 1
+Ciz1 Ndfip2 1
+Ciz1 Nfkbid 1
+Ciz1 Ptpn23 1
+Ciz1 Sar1b 1
+Ciz1 Slc25a16 1
+Ciz1 Taok1 1
+Ciz1 Tln1 1
+Ciz1 Tmem16h 1
+Ciz1 Tomm34 1
+Ciz1 Top1mt 1
+Ciz1 Ube2e1 1
+Ciz1 XM_131446 1
+Ciz1 XM_131446 1
+Ciz1 Xrcc1 1
+CJ068215 A530058N18Rik 1
+Ckap2l 2610016C23Rik 1
+Ckap2l 4930547N16Rik 1
+Ckap2l Aspm 1
+Ckap2l Aspm 1
+Ckap2l Aurkb 1
+Ckap2l Birc5 1
+Ckap2l Birc5 1
+Ckap2l Birc5 1
+Ckap2l C81431 1
+Ckap2l Ccna2 1
+Ckap2l Ccnb2 1
+Ckap2l Cdca3 1
+Ckap2l Cdca3 1
+Ckap2l Cdca3 1
+Ckap2l Cdca5 1
+Ckap2l Cdca8 1
+Ckap2l Cenpe 1
+Ckap2l Cenpe 1
+Ckap2l Cenpe 1
+Ckap2l Cenpf 1
+Ckap2l Cep55 1
+Ckap2l Ect2 1
+Ckap2l Fignl1 1
+Ckap2l MMT00004673 1
+Ckap2l Ncapg 1
+Ckap2l Nuf2 1
+Ckap2l Nusap1 1
+Ckap2l Nusap1 1
+Ckap2l Racgap1 1
+Ckap2l Racgap1 1
+Ckap2l Sdk2 1
+Ckap4 AK005483 1
+Ckap4 Atxn1l 1
+Ckap4 Col6a2 1
+Ckap4 MMT00014548 1
+Ckap4 Pcdhb10 1
+Ckap4 Plcxd3 1
+Ckap4 Siglech 1
+Ckap4 Tmcc1 1
+Ckap4 Tmem173 1
+Ckap5 A530045O15Rik 1
+Ckap5 AK033688 1
+Ckap5 E230024E03Rik 1
+Cklf 9030625A04Rik 1
+Cklf Aadat 1
+Cklf Aldh18a1 1
+Cklf BC024582 1
+Cklf Cdkn2b 1
+Cklf EG277089 1
+Cklf Nupr1 1
+Cklf Nupr1 1
+Cklf Olig2 1
+Cklf Phyh 1
+Cklf Samd4 1
+Cklf Slpi 1
+Cklf Stk10 1
+Ckm Acta1 1
+Ckm Ldb3 1
+Ckm Mb 1
+Ckm Myl1 1
+Ckm Pvalb 1
+Ckmt1 Chga 1
+Ckmt1 Tspan8 1
+Ckmt2 Ldb3 1
+Ckmt2 Myh4 1
+Ckmt2 Myh8 1
+Ckmt2 Myl1 1
+Ckm Tpm1 1
+Cks1b Csrp3 1
+Cks1b Dbf4 1
+Cks1b EG622657 1
+Cks1b Npc1 1
+Cks1b XM_123604 1
+Cks2 Bach1 1
+Cks2 Lrsam1 1
+CL211528 1700054A03Rik 1
+CL211528 4930423D24Rik 1
+CL211528 Clcn5 1
+CL211528 Cpa4 1
+CL211528 Cpa4 1
+CL211528 Gab3 1
+CL211528 Jam3 1
+CL211528 MMS010149390 1
+CL211528 MMT00009700 1
+CL211528 MMT00053142 1
+CL211528 MMT00075294 1
+CL211528 Olfr1115 1
+CL211528 Pcdhb1 1
+CL211528 Plekha2 1
+CL211528 Plekha2 1
+CL211528 Tmc3 1
+CL458932 MMT00061473 1
+CL458932 XM_358026 1
+Clasp1 Hspe1 1
+Clasp1 Nme3 1
+Clasp2 Azi2 1
+Clasp2 Sass6 1
+Clasp2 Setbp1 1
+Clca1 Cyr61 1
+Clca2 2010015P12Rik 1
+Clca2 Adh6-ps1 1
+Clca2 Ccdc80 1
+Clca2 Ccdc80 1
+Clca2 Clca1 1
+Clca2 Clca1 1
+Clca2 Fndc3b 1
+Clca2 Gbp1 1
+Clca2 Gbp1 1
+Clca2 Itgbl1 1
+Clca2 Nfkbiz 1
+Clca2 Tm6sf1 1
+Clca3 1190003M12Rik 1
+Clca3 1190003M12Rik 1
+Clca3 5830411K21Rik 1
+Clca3 Chga 1
+Clca3 Defcr8 1
+Clca3 Gsdmc2 1
+Clca3 Itln1 1
+Clca3 Olfr826 1
+Clca3 Pvalb 1
+Clca4 1700055M20Rik 1
+Clca4 Exph5 1
+Clca4 Fkbp10 1
+Clca4 Slco4c1 1
+Clca4 Slco4c1 1
+Clca6 2410039M03Rik 1
+Clca6 5830474E16Rik 1
+Clca6 6330418B08Rik 1
+Clca6 AW494482 1
+Clca6 AW494482 1
+Clca6 mCT163327 1
+Clca6 mCT163327 1
+Clca6 mCT51598 1
+Clca6 MMT00021024 1
+Clca6 MMT00059288 1
+Clca6 MMT00059288 1
+Clca6 MMT00063263 1
+Clca6 MMT00063263 1
+Clca6 Olfr275 1
+Clca6 Serpinb3d 1
+Clcc1 Csf2ra 1
+Clcc1 Gdap5 1
+Clcc1 Umodl1 1
+Clcn1 Lrrc19 1
+Clcn1 Mtap7d3 1
+Clcn1 Olfr1157 1
+Clcn1 Olfr118 1
+Clcn1 Slc7a13 1
+Clcn1 Tsga13 1
+Clcn2 2310016C08Rik 1
+Clcn2 6330514A18Rik 1
+Clcn2 Armcx3 1
+Clcn2 Cyp4f15 1
+Clcn2 Mmp14 1
+Clcn2 MMT00078660 1
+Clcn2 Ndrg2 1
+Clcn2 S1pr5 1
+Clcn2 Slc22a18 1
+Clcn2 Tmem216 1
+Clcn3 Hmox1 1
+Clcn3 Pex1 1
+Clcn4-2 EG667794 1
+Clcn4-2 EG667794 1
+Clcn4-2 MMT00004337 1
+Clcn4-2 Phyhip 1
+Clcn5 4933426D04Rik 1
+Clcn5 A530010L16Rik 1
+Clcn5 Col6a2 1
+Clcn5 Jam3 1
+Clcn5 MMS010059558 1
+Clcn5 MMT00001917 1
+Clcn5 Nhlrc1 1
+Clcn5 Nhlrc1 1
+Clcn5 NM_011222 1
+Clcn5 Serpinb13 1
+Clcn5 Timp1 1
+Clcn5 Timp1 1
+Clcn5 Timp1 1
+Clcn5 XM_284747 1
+Clcn6 Pgd 1
+Clcn6 Ptprk 1
+Clcn6 Tnnt1 1
+Clcn7 Anks1 1
+Cldn10 Prdm14 1
+Cldn11 Smox 1
+Cldn1 2900019G14Rik 1
+Cldn1 2900019G14Rik 1
+Cldn13 Grm2 1
+Cldn13 Sept1 1
+Cldn13 XM_194002 1
+Cldn14 2410137F16Rik 1
+Cldn1 4933415E08Rik 1
+Cldn14 Ifnar2 1
+Cldn14 Ifnar2 1
+Cldn14 Mx2 1
+Cldn14 Slc41a2 1
+Cldn15 Cpe 1
+Cldn15 Leptin_sqrt 1
+Cldn16 D2hgdh 1
+Cldn19 4921510J17Rik 1
+Cldn19 9330182L06Rik 1
+Cldn19 Chrm3 1
+Cldn1 Alas1 1
+Cldn1 Bach2 1
+Cldn1 BC042411 1
+Cldn1 Flnb 1
+Cldn1 Glt28d2 1
+Cldn1 Harbi1 1
+Cldn1 Hmgcr 1
+Cldn1 Hmgcr 1
+Cldn1 Hspa8 1
+Cldn1 Lipg 1
+Cldn1 Lipg 1
+Cldn1 Magi1 1
+Cldn1 MMT00026501 1
+Cldn1 MMT00035045 1
+Cldn1 MMT00081590 1
+Cldn1 Nampt 1
+Cldn1 Narf 1
+Cldn1 Pde9a 1
+Cldn1 Ppp1r3c 1
+Cldn1 Ptk6 1
+Cldn1 ri|2310043N10|ZX00040G07||2099 1
+Cldn1 Slc13a4 1
+Cldn1 Tapt1 1
+Cldn1 Tomm40l 1
+Cldn1 Tsku 1
+Cldn1 Tsku 1
+Cldn1 Tsku 1
+Cldn1 Tuba4a 1
+Cldn22 2310044H10Rik 1
+Cldn22 C79119 1
+Cldn23 Cttnbp2 1
+Cldn23 ENSMUST00000025557 1
+Cldn23 MMS010114361 1
+Cldn23 Ptchd1 1
+Cldn23 XM_357782 1
+Cldn23 Xpo6 1
+Cldn23 Xpo6 1
+Cldn2 Air 1
+Cldn2 Comt 1
+Cldn2 Ncald 1
+Cldn3 1300014I06Rik 1
+Cldn3 2410075B13Rik 1
+Cldn3 2610528J11Rik 1
+Cldn3 6330534C20Rik 1
+Cldn3 Amd1 1
+Cldn3 BC004044 1
+Cldn3 BC004044 1
+Cldn3 Chd1l 1
+Cldn3 Chd1l 1
+Cldn3 Cideb 1
+Cldn3 Dhrs3 1
+Cldn3 Fbxo33 1
+Cldn3 Fkbp5 1
+Cldn3 Fkbp5 1
+Cldn3 Gpr146 1
+Cldn3 Hisppd1 1
+Cldn3 Idh3a 1
+Cldn3 Lrrc3 1
+Cldn3 Lrwd1 1
+Cldn3 MMT00057399 1
+Cldn3 MMT00063804 1
+Cldn3 Nr1d1 1
+Cldn3 Nucks1 1
+Cldn3 Poll 1
+Cldn3 Pqlc1 1
+Cldn3 Pqlc1 1
+Cldn3 Taf6 1
+Cldn3 Taok3 1
+Cldn3 Thbd 1
+Cldn3 Tox 1
+Cldn3 Vcam1 1
+Cldn3 Wdr81 1
+Cldn3 Zh2c2 1
+Cldn3 Zh2c2 1
+Cldn5 Cyp4b1 1
+Cldn5 Tiam1 1
+Cldn6 Brd4 1
+Cldn6 Ccdc33 1
+Cldn6 EG668450 1
+Cldn6 Hoxb9 1
+Cldn6 Hoxb9 1
+Cldn6 Kcna2 1
+Cldn7 mCT154379 1
+Cldn8 Ugt8a 1
+Cldn9 Fusip1 1
+Cldn9 MMT00014760 1
+Cldn9 Rftn1 1
+Cldnd2 4930565D16Rik 1
+Clec11a E2f8 1
+Clec11a Klk1 1
+Clec11a MMT00042342 1
+Clec11a OTTMUSG00000007191 1
+Clec14a 2310014F07Rik 1
+Clec14a 9630050M13Rik 1
+Clec14a Cav2 1
+Clec14a Cav2 1
+Clec14a Cav2 1
+Clec14a Cd55 1
+Clec14a Chst7 1
+Clec14a Chst7 1
+Clec14a Cnrip1 1
+Clec14a F2r 1
+Clec14a Jam2 1
+Clec14a Marcks 1
+Clec14a Mrc1 1
+Clec14a Mylip 1
+Clec14a Oit3 1
+Clec14a Tbc1d4 1
+Clec14a Tfpi 1
+Clec14a Tfpi 1
+Clec14a Tspan7 1
+Clec16a 1810073O08Rik 1
+Clec16a A130090K04Rik 1
+Clec16a Arhgap10 1
+Clec16a Clgn 1
+Clec16a Defcr5 1
+Clec16a Ica1l 1
+Clec16a MMT00065981 1
+Clec16a Olfr17 1
+Clec16a Olfr695 1
+Clec16a Tmeff2 1
+Clec16a V1rg8 1
+Clec1b Abcc4 1
+Clec1b AK017535 1
+Clec1b Amz1 1
+Clec1b Ccdc148 1
+Clec1b Cd84 1
+Clec1b Clec4a2 1
+Clec1b Fabp7 1
+Clec1b Gngt2 1
+Clec1b H2-Eb1 1
+Clec1b Hmgb2 1
+Clec1b Hmox1 1
+Clec1b Itgbl1 1
+Clec1b Lyve1 1
+Clec1b Mef2c 1
+Clec1b Mmp27 1
+Clec1b Mmp27 1
+Clec1b Pik3ip1 1
+Clec1b Pros1 1
+Clec1b Rab31 1
+Clec1b Sgcb 1
+Clec1b Slc9a9 1
+Clec1b Spsb4 1
+Clec1b Srgn 1
+Clec1b Stk10 1
+Clec1b Trp53i11 1
+Clec1b Vcam1 1
+Clec2e C230067J06Rik 1
+Clec2e MMT00048650 1
+Clec2e MMT00052104 1
+Clec2e Olfr1427 1
+Clec2e Vwa2 1
+Clec2e XM_358207 1
+Clec2h BC048546 1
+Clec2h Bcl2l14 1
+Clec2h Gpr19 1
+Clec2h MMT00049957 1
+Clec2h MMT00049957 1
+Clec2h Mug2 1
+Clec2h Mug-ps1 1
+Clec2i Ksr2 1
+Clec2i Olfr1184 1
+Clec2i Zfp618 1
+Clec3b mCT154155 1
+Clec3b Nktr 1
+Clec3b Sec22c 1
+Clec3b Tmem16k 1
+Clec3b Wdr51a 1
+Clec3b Wdr51a 1
+Clec4a1 Batf 1
+Clec4a1 Cd300e 1
+Clec4a1 Clec4a3 1
+Clec4a1 Klf2 1
+Clec4a1 MMT00005483 1
+Clec4a1 MMT00008183 1
+Clec4a1 Pik3r5 1
+Clec4a1 Pik3r5 1
+Clec4a1 Pira3 1
+Clec4a1 Repin1 1
+Clec4a1 Spic 1
+Clec4a2 Cd14 1
+Clec4a2 Clec4a1 1
+Clec4a2 Clec4a3 1
+Clec4a2 Clec4b1 1
+Clec4a2 Clec4b1 1
+Clec4a2 Clec4n 1
+Clec4a2 Cpne8 1
+Clec4a2 Lyz1 1
+Clec4a2 Mmp27 1
+Clec4a2 Ppig 1
+Clec4a2 Spire1 1
+Clec4a2 Tlr13 1
+Clec4a3 1700029I01Rik 1
+Clec4a3 Ap1s2 1
+Clec4a3 Ap1s2 1
+Clec4a3 Ccr5 1
+Clec4a3 Cd14 1
+Clec4a3 Cd300e 1
+Clec4a3 Cd53 1
+Clec4a3 Cd5l 1
+Clec4a3 Cd84 1
+Clec4a3 Clec1b 1
+Clec4a3 Clec4a2 1
+Clec4a3 Clec4a2 1
+Clec4a3 Clec4b1 1
+Clec4a3 Ctss 1
+Clec4a3 Emr1 1
+Clec4a3 Emr1 1
+Clec4a3 Emr4 1
+Clec4a3 Emr4 1
+Clec4a3 Fcer1g 1
+Clec4a3 Fcgr4 1
+Clec4a3 Fyb 1
+Clec4a3 Fyb 1
+Clec4a3 Igsf6 1
+Clec4a3 Igsf6 1
+Clec4a3 Igsf6 1
+Clec4a3 Itgb2 1
+Clec4a3 Kcnk13 1
+Clec4a3 Klra2 1
+Clec4a3 Klra9 1
+Clec4a3 Lst1 1
+Clec4a3 Lst1 1
+Clec4a3 Ly86 1
+Clec4a3 mCT57345 1
+Clec4a3 mCT57345 1
+Clec4a3 MMT00032773 1
+Clec4a3 Ms4a6b 1
+Clec4a3 Ms4a6c 1
+Clec4a3 Ms4a6c 1
+Clec4a3 P2ry12 1
+Clec4a3 P2ry12 1
+Clec4a3 ri|5430408M01|PX00022B06||1187 1
+Clec4a3 Sirpb1 1
+Clec4a3 Sirpb1 1
+Clec4a3 Slc11a1 1
+Clec4a3 Spic 1
+Clec4a3 Spic 1
+Clec4a3 Tm6sf1 1
+Clec4a3 Tmsb4x 1
+Clec4a3 Vsig4 1
+Clec4a3 Vsig4 1
+Clec4b1 Car13 1
+Clec4b1 Cbr3 1
+Clec4b1 Clec4a2 1
+Clec4b1 Clec4a2 1
+Clec4b1 Kctd14 1
+Clec4b1 mCT57345 1
+Clec4b1 mCT57345 1
+Clec4b1 Mmp27 1
+Clec4b1 Sh2d1b1 1
+Clec4b1 X58216 1
+Clec4d Asprv1 1
+Clec4d Ch25h 1
+Clec4d Cxcl2 1
+Clec4d Cxcl2 1
+Clec4d D730048J04Rik 1
+Clec4d Gpr109a 1
+Clec4d Hdc 1
+Clec4d Klra21 1
+Clec4d Klra21 1
+Clec4d Klra9 1
+Clec4d Pf4 1
+Clec4d S100a8 1
+Clec4d S100a9 1
+Clec4d S100a9 1
+Clec4e Csf2 1
+Clec4e MMT00051323 1
+Clec4f Ccl24 1
+Clec4f Cd163 1
+Clec4f Cd5l 1
+Clec4f Cd5l 1
+Clec4f Fcna 1
+Clec4f Fcna 1
+Clec4f Folr2 1
+Clec4f Fpr1 1
+Clec4f Gngt2 1
+Clec4f Ifngr1 1
+Clec4f J00393 1
+Clec4f Kcnj16 1
+Clec4f Pcolce2 1
+Clec4f Vsig4 1
+Clec4g 4930573O21Rik 1
+Clec4g 4930573O21Rik 1
+Clec4g Aqp1 1
+Clec4g Atp11a 1
+Clec4g Ccl25 1
+Clec4g Cd300lg 1
+Clec4g Cyp4b1 1
+Clec4g E230013L22Rik 1
+Clec4g E230013L22Rik 1
+Clec4g Ehd3 1
+Clec4g Ehd3 1
+Clec4g Eltd1 1
+Clec4g Fcgr2b 1
+Clec4g Fcgr2b 1
+Clec4g Kcnq1 1
+Clec4g Lhfpl2 1
+Clec4g Pex11c 1
+Clec4g Pex11c 1
+Clec4g Pex11c 1
+Clec4g Plxnc1 1
+Clec4g Slc10a2 1
+Clec4g Slc10a2 1
+Clec4n Amz1 1
+Clec4n Arhgdib 1
+Clec4n Asb2 1
+Clec4n Cdca3 1
+Clec4n Clec7a 1
+Clec4n Csf2rb2 1
+Clec4n EG433865 1
+Clec4n Hexa 1
+Clec4n Hpx 1
+Clec4n Lcn2 1
+Clec4n Lyz1 1
+Clec4n Matn2 1
+Clec4n Mmp27 1
+Clec4n Mmp27 1
+Clec4n Ms4a6d 1
+Clec4n Pf4 1
+Clec4n Ppm1h 1
+Clec4n Slamf9 1
+Clec4n Tlr13 1
+Clec4n Tlr13 1
+Clec4n Tlr2 1
+Clec4n Ube2l6 1
+Clec7a 3830403N18Rik 1
+Clec7a Amz1 1
+Clec7a Anxa1 1
+Clec7a Bcl2a1b 1
+Clec7a Ccl24 1
+Clec7a Ccl6 1
+Clec7a Ccr2 1
+Clec7a Ccr2 1
+Clec7a Cd22 1
+Clec7a Cd83 1
+Clec7a Cd84 1
+Clec7a Clec4n 1
+Clec7a Egr2 1
+Clec7a Egr2 1
+Clec7a Fkbp7 1
+Clec7a Glipr1 1
+Clec7a Gpr137b-ps 1
+Clec7a Gpr177 1
+Clec7a Gpr35 1
+Clec7a Il1rn 1
+Clec7a Lama2 1
+Clec7a Lpl 1
+Clec7a Lpl 1
+Clec7a Lpxn 1
+Clec7a Mitf 1
+Clec7a Mmp12 1
+Clec7a Mmp13 1
+Clec7a Mmp13 1
+Clec7a NM_023123 1
+Clec7a Siglec5 1
+Clec7a Siglec5 1
+Clec7a Sirpa 1
+Clec7a Slc8a1 1
+Clec7a Spic 1
+Clec7a Srd5a1 1
+Clec7a St8sia4 1
+Clec7a Thbs2 1
+Clec7a Vcam1 1
+Clgn BC022687 1
+Clgn Ceacam5 1
+Clgn Clec16a 1
+Clgn Depdc1a 1
+Clgn Olfr1451 1
+Clgn Oog4 1
+Clgn Sass6 1
+Clic1 1200009I06Rik 1
+Clic1 A630077B13Rik 1
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+Cog7 Ercc5 1
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+Crnkl1 C76450 1
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+Csad Cga 1
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+Cs Csl 1
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+Csf1r MMT00015654 1
+Csf1r MMT00015654 1
+Csf1r MMT00073557 1
+Csf1r P2ry13 1
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+Csf1r Slc9a9 1
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+Csf2 AK089448 1
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+Csf2ra Arpc1b 1
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+Csf2ra Dmn 1
+Csf2ra Evl 1
+Csf2ra Fcer1g 1
+Csf2ra Inpp5d 1
+Csf2ra Lama1 1
+Csf2ra MMT00073557 1
+Csf2ra Mustn1 1
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+Csf2rb2 Adamts4 1
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+Csf2rb2 Ccl6 1
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+Csf2rb2 Pfkp 1
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+Csf2rb2 Tifab 1
+Csf2rb2 Twf2 1
+Csf2rb2 Vcam1 1
+Csf3 Abcd2 1
+Csf3r Tnfaip2 1
+Csgalnact1 4930456K20Rik 1
+Csgalnact1 Arhgap20 1
+Csgalnact1 Cox7b2 1
+Csgalnact1 Fstl1 1
+Csgalnact1 Tgfbrap1 1
+Csgalnact1 V1rc32 1
+Csgalnact1 XM_358735 1
+Cs Gstm2 1
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+Csk 5033414D02Rik 1
+Csk Arrb2 1
+Csk Eif3c 1
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+Csk Epor 1
+Csk Fmnl3 1
+Csk Hmha1 1
+Csk Kifc3 1
+Csk Nod1 1
+Csk Ppapdc3 1
+Csk Ptp4a1 1
+Csk Snx21 1
+Csk Socs1 1
+Csk Tcf19 1
+Csk Triobp 1
+Csk Zmynd15 1
+Csl 9830102E05Rik 1
+Csl Cs 1
+Csl Fasn 1
+Csl MMT00060232 1
+Cs Lrrc39 1
+Csmd1 5730591J02Rik 1
+Csmd1 Krtap6-3 1
+Csmd1 mCT146025 1
+Csmd1 Olfr1023 1
+Csmd1 Smr3a 1
+Csmd1 Tsx 1
+Csmd2 Asb12 1
+Csmd2 Hoxa2 1
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+Csmd3 Olfr678 1
+Csn1s1 1700074A11Rik 1
+Csn1s1 4930509K18Rik 1
+Csn1s1 4930509K18Rik 1
+Csn1s1 4930579M01Rik 1
+Csn1s1 AK004128 1
+Csn1s1 Cacna2d3 1
+Csn1s1 Chrdl2 1
+Csn1s1 D9Ertd596e 1
+Csn1s1 mCT153949 1
+Csn1s1 MMT00003977 1
+Csn1s1 NM_020571 1
+Csn1s1 Olfr847 1
+Csn1s1 Rmst 1
+Csn1s1 Slc13a1 1
+Csn1s1 Slc13a1 1
+Csn1s1 Xpo6 1
+Csn1s1 Xpo6 1
+Csn2 ENSMUSG00000033219 1
+Csn2 Odam 1
+Csn3 MMT00074102 1
+Csnk1a1 Olfr1427 1
+Csnk1e 1700025B11Rik 1
+Csnk1e 1700025B11Rik 1
+Csnk1e 2210021J22Rik 1
+Csnk1e 2210021J22Rik 1
+Csnk1e 3110048E14Rik 1
+Csnk1e 3110048E14Rik 1
+Csnk1e 3110048E14Rik 1
+Csnk1e 4930423K06Rik 1
+Csnk1e 6030439D06Rik 1
+Csnk1e Apol10a 1
+Csnk1e Apol10a 1
+Csnk1e Apol10a 1
+Csnk1e Apol10b 1
+Csnk1e Apol9b 1
+Csnk1e BC025446 1
+Csnk1e BC025446 1
+Csnk1e BC025446 1
+Csnk1e Celsr1 1
+Csnk1e Cyp2d22 1
+Csnk1e Lgals1 1
+Csnk1e Lgals1 1
+Csnk1e Lrrc24 1
+Csnk1e Lrrc24 1
+Csnk1e mCT116030 1
+Csnk1e MMT00057234 1
+Csnk1e MMT00075644 1
+Csnk1e Mon1a 1
+Csnk1e Ncf4 1
+Csnk1e Ncf4 1
+Csnk1e Ncf4 1
+Csnk1e Ndufa6 1
+Csnk1e Ndufa6 1
+Csnk1e Ndufa6 1
+Csnk1e Pcyt1a 1
+Csnk1e Pla2g6 1
+Csnk1e Pmm1 1
+Csnk1e Pnpla3 1
+Csnk1e Ppara 1
+Csnk1e Ppara 1
+Csnk1e Ppara 1
+Csnk1e Psca 1
+Csnk1e Rabl4 1
+Csnk1e Rabl4 1
+Csnk1e Rangap1 1
+Csnk1e Rpl3 1
+Csnk1e Rpl3 1
+Csnk1e Slc25a17 1
+Csnk1e Syngr1 1
+Csnk1e Taf7 1
+Csnk1e Ttll12 1
+Csnk1e XM_128790 1
+Csnk1e XM_142485 1
+Csnk1e XM_358035 1
+Csnk1e XM_359338 1
+Csnk1g2 Hmg20b 1
+Csnk1g2 Rpp21 1
+Csnk1g2 Snx21 1
+Csnk1g2 Thumpd3 1
+Csnk1g2 Usf2 1
+Csnk1g2 Xylt2 1
+Csnk1g3 1810013L24Rik 1
+Csnk1g3 Actr6 1
+Csnk1g3 Centa2 1
+Csnk1g3 Thpo 1
+Csnk2a1 Acss1 1
+Csnk2a1 Akt2 1
+Csnk2a1 Aph1a 1
+Csnk2a1 Commd7 1
+Csnk2a1 Hnrnpa3 1
+Csnk2a1 MMT00013656 1
+Csnk2a1 Myh9 1
+Csnk2a1 Nutf2 1
+Csnk2a1 Pdlim5 1
+Csnk2a1 Pxmp4 1
+Csnk2a1 Rbm47 1
+Csnk2a1 Rbm7 1
+Csnk2a1 Rnf208 1
+Csnk2a1 Upf1 1
+Csnk2a2 Ciapin1 1
+Csnk2a2 Es1 1
+Cspg4 Aplp1 1
+Cspg4 BC019586 1
+Cspg4 Kcng3 1
+Cspg4 mCT119792 1
+Cspg4 mCT183951 1
+Cspg5 Jakmip1 1
+Cspp1 2010015B12Rik 1
+Cspp1 4933415E08Rik 1
+Cspp1 5830418K08Rik 1
+Cspp1 AK081316 1
+Cspp1 AK082473 1
+Cspp1 AK084064 1
+Cspp1 B430203I24Rik 1
+Cspp1 BB124184 1
+Cspp1 BC068281 1
+Cspp1 Brwd1 1
+Cspp1 C130057M05Rik 1
+Cspp1 C530014P21Rik 1
+Cspp1 C87037 1
+Cspp1 D10Bwg1070e 1
+Cspp1 D19Ertd737e 1
+Cspp1 ENSMUSG00000075516 1
+Cspp1 Nsd1 1
+Cspp1 Pds5a 1
+Cspp1 Riok3 1
+Csrnp3 BG089777 1
+Csrnp3 Cd244 1
+Csrnp3 Prl6a1 1
+Csrnp3 Prl6a1 1
+Csrnp3 Sprr2f 1
+Csrnp3 XM_358168 1
+Csrp1 Cxcr4 1
+Csrp1 Kif21b 1
+Csrp1 Ltb 1
+Csrp1 Ptprc 1
+Csrp1 Rassf5 1
+Csrp2 Imp3 1
+Csrp3 AK006302 1
+Csrp3 Dct 1
+Csrp3 EG240549 1
+Csrp3 Hopx 1
+Csrp3 Hopx 1
+Csrp3 Inhbe 1
+Csrp3 Klk1b4 1
+Csrp3 Pik3r3 1
+Csrp3 Zfp655 1
+Cs Scd1 1
+Cst10 MMT00002368 1
+Cst11 1700109G15Rik 1
+Cst13 C330027C09Rik 1
+Cst13 Olfr1449 1
+Cst3 2310001A20Rik 1
+Cst3 2310001A20Rik 1
+Cst3 B2m 1
+Cst3 B2m 1
+Cst3 BF148022 1
+Cst3 Cd93 1
+Cst3 ENSMUST00000060640 1
+Cst3 Foxs1 1
+Cst3 Gins1 1
+Cst3 Gins1 1
+Cst3 Mapre1 1
+Cst3 MMT00014033 1
+Cst3 MMT00060440 1
+Cst3 Nat5 1
+Cst3 Ncoa3 1
+Cst3 Nola3 1
+Cst3 Pcdhgb6 1
+Cst3 Ptbp2 1
+Cst3 Rbck1 1
+Cst3 Rbm45 1
+Cst3 Rin2 1
+Cst3 Scand1 1
+Cst3 Zfp661 1
+Cstb 2210013O21Rik 1
+Cstb 2410004L22Rik 1
+Cstb A530050D06Rik 1
+Cstb Ccnd1 1
+Cstb Cyp2d13 1
+Cstb D730003I15Rik 1
+Cstb Edn1 1
+Cstb ENSMUSG00000074179 1
+Cstb Gpx4 1
+Cstb Hexa 1
+Cstb Hexa 1
+Cstb Matn2 1
+Cstb Nagk 1
+Cstb Plscr1 1
+Cstb Proz 1
+Cstb Ptgfr 1
+Cstb Rab34 1
+Cstb Rcl1 1
+Cstb Slpi 1
+Cstb Src 1
+Cstb Ubd 1
+Cstb Ubd 1
+Cstb Zfp655 1
+Cstf1 2010011I20Rik 1
+Cstf1 Snx12 1
+Cstf1 Zfp64 1
+Cstf2 3110001D03Rik 1
+Cstl1 AI551328 1
+Cstl1 Best1 1
+Cstl1 Bnipl 1
+Cstl1 Kcnk10 1
+Cstl1 Pogk 1
+Ctag2 Fcrls 1
+Ctag2 Olfr146 1
+Ctag2 Serpinb9e 1
+Ctag2 Serpinb9e 1
+Ctage5 BC048507 1
+Ctage5 Cpne3 1
+Ctage5 ENSMUSG00000074828 1
+Ctage5 Gys2 1
+Ctage5 Lipg 1
+Ctage5 Ncoa4 1
+Ctage5 Nudt12 1
+Ctage5 Sfxn1 1
+Ctage5 Slc2a2 1
+Ctage5 Slc2a2 1
+Ctage5 Stk40 1
+Ctbp2 2810427A07Rik 1
+Ctbp2 E330033B04Rik 1
+Ctbp2 MMT00020512 1
+Ctbs 1700096C16Rik 1
+Ctbs G6pc2 1
+Ctbs Lrrc15 1
+Ctbs Rspo3 1
+Ctcfl Serping1 1
+Ctdspl2 XM_124247 1
+Ctdspl2 XM_124247 1
+Ctf1 Ggt6 1
+Ctf1 Lmcd1 1
+Ctf1 Pcnxl3 1
+Ctf2 5730507C01Rik 1
+Ctf2 5730507C01Rik 1
+Ctf2 AF014454 1
+Ctf2 AY512958 1
+Ctf2 AY512958 1
+Ctf2 Krtap16-10 1
+Ctf2 Megf11 1
+Ctf2 MMT00043614 1
+Ctf2 MMT00072527 1
+Ctgf 4933429F08Rik 1
+Ctgf Adh4 1
+Ctgf Adm 1
+Ctgf Adm 1
+Ctgf Cebpd 1
+Ctgf Clec1a 1
+Ctgf Clip1 1
+Ctgf Mfsd2 1
+Ctgf Nrarp 1
+Ctgf Nrarp 1
+Ctgf Rasgef1b 1
+Ctgf Sgk1 1
+Ctgf Slc43a3 1
+Ctgf triglyceride_normal_None_W18 1
+Ctgf Vcam1 1
+Cth 2010305C02Rik 1
+Cth Aass 1
+Cth Agxt 1
+Cth Cfhr1 1
+Cth Dbt 1
+Cth Dbt 1
+Cth EG214403 1
+Cth Got1 1
+Cth Gsta2 1
+Cth Gsta2 1
+Cth Gstt3 1
+Cth Hapln1 1
+Cth MMT00043082 1
+Cth Ppat 1
+Cthrc1 MMS010114361 1
+Cth Rgs16 1
+Cth Slc16a11 1
+Ctla2a A330021E22Rik 1
+Ctla2a Alas2 1
+Ctla2a BC023892 1
+Ctla2a Boc 1
+Ctla2a Cd93 1
+Ctla2a Cd93 1
+Ctla2a Cd93 1
+Ctla2a Cldn15 1
+Ctla2a Fabp4 1
+Ctla2a Fabp4 1
+Ctla2a Fabp4 1
+Ctla2a Gja1 1
+Ctla2a Gpr120 1
+Ctla2a Ifi27 1
+Ctla2a Inhbb 1
+Ctla2a MMT00039922 1
+Ctla2a S100g 1
+Ctla2a Snx10 1
+Ctla2a Spink3 1
+Ctla2a Spon1 1
+Ctla2b 3830403N18Rik 1
+Ctla2b 3830403N18Rik 1
+Ctla2b 6720489N17Rik 1
+Ctla2b Aif1 1
+Ctla2b Casp4 1
+Ctla2b Cxcl9 1
+Ctla2b Hk3 1
+Ctla2b Hk3 1
+Ctla2b Klra2 1
+Ctla2b Mrc1 1
+Ctla2b Myo9a 1
+Ctla2b Myo9a 1
+Ctla2b Ptpn18 1
+Ctla2b Smc1b 1
+Ctla2b Tifab 1
+Ctla2b Trpm2 1
+Ctla2b Xlr 1
+Ctla2b Xlr 1
+Ctnna1 5730564C23Rik 1
+Ctnna1 AA501227 1
+Ctnna1 Gm412 1
+Ctnna1 Gm412 1
+Ctnna1 Pcdhb11 1
+Ctnna1 Suv420h1 1
+Ctnna1 Suv420h1 1
+Ctnna1 Suv420h1 1
+Ctnna1 Ucp3 1
+Ctnna2 Epyc 1
+Ctnna2 mCT168794 1
+Ctnna2 MMT00013951 1
+Ctnna2 MMT00073244 1
+Ctnna2 Olfr244 1
+Ctnna3 3110006E14Rik 1
+Ctnna3 6330418B08Rik 1
+Ctnna3 Atg2b 1
+Ctnna3 Atg2b 1
+Ctnna3 EG545136 1
+Ctnna3 MMS010092631 1
+Ctnna3 MMT00002879 1
+Ctnna3 MMT00049132 1
+Ctnna3 Olfr133 1
+Ctnna3 Pth 1
+Ctnna3 Pth 1
+Ctnna3 Retnlb 1
+Ctnna3 Tbx21 1
+Ctnnal1 Polr2h 1
+Ctnnb1 Fndc3a 1
+Ctnnbip1 Tcea3 1
+Ctnnbl1 Kcnk10 1
+Ctnnbl1 MMT00009927 1
+Ctnnbl1 Pfn4 1
+Ctnnd1 2010107E04Rik 1
+Ctnnd1 Arf4 1
+Ctnnd1 Cat 1
+Ctnnd1 Cat 1
+Ctnnd1 Cd74 1
+Ctnnd1 Fgfr4 1
+Ctnnd1 Jhdm1d 1
+Ctnnd1 Nfix 1
+Ctnnd1 Nfix 1
+Ctnnd1 Nudt1 1
+Ctnnd1 Olfr1061 1
+Ctnnd1 Pfn1 1
+Ctnnd1 Tmed9 1
+Ctnnd1 Tmed9 1
+Ctnnd1 Trib1 1
+Ctnnd1 Ywhaz 1
+Ctnnd2 MMT00024504 1
+Ctnnd2 Plunc 1
+Ctnnd2 Serpinb11 1
+Ctnnd2 Spaca1 1
+Ctnnd2 Tbx18 1
+Ctns EG217600 1
+Ctns EG217600 1
+Ctns EG217600 1
+Ctns Flcn 1
+Ctns MMT00004384 1
+Ctns Myh10 1
+Ctns Nle1 1
+Ctns Purb 1
+Ctns Purb 1
+Ctns Scarf1 1
+Ctns Scarf1 1
+Ctns Serpinf1 1
+Ctns Tax1bp3 1
+Ctns Tax1bp3 1
+Ctns Tax1bp3 1
+Ctns Tmem93 1
+Ctns Unc45b 1
+Ctns Unc45b 1
+Ctps 1700012G19Rik 1
+Ctps 1700012G19Rik 1
+Ctps 2610019F03Rik 1
+Ctps2 AA589562 1
+Ctps2 Appl1 1
+Ctps2 Cdkn2c 1
+Ctps2 EG432995 1
+Ctps2 Elovl3 1
+Ctps2 Heg1 1
+Ctps2 Hlcs 1
+Ctps2 Lig1 1
+Ctps2 Mr1 1
+Ctps2 Mrps36 1
+Ctps2 Mthfd1l 1
+Ctps2 Pafah1b3 1
+Ctps2 Rbm3 1
+Ctps2 Rras 1
+Ctps2 Tgfbi 1
+Ctps2 Zfp750 1
+Ctps Acot6 1
+Ctps Apoa4 1
+Ctps Dnaja1 1
+Ctps E430004N04Rik 1
+Ctps Enpp3 1
+Ctps Fry 1
+Ctps Kpna2 1
+Ctps Lamb3 1
+Ctps Nek6 1
+Ctps Ocln 1
+Ctps P4ha2 1
+Ctps Pik3r3 1
+Ctps Qrsl1 1
+Ctps Qrsl1 1
+Ctps Rcan1 1
+Ctps Rcan1 1
+Ctps Rrp12 1
+Ctps Scamp5 1
+Ctps Slc6a6 1
+Ctps Stk39 1
+Ctps Synpo 1
+Ctps Tcp11l2 1
+Ctps Trub2 1
+Ctps Tsku 1
+Ctps Vmn2r75 1
+Ctps XM_122761 1
+Ctrb1 Clps 1
+Ctrb1 Clps 1
+Ctrb1 EG386551 1
+Ctrb1 Gp2 1
+Ctrb1 Gp2 1
+Ctrb1 RP23-395H4.4 1
+Ctrb1 Try4 1
+Ctrl 1810010M01Rik 1
+Ctrl 1810010M01Rik 1
+Ctrl 1810010M01Rik 1
+Ctrl 2210010C04Rik 1
+Ctrl Cel 1
+Ctrl Clps 1
+Ctrl Cpa1 1
+Ctrl Cpa2 1
+Ctrl Cpb1 1
+Ctrl Cuzd1 1
+Ctrl Ela3 1
+Ctrl Gp2 1
+Ctrl Gp2 1
+Ctrl Klk1b16 1
+Ctrl MMT00058392 1
+Ctrl Pdia2 1
+Ctrl Pnliprp1 1
+Ctrl Pnliprp2 1
+Ctrl Pnliprp2 1
+Ctrl Prss2 1
+Ctrl Reg1 1
+Ctrl Reg3b 1
+Ctrl Sycn 1
+Ctrl Sycn 1
+Ctrl Try4 1
+Ctrl Xist 1
+Cts6 C130034I18Rik 1
+Cts6 Olfr668 1
+Cts6 Olfr994 1
+Cts6 Smr3a 1
+Cts7 2810429I04Rik 1
+Cts7 4930421C12Rik 1
+Cts7 A630036H22Rik 1
+Cts7 D130011O08Rik 1
+Cts7 D130011O08Rik 1
+Cts7 EG624685 1
+Cts7 Gad2 1
+Cts7 mCT55403 1
+Cts7 MMS010086060 1
+Cts7 MMS010086060 1
+Cts7 MMT00031355 1
+Cts7 MMT00031355 1
+Cts7 MMT00052302 1
+Cts7 MMT00052302 1
+Cts7 MMT00070027 1
+Cts7 Olfr335 1
+Cts7 Trim61 1
+Cts7 Unc13c 1
+Cts7 Unc13c 1
+Ctsa Cables1 1
+Ctsa Ctsz 1
+Ctsa Neil2 1
+Ctsa P2rx4 1
+Ctsa P2rx4 1
+Ctsb 2700022O18Rik 1
+Ctsb B4galt5 1
+Ctsb Dleu2 1
+Ctsc Baat 1
+Ctsc Prg4 1
+Ctsc Rab38 1
+Ctsd Dpp7 1
+Ctsd Gas6 1
+Ctsd Hexa 1
+Ctsd Itga6 1
+Ctsd MMT00007987 1
+Ctsd Ngp 1
+Ctsd Pgd 1
+Ctsd Pld3 1
+Ctse 3110009E18Rik 1
+Ctse Arl8a 1
+Ctse Cd55 1
+Ctse Cxcr4 1
+Ctse Ddx59 1
+Ctse Ddx59 1
+Ctse EG214403 1
+Ctse Etnk2 1
+Ctse Igfn1 1
+Ctse Insig2 1
+Ctse Lad1 1
+Ctse Lad1 1
+Ctse Mcm6 1
+Ctse MMT00033504 1
+Ctse MMT00067073 1
+Ctse MMT00067073 1
+Ctse Pkp1 1
+Ctse Pm20d1 1
+Ctse Pm20d1 1
+Ctse Ren1 1
+Ctse Ren1 1
+Ctse Ren1 1
+Ctse Serpinb8 1
+Ctse Tnnt2 1
+Ctsg Gp1ba 1
+Ctsg MMT00052238 1
+Ctsk AI504987 1
+Ctsk Clca2 1
+Ctsk Clca2 1
+Ctsk Efemp1 1
+Ctsk ENSMUST00000061199 1
+Ctsk Gpr177 1
+Ctsk Ibsp 1
+Ctsl Arg1 1
+Ctsl Lrp2bp 1
+Ctsl Olfr1289 1
+Ctsl Slc17a3 1
+Ctso 3110057O12Rik 1
+Ctso 5033411D12Rik 1
+Ctso 9530008L14Rik 1
+Ctso Fggy 1
+Ctso Flt1 1
+Ctso Gng10 1
+Ctso Hibch 1
+Ctso Hmga1 1
+Ctso MMT00031860 1
+Ctso MMT00072901 1
+Ctso Nxt2 1
+Ctso Pgk1 1
+Ctso Psd3 1
+Ctso Pts 1
+Ctso Tpk1 1
+Ctso Tpk1 1
+Ctsq Igh-VS107 1
+Ctsq Kcna1 1
+Ctsq Pcdh10 1
+Ctss AF251705 1
+Ctss Anxa3 1
+Ctss Anxa3 1
+Ctss C1qc 1
+Ctss Ccl6 1
+Ctss Ccr5 1
+Ctss Cd48 1
+Ctss Cd53 1
+Ctss Cd53 1
+Ctss Cd84 1
+Ctss Clec4a3 1
+Ctss Clec7a 1
+Ctss Csf1r 1
+Ctss Cybb 1
+Ctss Dock10 1
+Ctss Dock10 1
+Ctss Emr1 1
+Ctss Emr1 1
+Ctss Evi2a 1
+Ctss Fcgr4 1
+Ctss Fcgr4 1
+Ctss Gbp2 1
+Ctss Glipr1 1
+Ctss Gp49a 1
+Ctss H2-M3 1
+Ctss Havcr2 1
+Ctss Igsf6 1
+Ctss Itgal 1
+Ctss Lcp2 1
+Ctss Lilrb3 1
+Ctss Ly86 1
+Ctss Mpeg1 1
+Ctss Mpeg1 1
+Ctss Mpeg1 1
+Ctss Ms4a4c 1
+Ctss Ms4a6b 1
+Ctss Ms4a6d 1
+Ctss Ms4a6d 1
+Ctss Ngp 1
+Ctss Orm2 1
+Ctss Oxct1 1
+Ctss Oxct1 1
+Ctss Oxct1 1
+Ctss P2ry12 1
+Ctss Pilrb1 1
+Ctss Pira5 1
+Ctss Pkib 1
+Ctss Pla2g7 1
+Ctss Plac8 1
+Ctss Ptprc 1
+Ctss Rab19 1
+Ctss Rgs10 1
+Ctss Rgs10 1
+Ctss Rgs2 1
+Ctss Serpinb6a 1
+Ctss Sgcb 1
+Ctss Tyrobp 1
+Ctss Vsig4 1
+Ctsw Klhl23 1
+Ctsz 2210403K04Rik 1
+Ctsz 2610203C22Rik 1
+Ctsz 4930570C03Rik 1
+Ctsz Abcc12 1
+Ctsz Bex2 1
+Ctsz Comtd1 1
+Ctsz Ctsa 1
+Ctsz Ctsa 1
+Ctsz Gabarapl1 1
+Ctsz Hexa 1
+Ctsz Hexa 1
+Ctsz MMT00020361 1
+Ctsz MMT00020361 1
+Ctsz NM_020568 1
+Ctsz P2rx4 1
+Ctsz P2rx4 1
+Ctsz Ppap2c 1
+Ctsz Ppap2c 1
+Ctsz Prkra 1
+Ctsz Rhoc 1
+Ctsz Sergef 1
+Ctsz Tgm2 1
+Cttnbp2nl 2610207I05Rik 1
+Cttnbp2nl 4632428N05Rik 1
+Cttnbp2nl Adm 1
+Cttnbp2nl Adm 1
+Cttnbp2nl Als2cr13 1
+Cttnbp2nl Asah3l 1
+Cttnbp2nl C730048C13Rik 1
+Cttnbp2nl Ch25h 1
+Cttnbp2nl Chsy1 1
+Cttnbp2nl Ctgf 1
+Cttnbp2nl Cxcr7 1
+Cttnbp2nl Dnmbp 1
+Cttnbp2nl EG624219 1
+Cttnbp2nl F2r 1
+Cttnbp2nl Jdp2 1
+Cttnbp2nl Kcnj8 1
+Cttnbp2nl Lyve1 1
+Cttnbp2nl Nedd9 1
+Cttnbp2nl Pik3ip1 1
+Cttnbp2nl Pik3ip1 1
+Cttnbp2nl Pscd3 1
+Cttnbp2nl Pscdbp 1
+Cttnbp2nl Slc10a1 1
+Cttnbp2nl Spg21 1
+Cttnbp2nl Stard13 1
+Cttnbp2nl Stk17b 1
+Cttnbp2nl Tuba4a 1
+Cttnbp2 Shisa2 1
+Ctxn3 Atp6v1c2 1
+Ctxn3 MMT00016315 1
+Ctxn3 MMT00068555 1
+Ctxn3 Tnfsf11 1
+Ctxn3 V1ri7 1
+Ctxn3 XM_283621 1
+CU075549.1 1810032O08Rik 1
+CU075549.1 2610524H06Rik 1
+CU075549.1 Abcc3 1
+CU075549.1 Cyp2c70 1
+CU075549.1 Cyp7b1 1
+CU075549.1 Dio1 1
+CU075549.1 Gdf3 1
+CU075549.1 Krtap5-2 1
+CU075549.1 Krtap5-2 1
+CU075549.1 MMT00080694 1
+CU075549.1 Mup1 1
+CU075549.1 Mup1 1
+CU075549.1 Mup3 1
+CU075549.1 Mup4 1
+CU075549.1 Mup5 1
+CU075549.1 Olfr218 1
+CU075549.1 Sult5a1 1
+CU075549.1 XM_357378 1
+CU075549.1 XM_357378 1
+CU075549.1 XM_357378 1
+Cubn BC089491 1
+Cubn BC089491 1
+Cuedc1 A830058I18Rik 1
+Cuedc1 MMS010225692 1
+Cuedc1 MMT00011751 1
+Cuedc1 Olfr508 1
+Cugbp2 Bhlhb9 1
+Cugbp2 Chpt1 1
+Cugbp2 Chpt1 1
+Cugbp2 Il11ra1 1
+Cugbp2 Intu 1
+Cugbp2 Pdzrn3 1
+Cugbp2 Ppapdc3 1
+Cugbp2 Rasa2 1
+Cugbp2 Slc22a3 1
+Cugbp2 Ttc7b 1
+Cugbp2 Zfp191 1
+Cul4b Azi2 1
+Cul4b Eif4g2 1
+Cutc 4833409A17Rik 1
+Cutc Abcc2 1
+Cutc Lcor 1
+Cutc Sfxn2 1
+Cux1 AW049604 1
+Cux2 1700051K22Rik 1
+Cux2 1700051K22Rik 1
+Cux2 5730410E15Rik 1
+Cux2 Acta1 1
+Cux2 Cugbp2 1
+Cux2 Cxcl12 1
+Cux2 Dapk1 1
+Cux2 Fmo3 1
+Cux2 Fmo3 1
+Cux2 H2-Aa 1
+Cux2 Hgfac 1
+Cux2 Npal1 1
+Cux2 Prkab1 1
+Cux2 Setbp1 1
+Cux2 Setd4 1
+Cux2 Slc25a33 1
+Cux2 Trim24 1
+Cux2 Vipr2 1
+Cuzd1 1190003M12Rik 1
+Cuzd1 1700084C01Rik 1
+Cuzd1 1700084C01Rik 1
+Cuzd1 Lmo1 1
+Cuzd1 Prss3 1
+Cuzd1 Rnase1 1
+Cwc15 2210010B09Rik 1
+Cwc15 5730601F06Rik 1
+Cwc15 9030420J04Rik 1
+Cwc15 Ankrd49 1
+Cwc15 Ankrd49 1
+Cwc15 Cep57 1
+Cwc15 Cep57 1
+Cwc15 Col5a3 1
+Cwc15 Ilf3 1
+Cwc15 Mmp27 1
+Cwc15 Mmp27 1
+Cwc15 MMS010121572 1
+Cwc15 MMS010121572 1
+Cwc15 MMS010121572 1
+Cwc15 Mrpl4 1
+Cwc15 Rgl3 1
+Cwc15 S1pr5 1
+Cwc15 Sesn3 1
+Cwc15 Tmem123 1
+Cwc15 Tmem205 1
+Cwf19l1 4833409A17Rik 1
+Cwf19l1 MMS010016587 1
+Cwf19l1 Zfand2a 1
+Cwf19l2 Apol7e 1
+Cwf19l2 Atad1 1
+Cwf19l2 Cpeb4 1
+Cwf19l2 D15Ertd621e 1
+Cwf19l2 Daam1 1
+Cwf19l2 Ddx6 1
+Cwf19l2 Eif3s10 1
+Cwf19l2 Gtf2h1 1
+Cwf19l2 Kank2 1
+Cwf19l2 Mia2 1
+Cwf19l2 Mia2 1
+Cwf19l2 MMT00013656 1
+Cwf19l2 MMT00032015 1
+Cwf19l2 Nfia 1
+Cwf19l2 OTTMUSG00000010105 1
+Cwf19l2 Plekhm3 1
+Cwf19l2 Rdh18 1
+Cwf19l2 Styx 1
+Cwf19l2 Suhw4 1
+Cwf19l2 Trpm7 1
+Cwf19l2 Tspan12 1
+Cwf19l2 Usp47 1
+Cx3cr1 Havcr2 1
+Cxadr Birc3 1
+Cxadr D16Ertd472e 1
+Cxadr Dusp10 1
+Cxadr Insc 1
+Cxadr Mphosph6 1
+Cxcl10 1110003O08Rik 1
+Cxcl10 9830102E05Rik 1
+Cxcl10 Basp1 1
+Cxcl10 Ccl2 1
+Cxcl10 Ccl3 1
+Cxcl10 Ccl4 1
+Cxcl10 Ccl7 1
+Cxcl10 Cd274 1
+Cxcl10 Cd274 1
+Cxcl10 Cxcl9 1
+Cxcl10 Cxcl9 1
+Cxcl10 Cxcl9 1
+Cxcl10 Dennd2d 1
+Cxcl10 Gbp6 1
+Cxcl10 Igtp 1
+Cxcl10 Il1b 1
+Cxcl10 Il1rn 1
+Cxcl10 Irg1 1
+Cxcl10 Itih3 1
+Cxcl10 Mmp13 1
+Cxcl10 MMT00033536 1
+Cxcl10 Nrg1 1
+Cxcl10 Procr 1
+Cxcl10 Ripk2 1
+Cxcl10 Slamf7 1
+Cxcl10 Slamf8 1
+Cxcl10 Slamf8 1
+Cxcl10 Sunc1 1
+Cxcl10 Tmem140 1
+Cxcl10 Tnfaip3 1
+Cxcl10 Ubd 1
+Cxcl10 Ubd 1
+Cxcl10 Xcl1 1
+Cxcl11 2010109A12Rik 1
+Cxcl12 1810019D21Rik 1
+Cxcl12 Hebp1 1
+Cxcl12 Olfr469 1
+Cxcl12 Olfr734 1
+Cxcl12 Rerg 1
+Cxcl12 Rsc1a1 1
+Cxcl12 Sftpb 1
+Cxcl12 Sftpb 1
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+Dbil5 Plagl1 1
+Dbil5 Plagl1 1
+Dbil5 Tcerg1 1
+Dbil5 Zic1 1
+Dbn1 Aadat 1
+Dbn1 C030027H14Rik 1
+Dbn1 Ddah2 1
+Dbn1 Dll4 1
+Dbn1 Kctd10 1
+Dbn1 Pcdhb20 1
+Dbn1 Podxl 1
+Dbn1 Zfp651 1
+Dbndd1 2210411M09Rik 1
+Dbndd1 ENSMUSG00000072936 1
+Dbndd1 Galk2 1
+Dbndd1 Kcna6 1
+Dbndd1 mCT64833 1
+Dbndd1 MMS010202570 1
+Dbndd1 MMS010261929 1
+Dbndd1 NM_001002789 1
+Dbndd1 Spink6 1
+Dbndd2 Tctn2 1
+Dbp 3930402G23Rik 1
+Dbp 3930402G23Rik 1
+Dbp 4930406D14Rik 1
+Dbp 6430520M22Rik 1
+Dbp 6430520M22Rik 1
+Dbp 6430520M22Rik 1
+Dbp A930033H14Rik 1
+Dbp Adcy1 1
+Dbp Arntl 1
+Dbp Arntl 1
+Dbp AV336316 1
+Dbp Avpr1a 1
+Dbp Axud1 1
+Dbp BC016495 1
+Dbp Bhlhb2 1
+Dbp C730036D15Rik 1
+Dbp Cdkn1a 1
+Dbp Cdkn1a 1
+Dbp Ddef2 1
+Dbp Dedd2 1
+Dbp Emb 1
+Dbp G6pc 1
+Dbp Gadd45a 1
+Dbp Gdap10 1
+Dbp Hlf 1
+Dbpht2 Olfr676 1
+Dbp Lrfn3 1
+Dbp MMT00040704 1
+Dbp Nfil3 1
+Dbp Nfil3 1
+Dbp Npas2 1
+Dbp Npnt 1
+Dbp Nr1d1 1
+Dbp Nr1d1 1
+Dbp Pcsk4 1
+Dbp Pcsk4 1
+Dbp Pcsk4 1
+Dbp Ripk2 1
+Dbp Skil 1
+Dbp Slc2a5 1
+Dbp Slc2a5 1
+Dbp Slc45a3 1
+Dbp Slc45a3 1
+Dbp Slc45a3 1
+Dbp Sox7 1
+Dbp Spry4 1
+Dbp Stbd1 1
+Dbp Tef 1
+Dbp Tenc1 1
+Dbp Tjp3 1
+Dbp Tnfrsf19 1
+Dbp Usp2 1
+Dbt 4633401B06Rik 1
+Dbt 8430415E04Rik 1
+Dbt Abat 1
+Dbt Afmid 1
+Dbt Ahcy 1
+Dbt AK050209 1
+Dbt Dpyd 1
+Dbt Ppapdc3 1
+Dbt Ppm1k 1
+Dbt Ppm1k 1
+Dbt Ybx1 1
+Dbx2 ENSMUST00000029001 1
+Dbx2 Gdap9 1
+Dbx2 Stard9 1
+Dbx2 Tmem29 1
+Dbx2 XM_357222 1
+Dcakd EG432995 1
+Dcakd Purg 1
+Dcakd S100a9 1
+Dcbld2 8430439C15Rik 1
+Dcbld2 D230021J17Rik 1
+Dcbld2 Grm5 1
+Dcbld2 MMT00018202 1
+Dcbld2 MMT00027928 1
+Dcbld2 Rorc 1
+Dcbld2 Trpc5 1
+Dcbld2 Trps1 1
+Dcbld2 Tspan8 1
+Dcc A430088I17Rik 1
+Dcc Abca9 1
+Dcc AK089155 1
+Dcc BB177874 1
+Dcc E330001B16Rik 1
+Dcc Il2ra 1
+Dcc Nox1 1
+Dcc Pbk 1
+Dcc Tas2r117 1
+Dcc Tmem16j 1
+Dcc V1rc13 1
+Dcc V1rc13 1
+Dcdc2a AW492955 1
+Dchs1 6330503K22Rik 1
+Dchs1 Irf2 1
+Dchs1 Pea15a 1
+Dchs1 Pea15a 1
+Dchs1 Saa4 1
+Dci 9530008L14Rik 1
+Dci Acad11 1
+Dci Acadl 1
+Dci Acox1 1
+Dci Cpt2 1
+Dci Decr1 1
+Dci Ech1 1
+Dci Ehhadh 1
+Dci Ephx2 1
+Dci Etfdh 1
+Dci Hmgcl 1
+Dci Hsd17b11 1
+Dci Hsdl2 1
+Dci Map1lc3a 1
+Dci Mgll 1
+Dci Nid2 1
+Dci OTTMUSG00000021867 1
+Dci Retsat 1
+Dci XM_128212 1
+Dci XM_129965 1
+Dci XM_286114 1
+Dck 1110019K23Rik 1
+Dck 2010002N04Rik 1
+Dck 2610002J02Rik 1
+Dck Anxa3 1
+Dck Aplp2 1
+Dck Cenpc1 1
+Dck Cenpc1 1
+Dck Cnrip1 1
+Dck Commd10 1
+Dck Commd10 1
+Dck Eif2ak3 1
+Dck Epb4.1l3 1
+Dck Fbln7 1
+Dck Gyg 1
+Dck Hnrnpd 1
+Dck Il33 1
+Dck Iqwd1 1
+Dck Lrp12 1
+Dck MMT00040185 1
+Dck MMT00059455 1
+Dck Msr1 1
+Dck Ndc80 1
+Dck Pdgfc 1
+Dck Pole 1
+Dck Prkca 1
+Dck Rhoq 1
+Dck Slc17a2 1
+Dck Ssr1 1
+Dck Stap1 1
+Dck Tdp1 1
+Dck Tipin 1
+Dck Traf3 1
+Dck Ttrap 1
+Dck Ywhah 1
+Dck Zfp259 1
+Dclk1 5730507A11Rik 1
+Dclk1 AA409289 1
+Dclk1 AA409289 1
+Dclk1 Atoh1 1
+Dclk1 Creb5 1
+Dclk1 Dok5 1
+Dclk1 Dusp26 1
+Dclk1 EG435977 1
+Dclk1 EG435977 1
+Dclk1 Ereg 1
+Dclk1 Il1rapl2 1
+Dclk1 Kctd14 1
+Dclk1 MMT00012655 1
+Dclk1 Phf21b 1
+Dclk2 3010033K07Rik 1
+Dclk2 Aldh1l1 1
+Dclk2 Ass1 1
+Dclk2 Csad 1
+Dclk2 Dcxr 1
+Dclk2 Dnase2a 1
+Dclk2 Fgf21 1
+Dclk2 Fgf21 1
+Dclk2 Hk2 1
+Dclk2 Lrtm1 1
+Dclk2 Papss2 1
+Dclk2 Qdpr 1
+Dclk2 Slc27a5 1
+Dclk2 Slc6a12 1
+Dclk2 UREA_adj_s_s_sqrt_W16 1
+Dclk3 4933411B09Rik 1
+Dclk3 4933411B09Rik 1
+Dclk3 A930036K24Rik 1
+Dclk3 Abhd14b 1
+Dclk3 Acy1 1
+Dclk3 Acy1 1
+Dclk3 Acy1 1
+Dclk3 AI836954 1
+Dclk3 Clasp2 1
+Dclk3 Cmtm6 1
+Dclk3 EG631906 1
+Dclk3 EG665033 1
+Dclk3 Il20rb 1
+Dclk3 MMT00004439 1
+Dclk3 MMT00023639 1
+Dclk3 Nbeal2 1
+Dclk3 Nbeal2 1
+Dclk3 Slc38a3 1
+Dclre1a A530013C23Rik 1
+Dclre1a AK082140 1
+Dclre1a Aspa 1
+Dclre1a Atoh8 1
+Dclre1a Atp11a 1
+Dclre1a BC004728 1
+Dclre1a BC016495 1
+Dclre1a Cd9 1
+Dclre1a Ces3 1
+Dclre1a Ces3 1
+Dclre1a Cldn1 1
+Dclre1a Cldn1 1
+Dclre1a Cyp26a1 1
+Dclre1a Eps8l2 1
+Dclre1a Gpt2 1
+Dclre1a Insc 1
+Dclre1a Lmod2 1
+Dclre1a Map3k7ip2 1
+Dclre1a Mesp2 1
+Dclre1a Mesp2 1
+Dclre1a MMT00012324 1
+Dclre1a Nhlrc2 1
+Dclre1a Nrg4 1
+Dclre1a Nrg4 1
+Dclre1a Osbpl5 1
+Dclre1a Pdxk 1
+Dclre1a Pdxp 1
+Dclre1a Rhobtb1 1
+Dclre1a Rnf43 1
+Dclre1a Slc13a4 1
+Dclre1a Timp3 1
+Dclre1a Tnk2 1
+Dclre1a Usp6nl 1
+Dclre1c Bglap2 1
+Dclre1c Elmod2 1
+Dclre1c Fancb 1
+Dclre1c Myo1g 1
+Dcn Abcc9 1
+Dcn Colec11 1
+Dcn Ednra 1
+Dcn Ms4a4d 1
+Dcp1a Atp9b 1
+Dcp1a Olfr90 1
+Dcpp1 1700001J11Rik 1
+Dcpp1 4930547G20Rik 1
+Dcpp1 9530034A14Rik 1
+Dcpp1 Itpr2 1
+Dcpp1 MMT00007310 1
+Dcpp1 Pdzd4 1
+Dcpp3 AK040253 1
+Dcpp3 Kcne1 1
+Dcpp3 Megf11 1
+Dcst1 Cpb2 1
+Dcst1 MMT00076534 1
+Dcst2 EG245347 1
+Dcst2 Gm749 1
+Dcst2 MMT00014676 1
+Dcst2 Sntb2 1
+Dcst2 XM_357387 1
+Dct Asns 1
+Dct Bhmt 1
+Dct Cml3 1
+Dct Crygn 1
+Dct EG215895 1
+Dct EG620205 1
+Dct Lrtm1 1
+Dctn1 Slc16a10 1
+Dctn3 1810030N24Rik 1
+Dctn3 1810030N24Rik 1
+Dctn3 4930417M19Rik 1
+Dctn3 4930417M19Rik 1
+Dctn3 4930417M19Rik 1
+Dctn3 4933409K07Rik 1
+Dctn3 B230220N19Rik 1
+Dctn3 B230220N19Rik 1
+Dctn3 B230220N19Rik 1
+Dctn3 C78914 1
+Dctn3 Cd72 1
+Dctn3 Cd72 1
+Dctn3 Gabra3 1
+Dctn3 Gba2 1
+Dctn3 MMT00023085 1
+Dctn3 Mobkl2b 1
+Dctn3 Mrpl44 1
+Dctn3 Mylc2b 1
+Dctn3 Rusc2 1
+Dctn3 Slc35a1 1
+Dctn3 Spag8 1
+Dctn3 Tdrd7 1
+Dctn3 Tdrd7 1
+Dctn3 Unc13b 1
+Dctn4 C330018D20Rik 1
+Dctn4 C330018D20Rik 1
+Dctn4 Jmjd2d 1
+Dctn4 Jmjd2d 1
+Dctn4 MMT00070880 1
+Dctn4 MMT00071427 1
+Dctn4 MMT00071427 1
+Dctn4 MMT00076936 1
+Dctn4 MMT00076936 1
+Dctn4 Mppe1 1
+Dctn4 Mppe1 1
+Dctn4 Rbm22 1
+Dctn4 Rbm22 1
+Dctn4 Rps14 1
+Dctn4 Rps14 1
+Dctn4 XM_128127 1
+Dctn4 XM_128127 1
+Dctn6 MMT00022202 1
+Dct Spry2 1
+Dct Srd5a1 1
+Dct Srd5a1 1
+Dct Ugcgl2 1
+Dct Ugcgl2 1
+Dct Ugcgl2 1
+Dct Zap70 1
+Dcun1d1 BC062115 1
+Dcun1d1 Cfhr1 1
+Dcun1d1 Cnot4 1
+Dcun1d1 Eif2a 1
+Dcun1d1 Esrra 1
+Dcun1d1 Fat4 1
+Dcun1d1 Manbal 1
+Dcun1d1 Prr13 1
+Dcun1d1 Smc5 1
+Dcun1d1 Txndc1 1
+Dcun1d1 Vps4b 1
+Dcx Amac1 1
+Dcxr 2210010L05Rik 1
+Dcxr 2410131K14Rik 1
+Dcxr Adssl1 1
+Dcxr BB283961 1
+Dcxr Cd9 1
+Dcxr Clcn2 1
+Dcxr Gas1 1
+Dcxr Gnmt 1
+Dcx RP23-12I24.6 1
+Dcxr Pipox 1
+Dcxr Prodh2 1
+Dcxr Sardh 1
+Dcxr Sult1a1 1
+Ddah1 BE950716 1
+Ddah1 Ccnk 1
+Ddah1 Clca1 1
+Ddah1 Clca2 1
+Ddah1 Clca2 1
+Ddah1 Spag17 1
+Ddah2 Adamts10 1
+Ddah2 Akr1b8 1
+Ddah2 Akr1b8 1
+Ddah2 Akr1b8 1
+Ddah2 Bace2 1
+Ddah2 Bace2 1
+Ddah2 Cbx6 1
+Ddah2 Ccdc102a 1
+Ddah2 Chst12 1
+Ddah2 Dok4 1
+Ddah2 Eml1 1
+Ddah2 Epb4.1l2 1
+Ddah2 Filip1 1
+Ddah2 Fmnl3 1
+Ddah2 Fmnl3 1
+Ddah2 Gmpr 1
+Ddah2 MMT00031263 1
+Ddah2 Plxnd1 1
+Ddah2 Ralgds 1
+Ddah2 Rasip1 1
+Ddah2 Slc27a3 1
+Ddc 2310031A18Rik 1
+Ddc 4931408D14Rik 1
+Ddc Cabc1 1
+Ddc Cyp8b1 1
+Ddc Dtx4 1
+Ddc Grb10 1
+Ddc Leap2 1
+Ddc Lpin2 1
+Ddc Mreg 1
+Ddc Mreg 1
+Ddc Naaladl2 1
+Ddc Serpinb1a 1
+Ddc Tex12 1
+Ddc Tm4sf1 1
+Ddc Wdr89 1
+Ddef1 2410075B13Rik 1
+Ddef1 5530400N10Rik 1
+Ddef1 Aebp2 1
+Ddef1 Col14a1 1
+Ddef1 D530033C11Rik 1
+Ddef1 Enpp2 1
+Ddef1 Frmd6 1
+Ddef1 Map3k1 1
+Ddef1 Prkcd 1
+Ddef1 Wdr67 1
+Ddef1 Zhx1 1
+Ddef2 BB157440 1
+Ddef2 BC021767 1
+Ddef2 Btg1 1
+Ddef2 Ccne2 1
+Ddef2 Gldc 1
+Ddef2 Ndrg1 1
+Ddef2 Npas2 1
+Ddef2 Pdgfc 1
+Ddef2 Ppat 1
+Ddef2 Rfx4 1
+Ddef2 Smarcd1 1
+Ddef2 Smarcd1 1
+Ddef2 Smarcd1 1
+Ddef2 Stbd1 1
+Ddef2 Usp2 1
+Ddef2 XM_135450 1
+Ddefl1 Mx2 1
+Ddefl1 Pafah2 1
+Ddhd1 Prkcd 1
+Ddhd1 Wdfy4 1
+Ddhd2 Tmem20 1
+Ddi2 Cdkn2c 1
+Ddi2 Cyp2d13 1
+Ddit3 Gdf15 1
+Ddit4 1810011O10Rik 1
+Ddit4 2310067E19Rik 1
+Ddit4 6030422H21Rik 1
+Ddit4 AI461749 1
+Ddit4 Alkbh8 1
+Ddit4 Arl4d 1
+Ddit4 Arrdc3 1
+Ddit4 Cdca7 1
+Ddit4 Cdkn1a 1
+Ddit4 Cebpd 1
+Ddit4 Cebpd 1
+Ddit4 Dusp14 1
+Ddit4 Dusp14 1
+Ddit4 Gadd45g 1
+Ddit4 Gadd45g 1
+Ddit4 Gadd45g 1
+Ddit4 Gmeb1 1
+Ddit4 Ihpk2 1
+Ddit4 Irs2 1
+Ddit4l Arhgef16 1
+Ddit4l ENSMUSG00000073551 1
+Ddit4l Magea9 1
+Ddit4l Phlda3 1
+Ddit4 Mafb 1
+Ddit4 MMT00007773 1
+Ddit4 MMT00018326 1
+Ddit4 Mycn 1
+Ddit4 Osgin1 1
+Ddit4 Rasd1 1
+Ddit4 Rasd1 1
+Ddit4 Rgs16 1
+Ddit4 Rnd3 1
+Ddit4 Slc45a3 1
+Ddit4 Tmem100 1
+Ddit4 Trp53inp1 1
+Ddit4 Trp53inp1 1
+Ddit4 Tsc22d3 1
+Ddo Arhgap18 1
+Ddo Ech1 1
+Ddost Ddefl1 1
+Ddost E2f2 1
+Ddost XM_358120 1
+Ddr1 0610007N19Rik 1
+Ddr1 2210010L05Rik 1
+Ddr1 A430110N23Rik 1
+Ddr1 Arl2bp 1
+Ddr1 Boc 1
+Ddr1 Bspry 1
+Ddr1 Cd276 1
+Ddr1 Cdh3 1
+Ddr1 Chrnb1 1
+Ddr1 Chrnb1 1
+Ddr1 Dpt 1
+Ddr1 Efna4 1
+Ddr1 Emp1 1
+Ddr1 Foxj1 1
+Ddr1 Hn1 1
+Ddr1 Jag1 1
+Ddr1 Mll2 1
+Ddr1 Mll2 1
+Ddr1 Nck2 1
+Ddr1 Plp2 1
+Ddr1 Pygb 1
+Ddr1 Scube3 1
+Ddr1 Slc1a5 1
+Ddr1 Spon1 1
+Ddr1 Trhde 1
+Ddt 1200009F10Rik 1
+Ddt 4933425E08Rik 1
+Ddt 4933425E08Rik 1
+Ddt Gria3 1
+Ddt Gstt2 1
+Ddt Gstt2 1
+Ddt Lilrb4 1
+Ddt Slc41a2 1
+Ddt Srgn 1
+Ddx17 9430097D07Rik 1
+Ddx17 C130090K23Rik 1
+Ddx17 Exo1 1
+Ddx17 Map2k7 1
+Ddx17 Marveld3 1
+Ddx17 Olfr1305 1
+Ddx17 Pcnxl3 1
+Ddx17 Slc22a14 1
+Ddx17 Zkscan5 1
+Ddx19b 1110002N22Rik 1
+Ddx19b Adat1 1
+Ddx19b Nrp1 1
+Ddx20 Psmd6 1
+Ddx20 Ufd1l 1
+Ddx21 Cpox 1
+Ddx21 MMT00073305 1
+Ddx23 Acvrl1 1
+Ddx23 Ddx23 1
+Ddx23 Ddx23 1
+Ddx23 Dnase2a 1
+Ddx23 Dnase2a 1
+Ddx23 Irak4 1
+Ddx23 Irak4 1
+Ddx23 Irak4 1
+Ddx23 MMT00009923 1
+Ddx23 MMT00009923 1
+Ddx23 MMT00009923 1
+Ddx23 Senp1 1
+Ddx23 Sfrs2ip 1
+Ddx23 Slc38a4 1
+Ddx23 Tymp 1
+Ddx25 2700007P21Rik 1
+Ddx25 A630031M04Rik 1
+Ddx25 Herc1 1
+Ddx25 Punc 1
+Ddx26b A630033J11Rik 1
+Ddx26b Fat4 1
+Ddx26b Rbm12 1
+Ddx26b Sdc1 1
+Ddx26b Sox17 1
+Ddx27 2410006H16Rik 1
+Ddx27 2810026P18Rik 1
+Ddx27 2810026P18Rik 1
+Ddx27 Gmeb1 1
+Ddx27 Gmeb1 1
+Ddx27 Prkch 1
+Ddx27 Snhg6 1
+Ddx27 Tram1 1
+Ddx28 2610020H08Rik 1
+Ddx28 2610020H08Rik 1
+Ddx28 MMT00053383 1
+Ddx28 Slc41a2 1
+Ddx28 Ube2g2 1
+Ddx31 Uck1 1
+Ddx3x AK084191 1
+Ddx3x Api5 1
+Ddx3x Arl6ip2 1
+Ddx3x Cab39 1
+Ddx3x Centd2 1
+Ddx3x EG382769 1
+Ddx3x Fyttd1 1
+Ddx3x Jmjd1c 1
+Ddx3x Kif5b 1
+Ddx3x Lysmd3 1
+Ddx3x Man2b2 1
+Ddx3x Mfap3 1
+Ddx3x MMT00000525 1
+Ddx3x Sh3bp2 1
+Ddx3x Skiv2l2 1
+Ddx3x Stab1 1
+Ddx3x Zfp189 1
+Ddx3y Acsl6 1
+Ddx3y Cfd 1
+Ddx3y Cyp4a12b 1
+Ddx3y Eif2s3y 1
+Ddx3y Eif2s3y 1
+Ddx3y Eif2s3y 1
+Ddx3y Gpr173 1
+Ddx3y Grid2 1
+Ddx3y Jarid1d 1
+Ddx3y NM_011222 1
+Ddx3y Svs4 1
+Ddx3y Uty 1
+Ddx42 Arl4c 1
+Ddx42 Cdk5rap2 1
+Ddx42 Cdk5rap2 1
+Ddx42 Chrm1 1
+Ddx42 Gast 1
+Ddx42 Lrrc16a 1
+Ddx42 Malat1 1
+Ddx42 Mcc 1
+Ddx42 mCT165050 1
+Ddx42 Pphln1 1
+Ddx42 Ssxb1 1
+Ddx47 Ccdc79 1
+Ddx47 Pgm1 1
+Ddx5 1810011O10Rik 1
+Ddx59 1700012A16Rik 1
+Ddx59 4930596I21Rik 1
+Ddx59 4930596I21Rik 1
+Ddx59 Btnl9 1
+Ddx59 Csrp1 1
+Ddx59 Csrp1 1
+Ddx59 Kiss1 1
+Ddx59 MMT00067073 1
+Ddx59 MMT00067073 1
+Ddx5 Frmd4b 1
+Ddx5 Hnrpdl 1
+Ddx5 Med13 1
+Ddx5 MMT00078491 1
+Ddx5 MMT00078491 1
+Ddx5 MMT00078491 1
+Ddx60 4930528F23Rik 1
+Ddx60 BB517466 1
+Ddx60 Bst2 1
+Ddx60 EG634650 1
+Ddx60 Gbp3 1
+Ddx60 Herc5 1
+Ddx60 Herc5 1
+Ddx60 Ifih1 1
+Ddx60 Ifih1 1
+Ddx60 Iigp1 1
+Ddx60 Samd9l 1
+Ddx60 Usp18 1
+Ddx60 Zufsp 1
+Ddx6 Hsd17b7 1
+Ddx6 MMT00032015 1
+Ddx6 Pdlim5 1
+Ddx6 Rab6 1
+Decr1 1810015C04Rik 1
+Decr1 2310004N24Rik 1
+Decr1 Abcd3 1
+Decr1 Acmsd 1
+Decr1 Acot1 1
+Decr1 Chrna2 1
+Decr1 Chrna2 1
+Decr1 Cpt2 1
+Decr1 Decr2 1
+Decr1 ECHO_AORT_DIAMETER1_adj_s_slog_W10 1
+Decr1 Gpr98 1
+Decr1 Hadhb 1
+Decr1 Hmgcs2 1
+Decr1 Hmgcs2 1
+Decr1 Mgll 1
+Decr1 Pik3r1 1
+Decr1 Slc25a20 1
+Decr1 Slc27a2 1
+Decr1 Tlr5 1
+Decr1 Tmem56 1
+Decr1 Vnn1 1
+Decr1 Vnn1 1
+Decr2 0610010O12Rik 1
+Decr2 1600002H07Rik 1
+Decr2 1600002H07Rik 1
+Decr2 2200001I15Rik 1
+Decr2 6330415G19Rik 1
+Decr2 6330415G19Rik 1
+Decr2 9030612M13Rik 1
+Decr2 9030612M13Rik 1
+Decr2 A630033E08Rik 1
+Decr2 AV312629 1
+Decr2 AV312629 1
+Decr2 Enpp5 1
+Decr2 Gfer 1
+Decr2 Ggnbp1 1
+Decr2 Gm939 1
+Decr2 Gm939 1
+Decr2 Hn1l 1
+Decr2 Hn1l 1
+Decr2 Mcf2l 1
+Decr2 MMT00004867 1
+Decr2 MMT00070880 1
+Decr2 NM_172529 1
+Decr2 Park2 1
+Decr2 Phf1 1
+Decr2 Pla2g7 1
+Decr2 Ppil1 1
+Decr2 Ppil1 1
+Decr2 Rsph1 1
+Decr2 Tiam2 1
+Decr2 Tmem8 1
+Decr2 Tmem8 1
+Decr2 Tmem8 1
+Decr2 Vps52 1
+Dedd2 Cyp2a4 1
+Dedd2 Cyp2a5 1
+Dedd2 Gpi1 1
+Dedd2 Gpr146 1
+Dedd2 Tmem184a 1
+Dedd2 Trib3 1
+Def8 2810004N23Rik 1
+Def8 Acsf3 1
+Def8 Acsf3 1
+Def8 Aprt 1
+Def8 BC025816 1
+Def8 BC025816 1
+Def8 Cbfa2t3 1
+Def8 Hsd17b2 1
+Def8 Mlycd 1
+Def8 Mlycd 1
+Def8 Mlycd 1
+Def8 Rhou 1
+Def8 Rhou 1
+Def8 Tmed6 1
+Def8 Ttc13 1
+Def8 Ttc13 1
+Def8 XM_358694 1
+Defa1 2010204N08Rik 1
+Defa1 Defcr14 1
+Defa1 Reg3g 1
+Defb11 Fhad1 1
+Defb11 Fndc7 1
+Defb11 Olfr1026 1
+Defb11 Olfr998 1
+Defb12 1700019D03Rik 1
+Defb1 2400009B08Rik 1
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+Lamc3 Gm1653 1
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+Lamp2 Angptl3 1
+Lamp2 Angptl3 1
+Lamp2 Cyp2a12 1
+Lamp2 Pon1 1
+Lamp3 C030011L09Rik 1
+Lamp3 D930030K17Rik 1
+Lamp3 Dync1i1 1
+Lamp3 ENSMUST00000051949 1
+Lamp3 MMT00078143 1
+Lamp3 MMT00078143 1
+Lamp3 Olfr624 1
+Lamp3 Tcl1b3 1
+Lamp3 Vmn2r76 1
+Lancl1 4930567H12Rik 1
+Lancl1 Aacs 1
+Lancl1 Adam23 1
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+Lancl1 Smarcal1 1
+Lancl1 Tmbim1 1
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+Lancl2 Akap10 1
+Lancl2 EG629581 1
+Lancl2 EG668668 1
+Lancl2 Exoc2 1
+Lancl2 Gjb2 1
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+Lao1 Ank2 1
+Lao1 Armc4 1
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+Lao1 Olfr101 1
+Lao1 Olfr1076 1
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+Lap3 Cd38 1
+Lap3 Dtwd1 1
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+Laptm4b 2610002J02Rik 1
+Laptm4b 3110001A13Rik 1
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+Laptm4b Anp32-ps 1
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+Laptm4b Atp1a1 1
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+Laptm4b Hprt1 1
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+Laptm4b XM_128452 1
+Laptm4b XM_194143 1
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+Large 1200009F10Rik 1
+Large Cklf 1
+Large Csgalnact1 1
+Large Gpt2 1
+Large Lsm6 1
+Large Prkca 1
+Large Rhoq 1
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+Large Sh3bp4 1
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+Larp1 MMT00059796 1
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+Larp4 Tcof1 1
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+Larp5 Gabrb2 1
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+Lct OTTMUSG00000015946 1
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+Lct Scd1 1
+Lct Trap1a 1
+Lct Vnn3 1
+Lct Xpot 1
+Ldb2 1700081L11Rik 1
+Ldb2 A430093A21Rik 1
+Ldb2 Ang2 1
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+Ldb2 EG629036 1
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+Lepr Aldh1a1 1
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+NMR_FAT_MASS_normal_None_W16 Cdh1 1
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+NMR_FAT_MASS_normal_None_W16 Wdr67 1
+NMR_FAT_MASS_normal_None_W20 Olig1 1
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+Pcdhb1 MMT00051745 1
+Pcdhb1 MMT00051745 1
+Pcdhb1 MMT00067954 1
+Pcdhb1 MMT00078078 1
+Pcdhb1 MMT00078078 1
+Pcdhb1 MMT00079181 1
+Pcdhb1 Myh2 1
+Pcdhb1 Olfr1010 1
+Pcdhb1 Olfr1010 1
+Pcdhb1 OTTMUSG00000002191 1
+Pcdhb1 Serpinb13 1
+Pcdhb1 Serpinb13 1
+Pcdhb1 Sh2d3c 1
+Pcdhb1 Smr2 1
+Pcdhb1 Sort1 1
+Pcdhb1 Spsb1 1
+Pcdhb1 Tmed8 1
+Pcdhb1 Unc119 1
+Pcdhb1 XM_136587 1
+Pcdhb20 Gucy2g 1
+Pcdhb22 Ckmt1 1
+Pcdhb22 D130012P04Rik 1
+Pcdhb22 Klra22 1
+Pcdhb2 Bfsp2 1
+Pcdhb2 Dub1 1
+Pcdhb2 Hcn4 1
+Pcdhb2 Igh-V3609N 1
+Pcdhb2 Osr2 1
+Pcdhb3 4933434I06Rik 1
+Pcdhb3 8430420F16Rik 1
+Pcdhb3 EG667823 1
+Pcdhb3 Nfe2 1
+Pcdhb3 Olfr447 1
+Pcdhb3 Olfr447 1
+Pcdhb3 Sgcg 1
+Pcdhb3 Ttll9 1
+Pcdhb3 Zfp239 1
+Pcdhb4 Gzmb 1
+Pcdhb4 Kcns3 1
+Pcdhb4 Olfr521 1
+Pcdhb5 MMT00016556 1
+Pcdhb5 Sprr3 1
+Pcdhb6 4833413O15Rik 1
+Pcdhb6 MMS010171122 1
+Pcdhb6 V1rc18 1
+Pcdhga11 Pcdhgc3 1
+Pcdhga12 Pcdhgb8 1
+Pcdhga12 Tspan3 1
+Pcdhga2 Efemp2 1
+Pcdhga2 Gata2 1
+Pcdhga2 Pcdhga6 1
+Pcdhga2 Pcdhga7 1
+Pcdhga6 Nlgn2 1
+Pcdhga6 Osbpl7 1
+Pcdhga6 Pcdhga9 1
+Pcdhga6 Pcdhgb4 1
+Pcdhga6 Pcdhgc4 1
+Pcdhga9 Pcdhgb8 1
+Pcdhgb4 Cbx6 1
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+Pcdhgb6 Pcdhga8 1
+Pcdhgb8 Kif3b 1
+Pcdhgb8 Smchd1 1
+Pcdhgb8 Sorl1 1
+Pcdhgb8 Wdr35 1
+Pcf11 0910001L09Rik 1
+Pcf11 A130030D18Rik 1
+Pcf11 MMT00039884 1
+Pcf11 Tmem68 1
+Pcf11 Trpm7 1
+Pcf11 Trpm7 1
+Pcf11 Zbtb10 1
+Pcgf3 2900026A02Rik 1
+Pcgf3 2900026A02Rik 1
+Pcgf3 Acads 1
+Pcgf3 Enoph1 1
+Pcgf3 Enoph1 1
+Pcgf3 Fbxo21 1
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+Pcgf3 Gdap7 1
+Pcgf3 Gtpbp6 1
+Pcgf3 Gtpbp6 1
+Pcgf3 Pxmp2 1
+Pcgf3 Pxmp2 1
+Pcgf5 2210408F21Rik 1
+Pcgf5 5730455O13Rik 1
+Pcgf5 Fas 1
+Pcgf5 Lipa 1
+Pcgf5 MMS010016587 1
+Pcgf5 MMT00040704 1
+Pcgf5 MMT00050268 1
+Pcgf5 Rcl1 1
+Pcgf5 Sertad4 1
+Pcgf6 Etf1 1
+Pcgf6 Frat2 1
+Pcgf6 Jagn1 1
+Pcgf6 Pprc1 1
+Pcgf6 Zfp623 1
+Pck1 1300015D01Rik 1
+Pck1 1300015D01Rik 1
+Pck1 1300015D01Rik 1
+Pck1 2410075B13Rik 1
+Pck1 4833442J19Rik 1
+Pck1 9130208E07Rik 1
+Pck1 Angptl4 1
+Pck1 Arl4d 1
+Pck1 Atp5b 1
+Pck1 Cacna2d2 1
+Pck1 Cacna2d2 1
+Pck1 Cacna2d2 1
+Pck1 Ccrn4l 1
+Pck1 Errfi1 1
+Pck1 Man2a1 1
+Pck1 Mfsd2 1
+Pck1 Mfsd2 1
+Pck1 Per1 1
+Pck1 Pim3 1
+Pck1 Rasgef1b 1
+Pck1 Sds 1
+Pck1 Slc38a2 1
+Pck1 Slc38a2 1
+Pck1 Tat 1
+Pck1 Tat 1
+Pck1 Tat 1
+Pck1 Tmem20 1
+Pck1 Tsc22d3 1
+Pck1 Tsc22d3 1
+Pck2 2010004A03Rik 1
+Pck2 2310014G06Rik 1
+Pck2 AI504432 1
+Pck2 Cd99l2 1
+Pck2 EG432995 1
+Pck2 Gm600 1
+Pck2 MMT00021419 1
+Pck2 Sall2 1
+Pck2 Slc7a8 1
+Pck2 Slc7a8 1
+Pck2 Tbcel 1
+Pclo Olfm3 1
+Pcm1 AI314604 1
+Pcm1 Baz1a 1
+Pcmtd1 4732440D04Rik 1
+Pcmtd1 4732440D04Rik 1
+Pcmtd1 4921533L14Rik 1
+Pcmtd1 4921533L14Rik 1
+Pcmtd1 C330011F01Rik 1
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+Pcmtd1 Pdzd8 1
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+Pcmtd1 Snhg6 1
+Pcmtd2 A530082C11Rik 1
+Pcmtd2 Prex2 1
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+Pcnp 5033411D12Rik 1
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+Pcnp Abca1 1
+Pcnp Abca1 1
+Pcnp Calcrl 1
+Pcnp Dlg1 1
+Pcnp Kdelr1 1
+Pcnp Kdelr1 1
+Pcnp Lypla1 1
+Pcnp MMT00041689 1
+Pcnp Nek1 1
+Pcnp Pcgf6 1
+Pcnp Sec23a 1
+Pcnp Slc25a15 1
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+Pcnp Sypl 1
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+Pcnxl3 Bcas3 1
+Pcnxl3 C79407 1
+Pcnxl3 C79407 1
+Pcnxl3 Capn1 1
+Pcnxl3 Cubn 1
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+Pcnxl3 Foxn1 1
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+Pcnxl3 Lrrc4 1
+Pcnxl3 Lsm3 1
+Pcnxl3 Lypla2 1
+Pcnxl3 Mgat4b 1
+Pcnxl3 MMT00046285 1
+Pcnxl3 ORF61 1
+Pcnxl3 ORF61 1
+Pcnxl3 Pou2f1 1
+Pcnxl3 Ssbp3 1
+Pcolce2 1190002N15Rik 1
+Pcolce2 1190002N15Rik 1
+Pcolce2 Atp11a 1
+Pcolce2 Chst2 1
+Pcolce2 Ctsc 1
+Pcolce2 Ctsc 1
+Pcolce2 Ctsh 1
+Pcolce2 Dclk3 1
+Pcolce2 Nmnat3 1
+Pcolce2 Nmnat3 1
+Pcolce2 Paqr9 1
+Pcolce2 Slc9a9 1
+Pcolce2 Ube1l 1
+Pcolce Actb 1
+Pcolce Actb 1
+Pcolce Actb 1
+Pcolce Col14a1 1
+Pcolce Col1a1 1
+Pcolce Col4a2 1
+Pcolce Col5a1 1
+Pcolce Col6a1 1
+Pcolce Cygb 1
+Pcolce Cyp3a13 1
+Pcolce Dhcr7 1
+Pcolce Efemp2 1
+Pcolce Efemp2 1
+Pcolce Efemp2 1
+Pcolce Fasn 1
+Pcolce Hrbl 1
+Pcolce Klb 1
+Pcolce Leprel1 1
+Pcolce Ltbp3 1
+Pcolce Mafk 1
+Pcolce Matn2 1
+Pcolce Mmp2 1
+Pcolce MMT00032998 1
+Pcolce MMT00069147 1
+Pcolce Myadm 1
+Pcolce Olfml3 1
+Pcolce Pam 1
+Pcolce Pf4 1
+Pcolce Plvap 1
+Pcolce Rab11fip5 1
+Pcolce Rbp1 1
+Pcolce Rbp1 1
+Pcolce Rbp1 1
+Pcolce S100a6 1
+Pcolce Sod3 1
+Pcolce Sparc 1
+Pcolce Sparc 1
+Pcolce Sparc 1
+Pcolce Tgfbi 1
+Pcolce Tmsb10 1
+Pcolce Trim56 1
+Pcolce Vwf 1
+Pcp2 mCT51625 1
+Pcp2 MMS010091103 1
+Pcp2 Myst3 1
+Pcp4 Crtam 1
+Pcp4 Dleu2 1
+Pcp4l1 9130221J18Rik 1
+Pcp4l1 9130221J18Rik 1
+Pcp4l1 D1Ertd471e 1
+Pcp4l1 Endod1 1
+Pcp4l1 Endod1 1
+Pcp4l1 Nr1i3 1
+Pcp4 Olfr1013 1
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+Pcsk2 Sprr3 1
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+Pcsk4 6430520M22Rik 1
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+Pcsk4 Adc 1
+Pcsk4 AI605151 1
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+Pcsk4 D0H4S114 1
+Pcsk4 D0H4S114 1
+Pcsk4 Dbp 1
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+Pcsk4 Fgg 1
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+Pcsk4 Hlf 1
+Pcsk4 Hlf 1
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+Pcsk4 Ttc7 1
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+Pcsk5 1110034N17Rik 1
+Pcsk5 BC016495 1
+Pcsk5 C730048C13Rik 1
+Pcsk5 Dak 1
+Pcsk5 MMT00014015 1
+Pcsk5 MMT00014015 1
+Pcsk5 MMT00014015 1
+Pcsk5 Rfk 1
+Pcsk6 1110006G14Rik 1
+Pcsk6 Abcb4 1
+Pcsk6 Acsf2 1
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+Pcsk6 EG236844 1
+Pcsk6 mCT113910 1
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+Pcsk6 Tmem135 1
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+Pcsk9 Acat2 1
+Pcsk9 Hcrtr1 1
+Pcsk9 Hmgcr 1
+Pcsk9 Hmgcr 1
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+Pcsk9 Mid1ip1 1
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+Pctk3 Arl8a 1
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+Pctk3 Cd55 1
+Pctk3 Cd55 1
+Pctk3 Cdk5rap2 1
+Pctk3 Cyb5r1 1
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+Pctk3 Pcyt1a 1
+Pctk3 Pm20d1 1
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+Pctp 7030407E18Rik 1
+Pctp Acaca 1
+Pctp Arl5c 1
+Pctp Ubtf 1
+Pcyox1 Anxa4 1
+Pcyox1 Atp9a 1
+Pcyox1 Ermp1 1
+Pcyox1 Ermp1 1
+Pcyox1 Nagk 1
+Pcyt1a Glt1d1 1
+Pcyt1a Sash1 1
+Pdap1 D730040F13Rik 1
+Pdap1 Gcap11 1
+Pdc Cox7b2 1
+Pdcd10 1110019J04Rik 1
+Pdcd10 C230085J04Rik 1
+Pdcd10 MMT00066571 1
+Pdcd10 Polr2k 1
+Pdcd10 Prickle2 1
+Pdcd10 Tmem106b 1
+Pdcd11 0610010D20Rik 1
+Pdcd11 1700028B04Rik 1
+Pdcd11 4930429F11Rik 1
+Pdcd11 6030443O07Rik 1
+Pdcd11 6030443O07Rik 1
+Pdcd11 9130011E15Rik 1
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+Pdcd11 Ablim1 1
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+Pdcd11 BF459474 1
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+Pdcd11 Cutc 1
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+Pdcd11 Cyp2c70 1
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+Pdcd11 Dntt 1
+Pdcd11 Exosc1 1
+Pdcd11 Kif11 1
+Pdcd11 Kif11 1
+Pdcd11 Lcor 1
+Pdcd11 MMT00011493 1
+Pdcd11 MMT00035450 1
+Pdcd11 Nhlrc2 1
+Pdcd11 NM_012022 1
+Pdcd11 NM_012022 1
+Pdcd11 Nolc1 1
+Pdcd11 Nolc1 1
+Pdcd11 Olfr433 1
+Pdcd11 Olfr871 1
+Pdcd11 Pcgf6 1
+Pdcd11 Pik3ap1 1
+Pdcd11 Pik3ap1 1
+Pdcd11 Poll 1
+Pdcd11 Pprc1 1
+Pdcd11 Slk 1
+Pdcd11 Tcl1 1
+Pdcd4 Magi1 1
+Pdcd4 MMT00008758 1
+Pdcd4 MMT00077153 1
+Pdcd4 Rbpms 1
+Pdcd4 Slc24a6 1
+Pdcd4 Treh 1
+Pdc Ece1 1
+Pdcl 2010106G01Rik 1
+Pdcl Nudcd1 1
+Pdc Olfr1339 1
+Pde11a 5031408O05Rik 1
+Pde11a A930026C06Rik 1
+Pde11a Chodl 1
+Pde11a Defb14 1
+Pde11a mCT184347 1
+Pde11a MMT00078359 1
+Pde1a Cdca7 1
+Pde1a EG620205 1
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+Pde2a BC030183 1
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+Pde3b Aass 1
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+Pde3b Dhx15 1
+Pde3b Dpyd 1
+Pde3b Eif2s3x 1
+Pde3b Flvcr1 1
+Pde3b Laptm4b 1
+Pde3b MMT00014375 1
+Pde3b MMT00031271 1
+Pde3b MMT00047848 1
+Pde3b MMT00052614 1
+Pde3b MMT00058392 1
+Pde3b Nudt15 1
+Pde3b Olfr984 1
+Pde3b Pon2 1
+Pde3b Slc25a17 1
+Pde3b Stat6 1
+Pde3b Tob1 1
+Pde4a 5430432M24Rik 1
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+Pde4a Clasp1 1
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+Pde4a Efnb1 1
+Pde4a Pde4b 1
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+Pde4b Aatk 1
+Pde4b Wsb1 1
+Pde4b Wsb1 1
+Pde4b Wsb1 1
+Pde4c Gpr182 1
+Pde4c mCT168604 1
+Pde4c MMT00048601 1
+Pde4dip Fibp 1
+Pde4dip Kctd15 1
+Pde6b C030023E24Rik 1
+Pde6b MMT00014869 1
+Pde6b MMT00024648 1
+Pde6b MMT00046842 1
+Pde6b MMT00050401 1
+Pde6b Otp 1
+Pde6b S60053 1
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+Pde6c 5530400B01Rik 1
+Pde6c ENSMUSG00000074828 1
+Pde6c ENSMUSG00000075025 1
+Pde6c Fign 1
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+Pde6h Abcb4 1
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+Pde6h Acaa1b 1
+Pde6h Arhgdib 1
+Pde6h Arhgdib 1
+Pde6h Atf7ip 1
+Pde6h Atf7ip 1
+Pde6h Atp5g3 1
+Pde6h AV374850 1
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+Pde6h Erp27 1
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+Pde6h Gucy2c 1
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+Pde6h Klrd1 1
+Pde6h Lrig1 1
+Pde6h Plekhg5 1
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+Pde9a 4930539E08Rik 1
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+Pde9a Hs3st6 1
+Pde9a Hs3st6 1
+Pde9a Nfkbie 1
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+Pdgfa Eepd1 1
+Pdgfa Hapln1 1
+Pdgfa Myo1b 1
+Pdgfa Pld2 1
+Pdgfa Synj2 1
+Pdgfa Tax1bp3 1
+Pdgfc Cyp2a5 1
+Pdgfc Fn3k 1
+Pdgfc G0s2 1
+Pdgfc Gstt2 1
+Pdgfc MMT00060741 1
+Pdgfc Sbk1 1
+Pdgfc Setd4 1
+Pdgfc Skil 1
+Pdgfc Traf3 1
+Pdgfc Trim2 1
+Pdgfd Pcdha3 1
+Pdgfra Ezr 1
+Pdgfra Mamdc2 1
+Pdgfra Rbms3 1
+Pdgfrb 2810022L02Rik 1
+Pdgfrb A530057A03Rik 1
+Pdgfrb Adamts10 1
+Pdgfrb Adcyap1r1 1
+Pdgfrb Adra2b 1
+Pdgfrb Atp8b2 1
+Pdgfrb Atp8b2 1
+Pdgfrb Bicc1 1
+Pdgfrb Chsy1 1
+Pdgfrb Clip2 1
+Pdgfrb Col16a1 1
+Pdgfrb Col5a1 1
+Pdgfrb Col5a1 1
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+Pdgfrb Fbn1 1
+Pdgfrb Foxs1 1
+Pdgfrb Fscn1 1
+Pdgfrb Kpna2 1
+Pdgfrb Loxl1 1
+Pdgfrb Megf8 1
+Pdgfrb MMT00075074 1
+Pdgfrb Mtmr11 1
+Pdgfrb Myo9b 1
+Pdgfrb Phldb1 1
+Pdgfrb Plekha4 1
+Pdgfrb Plekha4 1
+Pdgfrb Ppargc1b 1
+Pdgfrb Prelp 1
+Pdgfrb Prss23 1
+Pdgfrb Rab11fip5 1
+Pdgfrb Rab11fip5 1
+Pdgfrb Rnf122 1
+Pdgfrb Rtn4rl1 1
+Pdgfrb Sox4 1
+Pdgfrb Tagln2 1
+Pdgfrb Tulp3 1
+Pdgfrb Whsc1 1
+Pdgfrb Wt1 1
+Pdgfrb Wt1 1
+Pdgfrb XM_125441 1
+Pdgfrl Tmem170b 1
+Pdha1 Cald1 1
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+Pdhb Apbb2 1
+Pdhb D14Ertd449e 1
+Pdhb mCT180896 1
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+Pdhx 2810002D19Rik 1
+Pdhx 2810427C15Rik 1
+Pdhx 2900042K05Rik 1
+Pdhx Caprin1 1
+Pdhx Cep250 1
+Pdhx Frzb 1
+Pdhx Slc4a11 1
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+Pdia2 Iapp 1
+Pdia3 AI182371 1
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+Pdia3 Hsp90b1 1
+Pdia3 Midn 1
+Pdia3 MMT00007878 1
+Pdia3 MMT00033070 1
+Pdia3 Rapgef4 1
+Pdia3 Sap18 1
+Pdia3 Sh3d19 1
+Pdia3 Slc25a20 1
+Pdia4 Atp8b1 1
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+Pdia4 Hsp90b1 1
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+Pdia6 Arrdc4 1
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+Pdia6 Derl3 1
+Pdia6 Iah1 1
+Pdia6 Iah1 1
+Pdia6 Id2 1
+Pdia6 Sec11c 1
+Pdik1l XM_284969 1
+Pdk1 2700094K13Rik 1
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+Pdk1 Aacs 1
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+Pdk1 Akr1d1 1
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+Pdk1 Cerkl 1
+Pdk1 Cisd1 1
+Pdk1 Dusp19 1
+Pdk1 ECHO_CARDIA_OUTPUT1_adj_s_s_sqrt_W10 1
+Pdk1 Enpep 1
+Pdk1 Ephb1 1
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+Pdk1 Fmnl2 1
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+Pdk1 Spc25 1
+Pdk1 Spc25 1
+Pdk1 Sunc1 1
+Pdk1 Ttrap 1
+Pdk1 XM_131446 1
+Pdk2 A530088I07Rik 1
+Pdk2 Abcd1 1
+Pdk2 Aco2 1
+Pdk2 Arl4d 1
+Pdk2 Arl4d 1
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+Plat Lox 1
+Plat Lum 1
+Plat Ndrg4 1
+Plat Sox7 1
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+Plau BB228170 1
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+Plaur Adamts4 1
+Plaur Adamts4 1
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+Plaur Ch25h 1
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+Plaur Sele 1
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+Plcd4 ENSMUST00000066413 1
+Plcd4 Olfr1049 1
+Plcd4 Olfr206 1
+Plcd4 V00829 1
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+Plcxd1 MMT00077828 1
+Plcxd2 Elmo1 1
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+Pld2 Dapk2 1
+Pld2 Fbxl19 1
+Pld2 Mast2 1
+Pld2 Mast2 1
+Pld2 MMT00022209 1
+Pld2 Nek8 1
+Pld2 Nek8 1
+Pld2 Nek8 1
+Pld2 Ppara 1
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+Plec1 4930534D22Rik 1
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+Plekha1 MMT00038794 1
+Plekha1 Mtnr1a 1
+Plekha1 Myo9b 1
+Plekha1 Nat8b 1
+Plekha1 NMR_FLUID_normal_None_W16 1
+Plekha1 Obfc2a 1
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+Plekha1 Slc43a1 1
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+Plekha1 Ubtd2 1
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+Plekha2 Jam3 1
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+Plekhb1 Trim68 1
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+Plekhb2 Il1r1 1
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+Plekhf1 Flt3l 1
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+Plekhf1 Hspa1b 1
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+Plekhf2 MMT00045283 1
+Plekhf2 OTTMUSG00000004551 1
+Plekhf2 Rab2a 1
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+Plekhg1 2310026I22Rik 1
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+Pllp Dnase2a 1
+Pllp Fam167b 1
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+Pllp March5 1
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+Pllp Tsc22d1 1
+Pln BC037032 1
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+Pln Mex3a 1
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+Plod3 BC037034 1
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+Plod3 Myd88 1
+Plod3 Nbeal2 1
+Plod3 Ncoa5 1
+Plod3 Nmt1 1
+Plod3 Nob1 1
+Plod3 Noc3l 1
+Plod3 Ogdh 1
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+Plod3 Ranbp3 1
+Plod3 Rasip1 1
+Plod3 Rce1 1
+Plod3 Sfrs1 1
+Plod3 Slc11a2 1
+Plod3 Them2 1
+Plod3 Wbscr16 1
+Plod3 Zc3h13 1
+Plod3 Znfx1 1
+Plp2 Calml4 1
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+Plp2 Cxcr4 1
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+Pls3 F13b 1
+Pls3 Mast2 1
+Pls3 MMT00035424 1
+Pls3 Ttpa 1
+Pls3 Ugt2a3 1
+Plscr1 Dynll1 1
+Plscr1 EG627788 1
+Plscr1 MMT00078660 1
+Plscr1 MMT00078660 1
+Plscr1 MMT00078660 1
+Plscr1 Rbpms 1
+Plscr2 1190002N15Rik 1
+Plscr2 1190002N15Rik 1
+Plscr2 1700057G04Rik 1
+Plscr2 1810022C23Rik 1
+Plscr2 1810055G02Rik 1
+Plscr2 Angptl3 1
+Plscr2 C730036D15Rik 1
+Plscr2 Ces2 1
+Plscr2 Ces5 1
+Plscr2 Cyb5r4 1
+Plscr2 Cyb5r4 1
+Plscr2 Farp1 1
+Plscr2 Hmgn3 1
+Plscr2 Marveld2 1
+Plscr2 Me1 1
+Plscr2 Me1 1
+Plscr2 MMT00078660 1
+Plscr2 MMT00078660 1
+Plscr2 Ncor2 1
+Plscr2 Nmnat3 1
+Plscr2 Nmnat3 1
+Plscr2 Nqo2 1
+Plscr2 Nt5e 1
+Plscr2 Pccb 1
+Plscr2 Pex7 1
+Plscr2 Sar1b 1
+Plscr2 Sort1 1
+Plscr2 Stap1 1
+Plscr2 Tmem14a 1
+Plscr2 Tspyl2 1
+Plscr2 Tspyl2 1
+Plscr2 XM_283945 1
+Plscr3 Nlgn2 1
+Plscr3 Rras 1
+Plscr4 AI574175 1
+Plscr4 B830007D08Rik 1
+Plscr4 Cep70 1
+Plscr4 Cgref1 1
+Plscr4 Clcn3 1
+Plscr4 Cyb561d2 1
+Plscr4 EG319225 1
+Plscr4 MMT00022661 1
+Plscr4 MMT00063278 1
+Plscr4 Nap1l1 1
+Plscr4 Rnf111 1
+Pltp 2310045A20Rik 1
+Pltp A030007L17Rik 1
+Pltp A030007L17Rik 1
+Pltp Adm 1
+Pltp Ccdc69 1
+Pltp Csad 1
+Pltp Cth 1
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+Pltp Ddit4 1
+Pltp Eaf2 1
+Pltp Ebf3 1
+Pltp Elovl5 1
+Pltp Frmd4b 1
+Pltp Gimap8 1
+Pltp H2-Q8 1
+Pltp Htra4 1
+Pltp Kcnk10 1
+Pltp Lrrc39 1
+Pltp Mesp1 1
+Pltp NM_010062 1
+Pltp NM_013525 1
+Pltp Prss8 1
+Pltp Reep5 1
+Pltp Reep5 1
+Pltp Robo1 1
+Pltp Spon2 1
+Pltp Tm7sf2 1
+Pltp Tmprss4 1
+Pltp Unc119 1
+Pltp Vegfb 1
+Pltp Vegfb 1
+Pltp Vps37b 1
+Pltp Vsig4 1
+Pltp Wfdc2 1
+Plunc 1810073O08Rik 1
+Plunc 2310057J18Rik 1
+Plunc 4930429F11Rik 1
+Plunc 4930429F11Rik 1
+Plunc AU022136 1
+Plunc B3galt5 1
+Plunc C030007I01Rik 1
+Plunc Gpr34 1
+Plunc Klhl29 1
+Plunc Lipk 1
+Plunc Megf10 1
+Plunc MMS010065000 1
+Plunc MMT00014003 1
+Plunc MMT00023003 1
+Plunc MMT00054519 1
+Plunc MMT00073846 1
+Plunc Shisa2 1
+Plvap Cbx6 1
+Plvap Lgi2 1
+Plvap Lgmn 1
+Plvap Lphn1 1
+Plvap Mxra8 1
+Plvap Nkd1 1
+Plxdc1 Hoxb2 1
+Plxdc2 AI120166 1
+Plxdc2 Cd72 1
+Plxdc2 Cyp1b1 1
+Plxdc2 Ncam1 1
+Plxdc2 Ncam1 1
+Plxdc2 Wt1 1
+Plxna2 2900035J10Rik 1
+Plxna2 2900035J10Rik 1
+Plxna2 2900035J10Rik 1
+Plxnb1 Aim1 1
+Plxnb1 Aprt 1
+Plxnb1 Cacng1 1
+Plxnb1 Cacng1 1
+Plxnb1 Cyb561 1
+Plxnb1 Eppk1 1
+Plxnb1 Gnat1 1
+Plxnb1 Nab2 1
+Plxnb1 Slco2a1 1
+Plxnb1 Tcf7 1
+Plxnb1 Tcf7 1
+Plxnb3 Cxcl5 1
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+Plxnc1 Clca2 1
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+Plxnc1 Hecw2 1
+Plxnc1 Il13ra2 1
+Plxnc1 Kdr 1
+Plxnc1 Ppap2a 1
+Plxnc1 Wnt2 1
+Pm20d1 5430435G22Rik 1
+Pm20d1 Arl8a 1
+Pm20d1 Arl8a 1
+Pm20d1 Cdk5rap2 1
+Pm20d1 Ctse 1
+Pm20d1 Ctse 1
+Pm20d1 Ddx59 1
+Pm20d1 Etnk2 1
+Pm20d1 Gas2l3 1
+Pm20d1 Gas2l3 1
+Pm20d1 Igfn1 1
+Pm20d1 Igfn1 1
+Pm20d1 Igfn1 1
+Pm20d1 Lad1 1
+Pm20d1 Mcm6 1
+Pm20d1 Mcm6 1
+Pm20d1 Mgat5 1
+Pm20d1 Mgat5 1
+Pm20d1 Mgat5 1
+Pm20d1 MMT00067073 1
+Pm20d1 Nr5a2 1
+Pm20d1 Pigr 1
+Pm20d1 Pigr 1
+Pm20d1 Ren1 1
+Pm20d1 Ren1 1
+Pm20d1 Sfmbt2 1
+Pm20d1 Smpd3 1
+Pm20d1 Yod1 1
+Pm20d2 Klrk1 1
+Pm20d2 Lsm11 1
+Pmaip1 Fabp7 1
+Pmaip1 Fabp7 1
+Pmaip1 Fer1l3 1
+Pmaip1 Lgals3 1
+Pmaip1 Slc2a6 1
+Pmaip1 Snx20 1
+Pmm1 1110028A07Rik 1
+Pmm1 MMT00010677 1
+Pmm1 Rhbdl1 1
+Pmm1 Tmem98 1
+Pmm1 Tnnc1 1
+Pmm2 Cdc42ep1 1
+Pmm2 MMT00082622 1
+Pmm2 Ppil2 1
+Pmp22 Alox5ap 1
+Pmp22 Icosl 1
+Pmp22 Sparc 1
+Pmp22 Tgfb3 1
+Pmpcb Apoc4 1
+Pmpcb Hadhb 1
+Pms2 4930517J16Rik 1
+Pms2 C130038G02Rik 1
+Pms2 Gm370 1
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+Ppara AK013765 1
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+Ppara Ppara 1
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+Ppara Rhobtb1 1
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+Ppargc1a Zfp385b 1
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+Ppp1r3g MMS010099730 1
+Ppp1r3g Rab30 1
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+Ppp2ca Cep68 1
+Ppp2ca Dpm3 1
+Ppp2ca EG434339 1
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+Psme4 Stra13 1
+Psmf1 Zfp618 1
+Psors1c2 Centg1 1
+Psors1c2 Centg1 1
+Psors1c2 Ctnnd2 1
+Psors1c2 Il19 1
+Psors1c2 Il19 1
+Psors1c2 Slc16a10 1
+Psors1c2 Slc16a10 1
+Pspc1 Trub2 1
+Psph 1700012L04Rik 1
+Psph 4930431P03Rik 1
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+Psph AV171342 1
+Psph B230219J02Rik 1
+Psph BC057170 1
+Psph Ccdc62 1
+Psph Cux1 1
+Psph Cyp3a57 1
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+Psph Kcnk12 1
+Psph MMT00010950 1
+Psph MMT00022922 1
+Psph MMT00062691 1
+Psph Nmnat2 1
+Psph Ptpn11 1
+Psph Slc15a4 1
+Psph Tardbp 1
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+Psrc1 Ccnd1 1
+Psrc1 Ddit4l 1
+Psrc1 Ddit4l 1
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+Psrc1 Phlda3 1
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+Pstpip2 Acss2 1
+Pstpip2 Bglap-rs1 1
+Pstpip2 Chrna4 1
+Pstpip2 Cyb5b 1
+Pstpip2 Cyp2r1 1
+Pstpip2 Fdft1 1
+Pstpip2 Gale 1
+Pstpip2 Gpam 1
+Pstpip2 MMT00069147 1
+Pstpip2 Paox 1
+Pstpip2 Pdzk1ip1 1
+Pstpip2 Pmvk 1
+Pstpip2 Polr3g 1
+Pstpip2 Rdh11 1
+Pstpip2 Sgsm1 1
+Pstpip2 Slc27a2 1
+Pstpip2 Thrsp 1
+Pstpip2 Uchl5ip 1
+Pstpip2 Uchl5ip 1
+Ptafr 1110034B05Rik 1
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+Ptafr 2810007J24Rik 1
+Ptafr 4930572J05Rik 1
+Ptafr Aadat 1
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+Ptafr Akr1c6 1
+Ptafr AW111846 1
+Ptafr Dennd3 1
+Ptafr EG214403 1
+Ptafr EG629203 1
+Ptafr Nox4 1
+Ptafr Nox4 1
+Ptafr Raver1 1
+Ptafr Tax1bp3 1
+Ptafr Tdo2 1
+Ptafr Unc119 1
+Ptar1 1700074H08Rik 1
+Ptar1 Mamdc2 1
+Ptar1 Olfr570 1
+Ptbp1 3110001I20Rik 1
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+Ptbp1 Ak2 1
+Ptbp1 Ak2 1
+Ptbp1 Ak2 1
+Ptbp1 Arl6ip5 1
+Ptbp1 BC062115 1
+Ptbp1 BC062115 1
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+Ptbp1 H2-Ke2 1
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+Ptbp1 Map1lc3a 1
+Ptbp1 Map1lc3a 1
+Ptbp1 MMT00002010 1
+Ptbp1 MMT00002010 1
+Ptbp1 MMT00002010 1
+Ptbp1 MMT00008875 1
+Ptbp1 MMT00008875 1
+Ptbp1 MMT00024854 1
+Ptbp1 MMT00073283 1
+Ptbp1 Nagpa 1
+Ptbp1 Nagpa 1
+Ptbp1 Nono 1
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+Ptbp1 Phf1 1
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+Ptbp1 Rab35 1
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+Ptbp1 Taf8 1
+Ptbp1 Tie1 1
+Ptbp1 Tie1 1
+Ptbp1 Tie1 1
+Ptbp2 Cadm1 1
+Ptbp2 Lepr 1
+Ptbp2 MMS010179677 1
+Ptcd3 Atp6v1e1 1
+Ptcd3 Bche 1
+Ptcd3 Zfyve19 1
+Ptch1 2310003H01Rik 1
+Ptch1 4930595M18Rik 1
+Ptch1 H2-M10.2 1
+Ptch1 Kcnj6 1
+Ptch1 Olfr1336 1
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+Ptchd1 Gm1553 1
+Ptchd1 MMS010063203 1
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+Ptchd3 Olfr1086 1
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+Ptchd3 Ssxb8 1
+Ptchd3 Ssxb8 1
+Ptcra A430108E01Rik 1
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+Ptcra MMT00025835 1
+Ptdss1 Cntnap5a 1
+Ptdss1 Dub1a 1
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+Pten 2700046G09Rik 1
+Pten 2700046G09Rik 1
+Pten 6530404N21Rik 1
+Pten 6530404N21Rik 1
+Pten AI450540 1
+Pten AI747699 1
+Pten AI747699 1
+Pten AK077243 1
+Pten AK077243 1
+Pten Aldh1a7 1
+Pten Aldh1a7 1
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+Pten BF466594 1
+Pten BF466594 1
+Pten Cbwd1 1
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+Pten Ermp1 1
+Pten Il33 1
+Pten Lrit1 1
+Pten MMT00014015 1
+Pten MMT00014015 1
+Pten MMT00014022 1
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+Pten Taok1 1
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+Pten Tfdp2 1
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+Pter Cpsf4l 1
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+Pter Enpep 1
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+Ptgdr 4933401J01Rik 1
+Ptgdr AW476263 1
+Ptgdr Dctd 1
+Ptgdr Evx1 1
+Ptgdr Ffar1 1
+Ptgdr Grm7 1
+Ptgdr Olfr108 1
+Ptgdr Olfr1288 1
+Ptgdr Pik3c2a 1
+Ptgdr Sox2 1
+Ptgdr Srst 1
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+Ptgds2 4930502E18Rik 1
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+Ptgds2 Atxn2l 1
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+Ptger3 Slc5a11 1
+Ptger3 Star 1
+Ptger4 Fry 1
+Ptges3 1810015C04Rik 1
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+Ptges3 Tox4 1
+Ptges3 Ube2q2 1
+Ptges MMT00016639 1
+Ptgfr 1500005A01Rik 1
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+Ptgfr Aim1l 1
+Ptgfr Aqp4 1
+Ptgfr Avpr1a 1
+Ptgfr Dock7 1
+Ptgfr Fmo2 1
+Ptgfr MMT00007367 1
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+Ptgfr Naip2 1
+Ptgfr Osbpl5 1
+Ptgfr Osbpl5 1
+Ptgfr Pla2g4f 1
+Ptgfr Siae 1
+Ptgfr Sulf2 1
+Ptgfr Tmc7 1
+Ptgir 1110001A07Rik 1
+Ptgir BC055111 1
+Ptgir Clec4n 1
+Ptgir Fstl1 1
+Ptgir Ly6g6e 1
+Ptgir Msr1 1
+Ptgir Nin 1
+Ptgir Ppp1r14a 1
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+Ptgis Slc41a1 1
+Ptgs1 Adrb1 1
+Ptgs1 Arhgap15 1
+Ptgs1 Arpc1b 1
+Ptgs1 Crlf2 1
+Ptgs1 Fabp7 1
+Ptgs1 Fabp7 1
+Ptgs1 Hc 1
+Ptgs1 Kal1 1
+Ptgs1 Kcna2 1
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+Ptgs1 Ly6g6e 1
+Ptgs1 Plxdc2 1
+Ptgs1 Rnasel 1
+Ptgs1 Tuba1b 1
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+Ptgs2 BC061194 1
+Pth 1110054O05Rik 1
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+Pth2r Ddr2 1
+Pth2r Sox17 1
+Pth 4930448I06Rik 1
+Pth Cryba1 1
+Pth Olfr920 1
+Pthr1 Adra2b 1
+Pthr1 Cldn5 1
+Pthr1 D630039A03Rik 1
+Pthr1 Ecm1 1
+Pthr1 Sod3 1
+Ptk2b 1500035H01Rik 1
+Ptk2b Tbc1d13 1
+Ptk2b Tmem34 1
+Ptk6 1700060H10Rik 1
+Ptk6 Cenpm 1
+Ptk6 Cyp2u1 1
+Ptk6 Tex12 1
+Ptma MMT00013076 1
+Ptma MMT00039922 1
+Ptn Mal 1
+Ptn MMS010123053 1
+Ptn MMT00018998 1
+Ptn Olfr1420 1
+Ptp4a1 1810015C04Rik 1
+Ptp4a1 3110001A13Rik 1
+Ptp4a1 Acsm3 1
+Ptp4a1 Atf6 1
+Ptp4a1 BC004728 1
+Ptp4a1 Bnip3 1
+Ptp4a1 C430048L16Rik 1
+Ptp4a1 Cxcl1 1
+Ptp4a1 ENSMUSG00000073019 1
+Ptp4a1 Gys2 1
+Ptp4a1 Hnmt 1
+Ptp4a1 Hsd17b2 1
+Ptp4a1 Ivns1abp 1
+Ptp4a1 MMT00044927 1
+Ptp4a1 MMT00044927 1
+Ptp4a1 MMT00044927 1
+Ptp4a1 MMT00054728 1
+Ptp4a1 Slc25a32 1
+Ptp4a1 Slc25a45 1
+Ptp4a1 Snx10 1
+Ptp4a1 Wnt5a 1
+Ptp4a2 MMT00017000 1
+Ptp4a3 Dennd3 1
+Ptp4a3 ENSMUSG00000075583 1
+Ptp4a3 Lynx1 1
+Ptpla 2310069G16Rik 1
+Ptpla 5730447C08Rik 1
+Ptplad1 1600014C10Rik 1
+Ptplad1 EG434197 1
+Ptplad1 Fads1 1
+Ptplad1 Igk-V4 1
+Ptplad1 Lipa 1
+Ptplad1 MMT00008599 1
+Ptplad1 MMT00008599 1
+Ptplad1 MMT00030928 1
+Ptplad1 MMT00039886 1
+Ptplad1 Olfr1196 1
+Ptplad1 Rab8a 1
+Ptplad1 Sec23a 1
+Ptplad1 Sec23a 1
+Ptplad1 Sh3rf2 1
+Ptplad1 Slc10a2 1
+Ptplad1 Snx18 1
+Ptplad1 XM_125034 1
+Ptplad1 Ykt6 1
+Ptpla Mras 1
+Ptpla Ormdl3 1
+Ptpla Pter 1
+Ptpla Pter 1
+Ptplb MMT00077446 1
+Ptplb MMT00077446 1
+Ptplb Popdc2 1
+Ptplb Robo1 1
+Ptplb Slc15a2 1
+Ptplb Slc15a2 1
+Ptpmt1 2700094K13Rik 1
+Ptpmt1 2700094K13Rik 1
+Ptpmt1 2700094K13Rik 1
+Ptpmt1 2810002D19Rik 1
+Ptpmt1 2810002D19Rik 1
+Ptpmt1 2810002D19Rik 1
+Ptpmt1 6030442H21Rik 1
+Ptpmt1 6030442H21Rik 1
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+Ptpmt1 Calcrl 1
+Ptpmt1 Calcrl 1
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+Ptpmt1 Ext2 1
+Ptpmt1 Frzb 1
+Ptpmt1 Gpr155 1
+Ptpmt1 Gtf2h5 1
+Ptpmt1 Harbi1 1
+Ptpmt1 Ldlrad3 1
+Ptpmt1 MMT00005552 1
+Ptpmt1 Nek6 1
+Ptpmt1 Olfr1033 1
+Ptpmt1 Olfr1033 1
+Ptpmt1 Olfr1033 1
+Ptpmt1 Olfr1034 1
+Ptpmt1 Olfr1034 1
+Ptpmt1 Olfr313 1
+Ptpn11 Fbxl10 1
+Ptpn11 MMT00016573 1
+Ptpn11 MMT00057426 1
+Ptpn12 Bcl2l13 1
+Ptpn12 Cdk8 1
+Ptpn12 Il17ra 1
+Ptpn12 Papd5 1
+Ptpn12 Phf17 1
+Ptpn12 XM_204303 1
+Ptpn13 Ahnak2 1
+Ptpn13 Robo2 1
+Ptpn18 5031425F14Rik 1
+Ptpn18 A630077B13Rik 1
+Ptpn18 A630077B13Rik 1
+Ptpn18 A630077B13Rik 1
+Ptpn18 AF067061 1
+Ptpn18 AJ409495 1
+Ptpn18 Akr1b3 1
+Ptpn18 Arhgap15 1
+Ptpn18 Arhgdib 1
+Ptpn18 Batf3 1
+Ptpn18 C1qa 1
+Ptpn18 Cd274 1
+Ptpn18 Clic1 1
+Ptpn18 Clic1 1
+Ptpn18 Coro1a 1
+Ptpn18 Cotl1 1
+Ptpn18 Cxcr6 1
+Ptpn18 Dok1 1
+Ptpn18 Gmfg 1
+Ptpn18 Gmfg 1
+Ptpn18 Gng2 1
+Ptpn18 Gpsm3 1
+Ptpn18 Gpsm3 1
+Ptpn18 H2-M3 1
+Ptpn18 H2-M3 1
+Ptpn18 H2-M3 1
+Ptpn18 Hcst 1
+Ptpn18 Il10rb 1
+Ptpn18 Iqgap2 1
+Ptpn18 Lfng 1
+Ptpn18 Lst1 1
+Ptpn18 MMT00075074 1
+Ptpn18 Naaa 1
+Ptpn18 Plcg2 1
+Ptpn18 Plekho1 1
+Ptpn18 Ppp1r14b 1
+Ptpn18 Rab19 1
+Ptpn18 Rgs10 1
+Ptpn18 Rims4 1
+Ptpn18 Rnase6 1
+Ptpn18 S100a9 1
+Ptpn18 Sash3 1
+Ptpn18 Slc12a8 1
+Ptpn18 Slc28a2 1
+Ptpn18 Tbxas1 1
+Ptpn18 Tmsb10 1
+Ptpn18 Tnfaip8l2 1
+Ptpn18 Ubash3b 1
+Ptpn20 Pcdha1 1
+Ptpn22 1700024P16Rik 1
+Ptpn22 AF067061 1
+Ptpn22 AK008541 1
+Ptpn22 Ccdc69 1
+Ptpn22 Ccl11 1
+Ptpn22 Cd2 1
+Ptpn22 Cd3g 1
+Ptpn22 Cd69 1
+Ptpn22 Grap2 1
+Ptpn22 Ikzf3 1
+Ptpn22 Ikzf3 1
+Ptpn22 Il7r 1
+Ptpn22 Magea3 1
+Ptpn22 Nkg7 1
+Ptpn22 Nlrc3 1
+Ptpn22 Rasgef1b 1
+Ptpn22 Satb1 1
+Ptpn22 Tspan13 1
+Ptpn23 1500010J02Rik 1
+Ptpn23 2610207I05Rik 1
+Ptpn23 4930402H24Rik 1
+Ptpn23 8430410A17Rik 1
+Ptpn23 Arf6 1
+Ptpn23 Arhgef5 1
+Ptpn23 Arl3 1
+Ptpn23 Bcl2l12 1
+Ptpn23 Dennd1a 1
+Ptpn23 Dlgap4 1
+Ptpn23 Git1 1
+Ptpn23 Git1 1
+Ptpn23 Gm1381 1
+Ptpn23 Jund 1
+Ptpn23 Mast2 1
+Ptpn23 MMT00061598 1
+Ptpn23 MMT00075355 1
+Ptpn23 Myh9 1
+Ptpn23 Myo1e 1
+Ptpn23 NM_177198 1
+Ptpn23 Ogfrl1 1
+Ptpn23 Pde4a 1
+Ptpn23 Ppp1r13l 1
+Ptpn23 Rhbdf2 1
+Ptpn23 Rhbdf2 1
+Ptpn23 Sar1b 1
+Ptpn23 Slc16a6 1
+Ptpn23 Stat5a 1
+Ptpn23 Tcfe3 1
+Ptpn23 Tomm34 1
+Ptpn3 Orm1 1
+Ptpn3 Orm3 1
+Ptpn3 Pappa 1
+Ptpn3 Tnc 1
+Ptpn6 5031414D18Rik 1
+Ptpn6 Gpr160 1
+Ptpn6 MMT00018015 1
+Ptpn6 Ucp2 1
+Ptpn7 Slc22a15 1
+Ptpn9 Tgif1 1
+Ptpra Megf10 1
+Ptprb 5330438D12Rik 1
+Ptprb 5330438D12Rik 1
+Ptprb Fhod3 1
+Ptprb Usp15 1
+Ptprc 0910001A06Rik 1
+Ptprc 0910001A06Rik 1
+Ptprc 0910001A06Rik 1
+Ptprc 4631422O05Rik 1
+Ptprc 4631422O05Rik 1
+Ptprc Actr3 1
+Ptprc AF251705 1
+Ptprc Angpt2 1
+Ptprc Ankrd57 1
+Ptprc Anxa3 1
+Ptprc Ap1s2 1
+Ptprc Ap1s2 1
+Ptprcap A130082M07Rik 1
+Ptprc Apbb1ip 1
+Ptprcap Ccl5 1
+Ptprcap Cd2 1
+Ptprcap Cd2 1
+Ptprcap Cd69 1
+Ptprcap Cxcr3 1
+Ptprcap Gpr18 1
+Ptprcap Il2rb 1
+Ptprcap Il2rb 1
+Ptprcap Ltb 1
+Ptprcap Slamf7 1
+Ptprcap Socs1 1
+Ptprcap Stat4 1
+Ptprcap Tbc1d10c 1
+Ptprcap Thy1 1
+Ptprcap Tnfrsf18 1
+Ptprc Arhgap15 1
+Ptprc BC028528 1
+Ptprc BC028528 1
+Ptprc BC028528 1
+Ptprc Bcl2a1a 1
+Ptprc Bcl2a1c 1
+Ptprc Casp4 1
+Ptprc Casp4 1
+Ptprc Cd44 1
+Ptprc Cd48 1
+Ptprc Cd48 1
+Ptprc Cd53 1
+Ptprc Cd53 1
+Ptprc Cd55 1
+Ptprc Cd86 1
+Ptprc Clec4a1 1
+Ptprc Clec4a2 1
+Ptprc Clec4a3 1
+Ptprc Clec7a 1
+Ptprc Clec7a 1
+Ptprc Clec7a 1
+Ptprc Cybb 1
+Ptprc Dock10 1
+Ptprc Dock10 1
+Ptprc Dock10 1
+Ptprc Dock11 1
+Ptprc Dock11 1
+Ptprc Dock11 1
+Ptprc Ebi3 1
+Ptprc Emr1 1
+Ptprc Emr4 1
+Ptprc ENSMUSG00000065522 1
+Ptprc Evi2a 1
+Ptprc Evl 1
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+Ptprc Filip1l 1
+Ptprc Frmd4a 1
+Ptprc Fyb 1
+Ptprc Fyb 1
+Ptprc Fyb 1
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+Ptprc Gpr65 1
+Ptprc Hmgb2 1
+Ptprc Igsf6 1
+Ptprc Il1b 1
+Ptprc Il2rg 1
+Ptprc Il2rg 1
+Ptprc Il2rg 1
+Ptprc Iqgap1 1
+Ptprc Iqgap1 1
+Ptprc Klra2 1
+Ptprc Lair1 1
+Ptprc Lcp2 1
+Ptprc Lcp2 1
+Ptprc Lilrb4 1
+Ptprc Lpcat2 1
+Ptprc Lpxn 1
+Ptprc Lpxn 1
+Ptprc Lyz1 1
+Ptprc Lyz1 1
+Ptprc Marcks 1
+Ptprc MMT00032773 1
+Ptprc MMT00054655 1
+Ptprc Mnda 1
+Ptprc Ms4a6b 1
+Ptprc Ms4a6b 1
+Ptprc Msn 1
+Ptprc Msr1 1
+Ptprc Msr1 1
+Ptprc P2ry10 1
+Ptprc Parp8 1
+Ptprc Pik3r5 1
+Ptprc Pira1 1
+Ptprc Pira1 1
+Ptprc Pira1 1
+Ptprc Pla2g4a 1
+Ptprc Pla2g4a 1
+Ptprc Plac8 1
+Ptprc Plac8 1
+Ptprc Ppbp 1
+Ptprc Rab39 1
+Ptprc Rab8b 1
+Ptprc Repin1 1
+Ptprc Samsn1 1
+Ptprc Samsn1 1
+Ptprc Sema6d 1
+Ptprc Serpinb6a 1
+Ptprc Sirpa 1
+Ptprc Sirpb1 1
+Ptprc Sirpb1 1
+Ptprc Sirpb1 1
+Ptprc Srgn 1
+Ptprc Srgn 1
+Ptprc St8sia4 1
+Ptprc Tgtp 1
+Ptprc Tmsb4x 1
+Ptprc Tmsb4x 1
+Ptprc Tnfaip8 1
+Ptprc Tnfaip8 1
+Ptprc Tnfaip8l2 1
+Ptprd 1810009A15Rik 1
+Ptprd 2210409O19Rik 1
+Ptprd 9130016M20Rik 1
+Ptprd Akr1c14 1
+Ptprd Col27a1 1
+Ptprd Cyp1a1 1
+Ptprd Hist1h1c 1
+Ptprd Hnmt 1
+Ptprd Idh1 1
+Ptprf Agpat6 1
+Ptprk Atp8b1 1
+Ptprk Osbpl9 1
+Ptprk Tcp11l2 1
+Ptprm Acaca 1
+Ptprm Glb1l3 1
+Ptprm MMS010198530 1
+Ptprn2 5830428H23Rik 1
+Ptprn2 9530049O05Rik 1
+Ptprn2 AK033688 1
+Ptprn2 AK089695 1
+Ptprn2 AK089695 1
+Ptprn2 D5Ertd577e 1
+Ptprn2 Grin3a 1
+Ptprn2 Olfr470 1
+Ptprn2 Sacs 1
+Ptprn2 Sacs 1
+Ptprn Unc13c 1
+Ptpro 1810046J19Rik 1
+Ptpro Actbl2 1
+Ptpro Blnk 1
+Ptpro Emp1 1
+Ptpro Eps8 1
+Ptpro Itih5 1
+Ptpro Klra17 1
+Ptpro Klra19 1
+Ptpro Klra2 1
+Ptpro Lpcat2 1
+Ptpro Mapre2 1
+Ptpro Mll2 1
+Ptpro MMT00010429 1
+Ptpro Ndufa3 1
+Ptpro Pdlim4 1
+Ptpro Sirpa 1
+Ptpro Sox17 1
+Ptprq Ercc8 1
+Ptprq Lcn8 1
+Ptprs 5730593F17Rik 1
+Ptprs Clasp2 1
+Ptprs Cygb 1
+Ptprs Galnt10 1
+Ptprs Itpr3 1
+Ptprs Myo9b 1
+Ptprs Nlgn2 1
+Ptprs Osbpl7 1
+Ptprs Osbpl7 1
+Ptprs Pkp3 1
+Ptprs Polb 1
+Ptprs Sqrdl 1
+Ptprt AK034704 1
+Ptprt AV265043 1
+Ptprt D5Ertd605e 1
+Ptprt Ndufb9 1
+Ptprt Tnmd 1
+Ptprz1 B3galt2 1
+Ptprz1 BC024997 1
+Ptprz1 MMS010194728 1
+Ptrf Olfr176 1
+Pts Tmem195 1
+Pttg1 0610010F05Rik 1
+Pttg1 1110031B06Rik 1
+Pttg1 1110031B06Rik 1
+Pttg1 1110031B06Rik 1
+Pttg1 3300001G02Rik 1
+Pttg1 3300001G02Rik 1
+Pttg1 4930543I03Rik 1
+Pttg1 Acyp2 1
+Pttg1 B130040O20Rik 1
+Pttg1 Camk2b 1
+Pttg1 Camk2b 1
+Pttg1 Camk2b 1
+Pttg1 Cpeb4 1
+Pttg1 Cpeb4 1
+Pttg1 Cyfip2 1
+Pttg1 Cyfip2 1
+Pttg1 Cyfip2 1
+Pttg1 Efemp1 1
+Pttg1 Ehbp1 1
+Pttg1 Fancl 1
+Pttg1 Fancl 1
+Pttg1 Fancl 1
+Pttg1 Gas7 1
+Pttg1ip Acox2 1
+Pttg1ip Epha3 1
+Pttg1 mCT16182 1
+Pttg1 Med7 1
+Pttg1 Med7 1
+Pttg1 Mfap3 1
+Pttg1 Mfap3 1
+Pttg1 MMT00009373 1
+Pttg1 MMT00009373 1
+Pttg1 MMT00048925 1
+Pttg1 Mtif2 1
+Pttg1 Mtif2 1
+Pttg1 Mtif2 1
+Pttg1 Nt5m 1
+Pttg1 Pnpt1 1
+Pttg1 Pwwp2a 1
+Pttg1 Pwwp2a 1
+Pttg1 Rshl3 1
+Pttg1 Rshl3 1
+Pttg1 Tgtp 1
+Pttg1 Tgtp 1
+Pttg1 Thg1l 1
+Pttg1 Thg1l 1
+Pttg1 Thg1l 1
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+Rcan1 Aacs 1
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+Rcan1 Antxr2 1
+Rcan1 Ctps 1
+Rcan1 Exph5 1
+Rcan1 Fermt2 1
+Rcan1 Fh1 1
+Rcan1 Hivep2 1
+Rcan1 Lpxn 1
+Rcan1 Obfc2a 1
+Rcan1 Ptgfr 1
+Rcan1 Tspan6 1
+Rcan2 Slc39a5 1
+Rcan3 Arrb1 1
+Rcan3 Dsn1 1
+Rcan3 MMT00058019 1
+Rcan3 Rab11fip5 1
+Rcan3 Slc41a1 1
+Rcan3 Smoc2 1
+Rcan3 Stmn1 1
+Rcan3 Tsc22d4 1
+Rcan3 Tuft1 1
+Rcbtb2 Dgkh 1
+Rcbtb2 Dgkh 1
+Rcbtb2 Dgkh 1
+Rchy1 Art3 1
+Rchy1 Cox18 1
+Rchy1 Gdap7 1
+Rchy1 Thap6 1
+Rcl1 Apon 1
+Rcn3 Clec11a 1
+Rcn3 Klk1 1
+Rcn3 Nipa1 1
+Rcn3 Plekhf1 1
+Rcn3 Rtn2 1
+Rcn3 Yif1b 1
+Rcor2 4930403O15Rik 1
+Rcor2 4930403O15Rik 1
+Rcor2 C78515 1
+Rcor2 Gpc5 1
+Rcor2 Grap2 1
+Rcor2 Grm5 1
+Rcsd1 5830411K21Rik 1
+Rcsd1 6720464F23Rik 1
+Rcsd1 A1300008O04Rik 1
+Rcsd1 AI119728 1
+Rcsd1 AI646485 1
+Rcsd1 AK081316 1
+Rcsd1 C230085J04Rik 1
+Rcsd1 F830004D09Rik 1
+Rcsd1 Fbxl7 1
+Rcsd1 MMT00049979 1
+Rcsd1 MMT00053294 1
+Rcsd1 Phxr4 1
+Rcsd1 Tcf12 1
+Rcsd1 Trim7 1
+Rcsd1 XM_134613 1
+Rcsd1 XM_357195 1
+Rcsd1 XM_357195 1
+Rcvrn 4930484H19Rik 1
+Rcvrn 5430428K19Rik 1
+Rcvrn MMT00005723 1
+Rcvrn MMT00043614 1
+Rdbp Cyld 1
+Rdh10 4921533L14Rik 1
+Rdh10 Cpne3 1
+Rdh10 Dhx9 1
+Rdh10 EG241041 1
+Rdh10 EG433270 1
+Rdh10 Fbxl5 1
+Rdh10 Klhl5 1
+Rdh10 Plekha1 1
+Rdh10 Serpinb1a 1
+Rdh10 Tmem70 1
+Rdh10 Tmem70 1
+Rdh10 Zmym6 1
+Rdh11 9030619P08Rik 1
+Rdh11 Acsl5 1
+Rdh11 Acss2 1
+Rdh11 Acss2 1
+Rdh11 Aqp8 1
+Rdh11 Aqp8 1
+Rdh11 Clcn6 1
+Rdh11 Dhcr7 1
+Rdh11 Dhcr7 1
+Rdh11 Fdps 1
+Rdh11 Fdps 1
+Rdh11 Hmgcs1 1
+Rdh11 Insig1 1
+Rdh11 Mum1 1
+Rdh11 Mum1 1
+Rdh11 Mum1 1
+Rdh11 NM_023431 1
+Rdh11 Nsdhl 1
+Rdh11 Pmvk 1
+Rdh11 Pmvk 1
+Rdh11 Pmvk 1
+Rdh11 Pstpip2 1
+Rdh11 Pstpip2 1
+Rdh11 Ropn1l 1
+Rdh11 Sc5d 1
+Rdh11 Sgsm1 1
+Rdh11 Sipa1l1 1
+Rdh11 Tfpi2 1
+Rdh11 Tkt 1
+Rdh11 Tlcd2 1
+Rdh12 Ncan 1
+Rdh14 1700016L15Rik 1
+Rdh16 2310040A07Rik 1
+Rdh16 AI452195 1
+Rdh16 Aipl1 1
+Rdh16 B230114P17Rik 1
+Rdh16 C8a 1
+Rdh16 Ces5 1
+Rdh16 Egfr 1
+Rdh16 Flvcr1 1
+Rdh16 Gpx5 1
+Rdh16 Kcnh3 1
+Rdh16 Me1 1
+Rdh16 Mmd2 1
+Rdh16 MMT00033197 1
+Rdh16 MMT00043082 1
+Rdh16 Pdk1 1
+Rdh16 Pgrmc2 1
+Rdh16 Plscr4 1
+Rdh16 Ppp1r3g 1
+Rdh16 Slc22a3 1
+Rdh16 Tmem135 1
+Rdh16 XM_357378 1
+Rdh16 Zfp60 1
+Rdh18 Abca8a 1
+Rdh18 Aqp9 1
+Rdh18 Ccl25 1
+Rdh18 Cyp2a12 1
+Rdh18 Cyp2d10 1
+Rdh18 Igfbp4 1
+Rdh18 Ksr2 1
+Rdh18 Map3k3 1
+Rdh18 Ppp2ca 1
+Rdh18 Rbbp7 1
+Rdh18 Rdh19 1
+Rdh18 Rdh19 1
+Rdh18 Rpia 1
+Rdh18 Tspan12 1
+Rdh19 Ahcy 1
+Rdh19 Ambp 1
+Rdh19 BC025446 1
+Rdh19 Bnip3 1
+Rdh19 Cflar 1
+Rdh19 Dpyd 1
+Rdh19 Klkb1 1
+Rdh19 Map3k3 1
+Rdh19 MMT00014600 1
+Rdh19 MMT00014600 1
+Rdh19 MMT00039886 1
+Rdh19 Nup85 1
+Rdh19 Rcl1 1
+Rdh19 Rdh18 1
+Rdh19 Rdh18 1
+Rdh19 Rdh7 1
+Rdh20 AI132189 1
+Rdh20 AI851790 1
+Rdh20 BC048355 1
+Rdh20 Cyp2d10 1
+Rdh20 Cyp2d13 1
+Rdh20 Cyp8b1 1
+Rdh20 EG226654 1
+Rdh20 Grip1 1
+Rdh20 Hsp25-ps1 1
+Rdh20 Inhbe 1
+Rdh20 Leap2 1
+Rdh20 Mbc2 1
+Rdh20 Nol3 1
+Rdh20 Rgs16 1
+Rdh20 Rhox5 1
+Rdh20 Rnf41 1
+Rdh20 RP23-34B24.1 1
+Rdh20 Smpd3 1
+Rdh20 Stat2 1
+Rdh20 Usp15 1
+Rdh20 Zfp207 1
+Rdh5 EG668033 1
+Rdh7 3830403N18Rik 1
+Rdh7 Apoc1 1
+Rdh7 Cdo1 1
+Rdh7 Hrh1 1
+Rdh7 Hsd3b7 1
+Rdh7 Rdh19 1
+Rdh7 Rdh19 1
+Rdh7 Scp2 1
+Rdh7 Sod1 1
+Rdh7 Uox 1
+Rdh9 MMS010030330 1
+Rdh9 Trib3 1
+Reck Cspg5 1
+Recql 2210419D22Rik 1
+Recql 5730419I09Rik 1
+Recql Dusp16 1
+Recql EG433229 1
+Recql Kcnj8 1
+Recql Magohb 1
+Recql Slco1a1 1
+Reep2 Arhgap8 1
+Reep3 Ado 1
+Reep3 Ankrd57 1
+Reep3 D10Ertd641e 1
+Reep3 D230019N24Rik 1
+Reep3 Ddt 1
+Reep3 Defb1 1
+Reep3 Dnajb12 1
+Reep3 Dnajb12 1
+Reep3 Gstt1 1
+Reep3 Jmjd1c 1
+Reep3 Jmjd1c 1
+Reep3 Med16 1
+Reep3 Rtkn2 1
+Reep3 Unc5b 1
+Reep3 X99384 1
+Reep3 X99384 1
+Reep3 Zwint 1
+Reep4 5033414D02Rik 1
+Reep4 Stx2 1
+Reep5 1700029I01Rik 1
+Reep5 Arl2bp 1
+Reep5 Arl2bp 1
+Reep5 Cd9 1
+Reep5 Golm1 1
+Reep5 Gusb 1
+Reep5 MMS010029708 1
+Reep5 MMS010234491 1
+Reep5 Nol3 1
+Reep5 Paip2 1
+Reep5 Ppm1h 1
+Reep5 Wfdc2 1
+Reep6 Cideb 1
+Reep6 Ggps1 1
+Reep6 Pcsk6 1
+Reep6 Pcyt1a 1
+Refbp2 9330156P08Rik 1
+Refbp2 Agl 1
+Refbp2 AK044722 1
+Refbp2 Atf6 1
+Refbp2 Atf6 1
+Refbp2 Cln3 1
+Refbp2 Cln3 1
+Refbp2 Copa 1
+Refbp2 Crp 1
+Refbp2 Dusp18 1
+Refbp2 Dusp23 1
+Refbp2 E230013L22Rik 1
+Refbp2 EG226654 1
+Refbp2 F11r 1
+Refbp2 MMS010155130 1
+Refbp2 MMT00029880 1
+Refbp2 MMT00033283 1
+Refbp2 MMT00082440 1
+Refbp2 Olfr1180 1
+Refbp2 OTTMUSG00000008584 1
+Refbp2 Ppox 1
+Refbp2 Rgs5 1
+Refbp2 Syt6 1
+Refbp2 XM_285768 1
+Reg1 1190003M12Rik 1
+Reg1 Agr2 1
+Reg1 Cpa1 1
+Reg1 Cpa2 1
+Reg1 Ctrb1 1
+Reg1 Gcg 1
+Reg1 Klk1b16 1
+Reg1 MMT00019353 1
+Reg1 Prss2 1
+Reg1 Xirp2 1
+Reg2 Chi3l3 1
+Reg2 mCT171504 1
+Reg2 Tmed6 1
+Reg3a 4932442G11Rik 1
+Reg3a Epdr1 1
+Reg3a Gata5 1
+Reg3a MMT00075449 1
+Reg3a MMT00080249 1
+Reg3a Narg2 1
+Reg3a XM_195759 1
+Reg3b 1810010M01Rik 1
+Reg3b Grm8 1
+Reg3b Ltf 1
+Reg3b Reg3g 1
+Reg3b Trat1 1
+Reg3b Trat1 1
+Reg3g 2410057H14Rik 1
+Reg3g 4631405J19Rik 1
+Reg3g Adh6a 1
+Reg3g Dmbt1 1
+Reg3g Oit1 1
+Reg3g Slc6a20a 1
+Reg4 MMS010208343 1
+Reg4 Slc7a10 1
+Rela Vps37c 1
+Relb 1200009I06Rik 1
+Relb Arid5a 1
+Relb Camk2n2 1
+Relb Casp4 1
+Relb Cd209b 1
+Relb Clec4d 1
+Relb Cxcl10 1
+Relb Flt3 1
+Relb Fscn1 1
+Relb Gpr109a 1
+Relb Ier5 1
+Relb Ikbke 1
+Relb Kcne3 1
+Relb Klf16 1
+Relb Klf6 1
+Relb MMT00061598 1
+Relb Mvp 1
+Relb Myc 1
+Relb Nfkb2 1
+Relb Nfkb2 1
+Relb Nfkb2 1
+Relb Nfkbie 1
+Relb Nfkbie 1
+Relb Pdgfrb 1
+Relb Prkcd 1
+Relb Prmt2 1
+Relb Psd4 1
+Relb Ralgds 1
+Relb Stk10 1
+Relb Sucnr1 1
+Relb Tagln2 1
+Relb Tagln2 1
+Relb Tlr2 1
+Relb Tmsb10 1
+Relb Tnfaip3 1
+Relb Tnip1 1
+Rell1 4933415E08Rik 1
+Rell1 Ccdc45 1
+Rell1 Gdf15 1
+Rell1 Impact 1
+Rell1 Nr4a1 1
+Rell1 Zkscan3 1
+Ren1 8030443L12Rik 1
+Ren1 AI594671 1
+Ren1 Cfhr3 1
+Ren1 Cxcr4 1
+Ren1 Ddx59 1
+Ren1 Dyrk3 1
+Ren1 Kiss1 1
+Ren1 Kiss1 1
+Ren1 MMT00000205 1
+Ren1 MMT00062379 1
+Ren1 MMT00067073 1
+Ren1 MMT00067073 1
+Ren1 Pm20d1 1
+Ren1 Ren2 1
+Ren1 Ren2 1
+Ren1 Ren2 1
+Ren1 Rnpep 1
+Ren1 Rnpep 1
+Ren1 Svs4 1
+Ren1 Svs4 1
+Ren1 Ubxd2 1
+Ren1 Uty 1
+Ren2 EG640370 1
+Ren2 Klhdc8a 1
+Ren2 MMT00000205 1
+Ren2 MMT00000205 1
+Ren2 MMT00052319 1
+Ren2 MMT00052319 1
+Ren2 MMT00060943 1
+Ren2 MMT00060943 1
+Ren2 MMT00060943 1
+Ren2 MMT00076063 1
+Ren2 MMT00076063 1
+Ren2 Rnpep 1
+Renbp Jazf1 1
+Renbp Tmem202 1
+Repin1 Atf3 1
+Repin1 Cyr61 1
+Repin1 Il1b 1
+Repin1 Il1b 1
+Repin1 Il1b 1
+Repin1 Nr4a1 1
+Repin1 Rgs1 1
+Rere Apoc4 1
+Rere C030015A19Rik 1
+Rere Cc2d2a 1
+Rere Lrp4 1
+Rere MMT00050414 1
+Rere Rnasen 1
+Rerg 8430419L09Rik 1
+Rerg Art4 1
+Rerg BC048546 1
+Rerg BC048546 1
+Rerg Crebl2 1
+Rerg Crebl2 1
+Rerg Crebl2 1
+Rerg Dera 1
+Rerg Dera 1
+Rerg Erp27 1
+Rerg Mgp 1
+Rerg Pde6h 1
+Resp18 4930547G20Rik 1
+Resp18 9330132A10Rik 1
+Resp18 9530056E24Rik 1
+Resp18 9530056E24Rik 1
+Resp18 A930021C24Rik 1
+Resp18 AK038003 1
+Resp18 AK038003 1
+Resp18 Ccdc62 1
+Resp18 Dnm3 1
+Resp18 Fgf16 1
+Resp18 Lce1m 1
+Resp18 Lce1m 1
+Resp18 MMS010239880 1
+Rest Adarb1 1
+Rest BB018844 1
+Rest Krr1 1
+Rest Lrig1 1
+Rest Trim33 1
+Retnla Cpe 1
+Retnla Fndc1 1
+Retsat BB517466 1
+Retsat Csad 1
+Retsat Cyp4a14 1
+Retsat Dci 1
+Retsat Dci 1
+Retsat Ech1 1
+Retsat Ehhadh 1
+Retsat Ephx1 1
+Retsat Ephx2 1
+Retsat Hsd17b4 1
+Retsat Leprel2 1
+Retsat Mmd 1
+Retsat MMT00016029 1
+Retsat Orm1 1
+Rev3l Arf6 1
+Rev3l Arhgap12 1
+Rev3l Atp6v0e 1
+Rev3l Chd9 1
+Rev3l Chd9 1
+Rev3l Cwf19l2 1
+Rev3l Dpyd 1
+Rev3l Dsg2 1
+Rev3l Ggnbp2 1
+Rev3l Hao1 1
+Rfc2 0610007L01Rik 1
+Rfc3 2310044G17Rik 1
+Rfc3 4930430E16Rik 1
+Rfc3 Apex2 1
+Rfc3 MMT00066815 1
+Rfc3 Ssbp3 1
+Rfc3 Trip13 1
+Rfc5 Kctd10 1
+Rfc5 MMT00078322 1
+Rfc5 MMT00078322 1
+Rfc5 MMT00078322 1
+Rfc5 Vps33a 1
+Rfesd 1700023H06Rik 1
+Rfesd EG639116 1
+Rfesd ENSMUSG00000071302 1
+Rfesd Glrx 1
+Rfesd Gtf2h5 1
+Rfesd Lrp2bp 1
+Rfesd Mctp1 1
+Rfesd Mctp1 1
+Rfesd Rnf24 1
+Rfesd Slc19a2 1
+Rfesd Tmem167 1
+Rfk A230083H22Rik 1
+Rfk Dhdh 1
+Rfpl4 4930404K13Rik 1
+Rfpl4 4930524O05Rik 1
+Rfpl4 Ccdc112 1
+Rfpl4 D3Ertd211e 1
+Rfpl4 Epha10 1
+Rfpl4 Epha10 1
+Rfpl4 MMT00040010 1
+Rfpl4 MMT00042270 1
+Rfpl4 MMT00042270 1
+Rfpl4 Otp 1
+Rfpl4 Rorc 1
+Rft1 AV089949 1
+Rft1 Cyyr1 1
+Rft1 Hist1h2bg 1
+Rft1 Olfr1251 1
+Rftn1 1110054M08Rik 1
+Rftn1 3300002I08Rik 1
+Rftn1 4933429K18Rik 1
+Rftn1 4933429K18Rik 1
+Rftn1 Ankrd13b 1
+Rftn1 Ankrd13b 1
+Rftn1 Ankrd13b 1
+Rftn1 Anks1b 1
+Rftn1 D730050C22Rik 1
+Rftn1 Defb29 1
+Rftn1 Defb29 1
+Rftn1 Krtap5-2 1
+Rftn1 Krtap5-2 1
+Rftn1 MMT00042937 1
+Rftn1 MMT00042937 1
+Rftn1 MMT00042937 1
+Rftn1 MMT00058793 1
+Rftn1 Mup3 1
+Rftn1 Prl5a1 1
+Rftn1 Setd2 1
+Rftn1 Susd4 1
+Rftn1 Trap1a 1
+Rftn1 Trap1a 1
+Rftn1 Ybx2 1
+Rftn2 Aox1 1
+Rftn2 Cps1 1
+Rftn2 Fzd6 1
+Rftn2 MMT00000549 1
+Rftn2 Tgfb2 1
+Rfwd2 Cyp2d10 1
+Rfwd2 EG668838 1
+Rfwd2 Hif1a 1
+Rfwd2 Impa1 1
+Rfwd2 Lypla1 1
+Rfwd2 MMT00014378 1
+Rfwd2 Psmg4 1
+Rfwd2 Rgl1 1
+Rfwd2 Uqcr 1
+Rfwd2 Vps4b 1
+Rfwd2 Zfand6 1
+Rfx1 4931409K22Rik 1
+Rfx1 6430571L13Rik 1
+Rfx1 Cbx3 1
+Rfx1 Chrac1 1
+Rfx1 Gm806 1
+Rfx1 Gna13 1
+Rfx1 Grb14 1
+Rfx1 Lhfpl4 1
+Rfx1 MMT00015015 1
+Rfx1 MMT00061618 1
+Rfx1 Mxra7 1
+Rfx1 Srp72 1
+Rfx1 Ssxb12 1
+Rfx3 8030411F24Rik 1
+Rfx4 6430573F11Rik 1
+Rfx4 A930033H14Rik 1
+Rfx4 A930033H14Rik 1
+Rfx4 Actr6 1
+Rfx4 AK051113 1
+Rfx4 AK076358 1
+Rfx4 Amdhd1 1
+Rfx4 Amdhd1 1
+Rfx4 BC021614 1
+Rfx4 BC021767 1
+Rfx4 BC062115 1
+Rfx4 Cenpm 1
+Rfx4 Cfd 1
+Rfx4 Cstb 1
+Rfx4 D630039A03Rik 1
+Rfx4 D930017K21Rik 1
+Rfx4 Hmgcr 1
+Rfx4 Lgr5 1
+Rfx4 Omd 1
+Rfx4 Pde9a 1
+Rfx4 Rin2 1
+Rfx4 Rnf125 1
+Rfx4 Slc10a2 1
+Rfx4 Stat5a 1
+Rfx4 Stat5a 1
+Rfx4 Tcp10b 1
+Rfx4 Timp3 1
+Rfx4 Zfp433 1
+Rfxap Ccnl1 1
+Rfxdc1 9330132A10Rik 1
+Rfxdc1 Actl7b 1
+Rfxdc1 B230215L15Rik 1
+Rfxdc1 B430203M17Rik 1
+Rfxdc1 B930007M17Rik 1
+Rfxdc1 Gm1323 1
+Rfxdc1 Gm1323 1
+Rfxdc1 MMT00033906 1
+Rfxdc1 MMT00033906 1
+Rg9mtd1 MMT00041235 1
+Rg9mtd1 Nat13 1
+Rg9mtd1 Tdg 1
+Rg9mtd1 Zfp51 1
+Rg9mtd1 Zfp750 1
+Rg9mtd2 Foxo6 1
+Rg9mtd3 Ccl21c 1
+Rg9mtd3 EG432768 1
+Rg9mtd3 F730047E07Rik 1
+Rg9mtd3 MMT00013301 1
+Rg9mtd3 Runx1t1 1
+Rg9mtd3 Shb 1
+Rg9mtd3 Shb 1
+Rg9mtd3 Slc43a3 1
+Rg9mtd3 Usp24 1
+Rg9mtd3 XM_194223 1
+Rg9mtd3 XM_207178 1
+Rgl1 Dll4 1
+Rgl1 Irf5 1
+Rgl1 Pcolce2 1
+Rgl1 Ptpre 1
+Rgl1 Rap2b 1
+Rgl1 Sp100 1
+Rgl2 Hmg20b 1
+Rgl2 Ik 1
+Rgl2 Smcr7 1
+Rgl2 Triobp 1
+Rgl3 Kank2 1
+Rgma NM_020568 1
+Rgma Prss8 1
+Rgmb 5930420B01Rik 1
+Rgmb Taf4b 1
+Rgn Agxt2 1
+Rgn Akr1c6 1
+Rgn Akr1c6 1
+Rgn Cyp2c37 1
+Rgn Cyp2c38 1
+Rgnef 1810073G21Rik 1
+Rgnef A930003O13Rik 1
+Rgnef AA407107 1
+Rgnef Aurka 1
+Rgnef B930095M22Rik 1
+Rgnef MMT00004409 1
+Rgnef MMT00020045 1
+Rgnef MMT00025329 1
+Rgnef Olfr64 1
+Rgnef Prox2 1
+Rgnef Spock3 1
+Rgn Emr4 1
+Rgn Ephb1 1
+Rgn Gstm6 1
+Rgn Mbl2 1
+Rgn Otc 1
+Rgn Pah 1
+Rgn Plekhf1 1
+Rgn Sqrdl 1
+Rgr 1700072E05Rik 1
+Rgr AK082325 1
+Rgr Asb7 1
+Rgr Cacna1d 1
+Rgr Tmprss11a 1
+Rgr Zfp369 1
+Rgs10 2210013O21Rik 1
+Rgs10 A730037C10Rik 1
+Rgs10 AF251705 1
+Rgs10 BC052328 1
+Rgs10 Cd84 1
+Rgs10 Clec4f 1
+Rgs10 Clec7a 1
+Rgs10 Dnase1l1 1
+Rgs10 Golm1 1
+Rgs10 Hexb 1
+Rgs10 Hvcn1 1
+Rgs10 Irf8 1
+Rgs10 Itgax 1
+Rgs10 Mgl1 1
+Rgs10 MMT00075074 1
+Rgs10 Oxct1 1
+Rgs10 P2ry14 1
+Rgs10 Pycard 1
+Rgs10 Pycard 1
+Rgs10 Tmem106a 1
+Rgs10 Tmem106a 1
+Rgs10 Tnfaip8 1
+Rgs10 Trem2 1
+Rgs10 Tsc22d4 1
+Rgs10 Vim 1
+Rgs12 Lcn13 1
+Rgs12 MMT00073565 1
+Rgs12 Olfr746 1
+Rgs13 BC011440 1
+Rgs13 Kcnip1 1
+Rgs13 Olfr204 1
+Rgs13 Sdk1 1
+Rgs13 Sdk1 1
+Rgs13 Slc1a3 1
+Rgs13 Usp10 1
+Rgs16 1810055G02Rik 1
+Rgs16 8430408G22Rik 1
+Rgs16 Hmox1 1
+Rgs16 Id3 1
+Rgs16 Klf10 1
+Rgs16 Snai2 1
+Rgs16 Snai2 1
+Rgs16 Txnip 1
+Rgs16 Zfp36 1
+Rgs17 2900057E15Rik 1
+Rgs17 Asxl3 1
+Rgs17 Asxl3 1
+Rgs17 Ebf3 1
+Rgs17 MMT00043793 1
+Rgs17 Rgs8 1
+Rgs18 Eomes 1
+Rgs18 Ppbp 1
+Rgs19 2310005E10Rik 1
+Rgs19 Akr1b8 1
+Rgs19 Aldh3b1 1
+Rgs19 Ap1s2 1
+Rgs19 Arhgap30 1
+Rgs19 Arhgdib 1
+Rgs19 Arhgdib 1
+Rgs19 B4galt4 1
+Rgs19 Camkk1 1
+Rgs19 Dse 1
+Rgs19 Frmd4a 1
+Rgs19 Fxyd5 1
+Rgs19 Gpsm3 1
+Rgs19 Gpsm3 1
+Rgs19 Me2 1
+Rgs19 Pdk3 1
+Rgs19 Pkib 1
+Rgs19 Plcg2 1
+Rgs19 Ptgir 1
+Rgs19 Ptpro 1
+Rgs19 Rab39 1
+Rgs19 Rhoh 1
+Rgs19 Rhoh 1
+Rgs19 Slc7a8 1
+Rgs1 BC013644 1
+Rgs1 C030014O09Rik 1
+Rgs1 Cebpd 1
+Rgs1 Ctgf 1
+Rgs1 Cxcr4 1
+Rgs1 Cxcr4 1
+Rgs1 Egr1 1
+Rgs1 Ifrd1 1
+Rgs1 Igfbp1 1
+Rgs1 Il1b 1
+Rgs1 S100a9 1
+Rgs1 Stk17b 1
+Rgs20 Klra16 1
+Rgs20 Vash2 1
+Rgs22 XM_138092 1
+Rgs2 Aspm 1
+Rgs2 Bcl2a1b 1
+Rgs2 Cd14 1
+Rgs2 Ctss 1
+Rgs2 Dse 1
+Rgs2 Ear1 1
+Rgs2 EG236451 1
+Rgs2 EG236451 1
+Rgs2 Entpd1 1
+Rgs2 Epb4.1l3 1
+Rgs2 Evi2a 1
+Rgs2 Fabp7 1
+Rgs2 Fabp7 1
+Rgs2 Fcgr1 1
+Rgs2 H2-M3 1
+Rgs2 Havcr2 1
+Rgs2 Lpcat2 1
+Rgs2 Ly86 1
+Rgs2 Naip2 1
+Rgs2 Pilrb1 1
+Rgs2 Pkib 1
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+Sele Ajap1 1
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+Snap91 MMT00021890 1
+Snap91 MMT00049044 1
+Snap91 Olfr584 1
+Snap91 Ptger3 1
+Snap91 Spaca1 1
+Snapc1 2210039B01Rik 1
+Snapc1 2210039B01Rik 1
+Snapc1 2810038D20Rik 1
+Snapc1 2810055F11Rik 1
+Snapc1 2810055F11Rik 1
+Snapc1 2810055F11Rik 1
+Snapc1 4930404H11Rik 1
+Snapc1 4930404H11Rik 1
+Snapc1 9630050P21Rik 1
+Snapc1 Adam4 1
+Snapc1 Adam4 1
+Snapc1 AV140144 1
+Snapc1 AV140144 1
+Snapc1 BE954240 1
+Snapc1 BE954240 1
+Snapc1 BE954240 1
+Snapc1 Colec11 1
+Snapc1 Dact1 1
+Snapc1 Dact1 1
+Snapc1 Dhrs7 1
+Snapc1 Dhrs7 1
+Snapc1 Fntb 1
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+Snapc1 Gdap5 1
+Snapc1 MMT00002192 1
+Snapc1 MMT00008776 1
+Snapc1 MMT00065088 1
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+Snapc1 Sipa1l1 1
+Snapc1 Sipa1l1 1
+Snapc1 Sipa1l1 1
+Snapc1 Slc38a6 1
+Snapc1 Slc38a6 1
+Snapc1 Snx6 1
+Snapc1 Trmt5 1
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+Snapc1 Ttc9 1
+Snapc5 Calml4 1
+Snapc5 Slc39a4 1
+Snca AK011092 1
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+Snca Bpgm 1
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+Snca Dppa2 1
+Snca Dppa2 1
+Snca Eraf 1
+Snca Eraf 1
+Snca Eraf 1
+Snca Hamp 1
+Snca Hamp 1
+Snca Hba-a1 1
+Snca Hbb-b1 1
+Snca Hbb-b1 1
+Snca Igh-6 1
+Snca MMT00011590 1
+Snca Slc4a1 1
+Snca Slc4a1 1
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+Sncb Zscan29 1
+Snd1 Ddost 1
+Snd1 Rangap1 1
+Sned1 Ceacam2 1
+Sned1 ENSMUST00000067672 1
+Sned1 Mlxipl 1
+Sned1 MMT00052485 1
+Sned1 Slc6a12 1
+Sned1 Ugt1a10 1
+Sned1 XM_126921 1
+Sned1 Zfp467 1
+Sned1 Zhx3 1
+Snf1lk 2310035P21Rik 1
+Snf1lk2 Btaf1 1
+Snf1lk2 Dhx33 1
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+Snf1lk AK081316 1
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+Snf1lk Arid5a 1
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+Snf1lk Bcl6b 1
+Snf1lk Ccrn4l 1
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+Snf1lk Klf10 1
+Snf1lk Nr4a1 1
+Snf1lk Nr4a1 1
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+Snf1lk Skil 1
+Snf1lk Slc25a25 1
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+Snhg6 Cdc45l 1
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+Snhg7 6430510M02Rik 1
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+Snhg7 Dpp7 1
+Snhg7 Dpp7 1
+Snhg7 Entpd2 1
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+Snhg7 Fcna 1
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+Snhg7 MMS010071814 1
+Snhg7 MMS010071814 1
+Snhg7 OTTMUSG00000011664 1
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+Snn Pdlim2 1
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+Snord116 1700001E04Rik 1
+Snord116 1700001F09Rik 1
+Snord116 1700094C09Rik 1
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+Snord116 Gm131 1
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+Snrpf MMT00034780 1
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+Sntg2 Ahr 1
+Sntg2 AI552429 1
+Sntg2 Aim1l 1
+Sntg2 AK020720 1
+Sntg2 Cys1 1
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+Sntg2 Gdap10 1
+Sntg2 Grhl1 1
+Sntg2 Grhl1 1
+Sntg2 ND5 1
+Sntg2 Sh3yl1 1
+Sntg2 Slc26a4 1
+Sntg2 Slc26a4 1
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+Sntg2 Tssc1 1
+Sntg2 Tssc1 1
+Sntg2 Tssc1 1
+Sntg2 XM_135639 1
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+Snurf Cadps2 1
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+Snw1 Nol5 1
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+Snx10 2610528J11Rik 1
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+Snx10 Acsl4 1
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+Snx10 B330016D10Rik 1
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+Snx10 Galnt7 1
+Snx10 Osbpl9 1
+Snx10 Phca 1
+Snx10 Prei4 1
+Snx10 Tmem150 1
+Snx11 Cyb561 1
+Snx11 Stat5a 1
+Snx11 Zfp398 1
+Snx12 1200016B10Rik 1
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+Snx12 Plekhm3 1
+Snx12 Rab15 1
+Snx16 Inhba 1
+Snx16 Nbeal1 1
+Snx18 Nbr1 1
+Snx18 Rest 1
+Snx19 Sorl1 1
+Snx20 2410004L22Rik 1
+Snx20 A830007P12Rik 1
+Snx20 Arhgap22 1
+Snx20 Arl5c 1
+Snx20 Capn1 1
+Snx20 Ccl4 1
+Snx20 Cd40 1
+Snx20 Cd72 1
+Snx20 Fxyd5 1
+Snx20 Fxyd5 1
+Snx20 H2-Ab1 1
+Snx20 Ly6c1 1
+Snx20 Lysmd2 1
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+Snx20 Ryr1 1
+Snx20 Sh3bgrl3 1
+Snx20 Slamf9 1
+Snx20 Slc15a3 1
+Snx20 Snn 1
+Snx20 Tmem176a 1
+Snx20 Tmem26 1
+Snx20 Twf2 1
+Snx21 Abcb10 1
+Snx21 Bad 1
+Snx21 Eif2s2 1
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+Snx21 MMT00017530 1
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+Snx22 Csn2 1
+Snx22 ENSMUSG00000072936 1
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+Snx22 Olfr415 1
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+Snx22 Otop2 1
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+Snx29 Cacnb1 1
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+Snx29 Gjd3 1
+Snx29 MMT00045085 1
+Snx29 Nek11 1
+Snx29 Nek11 1
+Snx29 Nek11 1
+Snx29 Wnt6 1
+Snx2 Krtcap2 1
+Snx2 NM_019643 1
+Snx31 6720456H09Rik 1
+Snx31 Cntnap2 1
+Snx31 Magea9 1
+Snx31 MMT00009535 1
+Snx31 Olfr1055 1
+Snx32 1700013N06Rik 1
+Snx32 2310057J18Rik 1
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+Snx32 2900073C17Rik 1
+Snx32 4930515G13Rik 1
+Snx32 4930515G13Rik 1
+Snx32 9630003H22Rik 1
+Snx32 Cdkn1b 1
+Snx32 D12Ertd125e 1
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+Snx32 Krt9 1
+Snx32 mCT50480 1
+Snx32 MMT00021509 1
+Snx32 Usp18 1
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+Snx4 EG637021 1
+Snx4 Igh-V3609N 1
+Snx4 MMT00078028 1
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+Snx4 Senp6 1
+Snx5 2510019K15Rik 1
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+Snx5 6330439K17Rik 1
+Snx5 8430406I07Rik 1
+Snx5 8430406I07Rik 1
+Snx5 8430406I07Rik 1
+Snx5 9030622O22Rik 1
+Snx5 Ahcy 1
+Snx5 Hao1 1
+Snx5 mCT155170 1
+Snx5 mCT155170 1
+Snx5 mCT155170 1
+Snx5 MMT00014033 1
+Snx5 Pfdn4 1
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+Snx5 Rin2 1
+Snx5 Rin2 1
+Snx6 1110002B05Rik 1
+Snx6 1110002B05Rik 1
+Snx6 1700047I17Rik1 1
+Snx6 1700047I17Rik1 1
+Snx6 1700047I17Rik1 1
+Snx6 AI447953 1
+Snx6 Cfl2 1
+Snx6 Cfl2 1
+Snx6 Mia2 1
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+Snx6 Srp54a 1
+Snx6 Srp54a 1
+Snx6 Srp54a 1
+Snx7 5730508B09Rik 1
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+Snx7 AK009002 1
+Snx7 Hist2h2aa1 1
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+Snx7 Olfm3 1
+Snx7 Ptbp2 1
+Snx7 Ptbp2 1
+Snx7 Slc35a3 1
+Snx7 Tmem77 1
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+Soat1 1700025G04Rik 1
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+Soat1 Arhgap30 1
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+Soat1 MMT00033504 1
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+Soat1 Tor1aip1 1
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+Sobp 2610037D02Rik 1
+Sobp Ikzf2 1
+Sobp Ikzf2 1
+Sobp MMT00042862 1
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+Socs1 Crtam 1
+Socs1 EG215895 1
+Socs1 Ncr1 1
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+Socs2 Onecut1 1
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+Socs3 Gadd45g 1
+Socs3 Gadd45g 1
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+Socs3 IL6_RBM_normal_None_W10 1
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+Socs3 Junb 1
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+Socs5 EG629383 1
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+Sod1 ORF28 1
+Sod1 ORF28 1
+Sod1 Pigp 1
+Sod1 XM_194273 1
+Sod1 XM_194273 1
+Sod1 XM_357089 1
+Sod3 Colec11 1
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+Sod3 XM_357089 1
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+Sorbs1 Adcy6 1
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+Sorbs1 Adcy6 1
+Sorbs1 AI452195 1
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+Sorbs1 Chkb 1
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+Sorbs1 Cpeb1 1
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+Sorbs1 MMT00071427 1
+Sorbs1 MMT00071427 1
+Sorbs1 Mobkl2b 1
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+Sorbs1 Sh3bgrl2 1
+Sorbs1 Synj2 1
+Sorbs1 Tlr5 1
+Sorbs1 Tmem141 1
+Sorbs1 Tmem184a 1
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+Sorbs1 U2af1 1
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+Sorbs3 Il7 1
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+Sorbs3 MMS010097975 1
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+Sorbs3 MMT00033380 1
+Sorbs3 MMT00038840 1
+Sorbs3 Paics 1
+Sorcs3 Gpr82 1
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+Sord 1200015F23Rik 1
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+Sord 1500003O03Rik 1
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+Sord 4933432K03Rik 1
+Sord 4933432K03Rik 1
+Sord 6330409N04Rik 1
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+Sord Scara5 1
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+Sord Shf 1
+Sord Slc24a5 1
+Sord Slc24a5 1
+Sord Slc24a5 1
+Sord Sqrdl 1
+Sord Sqrdl 1
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+Sord Tmem87a 1
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+Sorl1 9230110C19Rik 1
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+Sort1 2900042A17Rik 1
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+Sox17 Vmn2r107 1
+Sox17 Vmn2r107 1
+Sox18 Chst7 1
+Sox1 EG665828 1
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+Tmem140 Cald1 1
+Tmem140 D6Ertd160e 1
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+Tmem176a Tmem176b 1
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+Tmem176b BC006049 1
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+Tmem17 Chac2 1
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+Tmem182 5730507A11Rik 1
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+Tmem182 NM_173861 1
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+Tmem182 V1rd5 1
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+Tmem184a M6prbp1 1
+Tmem184a Mapk1ip1 1
+Tmem184a MMT00018015 1
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+Tmem184b AV371191 1
+Tmem184b AV371191 1
+Tmem184b C2cd2l 1
+Tmem184b ENSMUSG00000073100 1
+Tmem184b Fgfrl1 1
+Tmem184b Fgfrl1 1
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+Tmem184b Mesp1 1
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+Tmem184b Pla2g6 1
+Tmem184b Plxna2 1
+Tmem184b Sfmbt2 1
+Tmem184b Sfmbt2 1
+Tmem184b Slc25a19 1
+Tmem184b Sunc1 1
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+Tmem186 Slc27a1 1
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+Tmem189 Slc39a11 1
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+Tmem18 AI552429 1
+Tmem18 BB347868 1
+Tmem18 BB347868 1
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+Tmem18 MMT00009373 1
+Tmem18 Mppe1 1
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+Tmem18 Nfkbil1 1
+Tmem18 Pik3cg 1
+Tmem18 Pik3cg 1
+Tmem18 Pik3cg 1
+Tmem18 Slc26a4 1
+Tmem18 Slc26a4 1
+Tmem18 Sntg2 1
+Tmem18 Sntg2 1
+Tmem18 Socs2 1
+Tmem18 XM_147649 1
+Tmem18 XM_147649 1
+Tmem18 Xpot 1
+Tmem190 Tgfbrap1 1
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+Tmem195 Fggy 1
+Tmem195 Idh1 1
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+Tmem195 MMT00039432 1
+Tmem195 MMT00077789 1
+Tmem195 Nudt12 1
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+Tmem196 MMS010061265 1
+Tmem199 Mpdu1 1
+Tmem19 Abcc10 1
+Tmem19 Ccdc59 1
+Tmem19 Cpm 1
+Tmem19 Lyz1 1
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+Tmem19 Mbc2 1
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+Tmem19 Rdh20 1
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+Tmem19 Tmtc2 1
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+Tmem201 1700010L13Rik 1
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+Tmem201 MMT00036829 1
+Tmem201 Naip3 1
+Tmem201 Spata21 1
+Tmem201 Spata21 1
+Tmem201 Tcam1 1
+Tmem201 V1re7 1
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+Tmem202 Hkdc1 1
+Tmem204 Il17rd 1
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+Tmem205 Aasdhppt 1
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+Tmem206 Ints7 1
+Tmem206 Ints7 1
+Tmem206 Lyplal1 1
+Tmem206 MMT00001831 1
+Tmem206 MMT00019644 1
+Tmem206 MMT00019644 1
+Tmem206 MMT00019644 1
+Tmem206 Plxna2 1
+Tmem206 Plxna2 1
+Tmem206 Prox1 1
+Tmem206 Prox1 1
+Tmem206 Prox1 1
+Tmem207 1700009C05Rik 1
+Tmem207 Ccl12 1
+Tmem207 Mcpt-ps1 1
+Tmem207 Vmn2r26 1
+Tmem20 Chrdl2 1
+Tmem20 Fbxo30 1
+Tmem20 Fkbp5 1
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+Tmem216 Ndnl2 1
+Tmem218 4930459C07Rik 1
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+Tmem220 mCT4537 1
+Tmem22 Cpb2 1
+Tmem22 Itgae 1
+Tmem22 mCT152211 1
+Tmem22 MMT00061823 1
+Tmem22 MMT00061823 1
+Tmem22 Sall2 1
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+Tmem25 1810019D21Rik 1
+Tmem25 1810019D21Rik 1
+Tmem25 Cxadr 1
+Tmem25 Gas1 1
+Tmem25 Guca1b 1
+Tmem25 Nav2 1
+Tmem25 Ocln 1
+Tmem25 Siah2 1
+Tmem26 1200002N14Rik 1
+Tmem26 Bank1 1
+Tmem26 Ebi3 1
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+Tmem26 Prkx 1
+Tmem26 Rassf2 1
+Tmem26 Tmem132a 1
+Tmem28 Cxcl17 1
+Tmem29 1700019B03Rik 1
+Tmem29 Ccne2 1
+Tmem29 Crisp2 1
+Tmem2 Pawr 1
+Tmem30a AI461749 1
+Tmem30a C1galt1c1 1
+Tmem30a Cpsf6 1
+Tmem30a Fcho2 1
+Tmem30a Hiatl1 1
+Tmem30a Iigp1 1
+Tmem30a Lysmd3 1
+Tmem30a Psmc6 1
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+Tmem30b Cdkl1 1
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+Tmem30b mCT23184 1
+Tmem30b mCT23184 1
+Tmem30b Mia2 1
+Tmem30b Mia2 1
+Tmem30b Snapc1 1
+Tmem30b Snapc1 1
+Tmem30b Txndc1 1
+Tmem32 1810057I12Rik 1
+Tmem32 Bche 1
+Tmem32 Ddhd2 1
+Tmem32 E430028B21Rik 1
+Tmem32 Galnt1 1
+Tmem32 Irf2 1
+Tmem32 Lsm11 1
+Tmem32 Lsm11 1
+Tmem32 OTTMUSG00000000057 1
+Tmem32 Pde3b 1
+Tmem32 Rps20 1
+Tmem32 Rtn4 1
+Tmem32 Tgoln2 1
+Tmem32 Ube2l6 1
+Tmem32 XM_204303 1
+Tmem32 Yes1 1
+Tmem35 Aox3 1
+Tmem37 Insig2 1
+Tmem37 MMT00038313 1
+Tmem38a Cyp4f18 1
+Tmem38a Dnajb1 1
+Tmem38a Gatad2a 1
+Tmem38b Casp8ap2 1
+Tmem38b Zfp292 1
+Tmem39b Csf2ra 1
+Tmem39b Depdc5 1
+Tmem39b Lmnb1 1
+Tmem39b Sh3bp2 1
+Tmem41a Akr1c20 1
+Tmem41a Aox3 1
+Tmem41a Ap1s3 1
+Tmem41a Ccl27 1
+Tmem41a ENSMUST00000068192 1
+Tmem41a Mppe1 1
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+Tmem43 2010002N04Rik 1
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+Tmem43 2410089E03Rik 1
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+Tmem43 Afap1l2 1
+Tmem43 Arl2bp 1
+Tmem43 AW554918 1
+Tmem43 Bax 1
+Tmem43 Cep192 1
+Tmem43 Cep192 1
+Tmem43 Cks1b 1
+Tmem43 Cpne9 1
+Tmem43 Cxcl10 1
+Tmem43 Ddit4l 1
+Tmem43 Ddit4l 1
+Tmem43 Ddit4l 1
+Tmem43 Dyrk3 1
+Tmem43 Eda2r 1
+Tmem43 EG546623 1
+Tmem43 Gss 1
+Tmem43 Gtse1 1
+Tmem43 Ier5 1
+Tmem43 LEAN_MASS_sqrt 1
+Tmem43 Mcm2 1
+Tmem43 Mcm2 1
+Tmem43 MMT00007987 1
+Tmem43 NMR_LEAN_MASS 1
+Tmem43 Orm2 1
+Tmem43 Plk2 1
+Tmem43 Pparg 1
+Tmem43 Prune 1
+Tmem43 Prune 1
+Tmem43 Pxmp2 1
+Tmem43 Rcan1 1
+Tmem43 Slc1a4 1
+Tmem43 Srd5a3 1
+Tmem43 Srxn1 1
+Tmem43 Srxn1 1
+Tmem43 Tada3l 1
+Tmem43 Traf3 1
+Tmem43 Traf3 1
+Tmem43 Zmat3 1
+Tmem43 Zmat3 1
+Tmem44 BC028668 1
+Tmem44 BC028668 1
+Tmem44 MMT00072715 1
+Tmem45a 2610528E23Rik 1
+Tmem45a 5330420P17Rik 1
+Tmem45a Abi3bp 1
+Tmem45a Abi3bp 1
+Tmem45a Alcam 1
+Tmem45a Atp6v1a 1
+Tmem45a Atp6v1a 1
+Tmem45a BC027231 1
+Tmem45a BC043118 1
+Tmem45a BC043118 1
+Tmem45a BC043118 1
+Tmem45a Ccdc80 1
+Tmem45a Cggbp1 1
+Tmem45a Col8a1 1
+Tmem45a Cpox 1
+Tmem45a Ift57 1
+Tmem45a Ift57 1
+Tmem45a Impg2 1
+Tmem45a Impg2 1
+Tmem45a Ncbp2 1
+Tmem45a Nit2 1
+Tmem45a Pla1a 1
+Tmem45a Senp7 1
+Tmem45a Tctex1d2 1
+Tmem45a Tctex1d2 1
+Tmem45b Abcb1a 1
+Tmem45b Gm694 1
+Tmem45b Gm94 1
+Tmem45b Iqcg 1
+Tmem45b Rbm24 1
+Tmem45b Tspyl4 1
+Tmem50a 1700041L08Rik 1
+Tmem50a 2810452K05Rik 1
+Tmem50a 2810452K05Rik 1
+Tmem50a AI132189 1
+Tmem50a Aof2 1
+Tmem50a Arbp 1
+Tmem50a Arhgef12 1
+Tmem50a D4Wsu53e 1
+Tmem50a ENSMUSG00000076783 1
+Tmem50a Extl1 1
+Tmem50a Extl1 1
+Tmem50a Kdelc2 1
+Tmem50a Kdelc2 1
+Tmem50a Rgmb 1
+Tmem50a Rgmb 1
+Tmem50a Zfp346 1
+Tmem51 Batf3 1
+Tmem51 C1qc 1
+Tmem51 Card10 1
+Tmem51 Clcn6 1
+Tmem51 Clcn6 1
+Tmem51 Epha2 1
+Tmem51 Il16 1
+Tmem51 Masp2 1
+Tmem51 MMT00073557 1
+Tmem51 Plekhm2 1
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+Tmem51 Slc25a34 1
+Tmem51 Stambpl1 1
+Tmem53 1700040L02Rik 1
+Tmem55b 4930511A08Rik 1
+Tmem55b Apex1 1
+Tmem55b Osgep 1
+Tmem55b Osgep 1
+Tmem56 Abcb4 1
+Tmem56 Peg3 1
+Tmem57 Apob 1
+Tmem57 Apob 1
+Tmem57 Gigyf2 1
+Tmem57 Helb 1
+Tmem59l 5730510P18Rik 1
+Tmem59l Aytl1b 1
+Tmem59l D10Ertd494e 1
+Tmem59l D10Ertd494e 1
+Tmem59l D17Ertd648e 1
+Tmem59l D17Ertd648e 1
+Tmem59l EG328354 1
+Tmem59l ENSMUSG00000068293 1
+Tmem59l Gpa33 1
+Tmem59l Hoxc11 1
+Tmem59l Lingo1 1
+Tmem59l Mmp16 1
+Tmem59l MMT00049515 1
+Tmem59l MMT00064901 1
+Tmem59l Olfr811 1
+Tmem59l Olfr811 1
+Tmem59l Sh3md4 1
+Tmem59l Tcte1 1
+Tmem59l V1rc5 1
+Tmem61 Plxna3 1
+Tmem62 Fry 1
+Tmem62 Mgam 1
+Tmem62 Rbm47 1
+Tmem62 Slc25a15 1
+Tmem63b Aldh1b1 1
+Tmem63b AW548124 1
+Tmem63b Rnf10 1
+Tmem63b Sdc1 1
+Tmem63b Tpcn2 1
+Tmem63b Tsc22d1 1
+Tmem63c Kif14 1
+Tmem63c Lipi 1
+Tmem63c Syt16 1
+Tmem64 2310014D11Rik 1
+Tmem64 2410014A08Rik 1
+Tmem64 4930402H24Rik 1
+Tmem64 4931406C07Rik 1
+Tmem64 8430419L09Rik 1
+Tmem64 Cd81 1
+Tmem64 Clip2 1
+Tmem64 Cyp1a2 1
+Tmem64 Eps8 1
+Tmem64 Gclc 1
+Tmem64 Hmgcs1 1
+Tmem64 Lipa 1
+Tmem64 Lipa 1
+Tmem64 Lrat 1
+Tmem64 March8 1
+Tmem64 Nadk 1
+Tmem64 Paics 1
+Tmem64 Pdk4 1
+Tmem64 Prkca 1
+Tmem64 Rnf24 1
+Tmem64 Tmem135 1
+Tmem65 4930544F09Rik 1
+Tmem65 9930014A18Rik 1
+Tmem65 9930014A18Rik 1
+Tmem65 9930014A18Rik 1
+Tmem65 A1bg 1
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+Tmem65 Depdc6 1
+Tmem65 Xrcc6 1
+Tmem66 AI429214 1
+Tmem66 Ddhd2 1
+Tmem66 Leprotl1 1
+Tmem66 Leprotl1 1
+Tmem66 Rbpms 1
+Tmem66 Sdk2 1
+Tmem66 Slc25a15 1
+Tmem66 Slc7a2 1
+Tmem66 Thex1 1
+Tmem66 Ubxd6 1
+Tmem66 Zdhhc2 1
+Tmem68 2310030N02Rik 1
+Tmem68 B230117O15Rik 1
+Tmem68 Car8 1
+Tmem68 MMT00038794 1
+Tmem68 XM_203673 1
+Tmem70 1600012H06Rik 1
+Tmem70 1700034P13Rik 1
+Tmem70 Bet1 1
+Tmem70 Gt(ROSA)26Sor 1
+Tmem70 Ly96 1
+Tmem70 Ly96 1
+Tmem70 Sar1b 1
+Tmem71 Il7r 1
+Tmem77 Aasdhppt 1
+Tmem77 Abhd3 1
+Tmem77 Abhd5 1
+Tmem77 Acsm3 1
+Tmem77 Acsm4 1
+Tmem77 Actr6 1
+Tmem77 Bche 1
+Tmem77 Calm2 1
+Tmem77 Caprin1 1
+Tmem77 Cept1 1
+Tmem77 Cript 1
+Tmem77 Crk 1
+Tmem77 EG627624 1
+Tmem77 Hnmt 1
+Tmem77 NMR_FLUID_normal_Source+Sex_W8 1
+Tmem77 Rdh10 1
+Tmem77 Rnf11 1
+Tmem77 Sh3bgrl 1
+Tmem77 Sox12 1
+Tmem77 Stap1 1
+Tmem77 Syap1 1
+Tmem77 Tfpi2 1
+Tmem77 Tmem64 1
+Tmem77 Tpst2 1
+Tmem77 Ttc30b 1
+Tmem77 Ttrap 1
+Tmem77 Ugt2b37 1
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+Tmem82 Pqlc2 1
+Tmem82 Pqlc2 1
+Tmem82 Rap1gap 1
+Tmem82 Rap1gap 1
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+Tmem86a 2610208M17Rik 1
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+Tmem86a Cpne2 1
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+Tmem86a Hexa 1
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+Tmem86b Hsd17b7 1
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+Tmem87a Dnajc10 1
+Tmem87a Dnajc17 1
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+Tmem87a Exdl1 1
+Tmem87a Gabpb1 1
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+Tmem87a Hdc 1
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+Tmem87a MMT00034813 1
+Tmem87a MMT00034813 1
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+Tmem87a Nphp1 1
+Tmem87a Nphp1 1
+Tmem87a Nphp1 1
+Tmem87a Plcb2 1
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+Tmem87a Prom2 1
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+Tmem91 H2-Ob 1
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+Tmem91 Slc7a10 1
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+Tmem97 1700040L02Rik 1
+Tmem97 Acss2 1
+Tmem97 Acss2 1
+Tmem97 AI427122 1
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+Tmem97 Ces2 1
+Tmem97 Ces2 1
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+Tmem97 Dhcr7 1
+Tmem97 Dhcr7 1
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+Tmem97 EG226654 1
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+Tmem97 Fads1 1
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+Tmem97 Paox 1
+Tmem97 Pmvk 1
+Tmem97 Pmvk 1
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+Tmem97 Slc44a3 1
+Tmem97 Tmem106c 1
+Tmem97 Tmem14a 1
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+Tmem98 Ddah1 1
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+Tmlhe mCT59474 1
+Tmlhe MMS010225599 1
+Tmlhe N4bp2l2 1
+Tmlhe Nudt1 1
+T MMS010006233 1
+T MMT00013814 1
+T MMT00056244 1
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+Tmod4 S100a1 1
+Tmod4 S100a6 1
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+Tmpo Ftcd 1
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+Tmpo Gm872 1
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+Tmprss11e 4933424L21Rik 1
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+Tmprss11e Arhgap18 1
+Tmprss11e Gm725 1
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+Tmprss11e Met 1
+Tmprss11e MMT00002879 1
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+Tmprss11e MMT00020813 1
+Tmprss11e Nptx2 1
+Tmprss11e XM_358009 1
+Tmprss11e XM_358009 1
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+Tmprss11f Dnahc11 1
+Tmprss11f Elmod1 1
+Tmprss11f Elmod1 1
+Tmprss11f Rph3a 1
+Tmprss11f Stk33 1
+Tmprss11f Yipf7 1
+Tmprss12 6530401N04Rik 1
+Tmprss12 6530401N04Rik 1
+Tmprss12 Bmpr1b 1
+Tmprss12 Bmpr1b 1
+Tmprss12 Klhdc1 1
+Tmprss12 Mos 1
+Tmprss12 Mos 1
+Tmprss12 Odam 1
+Tmprss12 Popdc3 1
+Tmprss12 Sh3gl2 1
+Tmprss12 Smtnl1 1
+Tmprss12 Trem1 1
+Tmprss12 Vmn2r30 1
+Tmprss2 Adamts1 1
+Tmprss2 App 1
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+Tmprss2 Cldn14 1
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+Tmprss2 ENSMUSG00000074911 1
+Tmprss2 Hunk 1
+Tmprss2 MMT00066060 1
+Tmprss2 MMT00066060 1
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+Tmprss2 Mx2 1
+Tmprss2 Mx2 1
+Tmprss2 Mx2 1
+Tmprss2 N6amt1 1
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+Tmprss2 Snx33 1
+Tmprss2 Usp16 1
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+Tmprss4 Vil1 1
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+Tmprss6 Cyp2d26 1
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+Tmprss8 Olfr1275 1
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+Tmsb10 Ccdc109b 1
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+Tmsb10 Ccl19 1
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+Tmsb4x Rac2 1
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+Tmsb4x Slc2a6 1
+Tmsb4x Tmem26 1
+Tmsb4x Tubb1 1
+Tmtc1 Chtf18 1
+Tmtc2 Ccdc59 1
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+Tmtc2 Csrp2 1
+Tmtc2 Egf 1
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+Tmtc2 Ppap2c 1
+Tmtc2 Snrpf 1
+Tmtc4 Ghr 1
+Tmtc4 Nlrp12 1
+Tmtc4 Robo1 1
+Tmtc4 Tmlhe 1
+Tmub1 Pex11c 1
+Tnc Acta2 1
+Tnc Ccdc3 1
+Tnc Cd248 1
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+Tnc Ctnnal1 1
+Tnc Ctsk 1
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+Tnfaip2 AU040829 1
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+Tnfaip2 Efna1 1
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+Tnfaip2 Irf1 1
+Tnfaip2 Irf1 1
+Tnfaip2 Kifc3 1
+Tnfaip2 Lpin1 1
+Tnfaip2 MMT00047284 1
+Tnfaip2 MMT00082577 1
+Tnfaip2 Osgin1 1
+Tnfaip2 Plk2 1
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+Tnfaip2 Rhpn2 1
+Tnfaip2 Soat1 1
+Tnfaip2 Tifa 1
+Tnfaip2 Tmem16f 1
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+Tnfaip3 Amica1 1
+Tnfaip3 Ccl3 1
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+Tnfaip3 Cxcl1 1
+Tnfaip3 D17H6S56E-5 1
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+Tnfaip8 Capsl 1
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+Tnfaip8 Galnt7 1
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+Tnfaip8l1 Fgf21 1
+Tnfaip8l1 Hsp25-ps1 1
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+Tnfaip8l1 Pitpnc1 1
+Tnfaip8l1 Slc25a30 1
+Tnfaip8l1 Slc25a30 1
+Tnfaip8l2 Ccdc88a 1
+Tnfaip8l2 Scyl1bp1 1
+Tnfaip8l2 Spag11 1
+Tnfaip8 Lyz1 1
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+Tnfaip8 Msr1 1
+Tnfaip8 P2ry14 1
+Tnfaip8 Plau 1
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+Tnfaip8 Pole2 1
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+Tnfaip8 Tm6sf1 1
+Tnfaip8 Tnfrsf11b 1
+Tnfaip8 Ubtd2 1
+Tnfaip8 Wipf1 1
+Tnf Clec4d 1
+Tnf Hamp 1
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+Tnf MMS010083472 1
+Tnf MMT00044238 1
+Tnfrsf11b Efcab2 1
+Tnfrsf12a 3110001A13Rik 1
+Tnfrsf12a Ddx28 1
+Tnfrsf12a Dtymk 1
+Tnfrsf12a Ppl 1
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+Tnfrsf13b Ebi2 1
+Tnfrsf13b Hrasls3 1
+Tnfrsf13b Lat2 1
+Tnfrsf13b Pou2af1 1
+Tnfrsf13b Snn 1
+Tnfrsf14 2610204G22Rik 1
+Tnfrsf14 2810405K02Rik 1
+Tnfrsf14 AF155546 1
+Tnfrsf14 Angptl7 1
+Tnfrsf14 bw_chg_6_12_adj_s_s_sqrt_W16 1
+Tnfrsf14 MMT00044757 1
+Tnfrsf18 Cst7 1
+Tnfrsf18 Cst7 1
+Tnfrsf18 D10Ertd761e 1
+Tnfrsf18 ENSMUSG00000073019 1
+Tnfrsf18 Flt3 1
+Tnfrsf18 Nlrc4 1
+Tnfrsf18 Ptgs2 1
+Tnfrsf18 Sh2d1a 1
+Tnfrsf18 Siglec5 1
+Tnfrsf19 2600011E07Rik 1
+Tnfrsf19 5730593F17Rik 1
+Tnfrsf19 5730593F17Rik 1
+Tnfrsf19 AK012517 1
+Tnfrsf19 Card10 1
+Tnfrsf19 Gpr64 1
+Tnfrsf19 Myo1b 1
+Tnfrsf19 Nfil3 1
+Tnfrsf19 Rdh9 1
+Tnfrsf19 Rdh9 1
+Tnfrsf19 Scd2 1
+Tnfrsf19 Serpini2 1
+Tnfrsf19 Slc44a3 1
+Tnfrsf19 Slc44a3 1
+Tnfrsf1a C1s 1
+Tnfrsf1a Pzp 1
+Tnfrsf23 Olfr1448 1
+Tnfrsf25 Pgm5 1
+Tnfrsf9 Cxcl15 1
+Tnfrsf9 Olfr1316 1
+Tnfsf10 4632415L05Rik 1
+Tnfsf10 4632415L05Rik 1
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+Tnfsf10 6130401L20Rik 1
+Tnfsf10 6130401L20Rik 1
+Tnfsf10 A530082C11Rik 1
+Tnfsf10 Anxa5 1
+Tnfsf10 Exosc9 1
+Tnfsf10 Gnb4 1
+Tnfsf10 Gnb4 1
+Tnfsf10 Gnb4 1
+Tnfsf10 Mfn1 1
+Tnfsf10 MMT00033071 1
+Tnfsf10 MMT00048666 1
+Tnfsf10 MMT00048666 1
+Tnfsf10 MMT00059455 1
+Tnfsf10 Nfkbie 1
+Tnfsf10 Ppp1r14a 1
+Tnfsf10 Sirpb1 1
+Tnfsf10 Slc2a2 1
+Tnfsf10 Tnfaip8l3 1
+Tnfsf10 Trim21 1
+Tnfsf10 Tsnaxip1 1
+Tnfsf10 Ttc14 1
+Tnfsf10 Ttc14 1
+Tnfsf10 Zfp704 1
+Tnfsf10 Zmiz1 1
+Tnfsf11 a 1
+Tnfsf11 AK020752 1
+Tnfsf11 BB001228 1
+Tnfsf11 Ctxn3 1
+Tnfsf11 MMS010031922 1
+Tnfsf11 Wnt16 1
+Tnfsf13b Fhl2 1
+Tnfsf13b Tfdp1 1
+Tnfsf14 Grlf1 1
+Tnfsf14 Kcnb1 1
+Tnfsf14 Kcnb1 1
+Tnfsf14 Olfr1336 1
+Tnfsf15 EG625530 1
+Tnfsf15 Tmprss12 1
+Tnfsf8 1700010H22Rik 1
+Tnfsf8 4930544G11Rik 1
+Tnfsf8 A930009E05Rik 1
+Tnfsf8 Abi2 1
+Tnfsf8 Hcrtr2 1
+Tnfsf8 MMS010174087 1
+Tnfsf8 MMT00015608 1
+Tnfsf8 MMT00030638 1
+Tnfsf8 Slc17a4 1
+Tnfsf8 Slc17a4 1
+Tnfsf8 Vav3 1
+Tnf Tlr2 1
+Tnip1 Dhrs7b 1
+Tnip1 Dhrs7b 1
+Tnip1 Hint1 1
+Tnip1 Igk-V4 1
+Tnip1 Mapk7 1
+Tnip1 MMT00020436 1
+Tnip1 MMT00028135 1
+Tnip1 OTTMUSG00000005723 1
+Tnip1 Plscr3 1
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+Tnip1 Rapgef6 1
+Tnip1 Sparc 1
+Tnip1 Sparc 1
+Tnip1 Sparc 1
+Tnip1 Tmpo 1
+Tnip3 Bmp8a 1
+Tnip3 Cryba4 1
+Tnip3 Ifitm7 1
+Tnip3 Igfbpl1 1
+Tnip3 Stk32c 1
+Tnk2 Aspa 1
+Tnk2 Cdkn2c 1
+Tnk2 Insc 1
+Tnk2 Kctd15 1
+Tnk2 Pola1 1
+Tnk2 Trdmt1 1
+Tnks2 Acads 1
+Tnks2 Bmp7 1
+Tnks2 Gpx4 1
+Tnks2 Rbms1 1
+Tnks2 Rogdi 1
+Tnks2 Topors 1
+Tnks Creb3l2 1
+Tnks Epn2 1
+Tnn 2310040M23Rik 1
+Tnnc1 Caln1 1
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+Tnnc2 4930480K23Rik 1
+Tnnc2 Acta1 1
+Tnnc2 Acta1 1
+Tnnc2 Cfd 1
+Tnnc2 Cmya5 1
+Tnnc2 Cox6a2 1
+Tnnc2 Fbln2 1
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+Zrsr1 9130230N09Rik 1
+Zrsr1 Narg1l 1
+Zrsr2 Birc7 1
+Zrsr2 C230094A16Rik 1
+Zscan10 Faim2 1
+Zscan21 Bxdc2 1
+Zscan21 Cnpy4 1
+Zscan21 Glt1d1 1
+Zscan21 Gtf3a 1
+Zscan2 5730559C18Rik 1
+Zscan2 BC037156 1
+Zscan2 Brsk1 1
+Zscan2 C230066G23Rik 1
+Zscan4-ps1 1700025J12Rik 1
+Zscan4-ps1 4930456L15Rik 1
+Zscan4-ps1 Glipr1l1 1
+Zscan4-ps1 MMS010168722 1
+Zscan4-ps1 MMT00017780 1
+Zscan4-ps1 MMT00024412 1
+Zscan4-ps1 V1rc26 1
+Zscan5b Apaf1 1
+Zscan5b MMT00067661 1
+Zscan5b Nes 1
+Zswim2 Olfr75-ps1 1
+Zswim3 Mafb 1
+Zswim4 4933425B07Rik 1
+Zswim4 Arrdc5 1
+Zswim4 Serpinb7 1
+Zswim4 Wnt8a 1
+Zswim5 5730419F03Rik 1
+Zswim5 AK031320 1
+Zswim5 Esr2 1
+Zswim5 Mdn1 1
+Zswim5 MMT00077528 1
+Zswim5 Punc 1
+Zswim5 Tnks 1
+Zswim5 Zfp1 1
+Zufsp 1110021L09Rik 1
+Zufsp Itgav 1
+Zufsp Zfp160 1
+Zwilch AK015138 1
+Zwilch Asb9 1
+Zwilch Gpr35 1
+Zwilch Olfr1033 1
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+Zwint Ankrd24 1
+Zwint Gsto1 1
+Zwint Lrrc3 1
+Zwint Upb1 1
+Zxda 1700113I22Rik 1
+Zxda AW549542 1
+Zxda Lrrn3 1
+Zxda MMT00009268 1
+Zxda Srbd1 1
+Zxdc B4galt3 1
+Zxdc Hspa1b 1
+Zyg11b 1700024P16Rik 1
+Zyg11b 1810020D17Rik 1
+Zyg11b 2810410C14Rik 1
+Zyg11b C8b 1
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+Zyg11b Cdkn2c 1
+Zyg11b Echdc2 1
+Zyg11b Prkaa2 1
+Zyg11b Slc2a1 1
+Zyg11b Slc2a1 1
+Zyg11b Tmem125 1
+Zyg11b Tmem53 1
+Zyg11b Toe1 1
+Zzef1 Bche 1
+Zzef1 Bcl9 1
+Zzef1 Pcbp3 1
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+Zzz3 Rbbp6 1
+Zzz3 Usp33 1
diff --git a/inst/extdata/symbols.txt b/inst/extdata/symbols.txt
new file mode 100644
index 0000000..cf81aa0
--- /dev/null
+++ b/inst/extdata/symbols.txt
@@ -0,0 +1,20956 @@
+HUMAN MOUSE
+A1BG A1bg
+A1CF A1cf
+A2LD1 A2ld1
+A2M A2m
+A2ML1 Gm7298
+A3GALT2 A3galt2
+A4GALT A4galt
+A4GNT A4gnt
+AA063406 Prdx5
+AAAS Aaas
+AACS Aacs
+AADAC Aadac
+AADACL1 Aadacl1
+AADACL2 Aadacl2
+AADACL3 Aadacl3
+AADACL4 Gm13177
+AADAT Aadat
+AAED1 1110018J18Rik
+AAGAB Aagab
+AAK1 Aak1
+AAMP Aamp
+AANAT Aanat
+AAR2 0610011L14Rik
+AARD Aard
+AARS2 Aars2
+AARS Aars
+AARSD1 Aarsd1
+AASDH Aasdh
+AASDHPPT Aasdhppt
+AASS Aass
+AATF Aatf
+AATK Aatk
+AB002330 2610101N10Rik
+AB002339 C330016O10Rik
+AB006624 BC030440
+AB007861 D230004J03Rik
+AB007863 A130090K04Rik
+AB007864 1810013C15Rik
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+AB014514 AU042671
+AB014608 Parc
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+ACTL10 1700007I08Rik
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+ACVR1 Acvr1
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+ACY1 Acy1
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+ADA Ada
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+ADAMDEC1 Adamdec1
+ADAMTS10 Adamts10
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+ADAMTS13 Adamts13
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+ADAMTS15 Adamts15
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+ADAMTS7 Adamts7
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+ADAMTSL1 Adamtsl1
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+ADAMTSL3 Adamtsl3
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+ADAMTSL5 Adamtsl5
+ADAP1 Adap1
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+ADAR Adar
+ADARB1 Adarb1
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+ADC Adc
+ADCK1 Adck1
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+ADCY10 Adcy10
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+ADGB 9130014G24Rik
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+ADK Adk
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+ADO Ado
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+ADPGK Adpgk
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+ADPRM 2310004I24Rik
+ADRA1A Adra1a
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+ADRB1 Adrb1
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+ADRBK1 Adrbk1
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+ADRM1 Adrm1
+ADSL Adsl
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+ADTRP 9530008L14Rik
+AEBP1 Aebp1
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+AEN Aen
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+AF007149 2810459M11Rik
+AF038183 D15Wsu75e
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+AF052174 5530401J07Rik
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+AF318328 MMT00054490
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+AFF1 Aff1
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+AFG3L1 Afg3l1
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+AFM Afm
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+AGA Aga
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+AGBL1 Agbl1
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+AGPHD1 Agphd1
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+AGR2 Agr2
+AGR3 Agr3
+AGRN Agrn
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+AGT Agt
+AGTPBP1 Agtpbp1
+AGTR1 Agtr1a
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+AGTRAP Agtrap
+AGTRL1 Agtrl1
+AGXT2 Agxt2
+AGXT2L1 Agxt2l1
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+AGXT Agxt
+AHCTF1 Ahctf1
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+AI684822 Tbrg3
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+AIP Aip
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+AIRE Aire
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+AJAP1 Ajap1
+AJUBA Jub
+AK000539 AW146242
+AK000819 XM_354959
+AK000872 B130024G19Rik
+AK000877 2700045P11Rik
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+ALAD Alad
+ALAS1 Alas1
+ALAS2 Alas2
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+B2M B2m
+B3GALNT1 B3galnt1
+B3GALNT2 B3galnt2
+B3GALT1 B3galt1
+B3GALT2 B3galt2
+B3GALT4 B3galt4
+B3GALT5 B3galt5
+B3GALT6 B3galt6
+B3GALTL B3galtl
+B3GAT1 B3gat1
+B3GAT2 B3gat2
+B3GAT3 B3gat3
+B3GNT1 B3gnt1
+B3GNT2 B3gnt2
+B3GNT3 B3gnt3
+B3GNT4 B3gnt4
+B3GNT5 B3gnt5
+B3GNT6 B3gnt6
+B3GNT7 B3gnt7
+B3GNT8 B3gnt8
+B3GNT9 B3gnt9-ps
+B3GNTL1 6030413G23Rik
+B4GALNT1 B4galnt1
+B4GALNT2 B4galnt2
+B4GALNT3 B4galnt3
+B4GALNT4 B4galnt4
+B4GALT1 B4galt1
+B4GALT2 B4galt2
+B4GALT3 B4galt3
+B4GALT4 B4galt4
+B4GALT5 B4galt5
+B4GALT6 B4galt6
+B4GALT7 B4galt7
+B9D1 B9d1
+B9D2 B9d2
+BAALC Baalc
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+BACE1 Bace1
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+BACH1 Bach1
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+BAD Bad
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+BAG3 Bag3
+BAG4 Bag4
+BAG5 Bag5
+BAG6 Bag6
+BAHCC1 Bahcc1
+BAHD1 Bahd1
+BAI1 Bai1
+BAI2 Bai2
+BAI3 Bai3
+BAIAP2 Baiap2
+BAIAP2L1 Baiap2l1
+BAIAP2L2 Baiap2l2
+BAIAP3 Baiap3
+BAK1 Bak1
+BAMBI Bambi
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+BANF2 Banf2
+BANK1 Bank1
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+BAPX1 Bapx1
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+BATF Batf
+BAX Bax
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+BAZ1B Baz1b
+BAZ2A Baz2a
+BAZ2B Baz2b
+BBC3 Bbc3
+BBOX1 Bbox1
+BBS10 Bbs10
+BBS12 Bbs12
+BBS1 Bbs1
+BBS2 Bbs2
+BBS4 Bbs4
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+BBS9 Bbs9
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+BC019904 R74862
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+BC022570 C030019I05Rik
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+BC037827 MMT00014548
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+BC038512 MMT00060365
+BC038775 MMT00037357
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+BCAR1 Bcar1
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+BCAS3 Bcas3
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+BCCIP Bccip
+BCDIN3 Bcdin3
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+BCKDHA Bckdha
+BCKDHB Bckdhb
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+BCL10 Bcl10
+BCL11A Bcl11a
+BCL11B Bcl11b
+BCL2A1 Bcl2a1a
+BCL2 Bcl2
+BCL2L10 Bcl2l10
+BCL2L11 Bcl2l11
+BCL2L12 Bcl2l12
+BCL2L13 Bcl2l13
+BCL2L14 Bcl2l14
+BCL2L15 Bcl2l15
+BCL2L1 Bcl2l1
+BCL2L2 Bcl2l2
+BCL3 Bcl3
+BCL6B Bcl6b
+BCL6 Bcl6
+BCL7A Bcl7a
+BCL7B Bcl7b
+BCL7C Bcl7c
+BCL9 Bcl9
+BCL9L Bcl9l
+BCLAF1 Bclaf1
+BCMO1 Bcmo1
+BCO2 Bco2
+BCOR Bcor
+BCORL1 Bcorl1
+BCR Bcr
+BCS1L Bcs1l
+BDH1 Bdh1
+BDH2 Bdh2
+BDKRB1 Bdkrb1
+BDKRB2 Bdkrb2
+BDNF Bdnf
+BDP1 Bdp1
+BEAN1 Bean1
+BECN1 Becn1
+BEGAIN Begain
+BEND3 Bend3
+BEND4 Bend4
+BEND5 Bend5
+BEND6 Bend6
+BEND7 Bend7
+BEST1 Best1
+BEST2 Best2
+BEST3 Best3
+BEST4 Best4-ps
+BET1 Bet1
+BET1L Bet1l
+BET3L Bet3l
+BEX1 Bex1
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+BEX4 Bex4
+BF196512 9430067K14Rik
+BF360149 Olfr1364
+BF959359 6330532G10Rik
+BFAR Bfar
+BFSP1 Bfsp1
+BFSP2 Bfsp2
+BGLAP Bglap1
+BGN Bgn
+BHLHA15 Bhlha15
+BHLHA9 Bhlha9
+BHLHB2 Bhlhb2
+BHLHB4 Bhlhb4
+BHLHB5 Bhlhb5
+BHLHB8 Bhlhb8
+BHLHB9 Bhlhb9
+BHLHE22 Bhlhe22
+BHLHE23 Bhlhe23
+BHLHE40 Bhlhe40
+BHLHE41 Bhlhe41
+BHMT2 Bhmt2
+BHMT Bhmt
+BICC1 Bicc1
+BICD1 Bicd1
+BICD2 Bicd2
+BID Bid
+BIK Bik
+BIN1 Bin1
+BIN2 Bin2
+BIN3 Bin3
+BIRC1 Birc1b
+BIRC2 Birc2
+BIRC3 Birc3
+BIRC4 Birc4
+BIRC5 Birc5
+BIRC6 Birc6
+BIRC7 Birc7
+BIVM Bivm
+BLCAP Blcap
+BLK Blk
+BLM Blm
+BLMH Blmh
+BLNK Blnk
+BLOC1S1 Bloc1s1
+BLOC1S2 Bloc1s2
+BLOC1S3 Bloc1s3
+BLOC1S4 Bloc1s4
+BLOC1S5 Bloc1s5
+BLOC1S6 Bloc1s6
+BLR1 Blr1
+BLVRA Blvra
+BLVRB Blvrb
+BLZF1 Blzf1
+BMF Bmf
+BMI1 Bmi1
+BMP10 Bmp10
+BMP15 Bmp15
+BMP1 Bmp1
+BMP2 Bmp2
+BMP2K Bmp2k
+BMP3 Bmp3
+BMP4 Bmp4
+BMP5 Bmp5
+BMP6 Bmp6
+BMP7 Bmp7
+BMP8A Bmp8a
+BMP8B Bmp8b
+BMPER Bmper
+BMPR1A Bmpr1a
+BMPR1B Bmpr1b
+BMPR2 Bmpr2
+BMS1 Bms1
+BMS1L Bms1l
+BMX Bmx
+BNC1 Bnc1
+BNC2 Bnc2
+BNIP1 Bnip1
+BNIP2 Bnip2
+BNIP3 Bnip3
+BNIP3L Bnip3l
+BNIPL Bnipl
+BOC Boc
+BOD1 Bod1
+BOD1L1 Bod1l
+BOD1L2 Gm7843
+BOK Bok
+BOLA1 Bola1
+BOLA2 Bola2
+BOLA3 Bola3
+BOLL Boll
+BOP1 Bop1
+BORA 6720463M24Rik
+BPGM Bpgm
+BPHL Bphl
+BPI Bpi
+BPIFA1 Bpifa1
+BPIFA2 Bpifa2
+BPIFA3 Bpifa3
+BPIFB1 Bpifb1
+BPIFB2 Bpifb2
+BPIFB3 Bpifb3
+BPIFB4 Bpifb4
+BPIFB6 Bpifb6
+BPIFB9P Bpifb9a
+BPIFC Bpifc
+BPIL1 Bpil1
+BPIL2 Bpil2
+BPIL3 Bpil3
+BPNT1 Bpnt1
+BPTF Bptf
+BRAF Braf
+BRAP Brap
+BRAT1 Baat1
+BRCA1 Brca1
+BRCA2 Brca2
+BRCC3 Brcc3
+BRD1 Brd1
+BRD2 Brd2
+BRD3 Brd3
+BRD4 Brd4
+BRD7 Brd7
+BRD8 Brd8
+BRD9 AL022779
+BRDT Brdt
+BRE Bre
+BRF1 Brf1
+BRF2 Brf2
+BRI3BP Bri3bp
+BRI3 Bri3
+BRIP1 Brip1
+BRIX1 Brix1
+BRK1 6720456B07Rik
+BRMS1 Brms1
+BRMS1L Brms1l
+BROX Brox
+BRP44 Brp44
+BRP44L Brp44l
+BRPF1 Brpf1
+BRPF3 Brpf3
+BRS3 Brs3
+BRSK1 Brsk1
+BRSK2 Brsk2
+BRUNOL4 Brunol4
+BRUNOL6 Brunol6
+BRWD1 Brwd1
+BRWD2 Brwd2
+BRWD3 Brwd3
+BSCL2 Bscl2
+BSDC1 Bsdc1
+BSG AI325538
+BSN Bsn
+BSND Bsnd
+BSPH1 Bsph1
+BSPRY Bspry
+BST1 Bst1
+BST2 Bst2
+BSX Bsx
+BTAF1 Btaf1
+BTBD10 Btbd10
+BTBD11 Btbd11
+BTBD12 Btbd12
+BTBD14A Btbd14a
+BTBD14B Btbd14b
+BTBD16 Btbd16
+BTBD17 Btbd17
+BTBD18 Gm13718
+BTBD19 Btbd19
+BTBD1 Btbd1
+BTBD2 Btbd2
+BTBD3 Btbd3
+BTBD5 Btbd5
+BTBD6 Btbd6
+BTBD7 Btbd7
+BTBD8 Btbd8
+BTBD9 Btbd9
+BTC Btc
+BTD Btd
+BTF3 Btf3
+BTF3L4 Btf3l4
+BTG1 Btg1
+BTG2 Btg2
+BTG3 Btg3
+BTG4 Btg4
+BTK Btk
+BTLA Btla
+BTN1A1 Btn1a1
+BTN2A2 Btn2a2
+BTN3A2 Btnl4
+BTNL10 Btnl10
+BTNL2 Btnl2
+BTNL3 Btnl1
+BTNL9 Btnl9
+BTRC Btrc
+BUB1B Bub1b
+BUB1 Bub1
+BUB3 Bub3
+BUD13 Bud13
+BUD31 Bud31
+BVES Bves
+BX092137 2900092D14Rik
+BX105859 mCT53286
+BX110412 Nr1h5
+BXDC1 Bxdc1
+BXDC2 Bxdc2
+BXDC5 Bxdc5
+BYSL Bysl
+BZRAP1 Bzrap1
+BZRPL1 Bzrpl1
+BZW1 Bzw1
+BZW2 Bzw2
+C10orf104 D10Ertd641e
+C10orf105 Gm17455
+C10orf107 1700040L02Rik
+C10orf10 8430408G22Rik
+C10orf11 1700112E06Rik
+C10orf114 A930004D18Rik
+C10orf118 A630007B06Rik
+C10orf119 1110007A13Rik
+C10orf120 4933402N03Rik
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+C10orf130 XM_357082
+C10orf132 4933417O08Rik
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+C10orf18 BC016423
+C10orf22 Gm237
+C10orf26 D19Wsu162e
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+C10orf28 D19Ertd386e
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+C11orf1 1110032A03Rik
+C11orf11 Nt5dc3
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+C11orf17 D930014E17Rik
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+C11orf35 1600016N20Rik
+C11orf36 Mrgprg
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+C14orf104 1110034A24Rik
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+C14orf106 C79407
+C14orf108 4932432K03Rik
+C14orf109 D230037D09Rik
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+C14orf112 1810020G14Rik
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+C14orf130 5730410I19Rik
+C14orf131 2810455K09Rik
+C14orf132 D430019H16Rik
+C14orf133 AI413782
+C14orf139 MMT00003753
+C14orf140 2810002I04Rik
+C14orf142 AK010878
+C14orf143 2610021K21Rik
+C14orf145 4930534B04Rik
+C14orf147 1110002B05Rik
+C14orf148 4933437F05Rik
+C14orf149 2810055F11Rik
+C14orf152 EG544888
+C14orf153 2810002N01Rik
+C14orf155 MMT00082722
+C14orf156 1810035L17Rik
+C14orf159 9030617O03Rik
+C14orf161 4932415G16Rik
+C14orf164 Gm10332
+C14orf166 2700060E02Rik
+C14orf166B Gm6772
+C14orf169 2410016O06Rik
+C14orf172 6720458F09Rik
+C14orf173 2610204M08Rik
+C14orf174 Gm263
+C14orf176 G630016D24Rik
+C14orf179 1700019E19Rik
+C14orf180 A530016L24Rik
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+C14orf25 4921506M07Rik
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+C14orf32 C130032J12Rik
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+C14orf38 4930403N07Rik
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+C14orf46 5830457H20Rik
+C14orf49 4831426I19Rik
+C14orf50 Gm70
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+C14orf78 AI450948
+C14orf79 BC022687
+C14orf80 4930427A07Rik
+C14orf83 6330442E10Rik
+C14orf8 Gm77
+C14orf93 4931414P19Rik
+C14orf94 D14Ertd500e
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+C15orf17 2310046O06Rik
+C15orf20 AI449441
+C15orf23 D2Ertd750e
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+C15orf26 1700026D08Rik
+C15orf27 AI118078
+C15orf29 2410042D21Rik
+C15orf32 4930432H15Rik
+C15orf33 4930525F21Rik
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+C15orf41 BC052040
+C15orf42 5730590G19Rik
+C15orf43 4933406J08Rik
+C15orf44 2010321M09Rik
+C15orf48 AA467197
+C15orf52 A430105I19Rik
+C15orf55 BC125332
+C15orf57 Ccdc32
+C15orf59 6030419C18Rik
+C15orf60 2410076I21Rik
+C15orf61 2300009A05Rik
+C15orf62 Gm14137
+C15orf63 2310003F16Rik
+C16orf13 0610011F06Rik
+C16orf24 BC008155
+C16orf28 Unkl
+C16orf30 BC054438
+C16orf33 3300001G02Rik
+C16orf34 D17Ertd441e
+C16orf35 Mare
+C16orf42 0610007P22Rik
+C16orf44 BC025816
+C16orf45 2900011O08Rik
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+C9orf21 1110018J18Rik
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+CCP110 6330503K22Rik
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+CD101 Cd101
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+CD14 Cd14
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+CD160 Cd160
+CD163 Cd163
+CD164 Cd164
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+CD177 Cd177
+CD180 Cd180
+CD19 Cd19
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+CD200 Cd200
+CD200R1 Cd200r1
+CD200R1L Cd200r2
+CD207 Cd207
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+CD226 Cd226
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+CD274 Cd274
+CD276 Cd276
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+CD27 Cd27
+CD28 Cd28
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+CD47 Cd47
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+CD52 Cd52
+CD53 Cd53
+CD55 AW476241
+CD59 Cd59b
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+CD63 Cd63
+CD68 Cd68
+CD69 Cd69
+CD6 Cd6
+CD70 Cd70
+CD72 Cd72
+CD74 Cd74
+CD79A Cd79a
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+CD7 Cd7
+CD80 Cd80
+CD81 Cd81
+CD82 Cd82
+CD83 Cd83
+CD84 Cd84
+CD86 Cd86
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+CD93 Cd93
+CD96 Cd96
+CD97 Cd97
+CD99 Cd99
+CD99L2 Cd99l2
+CD9 Cd9
+CDA Cda
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+CDC2L1 Cdc2l1
+CDC2L5 Cdc2l5
+CDC2L6 Cdc2l6
+CDC34 Cdc34
+CDC37 Cdc37
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+CDCA1 Cdca1
+CDCA2 Cdca2
+CDCA3 Cdca3
+CDCA4 Cdca4
+CDCA5 Cdca5
+CDCA7 Cdca7
+CDCA7L Cdca7l
+CDCA8 Cdca8
+CDCP1 Cdcp1
+CDCP2 Cdcp2
+CDH10 Cdh10
+CDH11 Cdh11
+CDH12 Cdh12
+CDH13 Cdh13
+CDH15 Cdh15
+CDH16 Cdh16
+CDH17 Cdh17
+CDH18 B230220E17Rik
+CDH19 Cdh19
+CDH1 Cdh1
+CDH20 Cdh20
+CDH22 Cdh22
+CDH23 Cdh23
+CDH24 Cdh24
+CDH26 Cdh26
+CDH2 Cdh2
+CDH3 Cdh3
+CDH4 Cdh4
+CDH5 Cdh5
+CDH6 Cdh6
+CDH7 Cdh7
+CDH8 Cdh8
+CDH9 Cdh9
+CDHR1 Cdhr1
+CDHR2 Cdhr2
+CDHR3 Cdhr3
+CDHR4 Cdhr4
+CDHR5 Cdhr5
+CDIPT Cdipt
+CDK10 Cdk10
+CDK11B Cdk11b
+CDK12 Cdk12
+CDK13 Cdk13
+CDK14 Cdk14
+CDK15 Cdk15
+CDK16 Cdk16
+CDK17 Cdk17
+CDK18 Cdk18
+CDK19 Cdk19
+CDK1 Cdk1
+CDK20 Cdk20
+CDK2AP1 Cdk2ap1
+CDK2AP2 Cdk2ap2
+CDK2 Cdk2
+CDK3 Cdk3
+CDK4 Cdk4
+CDK5 Cdk5
+CDK5R1 Cdk5r1
+CDK5R2 Cdk5r2
+CDK5RAP1 Cdk5rap1
+CDK5RAP2 Cdk5rap2
+CDK5RAP3 Cdk5rap3
+CDK6 Cdk6
+CDK7 Cdk7
+CDK8 Cdk8
+CDK9 Cdk9
+CDKAL1 Cdkal1
+CDKL1 Cdkl1
+CDKL2 Cdkl2
+CDKL3 Cdkl3
+CDKL4 Cdkl4
+CDKL5 Cdkl5
+CDKN1A Cdkn1a
+CDKN1B Cdkn1b
+CDKN1C Cdkn1c
+CDKN2A Cdkn2a
+CDKN2AIP Cdkn2aip
+CDKN2AIPNL Cdkn2aipnl
+CDKN2B Cdkn2b
+CDKN2C Cdkn2c
+CDKN2D Cdkn2d
+CDKN3 Cdkn3
+CDNF Cdnf
+CDO1 Cdo1
+CDON Cdon
+CDPF1 2210021J22Rik
+CDR1 Cdr1
+CDR2 Cdr2
+CDR2L Cdr2l
+CDR3 Cdr3
+CDRT4 Cdrt4
+CDS1 Cds1
+CDS2 Cds2
+CDSN Cdsn
+CDT1 Cdt1
+CDV3 Cdv3
+CDX1 Cdx1
+CDX2 Cdx2
+CDX4 Cdx4
+CDYL2 Cdyl2
+CDYL Cdyl
+CEACAM16 Ceacam16
+CEACAM18 Ceacam18
+CEACAM19 Ceacam19
+CEACAM1 Ceacam1
+CEACAM20 Ceacam20
+CEACAM3 Ceacam3
+CEACAM4 ENSMUST00000066413
+CEACAM7 Ceacam14
+CEBPA Cebpa
+CEBPB Cebpb
+CEBPD Cebpd
+CEBPE Cebpe
+CEBPG Cebpg
+CEBPZ Cebpz
+CECR2 Cecr2
+CECR5 Cecr5
+CECR6 Cecr6
+CEECAM1 Ceecam1
+CELA1 Cela1
+CELA2A Cela2a
+CELA3A Gm13011
+CELA3B Cela3b
+CEL Cel
+CELF1 Celf1
+CELF2 Celf2
+CELF3 Celf3
+CELF4 Celf4
+CELF5 Celf5
+CELF6 Celf6
+CELSR1 Celsr1
+CELSR2 Celsr2
+CELSR3 Celsr3
+CEND1 Cend1
+CENPA Cenpa
+CENPB Cenpb
+CENPC1 Cenpc1
+CENPE Cenpe
+CENPF Cenpf
+CENPH Cenph
+CENPI Cenpi
+CENPJ Cenpj
+CENPK Cenpk
+CENPL Cenpl
+CENPM Cenpm
+CENPN 2610510J17Rik
+CENPO Cenpo
+CENPP Cenpp
+CENPQ Cenpq
+CENPT Cenpt
+CENPV Cenpv
+CENPW Cenpw
+CENTA1 Centa1
+CENTA2 Centa2
+CENTB1 Centb1
+CENTB2 Centb2
+CENTB5 Centb5
+CENTD1 Centd1
+CENTD3 Centd3
+CENTG1 Centg1
+CENTG2 Centg2
+CENTG3 Centg3
+CEP104 BC046331
+CEP110 Cep110
+CEP112 Ccdc46
+CEP120 Cep120
+CEP128 4930534B04Rik
+CEP135 Cep135
+CEP152 Cep152
+CEP164 Cep164
+CEP170 Cep170
+CEP19 1500031L02Rik
+CEP192 Cep192
+CEP250 Cep250
+CEP27 Cep27
+CEP290 Cep290
+CEP350 Cep350
+CEP41 Tsga14
+CEP44 BC088983
+CEP55 Cep55
+CEP57 Cep57
+CEP57L1 Cep57l1
+CEP63 Cep63
+CEP68 Cep68
+CEP70 Cep70
+CEP72 4933440J22Rik
+CEP76 Cep76
+CEP78 Cep78
+CEP85 Ccdc21
+CEP85L Gm9766
+CEP89 Ccdc123
+CEP95 Ccdc45
+CEP97 Cep97
+CEPT1 Cept1
+CER1 Cer1
+CERCAM Cercam
+CERK Cerk
+CERKL Cerkl
+CERS1 Lass1
+CERS2 Lass2
+CERS3 Lass3
+CERS4 Lass4
+CERS5 Lass5
+CERS6 Lass6
+CES1 Ces1g
+CES2 Ces2h
+CES3 Ces1d
+CES4A Ces4a
+CES5A Ces5a
+CES7 Ces7
+CETN1 Cetn1
+CETN2 Cetn2
+CETN3 Cetn3
+CETN4P Cetn4
+CFB Cfb
+CFC1 Cfc1
+CFD Cfd
+CFDP1 Cfdp1
+CFH Cfh
+CFHR1 Cfhr1
+CFI Cfi
+CFL1 Cfl1
+CFL2 Cfl2
+CFLAR Cflar
+CFM1 Cfm1
+CFP Cfp
+CFTR Cftr
+CGA Cga
+CGGBP1 Cggbp1
+CGN Cgn
+CGNL1 Cgnl1
+CGREF1 Cgref1
+CGRRF1 Cgrrf1
+CH25H Ch25h
+CHAC1 Chac1
+CHAC2 2510006C20Rik
+CHAD Chad
+CHADL Chadl
+CHAF1A Chaf1a
+CHAF1B Chaf1b
+CHAMP1 Zfp828
+CHAT Chat
+CHCHD10 Chchd10
+CHCHD1 Chchd1
+CHCHD2 Chchd2
+CHCHD3 Chchd3
+CHCHD4 Chchd4
+CHCHD5 Chchd5
+CHCHD6 Chchd6
+CHCHD7 Chchd7
+CHCHD8 Chchd8
+CHD1 Chd1
+CHD1L Chd1l
+CHD2 Chd2
+CHD3 Chd3
+CHD4 Chd4
+CHD5 Chd5
+CHD6 Chd6
+CHD7 Chd7
+CHD8 Chd8
+CHD9 Chd9
+CHDH Chdh
+CHEK1 Chek1
+CHEK2 Chek2
+CHERP Cherp
+CHES1 Ches1
+CHFR Chfr
+CHGA Chga
+CHGB Chgb
+CHI3L1 Chi3l1
+CHIA Chia
+CHIC1 Chic1
+CHIC2 Chic2
+CHID1 3110023E09Rik
+CHIT1 Chit1
+CHKA Chka
+CHKB Chkb
+CHKB-CPT1B BC090627
+CHL1 Chl1
+CHM Chm
+CHML Chml
+CHMP1A Chmp1a
+CHMP1B Chmp1b
+CHMP2A Chmp2a
+CHMP2B Chmp2b
+CHMP3 Chmp3
+CHMP4B Chmp4b
+CHMP4C 2310010I16Rik
+CHMP5 Chmp5
+CHMP6 Chmp6
+CHMP7 Chmp7
+CHN1 Chn1
+CHN2 Chn2
+CHODL Chodl
+CHORDC1 Chordc1
+CHP1 1500003O03Rik
+CHP2 2010110P09Rik
+CHPF2 Chpf2
+CHPF Chpf
+CHPT1 Chpt1
+CHRAC1 Chrac1
+CHRD Chrd
+CHRDL1 Chrdl1
+CHRDL2 Chrdl2
+CHRM1 Chrm1
+CHRM2 Chrm2
+CHRM3 Chrm3
+CHRM4 Chrm4
+CHRM5 Chrm5
+CHRNA10 Chrna10
+CHRNA1 Chrna1
+CHRNA2 Chrna2
+CHRNA3 Chrna3
+CHRNA4 Chrna4
+CHRNA5 Chrna5
+CHRNA6 Chrna6
+CHRNA7 Chrna7
+CHRNA9 Chrna9
+CHRNB1 Chrnb1
+CHRNB2 Chrnb2
+CHRNB3 Chrnb3
+CHRNB4 Chrnb4
+CHRND Chrnd
+CHRNE Chrne
+CHRNG Chrng
+CHST10 Chst10
+CHST11 Chst11
+CHST12 Chst12
+CHST13 1110067M19Rik
+CHST14 Chst14
+CHST15 Chst15
+CHST1 Chst1
+CHST2 Chst2
+CHST3 Chst3
+CHST4 Chst4
+CHST5 Chst5
+CHST7 Chst7
+CHST8 Chst8
+CHST9 Chst9
+CHSY1 Chsy1
+CHSY3 Chsy3
+CHTF18 Chtf18
+CHTF8 Chtf8
+CHTOP 2500003M10Rik
+CHUK Chuk
+CHURC1 Churc1
+CHX10 Chx10
+CIAO1 Ciao1
+CIAPIN1 Ciapin1
+CIAS1 Cias1
+CIB1 Cib1
+CIB2 Cib2
+CIB3 Cib3
+CIB4 Cib4
+CIC Cic
+CIDEA Cidea
+CIDEB Cideb
+CIDEC Cidec
+CIITA C2ta
+CILP2 Cilp2
+CILP Cilp
+CINP Cinp
+CIR1 Cir1
+CIRBP Cirbp
+CIRH1A Cirh1a
+CISD1 Cisd1
+CISD2 Cisd2
+CISD3 Cisd3
+CISH Cish
+CIT Cit
+CITED1 Cited1
+CITED2 Cited2
+CITED4 Cited4
+CIZ1 Ciz1
+CKAP1 Ckap1
+CKAP2 AA501227
+CKAP2L Ckap2l
+CKAP4 Ckap4
+CKAP5 Ckap5
+CKB Ckb
+CKLF Cklf
+CKM Ckm
+CKMT1B Ckmt1
+CKMT2 Ckmt2
+CKS1B Cks1b
+CKS1BP6 Gm6531
+CKS2 Cks2
+CLASP1 Clasp1
+CLASP2 Clasp2
+CLASRP Clasrp
+CLCA1 Clca1
+CLCA2 Clca5
+CLCA3 Clca4
+CLCA4 AI747448
+CLCC1 Clcc1
+CLC Clc
+CLCF1 Clcf1
+CLCN1 Clcn1
+CLCN2 Clcn2
+CLCN3 Clcn3
+CLCN4 Clcn4-2
+CLCN5 Clcn5
+CLCN6 Clcn6
+CLCN7 Clcn7
+CLCNKA Clcnka
+CLCNKB Clcnkb
+CLDN10 Cldn10
+CLDN11 Cldn11
+CLDN12 Cldn12
+CLDN14 Cldn14
+CLDN15 Cldn15
+CLDN16 Cldn16
+CLDN17 Cldn17
+CLDN18 Cldn18
+CLDN19 Cldn19
+CLDN1 Cldn1
+CLDN20 Cldn20
+CLDN22 Cldn22
+CLDN23 Cldn23
+CLDN24 Cldn24
+CLDN2 Cldn2
+CLDN3 Cldn3
+CLDN4 Cldn4
+CLDN5 Cldn5
+CLDN6 Cldn6
+CLDN7 Cldn7
+CLDN8 Cldn8
+CLDN9 Cldn9
+CLDND1 Cldn25
+CLDND2 Cldnd2
+CLEC10A Mgl2
+CLEC11A Clec11a
+CLEC12A Clec12a
+CLEC12B Clec12b
+CLEC14A Clec14a
+CLEC16A Clec16a
+CLEC18A Clec18a
+CLEC1A Clec1a
+CLEC1B Clec1b
+CLEC2D Clec2i
+CLEC2L Clec2l
+CLEC3A Clec3a
+CLEC3B Clec3b
+CLEC4A Clec4a2
+CLEC4C Clec4a1
+CLEC4D Clec4d
+CLEC4E Clec4e
+CLEC4F Clec4f
+CLEC4G Clec4g
+CLEC5A Clec5a
+CLEC6A Clec4n
+CLEC7A Clec7a
+CLEC9A Clec9a
+CLGN Clgn
+CLIC1 Clic1
+CLIC3 Clic3
+CLIC4 Clic4
+CLIC5 Clic5
+CLIC6 Clic6
+CLINT1 Clint1
+CLIP1 Clip1
+CLIP2 Clip2
+CLIP3 Clip3
+CLIP4 Clip4
+CLK1 Clk1
+CLK2 Clk2
+CLK3 Clk3
+CLK4 Clk4
+CLMN Clmn
+CLMP 9030425E11Rik
+CLN3 Cln3
+CLN5 Cln5
+CLN6 Cln6
+CLN8 Cln8
+CLNK Clnk
+CLNS1A Clns1a
+CLOCK Clock
+CLP1 Clp1
+CLPB Clpb
+CLPP Clpp
+CLPS Clps
+CLPSL2 Gm749
+CLPTM1 Clptm1
+CLPTM1L Clptm1l
+CLPX Clpx
+CLRN1 Clrn1
+CLRN2 Clrn2
+CLRN3 Clrn3
+CLSPN Clspn
+CLSTN1 Clstn1
+CLSTN2 Clstn2
+CLSTN3 Clstn3
+CLTA Clta
+CLTB Cltb
+CLTC Cltc
+CLUAP1 Cluap1
+CLU Clu
+CLUL1 MMT00070731
+CLVS1 Clvs1
+CLVS2 Clvs2
+CLYBL Clybl
+CMA1 Cma1
+CMAH Cmah
+CMAS Cmas
+CMBL Cmbl
+CMC1 Cmc1
+CMC2 2310061C15Rik
+CMIP Cmip
+CMKLR1 Cmklr1
+CMPK1 Cmpk1
+CMPK2 Cmpk2
+CMPK Cmpk
+CMTM1 Cmtm1
+CMTM2 Cmtm2a
+CMTM3 Cmtm3
+CMTM4 Cmtm4
+CMTM5 Cmtm5
+CMTM6 Cmtm6
+CMTM7 Cmtm7
+CMTM8 Cmtm8
+CMYA1 Cmya1
+CMYA3 Cmya3
+CMYA5 Cmya5
+CNBD1 MMT00025179
+CNBP Cnbp
+CNDP1 Cndp1
+CNDP2 Cndp2
+CNEP1R1 Tmem188
+CNFN Cnfn
+CNGA1 Cnga1
+CNGA2 Cnga2
+CNGA3 Cnga3
+CNGA4 Cnga4
+CNGB1 Cngb1
+CNGB3 Cngb3
+CNIH2 Cnih2
+CNIH3 Cnih3
+CNIH4 Cnih4
+CNIH Cnih
+CNKSR1 Cnksr1
+CNKSR2 Cnksr2
+CNKSR3 Cnksr3
+CNN1 Cnn1
+CNN2 Cnn2
+CNN3 Cnn3
+CNNM1 Cnnm1
+CNNM2 Cnnm2
+CNNM3 Cnnm3
+CNNM4 Cnnm4
+CNO Cno
+CNOT10 Cnot10
+CNOT1 Cnot1
+CNOT2 Cnot2
+CNOT3 Cnot3
+CNOT4 Cnot4
+CNOT6 Cnot6
+CNOT6L Cnot6l
+CNOT7 Cnot7
+CNOT8 Cnot8
+CNP Cnp
+CNPPD1 1810031K17Rik
+CNPY1 Cnpy1
+CNPY2 Cnpy2
+CNPY3 Cnpy3
+CNPY4 Cnpy4
+CNR1 Cnr1
+CNR2 Cnr2
+CNRIP1 Cnrip1
+CNST Cnst
+CNTD1 Cntd1
+CNTF Cntf
+CNTFR Cntfr
+CNTLN Cntln
+CNTN1 Cntn1
+CNTN2 Cntn2
+CNTN3 Cntn3
+CNTN4 Cntn4
+CNTN5 Cntn5
+CNTN6 Cntn6
+CNTNAP1 Cntnap1
+CNTNAP2 Cntnap2
+CNTNAP3 Cntnap3
+CNTNAP4 Cntnap4
+CNTNAP5 Cntnap5a
+CNTRL Cep110
+CNTROB Cntrob
+COA5 6330578E17Rik
+COASY Coasy
+COBL Cobl
+COBLL1 Cobll1
+COBRA1 Cobra1
+COCH Coch
+COG1 Cog1
+COG2 Cog2
+COG3 Cog3
+COG4 Cog4
+COG5 Cog5
+COG6 Cog6
+COG7 Cog7
+COG8 Cog8
+COIL Coil
+COL10A1 Col10a1
+COL11A1 Col11a1
+COL11A2 Col11a2
+COL12A1 Col12a1
+COL13A1 Col13a1
+COL14A1 Col14a1
+COL15A1 Col15a1
+COL16A1 Col16a1
+COL17A1 Col17a1
+COL18A1 Col18a1
+COL19A1 Col19a1
+COL1A1 Col1a1
+COL1A2 Col1a2
+COL20A1 Col20a1
+COL22A1 Col22a1
+COL23A1 Col23a1
+COL24A1 Col24a1
+COL25A1 Col25a1
+COL27A1 Col27a1
+COL28A1 Col28a1
+COL2A1 Col2a1
+COL3A1 Col3a1
+COL4A1 Col4a1
+COL4A2 Col4a2
+COL4A3BP Col4a3bp
+COL4A3 Col4a3
+COL4A4 Col4a4
+COL4A5 Col4a5
+COL4A6 Col4a6
+COL5A1 Col5a1
+COL5A2 Col5a2
+COL5A3 Col5a3
+COL6A1 Col6a1
+COL6A2 Col6a2
+COL6A3 Col6a3
+COL6A4P1 Col6a4
+COL6A5 Col6a5
+COL6A6 Col6a6
+COL7A1 Col7a1
+COL8A1 Col8a1
+COL8A2 Col8a2
+COL9A1 Col9a1
+COL9A2 Col9a2
+COL9A3 Col9a3
+COLEC10 Colec10
+COLEC11 Colec11
+COLEC12 Colec12
+COLQ Colq
+COMMD10 Commd10
+COMMD1 Commd1
+COMMD2 Commd2
+COMMD3 Commd3
+COMMD4 Commd4
+COMMD5 Commd5
+COMMD6 Commd6
+COMMD7 Commd7
+COMMD8 Commd8
+COMMD9 Commd9
+COMP Comp
+COMT Comt
+COMTD1 Comtd1
+Contig1111_RC 1700025K23Rik
+Contig1152_RC AK040871
+Contig12742_RC 4930452B06Rik
+Contig12820_RC AK220210
+Contig1320_RC 1110005A03Rik
+Contig13488_RC 4921511I16Rik
+Contig14015_RC 9630037P07Rik
+Contig14060_RC EG545802
+Contig14796_RC NM_177774
+Contig16161_RC 4930578N16Rik
+Contig17333_RC 2900078E11Rik
+Contig20846_RC C80638
+Contig21192 AI663975
+Contig21835_RC 2310002J21Rik
+Contig21988_RC AU020772
+Contig22175_RC Atp5o
+Contig23074_RC MMT00021760
+Contig23085_RC MMT00016295
+Contig23421_RC 2610100L16Rik
+Contig23475_RC A930021H16Rik
+Contig24252_RC 2210020M01Rik
+Contig24376_RC 6430514M23Rik
+Contig24594_RC A730046J19Rik
+Contig25809_RC 6330403M23Rik
+Contig26141_RC AI841971
+Contig26622_RC AY096003
+Contig26837_RC 2610203C20Rik
+Contig27082_RC E130009J12Rik
+Contig27591_RC XM_205232
+Contig27616_RC A830093I24Rik
+Contig27925_RC mCT51706
+Contig28226_RC 1200009F10Rik
+Contig28319_RC 1700129I04Rik
+Contig28418_RC EG623131
+Contig28483_RC 4933407P14Rik
+Contig2883_RC 2210013O21Rik
+Contig30023_RC BC042782
+Contig30593_RC 5730480H06Rik
+Contig31122_RC AI465301
+Contig31337_RC AI507611
+Contig31398_RC A330021E22Rik
+Contig31613_RC 1700029I15Rik
+Contig31661_RC 0610010E21Rik
+Contig31699_RC 4930424G05Rik
+Contig31771_RC AI595406
+Contig31979_RC MMT00074673
+Contig32123_RC D11Bwg0517e
+Contig32183_RC 2810457I06Rik
+Contig3228_RC 5630401D06Rik
+Contig32322_RC Dep1
+Contig32348 A230079K17Rik
+Contig32439_RC D830044I16Rik
+Contig32577_RC D030020J04Rik
+Contig32706_RC MMT00072326
+Contig32951_RC MMT00057885
+Contig33240_RC BC049635
+Contig33703_RC A430107D22Rik
+Contig33811_RC BC030870
+Contig33975_RC 9330180L21Rik
+Contig34129_RC A930001N09Rik
+Contig3464_RC MMT00067831
+Contig34719_RC BC048599
+Contig34986_RC 9630031F12Rik
+Contig35053_RC Surf5
+Contig35897_RC 4933429F08Rik
+Contig36101_RC 1700007E06Rik
+Contig36590_RC 4930549C01Rik
+Contig36739_RC MMT00043485
+Contig36836_RC A930001N09Rik
+Contig37117 4921506J03Rik
+Contig37371_RC C130065N10Rik
+Contig37397_RC MMT00014865
+Contig37538_RC 3732412D22Rik
+Contig37858_RC G430022H21Rik
+Contig37991_RC C80638
+Contig38029_RC 4930544O15Rik
+Contig3834_RC 2610001J05Rik
+Contig38476_RC 2610100L16Rik
+Contig38493_RC 3526401B18Rik
+Contig38557_RC A730055C05Rik
+Contig38580_RC B930095G15Rik
+Contig38613_RC D4Ertd429e
+Contig38736_RC 1700106J16Rik
+Contig38965_RC 6330531I01Rik
+Contig39100_RC MMT00076032
+Contig39193_RC MMS010141179
+Contig39591_RC 4921517N04Rik
+Contig398_RC C79267
+Contig40221_RC Gm1450
+Contig40600_RC D930049A15Rik
+Contig40673_RC AU042651
+Contig40929_RC B130024G19Rik
+Contig40964_RC MMT00049375
+Contig41210_RC AB041803
+Contig41360_RC mCT52294
+Contig41498_RC B230337E12Rik
+Contig41762 2310007A19Rik
+Contig41818_RC D830015G05Rik
+Contig41905_RC BC062109
+Contig41954_RC 6330411E07Rik
+Contig42012_RC D130004H04Rik
+Contig42270_RC 2900024O10Rik
+Contig42297_RC AW742931
+Contig42366_RC MMT00037357
+Contig42430_RC 2900009J20Rik
+Contig42559_RC AW125296
+Contig43034_RC C130071C03Rik
+Contig43096_RC 1700023L04Rik
+Contig43410_RC MMT00001950
+Contig43416_RC mCT165025
+Contig43540_RC BC028663
+Contig43542_RC A630082K20Rik
+Contig43586_RC 4930461P20Rik
+Contig43645_RC Tyki
+Contig43807_RC H47
+Contig43833_RC BC049816
+Contig44415 4930546H06Rik
+Contig44652_RC AK036014
+Contig44729_RC 1100001G20Rik
+Contig44758_RC 4833420G17Rik
+Contig45119_RC 1700016G05Rik
+Contig45266 MMT00062379
+Contig45328_RC Gm614
+Contig45443_RC D630014A15Rik
+Contig45455_RC D5Ertd135e
+Contig45505_RC 2300002D11Rik
+Contig45532_RC AK046858
+Contig45914_RC D9Ertd167e
+Contig45970_RC MMS010209881
+Contig46086_RC 2310032F03Rik
+Contig46232 9530033F24Rik
+Contig46631_RC A230078I05Rik
+Contig46677_RC C130071C03Rik
+Contig46942_RC 4930544G21Rik
+Contig47065_RC AV340377
+Contig47102_RC D8Ertd82e
+Contig47129 D230004J03Rik
+Contig47225_RC 1110028A07Rik
+Contig47326 Gm1661
+Contig47381_RC MMT00078406
+Contig47453_RC AI173486
+Contig475_RC MMT00063480
+Contig47865 2310016C16Rik
+Contig47895_RC A530098C11Rik
+Contig48014_RC E130112L23Rik
+Contig48270_RC AI846148
+Contig48402_RC Deb1
+Contig48473_RC MMT00022054
+Contig48481 AI448196
+Contig48712_RC Acsm2
+Contig48799_RC AI585793
+Contig48806_RC E130012A19Rik
+Contig48834_RC B230112C05Rik
+Contig48950_RC C330018D20Rik
+Contig49076_RC 1810021J13Rik
+Contig49175_RC 6620401M08Rik
+Contig49606_RC 4930505A04Rik
+Contig50241_RC AB112350
+Contig50355_RC 4921505C17Rik
+Contig50403_RC AK052334
+Contig50410 2310051F07Rik
+Contig50483 A630082K20Rik
+Contig50605_RC MMT00062579
+Contig50655_RC C730029A08Rik
+Contig50831_RC MMT00004229
+Contig50850_RC 2410004L22Rik
+Contig50938_RC D4Ertd429e
+Contig51037_RC BC024561
+Contig51328_RC 3110073H01Rik
+Contig51373_RC D15Wsu169e
+Contig51687_RC Krtdap
+Contig51943_RC C230094A16Rik
+Contig52214_RC 3110045G13Rik
+Contig52681_RC 3110006E14Rik
+Contig53080_RC 1110062M06Rik
+Contig5311_RC mCT1886
+Contig53242_RC 2010109K11Rik
+Contig53406_RC BC003965
+Contig53615_RC 1700012G19Rik
+Contig53703_RC 5830454D03Rik
+Contig53838_RC 2610203C20Rik
+Contig54012_RC 4921524J17Rik
+Contig54076_RC 1700019D03Rik
+Contig55242_RC AI606181
+Contig55334_RC D130054N24Rik
+Contig55497_RC 9030425E11Rik
+Contig55583_RC 2010003K15Rik
+Contig55766_RC EG547309
+Contig56023_RC 3110045C21Rik
+Contig56288_RC C030004M05Rik
+Contig56513_RC 1110018M03Rik
+Contig56848_RC 2810008M24Rik
+Contig57080_RC A430093A21Rik
+Contig57226_RC Nrarp
+Contig57230_RC 1110018F16Rik
+Contig57290_RC Nedd10
+Contig57330_RC C230081A13Rik
+Contig57419_RC 1810049H13Rik
+Contig57896_RC 9630054F20Rik
+Contig58042_RC 6330509M05Rik
+Contig58121_RC 0610030E20Rik
+Contig58279_RC 5430417L22Rik
+Contig58333 Tra2a
+Contig63625_RC 4631424J17Rik
+Contig63748_RC EG432995
+Contig65156_RC 2510048L02Rik
+Contig65401_RC 4933405A16Rik
+Contig66072 1810013D10Rik
+Contig667_RC 4833421E05Rik
+Contig66868_RC 1110065P20Rik
+Contig706_RC Ptpn14
+Contig9030_RC 2310020A21Rik
+Contig9229 BB169187
+COPA Copa
+COPB1 Copb1
+COPB2 Copb2
+COPE Cope
+COPG1 Copg
+COPG2 Copg2
+COPG Copg
+COPS2 Cops2
+COPS3 Cops3
+COPS4 Cops4
+COPS5 Cops5
+COPS6 Cops6
+COPS7A Cops7a
+COPS7B Cops7b
+COPS8 Cops8
+COPZ1 Copz1
+COPZ2 Copz2
+COQ10A Coq10a
+COQ10B Coq10b
+COQ2 Coq2
+COQ3 Coq3
+COQ4 Coq4
+COQ5 Coq5
+COQ6 Coq6
+COQ7 Coq7
+COQ9 Coq9
+CORIN Corin
+CORO1A Coro1a
+CORO1B Coro1b
+CORO1C Coro1c
+CORO2A Coro2a
+CORO2B Coro2b
+CORO6 Coro6
+CORO7 Coro7
+CORT Cort
+COTL1 Cotl1
+COVA1 Cova1
+COX10 Cox10
+COX11 Cox11
+COX14 2310016M24Rik
+COX15 Cox15
+COX16 Cox16
+COX17 Cox17
+COX18 Cox18
+COX19 2810437L13Rik
+COX1 mt-Co1
+COX20 Fam36a
+COX2 mt-Co2
+COX3 mt-Co3
+COX4I1 Cox4i1
+COX4I2 Cox4i2
+COX4NB Cox4nb
+COX5A Cox5a
+COX5B Cox5b
+COX6A1 Cox6a1
+COX6A2 Cox6a2
+COX6B1 Cox6b1
+COX6B2 Cox6b2
+COX6C AW476018
+COX7A1 Cox7a1
+COX7A2 Cox7a2
+COX7A2L Cox7a2l
+COX7B2 Cox7b2
+COX7B Cox7b
+COX7C Cox7c
+COX8A Cox8a
+COX8BP Cox8b
+COX8C Cox8c
+CPA1 Cpa1
+CPA2 Cpa2
+CPA3 Cpa3
+CPA4 Cpa4
+CPA5 Cpa5
+CPA6 Cpa6
+CPB1 Cpb1
+CPB2 Cpb2
+CP Cp
+CPD Cpd
+CPEB1 Cpeb1
+CPEB2 Cpeb2
+CPEB3 Cpeb3
+CPEB4 Cpeb4
+CPE Cpe
+CPED1 A430107O13Rik
+CPLX1 Cplx1
+CPLX2 Cplx2
+CPLX3 Cplx3
+CPLX4 Cplx4
+CPM Cpm
+CPN1 Cpn1
+CPN2 Cpn2
+CPNE1 Cpne1
+CPNE2 Cpne2
+CPNE3 Cpne3
+CPNE4 Cpne4
+CPNE5 Cpne5
+CPNE6 Cpne6
+CPNE7 Cpne7
+CPNE8 Cpne8
+CPNE9 Cpne9
+CPOX Cpox
+CPPED1 Cpped1
+CPQ Pgcp
+CPS1 Cps1
+CPSF1 Cpsf1
+CPSF2 Cpsf2
+CPSF3 Cpsf3
+CPSF3L Cpsf3l
+CPSF4 Cpsf4
+CPSF4L Cpsf4l
+CPSF6 Cpsf6
+CPSF7 Cpsf7
+CPT1A Cpt1a
+CPT1B Cpt1b
+CPT1C Cpt1c
+CPT2 Cpt2
+CPVL Cpvl
+CPXCR1 Cpxcr1
+CPXM1 Cpxm1
+CPXM2 Cpxm2
+CPZ Cpz
+CR1 Crry
+CR2 Cr2
+CR598768 2700089E24Rik
+CR617694 Nt5c3
+CR749513 Thbd
+CR749709 Diras1
+CRABP1 Crabp1
+CRABP2 Crabp2
+CRADD Cradd
+CRAMP1L Cramp1l
+CRAT Crat
+CRB1 Crb1
+CRB2 Crb2
+CRB3 Crb3
+CRBN Crbn
+CRCP Crcp
+CRCT1 Crct1
+CREB1 Creb1
+CREB3 Creb3
+CREB3L1 Creb3l1
+CREB3L2 Creb3l2
+CREB3L3 Creb3l3
+CREB3L4 Creb3l4
+CREB5 Creb5
+CREBBP Crebbp
+CREBL1 Crebl1
+CREBL2 Crebl2
+CREBRF A930001N09Rik
+CREBZF Crebzf
+CREG1 Creg1
+CREG2 Creg2
+CRELD1 Creld1
+CRELD2 Creld2
+CREM Crem
+CRHBP Crhbp
+CRH Crh
+CRHR1 Crhr1
+CRHR2 Crhr2
+CRIM1 Crim1
+CRIP1 Crip1
+CRIP2 Crip2
+CRIP3 Crip3
+CRIPT Cript
+CRISP1 Crisp4
+CRISP2 Crisp2
+CRISP3 Crisp1
+CRISPLD1 Crispld1
+CRISPLD2 Crispld2
+CRK Crk
+CRKL Crkl
+CRKRS Crkrs
+CRLF1 Crlf1
+CRLF2 Crlf2
+CRLF3 Crlf3
+CRLS1 Crls1
+CRMP1 Crmp1
+CRNKL1 Crnkl1
+CRNN Crnn
+CROCC Crocc
+CROT Crot
+CRP Crp
+CRSP2 Crsp2
+CRSP3 Crsp3
+CRSP6 Crsp6
+CRSP7 Crsp7
+CRSP8 Crsp8
+CRSP9 Crsp9
+CRTAC1 Crtac1
+CRTAM Crtam
+CRTAP Crtap
+CRTC1 Crtc1
+CRTC2 Crtc2
+CRTC3 Crtc3
+CRX Crx
+CRY1 Cry1
+CRY2 Cry2
+CRYAA Cryaa
+CRYAB Cryab
+CRYBA1 Cryba1
+CRYBA2 Cryba2
+CRYBA4 Cryba4
+CRYBB1 Crybb1
+CRYBB2 Crybb2
+CRYBB3 Crybb3
+CRYBG3 Crybg3
+CRYGA Cryga
+CRYGB Crygb
+CRYGC Crygc
+CRYGD Crygd
+CRYGN Crygn
+CRYGS Crygs
+CRYL1 Cryl1
+CRYM-AS1 Abca15
+CRYM Crym
+CRYZ Cryz
+CRYZL1 Cryzl1
+CSAD Csad
+CS Cs
+CSDA Csda
+CSDC2 Csdc2
+CSDE1 Csde1
+CSE1L Cse1l
+CSF1 Csf1
+CSF1R Csf1r
+CSF2 Csf2
+CSF2RA Csf2ra
+CSF2RB Csf2rb
+CSF3 Csf3
+CSF3R Csf3r
+CSGALNACT1 Csgalnact1
+CSGALNACT2 Csgalnact2
+CSK Csk
+CSMD1 Csmd1
+CSMD2 Csmd2
+CSMD3 Csmd3
+CSN1S1 Csn1s1
+CSN1S2A Csn1s2a
+CSN1S2BP Csn1s2b
+CSN2 Csn2
+CSN3 Csn3
+CSNK1A1 Csnk1a1
+CSNK1D Csnk1d
+CSNK1E Csnk1e
+CSNK1G1 Csnk1g1
+CSNK1G2 Csnk1g2
+CSNK1G3 Csnk1g3
+CSNK2A1 Csnk2a1
+CSNK2A2 Csnk2a2
+CSNK2B Csnk2b
+CSPG2 Cspg2
+CSPG3 Cspg3
+CSPG4 Cspg4
+CSPG5 Cspg5
+CSPP1 Cspp1
+CSRNP1 Csrnp1
+CSRNP2 Csrnp2
+CSRNP3 Csrnp3
+CSRP1 Csrp1
+CSRP2BP Csrp2bp
+CSRP2 Csrp2
+CSRP3 Csrp3
+CST11 Cst11
+CST3 Cst3
+CST5 Cst10
+CST6 Cst6
+CST7 Cst7
+CST8 Cst8
+CST9L Cst9
+CSTA EG433016
+CSTB Cstb
+CSTF1 Cstf1
+CSTF2 Cstf2
+CSTF2T Cstf2t
+CSTF3 Cstf3
+CSTL1 Cstl1
+CSTT Cst13
+CTA-216E10.6 4930407I10Rik
+CTA-221G9.4 2900026A02Rik
+CTAGE5 Ctage5
+CTB-1048E9.5 2810002G02Rik
+CTBP1 Ctbp1
+CTBP2 Ctbp2
+CTBS Ctbs
+CTC1 Ctc1
+CTCF Ctcf
+CTCFL Ctcfl
+CTDNEP1 Ctdnep1
+CTDP1 Ctdp1
+CTDSP1 Ctdsp1
+CTDSP2 Ctdsp2
+CTDSPL2 Ctdspl2
+CTDSPL Ctdspl
+CTF1 Ctf1
+CTF2P Ctf2
+CTGF Ctgf
+CTH Cth
+CTHRC1 Cthrc1
+CTIF Ctif
+CTLA4 Ctla4
+CTNNA1 Ctnna1
+CTNNA2 Ctnna2
+CTNNA3 Ctnna3
+CTNNAL1 Ctnnal1
+CTNNB1 Ctnnb1
+CTNNBIP1 Ctnnbip1
+CTNNBL1 Ctnnbl1
+CTNND1 Ctnnd1
+CTNND2 Ctnnd2
+CTNS Ctns
+CTPS1 Ctps
+CTPS2 Ctps2
+CTPS Ctps
+CTR9 Ctr9
+CTRB1 Ctrb1
+CTRC Ctrc
+CTRL Ctrl
+CTSA Ctsa
+CTSB Ctsb
+CTSC Ctsc
+CTSD Ctsd
+CTSE Ctse
+CTSF Ctsf
+CTSG Ctsg
+CTSH Ctsh
+CTSK Ctsk
+CTSL1 Ctsl
+CTSL1P8 Ctsll3
+CTSL Ctsl
+CTSO Ctso
+CTSS Ctss
+CTSW Ctsw
+CTSZ Ctsz
+CTTNBP2 Cttnbp2
+CTTNBP2NL Cttnbp2nl
+CTTN Cttn
+CTU1 Ctu1
+CTU2 Ctu2
+CTXN1 Ctxn1
+CTXN2 Ctxn2
+CTXN3 Ctxn3
+CUBN Cubn
+CUEDC1 Cuedc1
+CUEDC2 Cuedc2
+CUGBP1 Cugbp1
+CUGBP2 Cugbp2
+CUL1 Cul1
+CUL2 Cul2
+CUL3 Cul3
+CUL4A Cul4a
+CUL4B Cul4b
+CUL5 Cul5
+CUL7 Cul7
+CUL9 Cul9
+CUTA Cuta
+CUTC Cutc
+CUTL1 Cutl1
+CUTL2 Cutl2
+CUX1 Cux1
+CUX2 Cux2
+CUZD1 Cuzd1
+CWC15 Cwc15
+CWC22 Cwc22
+CWC25 Cwc25
+CWC27 Cwc27
+CWF19L1 Cwf19l1
+CWF19L2 Cwf19l2
+CWH43 Cwh43
+CX3CL1 Cx3cl1
+CX3CR1 Cx3cr1
+CXADR Cxadr
+CXCL10 Cxcl10
+CXCL11 Cxcl11
+CXCL12 Cxcl12
+CXCL13 Cxcl13
+CXCL14 Cxcl14
+CXCL16 Cxcl16
+CXCL17 Cxcl17
+CXCL1 Cxcl1
+CXCL3 Cxcl2
+CXCL6 Cxcl5
+CXCL9 Cxcl9
+CXCR1 Cxcr1
+CXCR2 Cxcr2
+CXCR3 Cxcr3
+CXCR4 Cxcr4
+CXCR5 Cxcr5
+CXCR6 Cxcr6
+CXCR7 Cxcr7
+CXorf1 MMT00066803
+CXorf21 5430427O19Rik
+CXorf22 Gm7173
+CXorf23 A830080D01Rik
+CXorf26 2610029G23Rik
+CXorf27 1700054O13Rik
+CXorf2 Gm1538
+CXorf30 Gm8787
+CXorf34 4732479N06Rik
+CXorf36 4930578C19Rik
+CXorf38 1810030O07Rik
+CXorf39 BC031748
+CXorf40A 1110012L19Rik
+CXorf41 E230019M04Rik
+CXorf43 D030011N01Rik
+CXorf46 MMT00045091
+CXorf48 4930502E18Rik
+CXorf49B Gm4779
+CXorf56 C330007P06Rik
+CXorf57 D330045A20Rik
+CXorf58 4932442L08Rik
+CXorf59 Gm3297
+CXorf64 1110059M19Rik
+CXorf65 Gm614
+CXorf66 Gm7073
+CXorf67 AU022751
+CXorf68 4930428E23Rik
+CXorf6 G630014P10Rik
+CXorf9 1200013B08Rik
+CXXC1 Cxxc1
+CXXC4 Cxxc4
+CXXC5 Cxxc5
+CXXC6 Cxxc6
+CYB561 Cyb561
+CYB561D1 Cyb561d1
+CYB561D2 Cyb561d2
+CYB5A Cyb5
+CYB5B Cyb5b
+CYB5D1 Cyb5d1
+CYB5D2 Cyb5d2
+CYB5R1 Cyb5r1
+CYB5R2 Cyb5r2
+CYB5R3 Cyb5r3
+CYB5R4 Cyb5r4
+CYB5RL Cyb5rl
+CYBA Cyba
+CYBASC3 Cybasc3
+CYBB Cybb
+CYBRD1 Cybrd1
+CYC1 Cyc1
+CYCS Cycs
+CYCTP Cyct
+CYFIP1 Cyfip1
+CYFIP2 Cyfip2
+CYGB Cygb
+CYHR1 Cyhr1
+CYLC1 Cylc1
+CYLC2 4930488P18Rik
+CYLD Cyld
+CYLN2 Cyln2
+CYMP Cym
+CYP11A1 Cyp11a1
+CYP11B1 Cyp11b1
+CYP11B2 Cyp11b2
+CYP17A1 Cyp17a1
+CYP19A1 Cyp19a1
+CYP1A1 Cyp1a1
+CYP1A2 Cyp1a2
+CYP1B1 Cyp1b1
+CYP20A1 Cyp20a1
+CYP21A1P Cyp21a1
+CYP24A1 Cyp24a1
+CYP26A1 Cyp26a1
+CYP26B1 Cyp26b1
+CYP26C1 Cyp26c1
+CYP27A1 Cyp27a1
+CYP27B1 Cyp27b1
+CYP2A13 Cyp2a4
+CYP2A7 Cyp2a5
+CYP2B6 Cyp2b10
+CYP2C18 Cyp2c50
+CYP2C19 Cyp2c29
+CYP2C9 Cyp2c55
+CYP2D6 Cyp2d22
+CYP2E1 Cyp2e1
+CYP2F1 Cyp2f2
+CYP2G1P Cyp2g1
+CYP2J2 Cyp2j6
+CYP2R1 Cyp2r1
+CYP2S1 Cyp2s1
+CYP2U1 Cyp2u1
+CYP2W1 Cyp2w1
+CYP39A1 Cyp39a1
+CYP3A43 Cyp3a57
+CYP3A4 Cyp3a13
+CYP3A5 Cyp3a41a
+CYP46A1 Cyp46a1
+CYP4A11 Cyp4a10
+CYP4A22 Cyp4a12b
+CYP4B1 Cyp4b1
+CYP4F11 Cyp4f40
+CYP4F12 Cyp4f14
+CYP4F22 Cyp4f39
+CYP4F2 Cyp4f18
+CYP4F3 Cyp4f13
+CYP4V2 Cyp4v3
+CYP4X1 Cyp4x1
+CYP51A1 Cyp51
+CYP7A1 Cyp7a1
+CYP7B1 Cyp7b1
+CYP8B1 Cyp8b1
+CYR61 Cyr61
+CYS1 Cys1
+CYSLTR1 Cysltr1
+CYSLTR2 Cysltr2
+CYSTM1 0610010O12Rik
+CYTB mt-Cytb
+CYTH1 Cyth1
+CYTH2 Cyth2
+CYTH3 Cyth3
+CYTH4 Cyth4
+CYTIP Cytip
+CYTL1 Cytl1
+CYYR1 Cyyr1
+D2HGDH D2hgdh
+D4S234E Nsg1
+D4ST1 D4st1
+D83778 A630042L21Rik
+D86985 D5Ertd579e
+D87438 2210010A19Rik
+D87445 3110001I20Rik
+D87454 2410127E18Rik
+DAAM1 Daam1
+DAAM2 Daam2
+DAB1 Dab1
+DAB2 Dab2
+DAB2IP Dab2ip
+DACH1 Dach1
+DACH2 Dach2
+DACT1 Dact1
+DACT2 Dact2
+DACT3-AS1 9330104G04Rik
+DACT3 Dact3
+DAD1 Dad1
+DAG1 Dag1
+DAGLA Dagla
+DAGLB Daglb
+DAK Dak
+DALRD3 Dalrd3
+DANCR 2700023E23Rik
+DAND5 Dand5
+DAO Dao
+DAP3 Dap3
+DAP Dap
+DAPK1 Dapk1
+DAPK2 Dapk2
+DAPK3 Dapk3
+DAPL1 Dapl1
+DAPP1 Dapp1
+DARC Darc
+DARS2 Dars2
+DARS Dars
+DAXX Daxx
+DAZAP1 Dazap1
+DAZAP2 Dazap2
+DAZL Dazl
+DBC1 Dbc1
+DBF4 Dbf4
+DBH Dbh
+DBI Dbi
+DBIL5P Dbil5
+DBN1 Dbn1
+DBNDD1 Dbndd1
+DBNDD2 Dbndd2
+DBNL Dbnl
+DBP Dbp
+DBR1 Dbr1
+DBT Dbt
+DBX1 Dbx1
+DBX2 Dbx2
+DCAF10 Dcaf10
+DCAF11 Dcaf11
+DCAF12 Dcaf12
+DCAF12L2 Dcaf12l2
+DCAF13 Dcaf13
+DCAF15 Dcaf15
+DCAF17 Dcaf17
+DCAF4 Dcaf4
+DCAF5 Dcaf5
+DCAF6 Dcaf6
+DCAF7 Dcaf7
+DCAF8 Dcaf8
+DCAKD Dcakd
+DCAMKL1 Dcamkl1
+DCAMKL2 Dcamkl2
+DCBLD1 Dcbld1
+DCBLD2 Dcbld2
+DCC Dcc
+DCDC2B Dcdc2b
+DCDC2 Dcdc2a
+DCDC5 Dcdc5
+DCHS1 Dchs1
+DCHS2 Dchs2
+DCI Dci
+DCK Dck
+DCLK1 Dclk1
+DCLK2 Dclk2
+DCLK3 Dclk3
+DCLRE1A Dclre1a
+DCLRE1B Dclre1b
+DCLRE1C Dclre1c
+DCN Dcn
+DCP1A Dcp1a
+DCP1B Dcp1b
+DCP2 Dcp2
+DCPS Dcps
+DCST1 Dcst1
+DCST2 Dcst2
+DCSTAMP Dcstamp
+DCT Dct
+DCTD Dctd
+DCTN1 Dctn1
+DCTN2 Dctn2
+DCTN3 Dctn3
+DCTN4 Dctn4
+DCTN5 Dctn5
+DCTN6 Dctn6
+DCTPP1 Dctpp1
+DCUN1D1 Dcun1d1
+DCUN1D2 AW495713
+DCUN1D3 Dcun1d3
+DCUN1D4 Dcun1d4
+DCUN1D5 Dcun1d5
+DCX Dcx
+DCXR Dcxr
+DDA1 Dda1
+DDAH1 Ddah1
+DDAH2 Ddah2
+DDB1 Ddb1
+DDB2 Ddb2
+DDC Ddc
+DDEF1 Ddef1
+DDEFL1 Ddefl1
+DDHD1 Ddhd1
+DDHD2 Ddhd2
+DDI1 1700011N24Rik
+DDI2 Ddi2
+DDIT3 Ddit3
+DDIT4 Ddit4
+DDIT4L Ddit4l
+DDN Ddn
+DDO Ddo
+DDOST Ddost
+DDR1 Ddr1
+DDR2 Ddr2
+DDRGK1 Ddrgk1
+DDT Ddt
+DDX10 Ddx10
+DDX11 Ddx11
+DDX17 Ddx17
+DDX18 Ddx18
+DDX19A Ddx19a
+DDX19B Ddx19b
+DDX1 Ddx1
+DDX20 Ddx20
+DDX21 Ddx21
+DDX23 Ddx23
+DDX24 Ddx24
+DDX25 Ddx25
+DDX26B 6330505F04Rik
+DDX27 Ddx27
+DDX28 Ddx28
+DDX31 Ddx31
+DDX39A Ddx39
+DDX39B Ddx39b
+DDX39 Ddx39
+DDX3X Ddx3x
+DDX3Y Ddx3y
+DDX41 Ddx41
+DDX42 Ddx42
+DDX43 BG089777
+DDX46 Ddx46
+DDX47 Ddx47
+DDX48 EG434080
+DDX49 Ddx49
+DDX4 Ddx4
+DDX50 Ddx50
+DDX51 Ddx51
+DDX52 Ddx52
+DDX54 Ddx54
+DDX55 Ddx55
+DDX56 Ddx56
+DDX58 Ddx58
+DDX59 Ddx59
+DDX5 Ddx5
+DDX60 Ddx60
+DDX6 Ddx6
+DEADC1 Deadc1
+DEAF1 Deaf1
+DECR1 Decr1
+DECR2 Decr2
+DEDD2 Dedd2
+DEDD Dedd
+DEF6 Def6
+DEF8 Def8
+DEFA1 Defa1
+DEFA4 MMT00080249
+DEFB103A Defb14
+DEFB105A Defb12
+DEFB106A Defb15
+DEFB110 Gm15386
+DEFB113 Defb18
+DEFB115 Defb28
+DEFB116 Defb29
+DEFB118 4930525K10Rik
+DEFB119 Defb19
+DEFB122 Defb45
+DEFB123 Defb36
+DEFB124 Defb25
+DEFB125 Defb26
+DEFB126 9230002F21Rik
+DEFB128 Defb20
+DEFB129 Defb23
+DEFB130 Defb47
+DEFB131 Defb43
+DEFB133 Defb44-ps
+DEFB135 Defb30
+DEFB136 Defb42
+DEFB1 Defb1
+DEFB4A Defb2
+DEGS1 Degs1
+DEGS2 Degs2
+DEK Dek
+DEM1 Dem1
+DENND1A Dennd1a
+DENND1B BC042698
+DENND1C Dennd1c
+DENND2A Dennd2a
+DENND2C Dennd2c
+DENND2D Dennd2d
+DENND3 Dennd3
+DENND4A Dennd4a
+DENND4B Dennd4b
+DENND4C 1700065A05Rik
+DENND5A Dennd5a
+DENND5B Dennd5b
+DENR Denr
+DEPDC1B Depdc1b
+DEPDC1 Depdc1a
+DEPDC2 C030045D06Rik
+DEPDC5 Depdc5
+DEPDC6 Depdc6
+DEPDC7 Depdc7
+DEPTOR Deptor
+DERA Dera
+DERL1 Derl1
+DERL2 Derl2
+DERL3 Derl3
+DES Des
+DESI1 Pppde2
+DESI2 Pppde1
+DET1 Det1
+DEXI Dexi
+DFFA Dffa
+DFFB Dffb
+DFNA5 Dfna5
+DFNB31 Whrn
+DFNB59 Dfnb59
+DGAT1 Dgat1
+DGAT2 Dgat2
+DGAT2L4 Dgat2l4
+DGAT2L6 Dgat2l6
+DGCR13 Tssk2
+DGCR14 Dgcr14
+DGCR2 Dgcr2
+DGCR6 Dgcr6
+DGCR8 Dgcr8
+DGKA Dgka
+DGKB Dgkb
+DGKD Dgkd
+DGKE Dgke
+DGKG Dgkg
+DGKH Dgkh
+DGKI Dgki
+DGKK Dgkk
+DGKQ Dgkq
+DGKZ Dgkz
+DGUOK Dguok
+DHCR24 Dhcr24
+DHCR7 Dhcr7
+DHDDS Dhdds
+DHDH Dhdh
+DHFR Dhfr
+DHH Dhh
+DHODH Dhodh
+DHPS Dhps
+DHRS10 Dhrs10
+DHRS11 Dhrs11
+DHRS13 Dhrs13
+DHRS1 Dhrs1
+DHRS2 Dhrs2
+DHRS3 Dhrs3
+DHRS4 Dhrs4
+DHRS7B Dhrs7b
+DHRS7C Dhrs7c
+DHRS7 Dhrs7
+DHRS8 Dhrs8
+DHRS9 Dhrs9
+DHRSX Dhrsx
+DHTKD1 Dhtkd1
+DHX15 Dhx15
+DHX16 Dhx16
+DHX29 Dhx29
+DHX30 Dhx30
+DHX32 Dhx32
+DHX33 Dhx33
+DHX34 Dhx34
+DHX35 Dhx35
+DHX36 Dhx36
+DHX37 Dhx37
+DHX38 Dhx38
+DHX40 Dhx40
+DHX57 Dhx57
+DHX58 Dhx58
+DHX8 Dhx8
+DHX9 Dhx9
+DIABLO Diablo
+DIAPH1 Diap1
+DIAPH2 Diap2
+DIAPH3 Diap3
+DICER1 Dicer1
+DIDO1 Dido1
+DIEXF Diexf
+DIMT1 Dimt1
+DIO1 Dio1
+DIO2 Dio2
+DIO3 Dio3
+DIO3OS Dio3os
+DIP2A Dip2a
+DIP2B AI317237
+DIP2C Dip2c
+DIRAS1 Diras1
+DIRAS2 Diras2
+DIRC2 Dirc2
+DIS3 Dis3
+DIS3L2 Dis3l2
+DIS3L Dis3l
+DISC1 Disc1
+DISP1 Disp1
+DISP2 Disp2
+DIXDC1 Dixdc1
+DKC1 BC068171
+DKFZP434H168 4930488L21Rik
+DKFZp686D0853 A730046J19Rik
+DKFZP761N09121 Copg2as2
+DKK1 Dkk1
+DKK2 Dkk2
+DKK3 Dkk3
+DKK4 Dkk4
+DKKL1 Dkkl1
+DLAT Dlat
+DLC1 Dlc1
+DLD Dld
+DLEC1 Dlec1
+DLEU2 Dleu2
+DLEU7 Dleu7
+DLG1 Dlg1
+DLG2 Dlg2
+DLG3 Dlg3
+DLG4 Dlg4
+DLG5 Dlg5
+DLG7 Dlg7
+DLGAP1 Dlgap1
+DLGAP2 Dlgap2
+DLGAP3 Dlgap3
+DLGAP4 Dlgap4
+DLGAP5 Dlgap5
+DLK1 Dlk1
+DLK2 Dlk2
+DLL1 Dll1
+DLL3 Dll3
+DLL4 Dll4
+DLST Dlst
+DLX1 Dlx1
+DLX2 Dlx2
+DLX3 Dlx3
+DLX4 Dlx4
+DLX5 Dlx5
+DLX6 Dlx6
+DMAP1 Dmap1
+DMBT1 Dmbt1
+DMBX1 Dmbx1
+DMC1 Dmc1
+DMD Dmd
+DMGDH Dmgdh
+DMKN Dmkn
+DMN Dmn
+DMP1 Dmp1
+DMPK Dmpk
+DMRT1 Dmrt1
+DMRT2 Dmrt2
+DMRT3 Dmrt3
+DMRTA1 Dmrta1
+DMRTA2 Dmrta2
+DMRTB1 Dmrtb1
+DMRTC1B Dmrtc1a
+DMRTC1 Dmrtc1b
+DMRTC2 Dmrtc2
+DMTF1 Dmtf1
+DMWD Dmwd
+DMXL1 Dmxl1
+DMXL2 Dmxl2
+DNA2 Dna2
+DNA2L Dna2l
+DNAAF1 Lrrc50
+DNAAF2 1110034A24Rik
+DNAAF3 6030429G01Rik
+DNAH10 Dnahc10
+DNAH11 Dnahc11
+DNAH12 Dnahc12
+DNAH14 Dnahc14
+DNAH17 Dnahc17
+DNAH1 Dnahc1
+DNAH2 Dnahc2
+DNAH3 Dnahc3
+DNAH5 Dnahc5
+DNAH7 Dnahc7a
+DNAH8 Dnahc8
+DNAH9 Dnahc9
+DNAI1 Dnaic1
+DNAI2 Dnaic2
+DNAJA1 Dnaja1
+DNAJA2 Dnaja2
+DNAJA3 Dnaja3
+DNAJA4 Dnaja4
+DNAJB11 Dnajb11
+DNAJB12 Dnajb12
+DNAJB13 Dnajb13
+DNAJB14 Dnajb14
+DNAJB1 Dnajb1
+DNAJB2 Dnajb10
+DNAJB3 Dnajb3
+DNAJB4 Dnajb4
+DNAJB5 Dnajb5
+DNAJB6 Dnajb6
+DNAJB7 Dnajb7
+DNAJB8 Dnajb8
+DNAJB9 Dnajb9
+DNAJC10 Dnajc10
+DNAJC11 Dnajc11
+DNAJC12 Dnajc12
+DNAJC13 Dnajc13
+DNAJC14 Dnajc14
+DNAJC15 Dnajc15
+DNAJC16 Dnajc16
+DNAJC17 Dnajc17
+DNAJC18 Dnajc18
+DNAJC19 Dnajc19
+DNAJC1 Dnajc1
+DNAJC21 Dnajc21
+DNAJC22 Dnajc22
+DNAJC24 Dnajc24
+DNAJC25 Dnajc25
+DNAJC27 Dnajc27
+DNAJC28 Dnajc28
+DNAJC2 Dnajc2
+DNAJC30 Dnajc30
+DNAJC3 Dnajc3
+DNAJC4 Dnajc4
+DNAJC5B Dnajc5b
+DNAJC5 Dnajc5
+DNAJC5G Dnajc5g
+DNAJC6 Dnajc6
+DNAJC7 Dnajc7
+DNAJC8 Dnajc8
+DNAJC9 Dnajc9
+DNAL1 Dnalc1
+DNAL4 Dnalc4
+DNALI1 Dnali1
+DNASE1 Dnase1
+DNASE1L1 Dnase1l1
+DNASE1L2 Dnase1l2
+DNASE1L3 Dnase1l3
+DNASE2B Dnase2b
+DNASE2 Dnase2a
+DND1 Dnd1
+DNER Dner
+DNHD3 Dnhd3
+DNLZ Dnlz
+DNM1 Dnm1
+DNM1L Dnm1l
+DNM2 Dnm2
+DNM3 Dnm3
+DNMBP Dnmbp
+DNMT1 Dnmt1
+DNMT3A Dnmt3a
+DNMT3B Dnmt3b
+DNMT3L Dnmt3l
+DNPEP Dnpep
+DNTT Dntt
+DNTTIP1 Dnttip1
+DNTTIP2 Dnttip2
+DOC2A Doc2a
+DOC2B Doc2b
+DOC2GP Doc2g
+DOCK10 Dock10
+DOCK11 Dock11
+DOCK1 Dock1
+DOCK2 Dock2
+DOCK3 Dock3
+DOCK4 Dock4
+DOCK5 Dock5
+DOCK6 Dock6
+DOCK7 Dock7
+DOCK8 Dock8
+DOCK9 Dock9
+DOHH Dohh
+DOK1 Dok1
+DOK2 Dok2
+DOK3 Dok3
+DOK4 Dok4
+DOK5 Dok5
+DOK6 Dok6
+DOK7 Dok7
+DOLK Dolk
+DOLPP1 Dolpp1
+DOM3Z Dom3z
+DONSON Donson
+DOPEY1 Dopey1
+DOPEY2 Dopey2
+DOT1L Dot1l
+DPAGT1 Dpagt1
+DPCD Dpcd
+DPCR1 Dpcr1
+DPEP1 Dpep1
+DPEP2 Dpep2
+DPEP3 Dpep3
+DPF1 Dpf1
+DPF2 Dpf2
+DPF3 Dpf3
+DPH1 Dph1
+DPH2 Dph2
+DPH3 Dph3
+DPH3P1 Dph3b-ps
+DPH3 Zcsl2
+DPH4 Zcsl3
+DPH5 Dph5
+DPM1 Dpm1
+DPM2 Dpm2
+DPM3 Dpm3
+DPP10 Dpp10
+DPP3 Dpp3
+DPP4 Dpp4
+DPP6 Dpp6
+DPP7 Dpp7
+DPP8 Dpp8
+DPP9 Dpp9
+DPPA2 Dppa2
+DPPA3 Dppa3
+DPPA4 Dppa4
+DPPA5 Dppa5
+DPRX Crxos1
+DPT Dpt
+DPY19L1 Dpy19l1
+DPY19L2 Dpy19l2
+DPY19L3 Dpy19l3
+DPY19L4 Dpy19l4
+DPY30 Dpy30
+DPYD Dpyd
+DPYS Dpys
+DPYSL2 Dpysl2
+DPYSL3 Dpysl3
+DPYSL4 Dpysl4
+DPYSL5 Dpysl5
+DQX1 Dqx1
+DR1 Dr1
+DRAM1 Dram1
+DRAM2 Dram2
+DRAP1 Drap1
+DRD1 Drd1a
+DRD1IP Drd1ip
+DRD2 Drd2
+DRD3 Drd3
+DRD4 Drd4
+DRD5 Drd5
+DRG1 Drg1
+DRG2 Drg2
+DROSHA Drosha
+DRP2 Drp2
+DSC1 Dsc1
+DSC2 Dsc2
+DSC3 Dsc3
+DSCAM Dscam
+DSCAML1 Dscaml1
+DSCC1 Dscc1
+DSCR1 Dscr1
+DSCR1L1 Dscr1l1
+DSCR1L2 Dscr1l2
+DSCR3 Dscr3
+DSCR6 Dscr6
+DSE Dse
+DSEL Dsel
+DSG1 Dsg1a
+DSG2 Dsg2
+DSG3 Dsg3
+DSG4 Dsg4
+DSN1 Dsn1
+DSP Dsp
+DSPG3 Dspg3
+DSPP Dspp
+DST Dst
+DSTN Dstn
+DSTYK Dstyk
+DTD1 Dtd1
+DTHD1 Gm17384
+DTL Dtl
+DTNA Dtna
+DTNB Dtnb
+DTNBP1 Dtnbp1
+DTWD1 Dtwd1
+DTWD2 Dtwd2
+DTX1 Dtx1
+DTX2 Dtx2
+DTX3 Dtx3
+DTX3L Dtx3l
+DTX4 Dtx4
+DTYMK Dtymk
+DULLARD Dullard
+DUOX1 Duox1
+DUOX2 Duox2
+DUOXA1 Duoxa1
+DUOXA2 9030623N16Rik
+DUPD1 Dupd1
+DUS1L Dus1l
+DUS2L Dus2l
+DUS3L Dus3l
+DUS4L Dus4l
+DUSP10 Dusp10
+DUSP11 Dusp11
+DUSP12 Dusp12
+DUSP13 Dusp13
+DUSP14 Dusp14
+DUSP15 Dusp15
+DUSP16 Dusp16
+DUSP18 Dusp18
+DUSP19 Dusp19
+DUSP1 Dusp1
+DUSP21 1700094E07Rik
+DUSP22 Dusp22
+DUSP23 Dusp23
+DUSP26 Dusp26
+DUSP27 Dusp27
+DUSP28 Dusp28
+DUSP2 Dusp2
+DUSP3 Dusp3
+DUSP4 Dusp4
+DUSP5 Dusp5
+DUSP6 Dusp6
+DUSP7 Dusp7
+DUSP8 Dusp8
+DUSP9 Dusp9
+DUT Dut
+DVL1 Dvl1
+DVL2 Dvl2
+DVL3 Dvl3
+DYDC1 Dydc1
+DYDC2 Dydc2
+DYM Dym
+DYNC1H1 Dync1h1
+DYNC1I1 Dync1i1
+DYNC1I2 Dync1i2
+DYNC1LI1 Dync1li1
+DYNC1LI2 Dync1li2
+DYNC2H1 Dync2h1
+DYNC2LI1 Dync2li1
+DYNLL1 Dynll1
+DYNLL2 Dynll2
+DYNLRB1 Dynlrb1
+DYNLRB2 Dynlrb2
+DYNLT1 Dynlt1b
+DYNLT3 Dynlt3
+DYRK1A Dyrk1a
+DYRK1B Dyrk1b
+DYRK2 Dyrk2
+DYRK3 Dyrk3
+DYRK4 Dyrk4
+DYSF Dysf
+DYSFIP1 Dysfip1
+DYTN Dytn
+DYX1C1 Dyx1c1
+DZANK1 6330439K17Rik
+DZIP1 Dzip1
+DZIP1L Dzip1l
+DZIP3 Dzip3
+E2F1 E2f1
+E2F2 E2f2
+E2F3 E2f3
+E2F4 E2f4
+E2F5 E2f5
+E2F6 E2f6
+E2F7 E2f7
+E2F8 E2f8
+E4F1 E4f1
+EAF1 Eaf1
+EAF2 Eaf2
+EAPP Eapp
+EARS2 Ears2
+EBAG9 Ebag9
+EBF1 Ebf1
+EBF2 Ebf2
+EBF3 Ebf3
+EBF4 Ebf4
+EBI2 Ebi2
+EBI3 Ebi3
+EBNA1BP2 Ebna1bp2
+EBP Ebp
+EBPL Ebpl
+ECD Ecd
+ECE1 Ece1
+ECE2 Ece2
+ECEL1 Ecel1
+ECGF1 Ecgf1
+ECH1 Ech1
+ECHDC1 Echdc1
+ECHDC2 Echdc2
+ECHDC3 Echdc3
+ECHS1 Echs1
+ECI1 Eci1
+ECI2 Eci2
+ECM1 Ecm1
+ECM2 Ecm2
+ECSCR Ecscr
+ECSIT Ecsit
+ECT2 Ect2
+ECT2L Ect2l
+EDA2R Eda2r
+EDA Eda
+EDARADD Edaradd
+EDAR Edar
+EDC3 Edc3
+EDC4 Edc4
+EDD1 Edd1
+EDDM3B Eddm3b
+EDEM1 Edem1
+EDEM2 Edem2
+EDEM3 Edem3
+EDF1 Edf1
+EDG1 Edg1
+EDG2 Edg2
+EDG3 Edg3
+EDG4 Edg4
+EDG6 Edg6
+EDG7 Edg7
+EDG8 Edg8
+EDIL3 Edil3
+EDN1 Edn1
+EDN2 Edn2
+EDN3 Edn3
+EDNRA Ednra
+EDNRB Ednrb
+EEA1 Eea1
+EED Eed
+EEF1A1 Eef1a1
+EEF1A2 Eef1a2
+EEF1B2 Eef1b2
+EEF1D Eef1d
+EEF1E1 Eef1e1
+EEF1G Eef1g
+EEF2 Eef2
+EEF2K Eef2k
+EEFSEC Eefsec
+EEPD1 Eepd1
+EFCAB10 Efcab10
+EFCAB11 2610021K21Rik
+EFCAB12 BC060267
+EFCAB1 Efcab1
+EFCAB2 Efcab2
+EFCAB3 Efcab3
+EFCAB4A Efcab4a
+EFCAB4B Efcab4b
+EFCAB5 Efcab5
+EFCAB6 Efcab6
+EFCAB7 Efcab7
+EFCAB9 Efcab9
+EFCBP1 Efcbp1
+EFCBP2 Efcbp2
+EFEMP1 Efemp1
+EFEMP2 Efemp2
+EFHA1 Efha1
+EFHA2 Efha2
+EFHB Efhb
+EFHC1 Efhc1
+EFHC2 Efhc2
+EFHD1 Efhd1
+EFHD2 Efhd2
+EFNA1 Efna1
+EFNA2 Efna2
+EFNA3 Efna3
+EFNA4 Efna4
+EFNA5 Efna5
+EFNB1 Efnb1
+EFNB2 Efnb2
+EFNB3 Efnb3
+EFR3A Efr3a
+EFR3B Efr3b
+EFS Efs
+EFTUD1 Eftud1
+EFTUD2 Eftud2
+EGF Egf
+EGFEM1P Egfem1
+EGFL6 Egfl6
+EGFL7 Egfl7
+EGFL8 Egfl8
+EGFL9 Egfl9
+EGFLAM AU040377
+EGFR Egfr
+EGLN1 Egln1
+EGLN2 Egln2
+EGLN3 Egln3
+EGR1 Egr1
+EGR2 Egr2
+EGR3 Egr3
+EGR4 Egr4
+EHBP1 Ehbp1
+EHBP1L1 Ehbp1l1
+EHD1 Ehd1
+EHD2 Ehd2
+EHD3 Ehd3
+EHD4 Ehd4
+EHF Ehf
+EHHADH Ehhadh
+EHMT1 Ehmt1
+EHMT2 Ehmt2
+EI24 Ei24
+EID1 Eid1
+EID2B AK044722
+EID2 Eid2
+EID3 Eid3
+EIF1AD Eif1ad
+EIF1AX Eif1a
+EIF1B Eif1b
+EIF1 Eif1
+EIF2A Eif2a
+EIF2AK1 Eif2ak1
+EIF2AK2 Eif2ak2
+EIF2AK3 Eif2ak3
+EIF2AK4 Eif2ak4
+EIF2B1 Eif2b1
+EIF2B2 Eif2b2
+EIF2B3 Eif2b3
+EIF2B4 Eif2b4
+EIF2B5 Eif2b5
+EIF2C1 Eif2c1
+EIF2C2 Eif2c2
+EIF2C3 Eif2c3
+EIF2C4 Eif2c4
+EIF2D Eif2d
+EIF2S1 Eif2s1
+EIF2S2 Eif2s2
+EIF2S3 Eif2s3x
+EIF3A Eif3a
+EIF3B Eif3b
+EIF3C Eif3c
+EIF3D Eif3d
+EIF3E Eif3e
+EIF3F Eif3f
+EIF3G Eif3g
+EIF3H Eif3h
+EIF3I Eif3i
+EIF3J Eif3j
+EIF3K Eif3k
+EIF3L Eif3l
+EIF3M Eif3m
+EIF3S10 Eif3s10
+EIF3S1 Eif3s1
+EIF3S2 Eif3s2
+EIF3S3 Eif3s3
+EIF3S4 Eif3s4
+EIF3S6IP Eif3s6ip
+EIF3S7 Eif3s7
+EIF3S8 Eif3s8
+EIF3S9 Eif3s9
+EIF4A1 Eif4a1
+EIF4A2 Eif4a2
+EIF4A3 Eif4a3
+EIF4B Eif4b
+EIF4E1B Eif4e1b
+EIF4E2 Eif4e2
+EIF4E3 Eif4e3
+EIF4EBP1 Eif4ebp1
+EIF4EBP2 Eif4ebp2
+EIF4EBP3 Eif4ebp3
+EIF4E Eif4e
+EIF4ENIF1 Eif4enif1
+EIF4G1 Eif4g1
+EIF4G2 Eif4g2
+EIF4G3 Eif4g3
+EIF4H Eif4h
+EIF5A2 Eif5a2
+EIF5A Eif5a
+EIF5B Eif5b
+EIF5 Eif5
+EIF6 Eif6
+ELA1 Ela1
+ELA2 MMT00014773
+ELA3B Ela3
+ELAC1 Elac1
+ELAC2 Elac2
+ELANE Elane
+ELAVL1 Elavl1
+ELAVL2 Elavl2
+ELAVL3 Elavl3
+ELAVL4 Elavl4
+ELF1 Elf1
+ELF2 Elf2
+ELF3 Elf3
+ELF4 Elf4
+ELF5 Elf5
+ELFN1 Elfn1
+ELFN2 Elfn2
+ELK1 Elk1
+ELK3 Elk3
+ELK4 Elk4
+ELL2 Ell2
+ELL3 Ell3
+ELL Ell
+ELMO1 Elmo1
+ELMO2 Elmo2
+ELMO3 Elmo3
+ELMOD1 Elmod1
+ELMOD2 Elmod2
+ELMOD3 Elmod3
+ELN Eln
+ELOF1 Elof1
+ELOVL1 Elovl1
+ELOVL2 Elovl2
+ELOVL3 Elovl3
+ELOVL4 Elovl4
+ELOVL5 Elovl5
+ELOVL6 Elovl6
+ELOVL7 Elovl7
+ELP2 Elp2
+ELP3 Elp3
+ELP4 Elp4
+ELTD1 Eltd1
+EMB Emb
+EMC10 2310044H10Rik
+EMC1 C230096C10Rik
+EMC2 Ttc35
+EMC3 Tmem111
+EMC4 Tmem85
+EMC6 Tmem93
+EMC7 2900064A13Rik
+EMC8 Cox4nb
+EMC9 Fam158a
+EMCN Emcn
+EMD Emd
+EME1 Eme1
+EME2 Eme2
+EMG1 Emg1
+EMID1 Emid1
+EMID2 Emid2
+EMILIN1 Emilin1
+EMILIN2 Emilin2
+EMILIN3 Emilin3
+EML1 Eml1
+EML2 Eml2
+EML3 Eml3
+EML4 Eml4
+EML5 Eml5
+EML6 Eml6
+EMP1 Emp1
+EMP2 Emp2
+EMP3 Emp3
+EMR1 Emr1
+EMX1 Emx1
+EMX2 Emx2
+EMX2OS Emx2os
+EN1 En1
+EN2 En2
+ENAH Enah
+ENAM Enam
+ENC1 Enc1
+ENDOD1 Endod1
+ENDOG Endog
+ENDOGL1 Endogl1
+ENDOU Endou
+ENDOV A730011L01Rik
+ENGASE Engase
+ENG Eng
+ENHO Enho
+ENKUR Enkur
+ENO1 CL302992
+ENO2 Eno2
+ENO3 Eno3
+ENO4 Eno4
+ENOPH1 Enoph1
+ENOX1 Enox1
+ENOX2 Enox2
+ENPEP Enpep
+ENPP1 Enpp1
+ENPP2 Enpp2
+ENPP3 Enpp3
+ENPP4 Enpp4
+ENPP5 Enpp5
+ENPP6 Enpp6
+ENPP7 Enpp7
+ENSA Ensa
+ENST00000219837 2310008H09Rik
+ENST00000223140 Nobox
+ENST00000226272 Tmprss11e
+ENST00000229384 2010003O18Rik
+ENST00000238638 MMT00079857
+ENST00000247029 Og9x
+ENST00000248346 1110002H13Rik
+ENST00000251483 Sult1c1
+ENST00000254627 Oc90
+ENST00000257665 Pom121
+ENST00000261334 4933425B16Rik
+ENST00000269091 3000004C01Rik
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+FAS Fas
+FASLG Fasl
+FASN Fasn
+FASTKD1 Fastkd1
+FASTKD2 Fastkd2
+FASTKD3 Fastkd3
+FASTKD5 Fastkd5
+FASTK Fastk
+FAT1 Fat1
+FAT2 Fat2
+FAT3 Fat3
+FAT4 Fat4
+FATE1 Fate1
+FAT Fath
+FAU ENSMUST00000052867
+FAXC 6230409E13Rik
+FBF1 Fbf1
+FBL Fbl
+FBLIM1 Fblim1
+FBLL1 Fbll1
+FBLN1 Fbln1
+FBLN2 Fbln2
+FBLN5 Fbln5
+FBLN7 Fbln7
+FBN1 Fbn1
+FBN2 Fbn2
+FBP1 Fbp1
+FBP2 Fbp2
+FBRS Fbrs
+FBRSL1 Fbrsl1
+FBS1 Fbs1
+FBXL10 Fbxl10
+FBXL11 Fbxl11
+FBXL12 Fbxl12
+FBXL13 Fbxl13
+FBXL14 Fbxl14
+FBXL15 Fbxl15
+FBXL16 Fbxl16
+FBXL17 Fbxl17
+FBXL18 Fbxl18
+FBXL19 Fbxl19
+FBXL20 Fbxl20
+FBXL21 Fbxl21
+FBXL22 Fbxl22
+FBXL2 Fbxl2
+FBXL3 Fbxl3
+FBXL4 Fbxl4
+FBXL5 Fbxl5
+FBXL6 Fbxl6
+FBXL7 Fbxl7
+FBXL8 Fbxl8
+FBXO10 Fbxo10
+FBXO11 Fbxo11
+FBXO15 Fbxo15
+FBXO16 Fbxo16
+FBXO17 Fbxo17
+FBXO18 Fbxo18
+FBXO21 Fbxo21
+FBXO22 Fbxo22
+FBXO24 Fbxo24
+FBXO25 Fbxo25
+FBXO27 Fbxo27
+FBXO28 Fbxo28
+FBXO2 Fbxo2
+FBXO30 Fbxo30
+FBXO31 Fbxo31
+FBXO32 Fbxo32
+FBXO33 Fbxo33
+FBXO34 Fbxo34
+FBXO36 Fbxo36
+FBXO38 Fbxo38
+FBXO39 Fbxo39
+FBXO3 Fbxo3
+FBXO40 Fbxo40
+FBXO41 Fbxo41
+FBXO42 Fbxo42
+FBXO43 Fbxo43
+FBXO44 Fbxo44
+FBXO45 Fbxo45
+FBXO46 Fbxo46
+FBXO47 Fbxo47
+FBXO48 Fbxo48
+FBXO4 Fbxo4
+FBXO5 Fbxo5
+FBXO6 Fbxo6
+FBXO7 Fbxo7
+FBXO8 Fbxo8
+FBXO9 Fbxo9
+FBXW10 Fbxw10
+FBXW11 Fbxw11
+FBXW12 Fbxw23
+FBXW2 Fbxw2
+FBXW4 Fbxw4
+FBXW5 Fbxw5
+FBXW7 Fbxw7
+FBXW8 Fbxw8
+FBXW9 Fbxw9
+FCAMR Fcamr
+FCER1A Fcer1a
+FCER1G Fcer1g
+FCER2 Fcer2a
+FCF1 Fcf1
+FCGBP Fcgbp
+FCGR1A Fcgr1
+FCGR2A Fcgr3
+FCGR2B Fcgr2b
+FCGR3A Fcgr3a
+FCGRT Fcgrt
+FCHO1 Fcho1
+FCHO2 Fcho2
+FCHSD1 Fchsd1
+FCHSD2 Fchsd2
+FCMD Fcmd
+FCN1 Fcna
+FCN3 MMT00062424
+FCRL1 Fcrl1
+FCRL3 Fcrl5
+FCRL6 Fcrl6
+FCRLA Fcrla
+FCRLB Fcrlb
+FDFT1 Fdft1
+FDPS Fdps
+FDX1 Fdx1
+FDX1L Fdx1l
+FDXACB1 Fdxacb1
+FDXR Fdxr
+FECH Fech
+FEM1A Fem1a
+FEM1B Fem1b
+FEM1C Fem1c
+FEN1 Fen1
+FER1L3 Fer1l3
+FER1L4 Fer1l4
+FER1L5 Fer1l5
+FER1L6 Fer1l6
+FERD3L Ferd3l
+FER Fer
+FERMT1 Fermt1
+FERMT2 Fermt2
+FERMT3 Fermt3
+FES Fes
+FETUB Fetub
+FEV Fev
+FEZ1 Fez1
+FEZ2 Fez2
+FEZF1 Fezf1
+FEZF2 Fezf2
+FFAR1 Ffar1
+FFAR2 Ffar2
+FFAR3 Ffar3
+FGA Fga
+FGB Fgb
+FGD1 Fgd1
+FGD2 Fgd2
+FGD3 Fgd3
+FGD4 Fgd4
+FGD5 Fgd5
+FGD6 Fgd6
+FGF10 Fgf10
+FGF11 Fgf11
+FGF12 Fgf12
+FGF13 Fgf13
+FGF14 Fgf14
+FGF16 Fgf16
+FGF17 Fgf17
+FGF18 Fgf18
+FGF19 Fgf15
+FGF1 Fgf1
+FGF20 Fgf20
+FGF21 Fgf21
+FGF22 Fgf22
+FGF23 Fgf23
+FGF2 Fgf2
+FGF3 Fgf3
+FGF4 Fgf4
+FGF5 Fgf5
+FGF6 Fgf6
+FGF7 Fgf7
+FGF8 Fgf8
+FGF9 Fgf9
+FGFBP1 Fgfbp1
+FGFBP3 Fgfbp3
+FGFR1 Fgfr1
+FGFR1OP2 Fgfr1op2
+FGFR1OP Fgfr1op
+FGFR2 Fgfr2
+FGFR3 Fgfr3
+FGFR4 Fgfr4
+FGFRL1 Fgfrl1
+FGG Fgg
+FGGY Fggy
+FGL1 Fgl1
+FGL2 Fgl2
+FGR Fgr
+FHAD1 Fhad1
+FHDC1 Fhdc1
+FH Fh1
+FHIT Fhit
+FHL1 Fhl1
+FHL2 Fhl2
+FHL3 Fhl3
+FHL5 Fhl5
+FHOD1 Fhod1
+FHOD3 Fhod3
+FIBCD1 Fibcd1
+FIBIN Fibin
+FIBP Fibp
+FICD Ficd
+FIG4 Fig4
+FIGF Figf
+FIGLA Figla
+FIGN Fign
+FIGNL1 Fignl1
+FIGNL2 Fignl2
+FILIP1 Filip1
+FILIP1L Filip1l
+FIM1 Fim1
+FIM3 Fim3
+FIP1L1 Fip1l1
+FIS1 Fis1
+FITM1 Fitm1
+FITM2 Fitm2
+FIZ1 Fiz1
+FJX1 Fjx1
+FKBP10 Fkbp10
+FKBP11 Fkbp11
+FKBP14 Fkbp14
+FKBP15 Fkbp15
+FKBP1A Fkbp1a
+FKBP1B Fkbp1b
+FKBP2 Fkbp2
+FKBP3 Fkbp3
+FKBP4 Fkbp4
+FKBP5 Fkbp5
+FKBP6 Fkbp6
+FKBP7 Fkbp7
+FKBP8 Fkbp8
+FKBP9 Fkbp9
+FKBPL Fkbpl
+FKHL18 Fkhl18
+FKRP Fkrp
+FKSG83 Vmn1r87
+FKTN Fktn
+FLAD1 Flad1
+FLCN Flcn
+FLG2 Flg2
+FLG Flg
+FLI1 Fli1
+FLII Flii
+FLJ13224 3010003L21Rik
+FLJ21408 4921524M04Rik
+FLJ22184 BC068157
+FLJ22763 Gm5485
+FLJ23152 Gm1574
+FLJ27352 Gm5918
+FLJ30064 Gm15708
+FLJ30092 Gm15800
+FLJ35776 5031415H12Rik
+FLJ37396 Gm6976
+FLJ37453 B330016D10Rik
+FLJ37505 4933438B17Rik
+FLJ40606 4930445K14Rik
+FLJ42875 Gm13111
+FLJ43390 1700086L19Rik
+FLJ45079 Dbpht2
+FLNA Flna
+FLNB Flnb
+FLNC Flnc
+FLOT1 Flot1
+FLOT2 Flot2
+FLRT1 Flrt1
+FLRT2 Flrt2
+FLRT3 Flrt3
+FLT1 Flt1
+FLT3 Flt3
+FLT3LG Flt3l
+FLT4 Flt4
+FLVCR1 Mfsd7b
+FLVCR2 Mfsd7c
+FLYWCH1 Flywch1
+FLYWCH2 Flywch2
+FMN1 Fmn1
+FMN2 Fmn2
+FMNL1 Fmnl1
+FMNL2 Fmnl2
+FMNL3 Fmnl3
+FMO1 Fmo1
+FMO2 Fmo2
+FMO3 Fmo3
+FMO4 Fmo4
+FMO5 Fmo5
+FMO6 Fmo6
+FMO9P Fmo9
+FMOD Fmod
+FMR1 Fmr1
+FMR1NB Fmr1nb
+FN1 Fn1
+FN3K Fn3k
+FN3KRP Fn3krp
+FNBP1 Fnbp1
+FNBP1L Fnbp1l
+FNBP4 Fnbp4
+FNDC1 Fndc1
+FNDC3A Fndc3a
+FNDC3B Fndc3b
+FNDC4 Fndc4
+FNDC5 Fndc5
+FNDC7 Fndc7
+FNDC8 Fndc8
+FNDC9 C030019I05Rik
+FNIP1 A730024A03Rik
+FNIP2 Fnip2
+FNTA Fnta
+FNTB Fntb
+FOCAD BC057079
+FOLH1 Folh1
+FOLR1 Folr1
+FOLR2 Folr2
+FOLR4 Folr4
+FOPNL 0610037P05Rik
+FOSB Fosb
+FOS Fos
+FOSL1 Fosl1
+FOSL2 Fosl2
+FOXA1 Foxa1
+FOXA2 Foxa2
+FOXA3 Foxa3
+FOXB1 Foxb1
+FOXB2 Foxb2
+FOXC1 Foxc1
+FOXC2 Foxc2
+FOXD1 Foxd1
+FOXD2 Foxd2
+FOXD3 Foxd3
+FOXD4 Foxd4
+FOXE1 Foxe1
+FOXE3 Foxe3
+FOXF1 Foxf1
+FOXF2 Foxf2
+FOXG1A Foxg1
+FOXH1 Foxh1
+FOXI1 Foxi1
+FOXI2 Foxi2
+FOXI3 Foxi3
+FOXJ1 Foxj1
+FOXJ2 Foxj2
+FOXJ3 Foxj3
+FOXK1 Foxk1
+FOXK2 Foxk2
+FOXL1 Foxl1
+FOXL2 Foxl2
+FOXM1 Foxm1
+FOXN1 Foxn1
+FOXN2 Foxn2
+FOXN3 Foxn3
+FOXN4 Foxn4
+FOXO1A Foxo1
+FOXO3A Foxo3a
+FOXO3 Foxo3
+FOXO4 Foxo4
+FOXP1 Foxp1
+FOXP2 Foxp2
+FOXP3 Foxp3
+FOXP4 Foxp4
+FOXQ1 Foxq1
+FOXR1 Foxr1
+FOXR2 EG436240
+FOXRED1 Foxred1
+FOXRED2 Foxred2
+FOXS1 Foxs1
+FPGS Fpgs
+FPGT Fpgt
+FPR1 Fpr1
+FPR2 Fpr3
+FPRL1 Fpr-rs2
+FRA10AC1 Fra10ac1
+FRAP1 Frap1
+FRAS1 Fras1
+FRAT1 Frat1
+FRAT2 Frat2
+FREM1 Frem1
+FREM2 Frem2
+FREM3 Frem3
+FREQ Freq
+FRG1 Frg1
+FRG2 Gm6219
+FRK Frk
+FRMD1 Gm5092
+FRMD3 Frmd3
+FRMD4A Frmd4a
+FRMD4B Frmd4b
+FRMD5 A930004K21Rik
+FRMD6 Frmd6
+FRMD7 Frmd7
+FRMD8 Frmd8
+FRMPD1 Frmpd1
+FRMPD2 Gm626
+FRMPD3 Frmpd3
+FRMPD4 Frmpd4
+FRRS1 Frrs1
+FRRS1L 6430704M03Rik
+FRS2 Frs2
+FRS3 Frs3
+FRY Fry
+FRYL 9030227G01Rik
+FRZB Frzb
+FSCB Fscb
+FSCN1 Fscn1
+FSCN2 Fscn2
+FSCN3 Fscn3
+FSD1 Fsd1
+FSD1L Fsd1l
+FSD2 Fsd2
+FSHB Fshb
+FSHR Fshr
+FSIP1 Fsip1
+FSIP2 Fsip2
+FST Fst
+FSTL1 Fstl1
+FSTL3 Fstl3
+FSTL4 Fstl4
+FSTL5 Fstl5
+FTCD Ftcd
+FTH1 Fth1
+FTHL17 Fthl17
+FTL Ftl1
+FTMT Ftmt
+FTO Fto
+FTS Fts
+FTSJ1 Ftsj1
+FTSJ2 Ftsj2
+FTSJ3 Ftsj3
+FTSJD1 Ftsjd1
+FTSJD2 Ftsjd2
+FUBP1 Fubp1
+FUBP3 Fubp3
+FUCA1 Fuca1
+FUCA2 Fuca2
+FUK Fuk
+FUNDC1 Fundc1
+FUNDC2 Fundc2
+FUOM Fuom
+FURIN Furin
+FUS Fus
+FUT10 Fut10
+FUT11 Fut11
+FUT1 Fut1
+FUT2 Fut2
+FUT4 Fut4
+FUT5 Fut4-ps1
+FUT7 Fut7
+FUT8 Fut8
+FUT9 Fut9
+FUZ Fuz
+FVT1 Fvt1
+FXC1 Fxc1
+FXN Fxn
+FXR1 Fxr1
+FXR2 Fxr2
+FXYD1 Fxyd1
+FXYD2 Fxyd2
+FXYD3 Fxyd3
+FXYD4 Fxyd4
+FXYD5 Fxyd5
+FXYD6 Fxyd6
+FXYD7 Fxyd7
+FYB Fyb
+FYCO1 Fyco1
+FYN Fyn
+FYTTD1 Fyttd1
+FZD10 Fzd10
+FZD1 Fzd1
+FZD2 Fzd2
+FZD3 Fzd3
+FZD4 Fzd4
+FZD5 Fzd5
+FZD6 Fzd6
+FZD7 Fzd7
+FZD8 Fzd8
+FZD9 Fzd9
+FZR1 Fzr1
+G0S2 G0s2
+G2E3 G2e3
+G3BP1 G3bp1
+G3BP2 G3bp2
+G6PC2 G6pc2
+G6PC3 G6pc3
+G6PC G6pc
+G6PD G6pdx
+GAA Gaa
+GAB1 Gab1
+GAB2 Gab2
+GAB3 Gab3
+GABARAP Gabarap
+GABARAPL1 Gabarapl1
+GABARAPL2 Gabarapl2
+GABBR1 Gabbr1
+GABBR2 Gabbr2
+GABPA Gabpa
+GABPB1 Gabpb1
+GABRA1 Gabra1
+GABRA2 Gabra2
+GABRA3 Gabra3
+GABRA4 Gabra4
+GABRA5 Gabra5
+GABRA6 Gabra6
+GABRB1 Gabrb1
+GABRB2 Gabrb2
+GABRB3 Gabrb3
+GABRD Gabrd
+GABRE Gabre
+GABRG1 Gabrg1
+GABRG2 Gabrg2
+GABRG3 Gabrg3
+GABRP Gabrp
+GABRQ Gabrq
+GABRR1 Gabrr1
+GABRR2 Gabrr2
+GABRR3 Gabrr3
+GAD1 Gad1
+GAD2 Gad2
+GADD45A Gadd45a
+GADD45B Gadd45b
+GADD45G Gadd45g
+GADD45GIP1 Gadd45gip1
+GADL1 Gadl1
+GAK Gak
+GAL3ST1 Gal3st1
+GAL3ST2 Gal3st2
+GAL3ST3 Gal3st3
+GAL3ST4 Gal3st4
+GALC Galc
+GALE Gale
+GAL Gal
+GALK1 Galk1
+GALK2 Galk2
+GALM Galm
+GALNS Galns
+GALNT10 Galnt10
+GALNT11 Galnt11
+GALNT12 Galnt12
+GALNT13 Galnt13
+GALNT14 Galnt14
+GALNT1 Galnt1
+GALNT2 Galnt2
+GALNT3 Galnt3
+GALNT4 Galnt4
+GALNT5 Galnt5
+GALNT6 Galnt6
+GALNT7 Galnt7
+GALNT8 MMT00036241
+GALNT9 Galnt9
+GALNTL1 Galntl1
+GALNTL2 Galntl2
+GALNTL4 Galntl4
+GALNTL5 Galntl5
+GALNTL6 Galntl6
+GALP Galp
+GALR1 Galr1
+GALR2 Galr2
+GALR3 Galr3
+GALT Galt
+GAMT Gamt
+GANAB Ganab
+GANC Ganc
+GAN Gan
+GAP43 Gap43
+GAPDH AK081405
+GAPDHS Gapdhs
+GAPT Gapt
+GAPVD1 Gapvd1
+GAR1 Gar1
+GARNL1 Garnl1
+GARNL3 Garnl3
+GARNL4 Garnl4
+GARS Gars
+GART Gart
+GAS1 Gas1
+GAS2 Gas2
+GAS2L1 Gas2l1
+GAS2L2 Gas2l2
+GAS2L3 Gas2l3
+GAS6 Gas6
+GAS7 Gas7
+GAS8 Gas8
+GAST Gast
+GATA1 Gata1
+GATA2 Gata2
+GATA3 Gata3
+GATA4 Gata4
+GATA5 Gata5
+GATA6 Gata6
+GATAD1 Gatad1
+GATAD2A Gatad2a
+GATAD2B Gatad2b
+GATC Gatc
+GATM Gatm
+GATSL2 Gatsl2
+GATSL3 Gatsl3
+GBA2 Gba2
+GBA3 mCT51200
+GBA Gba
+GBAS Gbas
+GBE1 Gbe1
+GBF1 Gbf1
+GBGT1 Gbgt1
+GBP1 Gbp2
+GBP2 Gbp1
+GBP4 Gbp3
+GBP5 Gbp5
+GBP6 BC057170
+GBP7 Gbp7
+GBX1 Gbx1
+GBX2 Gbx2
+GCA Gca
+GCAT Gcat
+GCC1 Gcc1
+GCC2 Gcc2
+GCDH Gcdh
+GCET2 Gcet2
+GCFC1 Gcfc1
+GCFC2 AW146020
+GC Gc
+GCG Gcg
+GCGR Gcgr
+GCH1 Gch1
+GCHFR Gchfr
+GCK Gck
+GCKR Gckr
+GCLC Gclc
+GCLM Gclm
+GCM1 Gcm1
+GCM2 Gcm2
+GCN1L1 Gcn1l1
+GCN5L2 Gcn5l2
+GCNT1 Gcnt1
+GCNT2 Gcnt2
+GCNT3 Gcnt3
+GCNT4 Gcnt4
+GCNT7 Gcnt7
+GCOM1 Myzap
+GCSH Gcsh
+GDA Gda
+GDAP1 Gdap1
+GDAP1L1 Gdap1l1
+GDAP2 Gdap2
+GDE1 Gde1
+GDF10 Gdf10
+GDF11 Gdf11
+GDF15 Gdf15
+GDF1 Gdf1
+GDF2 Gdf2
+GDF3 Gdf3
+GDF5 Gdf5
+GDF6 Gdf6
+GDF7 Gdf7
+GDF8 Gdf8
+GDF9 Gdf9
+GDI1 Gdi1
+GDI2 Gdi2
+GDNF Gdnf
+GDPD1 Gdpd1
+GDPD2 Gdpd2
+GDPD3 Gdpd3
+GDPD4 Gdpd4
+GDPD5 Gdpd5
+GDPGP1 D330012F22Rik
+GEM Gem
+GEMIN2 Sip1
+GEMIN4 Gemin4
+GEMIN5 Gemin5
+GEMIN6 Gemin6
+GEMIN7 Gemin7
+GEMIN8 Gemin8
+GEN1 Gen1
+GET4 Get4
+GFAP Gfap
+GFER Gfer
+GFI1B Gfi1b
+GFI1 Gfi1
+GFM1 Gfm1
+GFM2 Gfm2
+GFOD1 Gfod1
+GFOD2 Gfod2
+GFPT1 Gfpt1
+GFPT2 Gfpt2
+GFRA1 Gfra1
+GFRA2 Gfra2
+GFRA3 Gfra3
+GFRA4 Gfra4
+GFRAL Gfral
+GGA1 Gga1
+GGA2 Gga2
+GGA3 Gga3
+GGCT Ggct
+GGCX Ggcx
+GGH Ggh
+GGNBP1 Ggnbp1
+GGNBP2 Ggnbp2
+GGN Ggn
+GGPS1 Ggps1
+GGT1 Ggt1
+GGT5 Ggt5
+GGT6 Ggt6
+GGT7 Ggt7
+GGTA1 Ggta1
+GGTL3 Ggtl3
+GGTLA1 Ggtla1
+GH1 a
+GHDC Ghdc
+GHITM Ghitm
+GHR Ghr
+GHRH Ghrh
+GHRHR Ghrhr
+GHRL Ghrl
+GHSR Ghsr
+GID4 4933439F18Rik
+GID8 2310003C23Rik
+GIF Gif
+GIGYF1 Gigyf1
+GIGYF2 Gigyf2
+GIMAP1 Gimap1
+GIMAP4 Gimap4
+GIMAP5 Gimap5
+GIMAP6 Gimap6
+GIMAP7 Gimap7
+GIMAP8 Gimap8
+GIMD1 Gm5549
+GIN1 Gin1
+GINM1 BC013529
+GINS1 Gins1
+GINS2 4833427B12Rik
+GINS3 Gins3
+GINS4 Gins4
+GIPC1 Gipc1
+GIPC2 Gipc2
+GIPC3 Gipc3
+GIP Gip
+GIPR Gipr
+GIT1 Git1
+GIT2 Git2
+GIYD2 Giyd2
+GJA10 Gja10
+GJA12 Gja12
+GJA1 Gja1
+GJA3 Gja3
+GJA4 Gja4
+GJA5 Gja5
+GJA7 Gja7
+GJA8 Gja8
+GJB1 Gjb1
+GJB2 Gjb2
+GJB3 Gjb3
+GJB4 Gjb4
+GJB5 Gjb5
+GJB6 Gjb6
+GJC1 Gjc1
+GJC2 Gjc2
+GJC3 Gjc3
+GJD2 Gjd2
+GJD3 Gjd3
+GJD4 Gjd4
+GJE1 Gje1
+GK2 Gk2
+GK5 Gk5
+GKAP1 Gkap1
+GK Gyk
+GKN1 Gkn1
+GKN2 Gkn2
+GLA Gla
+GLB1 Glb1
+GLB1L2 Glb1l2
+GLB1L3 4921509F24Rik
+GLB1L Glb1l
+GLCCI1 Glcci1
+GLCE Glce
+GLDC Gldc
+GLDN Gldn
+GLE1 Gle1
+GLG1 Glg1
+GLI1 Gli1
+GLI2 Gli2
+GLI3 Gli3
+GLIPR1 Glipr1
+GLIPR1L1 Glipr1l1
+GLIPR1L2 Glipr1l2
+GLIPR2 Glipr2
+GLIS1 Glis1
+GLIS2 Glis2
+GLIS3 Glis3
+GLMN Glmn
+GLO1 Glo1
+GLOD4 Glod4
+GLOD5 Glod5
+GLOXD1 Gloxd1
+GLP1R Glp1r
+GLP2R Glp2r
+GLRA1 Glra1
+GLRA2 Glra2
+GLRA3 Glra3
+GLRA4 Glra4
+GLRB Glrb
+GLRX2 Glrx2
+GLRX3 Glrx3
+GLRX5 Glrx5
+GLRX Glrx
+GLS2 Gls2
+GLS Gls
+GLT1D1 Glt1d1
+GLT25D1 Glt25d1
+GLT25D2 Glt25d2
+GLT28D1 Glt28d1
+GLT6D1 Glt6d1
+GLT8D1 Glt8d1
+GLT8D2 Glt8d2
+GLTPD1 Gltpd1
+GLTPD2 Gltpd2
+GLTP Gltp
+GLTSCR1 Gltscr1
+GLTSCR2 Gltscr2
+GLUD1 Glud1
+GLULD1 Gluld1
+GLUL Glul
+GLYAT Glyat
+GLYATL3 Glyatl3
+GLYCAM1 Glycam1
+GLYCTK Glyctk
+GLYR1 Glyr1
+GM2A Gm2a
+GMCL1 Gmcl1
+GMDS Gmds
+GMEB1 Gmeb1
+GMEB2 Gmeb2
+GMFB Gmfb
+GMFG Gmfg
+GMIP Gmip
+GML Gml
+GMNC Gm606
+GMNN Gmnn
+GMPPA Gmppa
+GMPPB Gmppb
+GMPR2 Gmpr2
+GMPR Gmpr
+GMPS Gmps
+GNA11 Gna11
+GNA12 Gna12
+GNA13 Gna13
+GNA14 Gna14
+GNA15 Gna15
+GNAI1 Gnai1
+GNAI2 Gnai2
+GNAI3 Gnai3
+GNAL Gnal
+GNAO1 Gnao1
+GNAQ Gnaq
+GNAS-AS1 Nespas
+GNAS Gnas
+GNAT1 Gnat1
+GNAT2 Gnat2
+GNAT3 Gnat3
+GNAZ Gnaz
+GNB1 Gnb1
+GNB1L Gnb1l
+GNB2 Gnb2
+GNB2L1 Gnb2l1
+GNB3 Gnb3
+GNB4 Gnb4
+GNB5 Gnb5
+GNE Gne
+GNG10 Gng10
+GNG11 Gng11
+GNG12 Gng12
+GNG13 Gng13
+GNG2 Gng2
+GNG3 Gng3
+GNG4 Gng4
+GNG5 Gng5
+GNG7 Gng7
+GNG8 Gng8
+GNGT1 Gngt1
+GNGT2 Gngt2
+GNL1 Gnl1
+GNL2 Gnl2
+GNL3 Gnl3
+GNL3L Gnl3l
+GNMT Gnmt
+GNN BC030307
+GNPAT Gnpat
+GNPDA1 Gnpda1
+GNPDA2 Gnpda2
+GNPNAT1 Gnpnat1
+GNPTAB EG432486
+GNPTG Gnptg
+GNRH1 Gnrh1
+GNRHR Gnrhr
+GNS Gns
+GOLGA1 Golga1
+GOLGA2 Golga2
+GOLGA3 Golga3
+GOLGA4 Golga4
+GOLGA5 Golga5
+GOLGA7B Golga7b
+GOLGA7 Golga7
+GOLGB1 Golgb1
+GOLIM4 Golim4
+GOLM1 Golm1
+GOLPH2 Golph2
+GOLPH3 Golph3
+GOLPH3L Golph3l
+GOLPH4 Golph4
+GOLT1A Golt1a
+GOLT1B Golt1b
+GON4L Gon4l
+GOPC Gopc
+GORAB Gorab
+GORASP1 Gorasp1
+GORASP2 Gorasp2
+GOSR1 Gosr1
+GOSR2 Gosr2
+GOT1 Got1
+GOT1L1 Got1l1
+GOT2 Got2
+GP1BA Gp1ba
+GP1BB Gp1bb
+GP2 Gp2
+GP5 Gp5
+GP6 Gp6
+GP9 Gp9
+GPA33 Gpa33
+GPAA1 Gpaa1
+GPAM Gpam
+GPANK1 Gpank1
+GPAT2 Gpat2
+GPATC1 Gpatc1
+GPATC2 Gpatc2
+GPATC3 Gpatc3
+GPATC4 Gpatc4
+GPATC8 Gpatc8
+GPATCH1 Gpatch1
+GPATCH2 Gpatch2
+GPATCH3 Gpatch3
+GPATCH4 Gpatch4
+GPATCH8 Gpatch8
+GPBAR1 Gpbar1
+GPBP1 Gpbp1
+GPBP1L1 Gpbp1l1
+GPC1 Gpc1
+GPC2 Gpc2
+GPC3 Gpc3
+GPC4 Gpc4
+GPC5 Gpc5
+GPC6 Gpc6
+GPCPD1 Gpcpd1
+GPD1 Gpd1
+GPD1L Gpd1l
+GPD2 Gpd2
+GPER Gpr30
+GPHA2 Gpha2
+GPHB5 Gphb5
+GPHN Gphn
+GPIAP1 Gpiap1
+GPI Gpi1
+GPIHBP1 Gpihbp1
+GPKOW Gpkow
+GPLD1 Gpld1
+GPM6A Gpm6a
+GPM6B Gpm6b
+GPN1 Gpn1
+GPN2 Gpn2
+GPN3 Gpn3
+GPNMB Gpnmb
+GPR101 Gpr101
+GPR103 Gpr103
+GPR107 AI790205
+GPR108 Gpr108
+GPR109A Gpr109a
+GPR110 Gpr110
+GPR111 Gpr111
+GPR112 Gpr112
+GPR113 Gpr113
+GPR114 Gpr114
+GPR115 Gpr115
+GPR116 Gpr116
+GPR119 Gpr119
+GPR120 Gpr120
+GPR123 Gpr123
+GPR124 Gpr124
+GPR125 Gpr125
+GPR126 Gpr126
+GPR128 Gpr128
+GPR12 Gpr12
+GPR132 Gpr132
+GPR133 Gpr133
+GPR135 Gpr135
+GPR137B Gpr137b
+GPR137C 6330416L11Rik
+GPR137 Gpr137
+GPR139 Gpr139
+GPR141 Gpr141
+GPR142 Gpr142
+GPR143 Gpr143
+GPR144 XM_355327
+GPR146 Gpr146
+GPR149 Gpr149
+GPR150 Gpr150
+GPR151 Gpr151
+GPR152 Gpr152
+GPR153 Gpr153
+GPR155 Gpr155
+GPR156 Gpr156
+GPR157 Gpr157
+GPR158 Gpr158
+GPR15 Gpr15
+GPR160 Gpr160
+GPR161 Gpr161
+GPR162 Gpr162
+GPR165P Gpr165
+GPR171 Gpr171
+GPR172B Gpr172b
+GPR173 Gpr173
+GPR174 Gpr174
+GPR176 Gpr176
+GPR177 Gpr177
+GPR179 Gpr179
+GPR17 Gpr17
+GPR180 Gpr180
+GPR182 Gpr182
+GPR183 Gpr183
+GPR18 Gpr18
+GPR19 Gpr19
+GPR1 Gpr1
+GPR20 Gpr20
+GPR21 Gpr21
+GPR22 Gpr22
+GPR23 Gpr23
+GPR25 Gpr25
+GPR26 Gpr26
+GPR27 Gpr27
+GPR30 Gpr30
+GPR31 Gpr31b
+GPR32 XM_357749
+GPR33 Gpr33
+GPR34 Gpr34
+GPR35 Gpr35
+GPR37 Gpr37
+GPR37L1 Gpr37l1
+GPR39 Gpr39
+GPR3 Gpr3
+GPR44 Gpr44
+GPR45 Gpr45
+GPR4 Gpr4
+GPR50 Gpr50
+GPR52 EG620246
+GPR55 Gpr55
+GPR56 Gpr56
+GPR61 Gpr61
+GPR62 Gpr62
+GPR63 Gpr63
+GPR64 Gpr64
+GPR65 Gpr65
+GPR68 Gpr68
+GPR6 Gpr6
+GPR75 Gpr75
+GPR77 Gpr77
+GPR81 Gpr81
+GPR82 Gpr82
+GPR83 Gpr83
+GPR84 Gpr84
+GPR85 Gpr85
+GPR87 Gpr87
+GPR88 Gpr88
+GPR89A Gpr89
+GPR92 Gpr92
+GPR97 Gpr97
+GPR98 Gpr98
+GPRASP1 Gprasp1
+GPRASP2 Gprasp2
+GPRC5A Gprc5a
+GPRC5B Gprc5b
+GPRC5C Gprc5c
+GPRC5D Gprc5d
+GPRC6A Gprc6a
+GPRIN1 Gprin1
+GPRIN2 Gprin2
+GPRIN3 Gprin3
+GPS1 Gps1
+GPS2 Gps2
+GPSM1 Gpsm1
+GPSM2 Gpsm2
+GPSM3 Gpsm3
+GPSN2 Gpsn2
+GPT2 Gpt2
+GPT Gpt
+GPX1 Gpx1
+GPX2 Gpx2
+GPX3 Gpx3
+GPX4 Gpx4
+GPX5 Gpx5
+GPX6 Gpx6
+GPX7 Gpx7
+GPX8 Gpx8
+GRAMD1A Gramd1a
+GRAMD1B Gramd1b
+GRAMD1C Gramd1c
+GRAMD2 Gramd2
+GRAMD3 Gramd3
+GRAMD4 Gramd4
+GRAP2 Grap2
+GRAP Grap
+GRASP Grasp
+GRB10 Grb10
+GRB14 Grb14
+GRB2 Grb2
+GRB7 Grb7
+GREB1 Greb1
+GREB1L Greb1l
+GREM1 Grem1
+GREM2 Grem2
+GRHL1 Grhl1
+GRHL2 Grhl2
+GRHL3 Grhl3
+GRHPR Grhpr
+GRIA1 Gria1
+GRIA2 Gria2
+GRIA3 Gria3
+GRIA4 Gria4
+GRID1 Grid1
+GRID2 Grid2
+GRID2IP Grid2ip
+GRIFIN Grifin
+GRIK1 Grik1
+GRIK2 Grik2
+GRIK3 Grik3
+GRIK4 Grik4
+GRIK5 Grik5
+GRIN1 Grin1
+GRIN2A Grin2a
+GRIN2B Grin2b
+GRIN2C Grin2c
+GRIN2D Grin2d
+GRIN3A Grin3a
+GRIN3B Grin3b
+GRINA Grina
+GRINL1A Grinl1a
+GRIP1 Grip1
+GRIP2 Grip2
+GRIPAP1 Gripap1
+GRK1 Grk1
+GRK4 Gprk2l
+GRK5 Gprk5
+GRK6 Gprk6
+GRLF1 Grlf1
+GRM1 Grm1
+GRM2 Grm2
+GRM3 Grm3
+GRM4 Grm4
+GRM5 Grm5
+GRM6 Grm6
+GRM7 Grm7
+GRM8 Grm8
+GRN Grn
+GRPEL1 Grpel1
+GRPEL2 Grpel2
+GRP Grp
+GRPR Grpr
+GRRP1 Grrp1
+GRSF1 Grsf1
+GRTP1 Grtp1
+GRWD1 Grwd1
+GRXCR1 Grxcr1
+GRXCR2 Grxcr2
+GSC2 Gsc2
+GSC Gsc
+GSCL Gscl
+GSDM1 Gsdm1
+GSDMA Gsdma
+GSDMC Gsdmc
+GSDMDC1 Gsdmdc1
+GSDMD Gsdmd
+GSG1 Gsg1
+GSG1L Gsg1l
+GSG2 Gsg2
+GSH1 Gsh1
+GSK3A Gsk3a
+GSK3B Gsk3b
+GSN Gsn
+GSPT1 Gspt1
+GSPT2 Gspt2
+GSR Gsr
+GSS Gss
+GSTA1 Gsta2
+GSTA3 Gsta3
+GSTA4 Gsta4
+GSTA5 Gm3776
+GSTCD Gstcd
+GSTK1 Gstk1
+GSTM1 Gstm1
+GSTM2 Gstm2
+GSTM2P1 Gstm2-ps1
+GSTM3 Gstm3
+GSTM4 Gstm4
+GSTO1 Gsto1
+GSTO2 Gsto2
+GSTP1 Gstp1
+GSTT1 Gstt1
+GSTT2 Gstt2
+GSTTP2 Gstt4
+GSTZ1 Gstz1
+GSX1 Gsx1
+GSX2 Gsx2
+GTDC1 Gtdc1
+GTDC2 Gtdc2
+GTF2A1 Gtf2a1
+GTF2A1L Gtf2a1l
+GTF2A2 Gtf2a2
+GTF2B Gtf2b
+GTF2E1 Gtf2e1
+GTF2E2 Gtf2e2
+GTF2F1 Gtf2f1
+GTF2F2 Gtf2f2
+GTF2H1 Gtf2h1
+GTF2H2 Gtf2h2
+GTF2H3 Gtf2h3
+GTF2H4 Gtf2h4
+GTF2H5 Gtf2h5
+GTF2I Gtf2i
+GTF2IRD1 Gtf2ird1
+GTF2IRD2 Gtf2ird2
+GTF3A Gtf3a
+GTF3C1 Gtf3c1
+GTF3C2 Gtf3c2
+GTF3C3 Gtf3c3
+GTF3C4 Gtf3c4
+GTF3C5 Gtf3c5
+GTF3C6 Gtf3c6
+GTPBP10 Gtpbp10
+GTPBP1 Gtpbp1
+GTPBP2 Gtpbp2
+GTPBP3 Gtpbp3
+GTPBP4 Gtpbp4
+GTPBP5 Gtpbp5
+GTPBP6 Gtpbp6
+GTPBP8 Gtpbp8
+GTPBP9 Gtpbp9
+GTSE1 Gtse1
+GTSF1 Gtsf1
+GTSF1L Gtsf1l
+GUCA1A Guca1a
+GUCA1B Guca1b
+GUCA2A Guca2a
+GUCA2B Guca2b
+GUCY1A2 Gucy1a2
+GUCY1A3 Gucy1a3
+GUCY1B2 Gucy1b2
+GUCY1B3 Gucy1b3
+GUCY2C Gucy2c
+GUCY2D Gucy2e
+GUCY2E Gucy2d
+GUCY2F Gucy2f
+GUCY2GP Gucy2g
+GUF1 Guf1
+GUK1 Guk1
+GULOP Gulo
+GULP1 Gulp1
+GUP1 Gup1
+GUSB Gusb
+GVINP1 Gvin1
+GXYLT1 Gxylt1
+GXYLT2 Gxylt2
+GYG1 Gyg
+GYLTL1B Gyltl1b
+GYPC Gypc
+GYS1 Gys1
+GYS2 Gys2
+GZF1 Gzf1
+GZMA Gzma
+GZMB Gzmb
+GZMH Gzme
+GZMK Gzmk
+GZMM Gzmm
+H19 H19
+H1F0 H1f0
+H1FNT H1fnt
+H1FOO H1foo
+H1FX H1fx
+H2AFB1 H2afb1
+H2AFB2 H2afb3-ps
+H2AFJ H2afj
+H2AFV H2afv
+H2AFX H2afx
+H2AFY2 H2afy2
+H2AFY H2afy
+H2AFZ H2afz
+H2BFM H2bfm
+H3F3A H3f3a
+H3F3B H3f3b
+H3F3C H3f3c
+H6PD H6pd
+HAAO Haao
+HABP2 Habp2
+HABP4 Habp4
+HACE1 Hace1
+HACL1 Hacl1
+HADH2 Hadh2
+HADHA Hadha
+HADHB Hadhb
+HADH Hadh
+HAGH Hagh
+HAGHL Haghl
+HAL Hal
+HAMP Hamp
+HAND1 Hand1
+HAND2 Hand2
+HAO1 Hao1
+HAO2 Hao2
+HAP1 Hap1
+HAPLN1 Hapln1
+HAPLN2 Hapln2
+HAPLN3 Hapln3
+HAPLN4 Hapln4
+HARBI1 Harbi1
+HARS2 Hars2
+HARS Hars
+HARSL Harsl
+HAS1 Has1
+HAS2-AS1 Has2as
+HAS2 Has2
+HAS3 Has3
+HAT1 Hat1
+HAUS1 Haus1
+HAUS2 Haus2
+HAUS3 Haus3
+HAUS4 Haus4
+HAUS5 Haus5
+HAUS6 Haus6
+HAUS7 Haus7
+HAUS8 Haus8
+HAVCR1 Havcr1
+HAVCR2 Havcr2
+HAX1 Hax1
+HBA1 Hba-a2
+HBA2 Hba-a1
+HBB Hbb
+HBD Hbb-bh2
+HBE1 Hbb-y
+HBEGF Hbegf
+HBG1 Hbb-bh1
+HBLD1 Hbld1
+HBLD2 Hbld2
+HBP1 Hbp1
+HBQ1 Hbq1
+HBS1L Hbs1l
+HBXIP ENSMUST00000060354
+HBZ Hba-x
+HCAR1 Gpr81
+HCAR2 Niacr1
+HCCS Hccs
+HCFC1 Hcfc1
+HCFC1R1 Hcfc1r1
+HCFC2 Hcfc2
+HCG4 5430410E06Rik
+HCK Hck
+HCLS1 Hcls1
+HCN1 Hcn1
+HCN2 Hcn2
+HCN3 Hcn3
+HCN4 Hcn4
+HCRT Hcrt
+HCRTR1 Hcrtr1
+HCRTR2 Hcrtr2
+HCST Hcst
+hCT10916.2 2010001H14Rik
+hCT11512 Adam4
+hCT11714 A930005I04Rik
+hCT11924 1700001C02Rik
+hCT12019.3 2200002K05Rik
+hCT12152 Doc2g
+hCT13065 3830431G21Rik
+hCT13289.3 Nkx2-4
+hCT13519.3 2610003J06Rik
+hCT14042 0610012D14Rik
+hCT1640771.3 4921520G13Rik
+hCT1641728.3 A230062G08Rik
+hCT1642255.2 1700013G10Rik
+hCT1642314.3 Gm412
+hCT1642608 Uncx4.1
+hCT1642908.3 4933405L10Rik
+hCT1643171.2 0610010E21Rik
+hCT1643345.3 1700007N14Rik
+hCT1643472.2 2410012H22Rik
+hCT1643649.2 Ndufs5
+hCT1643839.3 Folr4
+hCT1644314 Dkkl1
+hCT1644338.2 B230333C21Rik
+hCT1644481.3 1700041E20Rik
+hCT1644917.1 BC052484
+hCT1644927.3 Gm606
+hCT1645078.2 5430427O21Rik
+hCT1645339.1 Gbx1
+hCT1645854 5430433E21Rik
+hCT1645981.3 MMT00022854
+hCT1645995.2 2310039H08Rik
+hCT1646155.3 2610034M16Rik
+hCT1646198.1 MMT00057885
+hCT1646611.2 BF011300
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+hCT1647551.2 Rpl9
+hCT1647780.3 3110009E18Rik
+hCT1648107.2 NM_178245
+hCT1648323.2 Olfr767
+hCT1648352.2 A830073O21Rik
+hCT1650928 EG436049
+hCT1653189.3 1700101E01Rik
+hCT1653363.1 MMT00070072
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+hCT1654767 2510009E07Rik
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+hCT1656787.3 1700007B14Rik
+hCT1686533.1 Olfr560
+hCT1686568.2 Olfr561
+hCT1686854.2 Olfr1306
+hCT1686867.2 Olfr1106
+hCT17075.2 BC048390
+hCT1768041.1 1700012B09Rik
+hCT17691.2 Lrrc37a
+hCT1770931.2 Rfpl4
+hCT1771270.2 AI662250
+hCT1782071 4930579G24Rik
+hCT1785709 1110059M19Rik
+hCT1813262 MMT00072637
+hCT1813828.1 Uox
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+hCT1815076.2 MMT00009081
+hCT1815166.1 4933427E13Rik
+hCT1815592.2 MMT00005540
+hCT1816254.1 MMT00052820
+hCT1816922.2 Gm421
+hCT1819091.2 Cd200r2
+hCT1822191.2 MMT00015749
+hCT1823022.2 2410078J06Rik
+hCT1823092.1 4921517D21Rik
+hCT1824922.2 4930403N07Rik
+hCT1826563.2 4930558O21Rik
+hCT1828339.1 4930417M19Rik
+hCT1830864.1 Olfr683
+hCT1831218 Sp9
+hCT1832349.2 Olfr1365
+hCT1833357.2 mCT123331
+hCT1833562 Zfp92
+hCT1833792.1 1810005K13Rik
+hCT1834285 Sult1c1
+hCT1834401.1 Olfr993
+hCT1835238.2 A330050F15Rik
+hCT1835348.2 Olfr906
+hCT1837134 4931440L10Rik
+hCT1842107 AU020772
+hCT1846498.1 Olfr433
+hCT1846812.1 1700123I01Rik
+hCT1847002.1 mCT59770
+hCT1849298 Txnrd3
+hCT19267.2 G630049C14Rik
+hCT1950109.1 1700034E13Rik
+hCT1951975.1 C330005M16Rik
+hCT1952131.1 Rfpl4
+hCT1953847.1 1810015A11Rik
+hCT1958452.1 V1rc10
+hCT1964895 A930017N06Rik
+hCT1967331.1 0910001L09Rik
+hCT1970462 BC018371
+hCT1970902 XM_138060
+hCT1971113 A930001M12Rik
+hCT1971147 Exosc3
+hCT1971190 BC018507
+hCT1971294.1 Gm1964
+hCT1971349.1 EG546143
+hCT1972710.1 AI661453
+hCT19878.2 2010305C02Rik
+hCT21172 Irg1
+hCT21474 BC025575
+hCT21996.2 EG231903
+hCT22315.2 MMT00024260
+hCT2257376 AY078069
+hCT2257528 BC038613
+hCT2263673 A730011L01Rik
+hCT2263847 2010001H14Rik
+hCT2268375 Olfr1258
+hCT2270810 5330434G04Rik
+hCT2272945 Ubb
+hCT2275836 B930041F14Rik
+hCT2275841 EG381582
+hCT2278726 5830482F20Rik
+hCT2280706 Pnliprp2
+hCT2281958 A430105I19Rik
+hCT2287214 EG546896
+hCT2288870 Cnot6l
+hCT2289405 Ddx11
+hCT2292694 Htr5b
+hCT2294904 Jam4
+hCT2299603 Ssbp3
+hCT2305975 Crim2
+hCT2309614 1810059G22Rik
+hCT2313938 Bak1
+hCT2319468 Bzw1
+hCT2326460 A230062G08Rik
+hCT2326510 9030416H16Rik
+hCT2327718 4933405A16Rik
+hCT2336637 MMT00001542
+hCT2340581 1700067K01Rik
+hCT2341037 EG433653
+hCT2341041 E130113E03Rik
+hCT2343244 6330512M04Rik
+hCT2345203 2210417D09Rik
+hCT2345383 Olfr549
+hCT2345384 Onecut3
+hCT2347392 1810008K16Rik
+hCT2347429 Helt
+hCT23889.2 C730027E14Rik
+hCT23935 Prrxl1
+hCT24073.3 4930443G12Rik
+hCT24116.2 6332401O19Rik
+hCT25827 0610030E20Rik
+hCT29167.3 4930405M20Rik
+hCT29424 Cetn4
+hCT30187.2 4933412E14Rik
+hCT31203.2 Olfr424
+hCT31672.3 Prr13
+hCT31917.3 Txnrd3
+hCT32285.2 Olfr1165
+hCT32484.3 MMT00004368
+hCT32574.3 0610009J22Rik
+hCT33070.3 2210021J22Rik
+hCT33521.2 Gm249
+hCT5910 Dbx1
+hCT6221 Eif3s5
+hCT6428.3 4930511J11Rik
+hCT9009.2 Hmgn1
+hCT9327.3 4933401F05Rik
+hCT9493 Grifin
+hCT9675.2 AA673488
+hCT9688.3 Abca15
+hCT9895.2 1700016K13Rik
+HDAC10 Hdac10
+HDAC11 Hdac11
+HDAC1 Hdac1
+HDAC2 Hdac2
+HDAC3 Hdac3
+HDAC4 Hdac4
+HDAC5 Hdac5
+HDAC6 Hdac6
+HDAC7A Hdac7a
+HDAC7 Hdac7
+HDAC8 Hdac8
+HDAC9 Hdac9
+HDC Hdc
+HDDC2 Hddc2
+HDDC3 Hddc3
+HDGF Hdgf
+HDGFL1 Hdgfl1
+HDGFRP2 Hdgfrp2
+HDGFRP3 Hdgfrp3
+HDHD1A Hdhd1a
+HDHD2 Hdhd2
+HDHD3 Hdhd3
+HD Hdh
+HDLBP Hdlbp
+HDX Hdx
+HEATR1 Heatr1
+HEATR2 Heatr2
+HEATR3 Heatr3
+HEATR4 XM_153454
+HEATR5A Heatr5a
+HEATR5B Heatr5b
+HEATR6 Heatr6
+HEATR7A Heatr7a
+HEATR7B1 Heatr7b1
+HEATR7B2 Heatr7b2
+HEATR8 Heatr8
+HEBP1 Hebp1
+HEBP2 Hebp2
+HECA Heca
+HECTD1 Hectd1
+HECTD2 Hectd2
+HECTD3 Hectd3
+HECW1 Hecw1
+HECW2 Hecw2
+HEG1 Heg1
+HELB Helb
+HELLS Hells
+HELQ Helq
+HELT Helt
+HELZ Helz
+HEMGN Hemgn
+HEMK1 Hemk1
+HEMK2 Hemk2
+HENMT1 4921515J06Rik
+HEPACAM2 Hepacam2
+HEPACAM Hepacam
+HEPH Heph
+HEPHL1 Hephl1
+HERC1 Herc1
+HERC2 Herc2
+HERC3 Herc3
+HERC4 Herc4
+HERC6 Herc5
+HERPUD1 Herpud1
+HERPUD2 Herpud2
+HES1 Hes1
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+HEXDC Hexdc
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+HISPPD1 Hisppd1
+HIST1H1A Hist1h1a
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+HIST1H2AB Hist1h2ab
+HIST1H2AC Hist1h2ac
+HIST1H2AD Hist1h2ad
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+HIST1H2AG Hist1h2ag
+HIST1H2AH Hist1h2ah
+HIST1H2AI Hist1h2ai
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+HIST1H2AK Hist1h2ak
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+HIST2H2AB EG621893
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+HSS00141096 MMT00000048
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+HSS00289233 2810451A06Rik
+HSS0029163.3 Txnl5
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+HSS0029421.3_rc Man1b1
+HSS0029468.4 Ctsz
+HSS0029478.3_rc Slc3a2
+HSS0029497.4 Bphl
+HSS0029512.2 Ndufb4
+HSS00296948 AK033336
+HSS00305022 2900022I03Rik
+HSS00305517 4933407H18Rik
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+HSS00378287 MMT00023737
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+HSS00386249 BC048403
+HSS00389911 A530088I07Rik
+HSS0073883.5_rc Fkbp1a
+HSS0143641.3_rc Ftl1
+HSS0157490.3_rc Cstb
+HSS0187880.2_rc Prkab1
+HSS0367367.2_rc Hba-a1
+HSS0373348.2 Akr1c21
+HSS0431762.2_rc Cycs
+HSS0497042.1_rc Pnliprp1
+HSS0499419.1_rc Vbp1
+HSS0516391.1_rc Tmem27
+HSS0553933.1 Clps
+HSS0585063.1_rc Rpl13a
+HSS0589063.1_rc Rps26
+HSS0605379.1 Agt
+HSS0608057.1_rc Klk1
+HSS0620970.1_rc Napsa
+HTATIP2 Htatip2
+HTATIP Htatip
+HTATSF1 Htatsf1
+HTR1A Htr1a
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+IL10 Il10
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+IL1RL1 Il1rl1
+IL1RL2 Il1rl2
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+IL20 Il20
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+KPNA4 Kpna4
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+KRTAP13-2 EG546672
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+KRTAP20-2 Gm6379
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+LDLRAD3 Ldlrad3
+LDLRAP1 Ldlrap1
+LDLR Ldlr
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+LOC730755 Gm11938
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+LOR Lor
+LOXHD1 Loxhd1
+LOXL1 Loxl1
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+LOX Lox
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+LRRC16 Lrrc16
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+LRRC1 Lrrc1
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+LRRC23 Lrrc23
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+LRRC44 Lrrc44
+LRRC45 Lrrc45
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+LRRC4 Lrrc4
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+LRRC52 Lrrc52
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+LRRC73 Gm88
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+LRRCC1 Lrrcc1
+LRRD1 4932412H11Rik
+LRRFIP1 Lrrfip1
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+LRRIQ1 Lrriq1
+LRRIQ2 Lrriq2
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+LRRIQ4 Lrriq4
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+LRRN4 Lrrn4
+LRRN5 Lrrn2
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+LRRTM1 Lrrtm1
+LRRTM2 Lrrtm2
+LRRTM3 Lrrtm3
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+NOD2 Nod2
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+NOL11 Nol11
+NOL12 Nol12
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+NOL4 Nol4
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+NOL8 Nol8
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+NOLA3 Nola3
+NOLC1 MMT00052601
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+NPTXR Nptxr
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+NPW Npw
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+NQO2 Nqo2
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+NRK Nrk
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+NRTN Nrtn
+NRXN1 Nrxn1
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+NRXN3 Nrxn3
+NSA2 Nsa2
+NSBP1 Nsbp1
+NSD1 Nsd1
+NSDHL Nsdhl
+NSFL1C Nsfl1c
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+PBX3 Pbx3
+PBX4 Pbx4
+PBXIP1 Pbxip1
+PCAF Pcaf
+PCBD1 Pcbd1
+PCBD2 Pcbd2
+PCBP1 Pcbp1
+PCBP2 Pcbp2
+PCBP3 Pcbp3
+PCBP4 Pcbp4
+PCCA Pcca
+PCCB Pccb
+PCDH10 Pcdh10
+PCDH11X Pcdh11x
+PCDH12 Pcdh12
+PCDH15 Pcdh15
+PCDH17 Pcdh17
+PCDH18 Pcdh18
+PCDH19 AK028278
+PCDH1 Pcdh1
+PCDH20 Pcdh20
+PCDH21 Pcdh21
+PCDH7 Pcdh7
+PCDH8 Pcdh8
+PCDH9 Pcdh9
+PCDHA10 Pcdha10
+PCDHA11 Pcdha11
+PCDHA12 Pcdha9
+PCDHA13 Pcdha12
+PCDHA1 Pcdha1
+PCDHA2 Pcdha2
+PCDHA3 Pcdha3
+PCDHA4 Pcdha4
+PCDHA5 Pcdha5
+PCDHA6 Pcdha6
+PCDHA7 Pcdha7
+PCDHA8 Pcdha8
+PCDHAC1 Pcdhac1
+PCDHAC2 Pcdhac2
+PCDHB10 Pcdhb18
+PCDHB11 Pcdhb19
+PCDHB14 Pcdhb20
+PCDHB15 Pcdhb22
+PCDHB16 Pcdhb17
+PCDHB17 Pcdhb14
+PCDHB1 Pcdhb1
+PCDHB2 Pcdhb2
+PCDHB3 Pcdhb3
+PCDHB4 Pcdhb4
+PCDHB5 Pcdhb5
+PCDHB6 Pcdhb13
+PCDHB7 Pcdhb15
+PCDHB8 Pcdhb16
+PCDHGA10 Pcdhga10
+PCDHGA11 Pcdhga11
+PCDHGA12 Pcdhga12
+PCDHGA1 Pcdhga1
+PCDHGA2 Pcdhga2
+PCDHGA3 Pcdhga3
+PCDHGA4 Pcdhga4
+PCDHGA5 Pcdhga5
+PCDHGA6 Pcdhga6
+PCDHGA7 Pcdhga7
+PCDHGA8 Pcdhga8
+PCDHGA9 Pcdhga9
+PCDHGB1 Pcdhgb1
+PCDHGB2 Pcdhgb2
+PCDHGB4 Pcdhgb4
+PCDHGB5 Pcdhgb5
+PCDHGB6 Pcdhgb6
+PCDHGB7 Pcdhgb7
+PCDHGB8P Pcdhgb8
+PCDHGC3 Pcdhgc3
+PCDHGC4 Pcdhgc4
+PCDHGC5 Pcdhgc5
+PCDP1 Gm101
+PCED1A Fam113a
+PCED1B Fam113b
+PCED1CP Gm4958
+PCF11 Pcf11
+PCGF1 Pcgf1
+PCGF2 Pcgf2
+PCGF3 Pcgf3
+PCGF5 Pcgf5
+PCGF6 Pcgf6
+PCID1 Ga17
+PCID2 Pcid2
+PCIF1 Pcif1
+PCK1 Pck1
+PCK2 Pck2
+PCLO Pclo
+PCM1 Pcm1
+PCMT1 Pcmt1
+PCMTD1 Pcmtd1
+PCMTD2 Pcmtd2
+PCNA Pcna
+PCNP Pcnp
+PCNT Pcnt
+PCNXL2 E330039K12Rik
+PCNXL3 Pcnxl3
+PCNXL4 1810048J11Rik
+PCNX Pcnx
+PCOLCE2 Pcolce2
+PCOLCE Pcolce
+PCP2 Pcp2
+PCP4L1 Pcp4l1
+PCP4 Pcp4
+PC Pcx
+PCQAP Pcqap
+PCSK1N Pcsk1n
+PCSK1 Pcsk1
+PCSK2 Pcsk2
+PCSK4 Pcsk4
+PCSK5 Pcsk5
+PCSK6 Pcsk6
+PCSK7 Pcsk7
+PCSK9 Pcsk9
+PCTK1 Pctk1
+PCTK2 Pctk2
+PCTK3 Pctk3
+PCTP Pctp
+PCYOX1L Pcyox1l
+PCYOX1 Pcyox1
+PCYT1A Pcyt1a
+PCYT1B Pcyt1b
+PCYT2 Pcyt2
+PDAP1 Pdap1
+PDCD10 Pdcd10
+PDCD11 Pdcd11
+PDCD1LG2 Pdcd1lg2
+PDCD1 Pdcd1
+PDCD2L Pdcd2l
+PDCD2 Pdcd2
+PDCD4 Pdcd4
+PDCD5 Pdcd5
+PDCD6IP Pdcd6ip
+PDCD6 Pdcd6
+PDCD7 Pdcd7
+PDCD8 Pdcd8
+PDCL2 Pdcl2
+PDCL3 Pdcl3
+PDCL Pdcl
+PDC Pdc
+PDDC1 Pddc1
+PDE10A Pde10a
+PDE11A Pde11a
+PDE12 Pde12
+PDE1A Pde1a
+PDE1B Pde1b
+PDE1C Pde1c
+PDE2A Pde2a
+PDE3A Pde3a
+PDE3B Pde3b
+PDE4A Pde4a
+PDE4B Pde4b
+PDE4C Pde4c
+PDE4DIP Pde4dip
+PDE4D Pde4d
+PDE5A Pde5a
+PDE6A Pde6a
+PDE6B Pde6b
+PDE6C Pde6c
+PDE6D Pde6d
+PDE6G Pde6g
+PDE6H Pde6h
+PDE7A Pde7a
+PDE7B Pde7b
+PDE8A Pde8a
+PDE8B Pde8b
+PDE9A Pde9a
+PDF Pdf
+PDGFA Pdgfa
+PDGFB Pdgfb
+PDGFC Pdgfc
+PDGFD Pdgfd
+PDGFRA Pdgfra
+PDGFRB Pdgfrb
+PDGFRL Pdgfrl
+PDHA1 Pdha1
+PDHA2 Pdha2
+PDHB Pdhb
+PDHX Pdhx
+PDIA2 Pdia2
+PDIA3 Pdia3
+PDIA4 Pdia4
+PDIA5 Pdia5
+PDIA6 Pdia6
+PDIK1L Pdik1l
+PDILT Pdilt
+PDK1 Pdk1
+PDK2 Pdk2
+PDK3 Pdk3
+PDK4 Pdk4
+PDLIM1 Pdlim1
+PDLIM2 Pdlim2
+PDLIM3 Pdlim3
+PDLIM4 Pdlim4
+PDLIM5 Pdlim5
+PDLIM7 Pdlim7
+PDP1 Pdp1
+PDP2 Pdp2
+PDPK1 Pdpk1
+PDPN Pdpn
+PDPR Pdpr
+PDRG1 Pdrg1
+PDS5A Pds5a
+PDS5B Pds5b
+PDSS1 Pdss1
+PDSS2 Pdss2
+PDX1 Pdx1
+PDXDC1 Pdxdc1
+PDXK Pdxk
+PDXP Pdxp
+PDYN Pdyn
+PDZD11 Pdzd11
+PDZD2 Pdzd2
+PDZD3 Pdzd3
+PDZD4 Pdzd4
+PDZD7 Pdzd7
+PDZD8 Pdzd8
+PDZD9 Pdzd9
+PDZK1IP1 Pdzk1ip1
+PDZK1 Pdzk1
+PDZRN3 Pdzrn3
+PDZRN4 Pdzrn4
+PEA15 Pea15
+PEAK1 C230081A13Rik
+PEAR1 Pear1
+PEBP1 Pebp1
+PEBP4 Pebp4
+PECAM1 Pecam1
+PECI Peci
+PECR Pecr
+PEF1 Pef1
+PEG10 Peg10
+PEG3 Peg3
+PELI1 Peli1
+PELI2 Peli2
+PELI3 Peli3
+PELO Pelo
+PELP1 Pelp1
+PEMT Pemt
+PENK Penk
+PEO1 Peo1
+PEPB Pepb
+PEPC Pepc
+PEPD Pepd
+PER1 Per1
+PER2 Per2
+PER3 Per3
+PERLD1 Perld1
+PERP Perp
+PERQ1 Perq1
+PES1 Pes1
+PET100 2900053A13Rik
+PET112L Pet112l
+PET117 Gm20571
+PEX10 Pex10
+PEX11A Pex11a
+PEX11B Pex11b
+PEX11G Pex11c
+PEX12 Pex12
+PEX13 Pex13
+PEX14 Pex14
+PEX16 Pex16
+PEX19 Pex19
+PEX1 Pex1
+PEX26 Pex26
+PEX2 Pex2
+PEX3 Pex3
+PEX5L Pex2
+PEX5 Pex5
+PEX6 Pex6
+PEX7 Pex7
+PF4 Cxcl4
+PFAS Pfas
+PFDN1 Pfdn1
+PFDN2 Pfdn2
+PFDN4 Pfdn4
+PFDN5 Pfdn5
+PFDN6 H2-Ke2
+PFKFB1 Pfkfb1
+PFKFB2 Pfkfb2
+PFKFB3 Pfkfb3
+PFKFB4 Pfkfb4
+PFKL Pfkl
+PFKM Pfkm
+PFKP Pfkp
+PFN1 Pfn1
+PFN2 Pfn2
+PFN3 Pfn3
+PFN4 Pfn4
+PFTK1 Pftk1
+PGAM1 Pgam1
+PGAM2 Pgam2
+PGAM5 Pgam5
+PGAP1 Pgap1
+PGAP2 Pgap2
+PGAP3 Pgap3
+PGBD1 4921509E05Rik
+PGBD5 Pgbd5
+PGC Pgc
+PGD Pgd
+PGEA1 Pgea1
+PGF Pgf
+PGGT1B Pggt1b
+PGK1 Pgk1
+PGK2 Pgk2
+PGLS Pgls
+PGLYRP1 Pglyrp1
+PGLYRP2 Pglyrp2
+PGLYRP3 Pglyrp3
+PGLYRP4 Pglyrp4
+PGM1 Pgm2
+PGM2L1 Pgm2l1
+PGM2 Pgm1
+PGM3 Pgm3
+PGM5 Pgm5
+PGPEP1L Pgpep1l
+PGPEP1 Pgpep1
+PGP Pgp
+PGRMC1 Pgrmc1
+PGRMC2 Pgrmc2
+PGR Pgr
+PGS1 Pgs1
+PHACTR1 Phactr1
+PHACTR2 Phactr2
+PHACTR3 Phactr3
+PHACTR4 Phactr4
+PHAX Phax
+PHB2 Phb2
+PHB Phb
+PHC1 Phc1
+PHC2 Phc2
+PHC3 Phc3
+PHCA Phca
+PHEX Phex
+PHF10 Phf10
+PHF11 AW476263
+PHF12 Phf12
+PHF13 Phf13
+PHF14 Phf14
+PHF15 Phf15
+PHF16 Phf16
+PHF17 Phf17
+PHF19 Phf19
+PHF1 Phf1
+PHF20L1 Phf20l1
+PHF20 Phf20
+PHF21A Phf21a
+PHF21B Phf21b
+PHF23 Phf23
+PHF2 Phf2
+PHF3 Phf3
+PHF5A Phf5a
+PHF6 Phf6
+PHF7 Phf7
+PHF8 Phf8
+PHGDHL1 Phgdhl1
+PHGDH Phgdh
+PHGR1 Phgr1
+PHIP Phip
+PHKA1 Phka1
+PHKA2 Phka2
+PHKB Phkb
+PHKG1 Phkg1
+PHKG2 Phkg2
+PHLDA1 Phlda1
+PHLDA2 Phlda2
+PHLDA3 Phlda3
+PHLDB1 Phldb1
+PHLDB2 Phldb2
+PHLDB3 Phldb3
+PHLPP1 Phlpp1
+PHLPP2 Phlpp2
+PHLPPL Phlppl
+PHLPP Phlpp
+PHOSPHO1 Phospho1
+PHOSPHO2 Phospho2
+PHOX2A Phox2a
+PHOX2B Phox2b
+PHPT1 Phpt1
+PHRF1 Phrf1
+PHTF1 Phtf1
+PHTF2 Phtf2
+PHYHD1 Phyhd1
+PHYHIPL Phyhipl
+PHYHIP Phyhip
+PHYH Phyh
+PI15 Pi15
+PI16 Pi16
+PI4K2A Pi4k2a
+PI4K2B Pi4k2b
+PI4KA Pi4ka
+PI4KB Pi4kb
+PIAS1 Pias1
+PIAS2 Pias2
+PIAS3 Pias3
+PIAS4 Pias4
+PIB5PA Pib5pa
+PIBF1 Pibf1
+PICALM Picalm
+PICK1 Pick1
+PID1 Pid1
+PIDD Lrdd
+PIEZO1 Fam38a
+PIEZO2 Fam38b
+PIF1 Pif1
+PIGA Piga
+PIGB Pigb
+PIGC Pigc
+PIGF Pigf
+PIGG Pigg
+PIGH Pigh
+PIGK Pigk
+PIGL Pigl
+PIGM Pigm
+PIGN Pign
+PIGO Pigo
+PIGP Pigp
+PIGQ Pigq
+PIGR Pigr
+PIGS Pigs
+PIGT Pigt
+PIGU Pigu
+PIGV Pigv
+PIGW Pigw
+PIGX Pigx
+PIGY Pigy
+PIGZ Pigz
+PIH1D1 Pih1d1
+PIH1D2 Pih1d2
+PIH1D3 4930521A18Rik
+PIK3AP1 Pik3ap1
+PIK3C2A Pik3c2a
+PIK3C2B Pik3c2b
+PIK3C2G Pik3c2g
+PIK3C3 Pik3c3
+PIK3CA Pik3ca
+PIK3CB Pik3cb
+PIK3CD Pik3cd
+PIK3CG Pik3cg
+PIK3IP1 Pik3ip1
+PIK3R1 Pik3r1
+PIK3R2 Pik3r2
+PIK3R3 Pik3r3
+PIK3R4 Pik3r4
+PIK3R5 Pik3r5
+PIK3R6 Pik3r6
+PIK4CA Pik4ca
+PIK4CB Pik4cb
+PIKFYVE Pikfyve
+PILRA Pilra
+PILRB Pilrb1
+PIM1 Pim1
+PIM2 Pim2
+PIM3 Pim3
+PIN1 mCT21996
+PIN1P1 Pin1-ps1
+PIN1 Pin1
+PIN4 Pin4
+PINK1 Pink1
+PINLYP 2310033E01Rik
+PINX1 Pinx1
+PION Pion
+PIP4K2A Pip4k2a
+PIP4K2B Pip4k2b
+PIP4K2C Pip4k2c
+PIP5K1A Pip5k1a
+PIP5K1C Pip5k1c
+PIP5K2A Pip5k2a
+PIP5K2B Pip5k2b
+PIP5K2C Pip5k2c
+PIP5K3 Pip5k3
+PIP5KL1 Pip5kl1
+PIPOX Pipox
+PIP Pip
+PIR Pir
+PIRT Pirt
+PISD Pisd
+PITHD1 1110049F12Rik
+PITPNA Pitpna
+PITPNB Pitpnb
+PITPNC1 Pitpnc1
+PITPNM1 Pitpnm1
+PITPNM2 Pitpnm2
+PITPNM3 A330068P14Rik
+PITRM1 Pitrm1
+PITX1 Pitx1
+PITX2 Pitx2
+PITX3 Pitx3
+PIWIL1 Piwil1
+PIWIL2 Piwil2
+PIWIL4 Piwil4
+PJA1 Pja1
+PJA2 Pja2
+PKD1L1 Pkd1l1
+PKD1L2 Pkd1l2
+PKD1L3 Pkd1l3
+PKD1 Pkd1
+PKD2L1 Pkd2l1
+PKD2L2 Pkd2l2
+PKD2 Pkd2
+PKDCC Pkdcc
+PKDREJ Pkdrej
+PKHD1L1 Pkhd1l1
+PKHD1 Pkhd1
+PKIA Pkia
+PKIB Pkib
+PKIG Pkig
+PKLR Pklr
+PKM2 Pkm2
+PKM Pkm
+PKMYT1 Pkmyt1
+PKN1 Pkn1
+PKN2 Pkn2
+PKN3 Pkn3
+PKNOX1 Pknox1
+PKNOX2 Pknox2
+PKP1 Pkp1
+PKP2 Pkp2
+PKP3 Pkp3
+PKP4 Pkp4
+PLA1A Pla1a
+PLA2G10 Pla2g10
+PLA2G12A Pla2g12a
+PLA2G12B Pla2g12b
+PLA2G15 Pla2g15
+PLA2G16 Pla2g16
+PLA2G1B Pla2g1b
+PLA2G2A Pla2g2a
+PLA2G2C Pla2g2c
+PLA2G2D Pla2g2d
+PLA2G2E Pla2g2e
+PLA2G2F Pla2g2f
+PLA2G3 Pla2g3
+PLA2G4A Pla2g4a
+PLA2G4B Pla2g4b
+PLA2G4C Pla2g4c
+PLA2G4D Pla2g4d
+PLA2G4E Pla2g4e
+PLA2G4F Pla2g4f
+PLA2G5 Pla2g5
+PLA2G6 Pla2g6
+PLA2G7 Pla2g7
+PLA2R1 Pla2r1
+PLAA Plaa
+PLAC1L Plac1l
+PLAC1 Plac1
+PLAC8L1 Plac8l1
+PLAC8 Plac8
+PLAG1 Plag1
+PLAGL1 Plagl1
+PLAGL2 Plagl2
+PLAT Plat
+PLAU Plau
+PLAUR Plaur
+PLB1 Plb1
+PLBD1 Plbd1
+PLBD2 Plbd2
+PLCB1 Plcb1
+PLCB2 Plcb2
+PLCB3 Plcb3
+PLCB4 Plcb4
+PLCD1 Plcd1
+PLCD3 Plcd3
+PLCD4 Plcd4
+PLCE1 Plce1
+PLCG1 Plcg1
+PLCG2 Plcg2
+PLCH1 Plch1
+PLCH2 Plch2
+PLCL1 Plcl1
+PLCL2 Plcl2
+PLCXD1 Plcxd1
+PLCXD2 Plcxd2
+PLCXD3 Plcxd3
+PLCZ1 Plcz1
+PLD1 Pld1
+PLD2 Pld2
+PLD3 Pld3
+PLD4 Pld4
+PLD5 Pld5
+PLD6 Pld6
+PLDN Pldn
+PLEC1 Plec1
+PLEC Plec
+PLEK2 Plek2
+PLEKHA1 Plekha1
+PLEKHA2 Plekha2
+PLEKHA3 Plekha3
+PLEKHA4 Plekha4
+PLEKHA5 Plekha5
+PLEKHA6 Plekha6
+PLEKHA7 Plekha7
+PLEKHA8 Plekha8
+PLEKHB1 Plekhb1
+PLEKHB2 Plekhb2
+PLEKHC1 Plekhc1
+PLEKHD1 3830431G21Rik
+PLEKHF1 Plekhf1
+PLEKHF2 Plekhf2
+PLEKHG1 Plekhg1
+PLEKHG2 Plekhg2
+PLEKHG3 Plekhg3
+PLEKHG4 Plekhg4
+PLEKHG5 Plekhg5
+PLEKHG6 Plekhg6
+PLEKHH1 Plekhh1
+PLEKHH2 Plekhh2
+PLEKHH3 Plekhh3
+PLEKHJ1 Plekhj1
+PLEKHK1 Plekhk1
+PLEKHM1 Plekhm1
+PLEKHM2 Plekhm2
+PLEKHM3 Plekhm3
+PLEKHN1 Plekhn1
+PLEKHO1 Plekho1
+PLEKHO2 Plekho2
+PLEKHQ1 Plekhq1
+PLEKHS1 9930023K05Rik
+PLEK Plek
+PLG Plg
+PLGRKT 5033414D02Rik
+PLIN1 Plin1
+PLIN2 Plin2
+PLIN3 Plin3
+PLIN4 Plin4
+PLIN5 Plin5
+PLIN Plin
+PLK1 Plk1
+PLK1S1 Plk1s1
+PLK2 Plk2
+PLK3 Plk3
+PLK4 Plk4
+PLK5 Plk5
+PLLP Pllp
+PLN AK031320
+PLOD1 Plod1
+PLOD2 Plod2
+PLOD3 Plod3
+PLP1 Plp1
+PLP2 Plp2
+PLRG1 Plrg1
+PLS1 AI427122
+PLS3 Pls3
+PLSCR1 Plscr1
+PLSCR3 Plscr3
+PLSCR4 Plscr4
+PLSCR5 Plscr5
+PLTP Pltp
+PLUNC Plunc
+PLVAP Plvap
+PLXDC1 Plxdc1
+PLXDC2 Plxdc2
+PLXNA1 Plxna1
+PLXNA2 Plxna2
+PLXNA3 Plxna3
+PLXNA4A Plxna4
+PLXNB1 Plxnb1
+PLXNB2 Plxnb2
+PLXNB3 Plxnb3
+PLXNC1 Plxnc1
+PLXND1 Plxnd1
+PM20D1 Pm20d1
+PM20D2 Pm20d2
+PMAIP1 Pmaip1
+PMCH Pmch
+PMEL Pmel
+PMEPA1 Pmepa1
+PMF1 Pmf1
+PMFBP1 Pmfbp1
+PML Pml
+PMM1 Pmm1
+PMM2 Pmm2
+PMP22 Pmp22
+PMP2 Pmp2
+PMPCA Pmpca
+PMPCB Pmpcb
+PMS1 Pms1
+PMS2 Pms2
+PMVK Pmvk
+PNCK Pnck
+PNISR Sfrs18
+PNKD Pnkd
+PNKP Pnkp
+PNLDC1 Pnldc1
+PNLIP Pnlip
+PNLIPRP1 Pnliprp1
+PNLIPRP2 Pnliprp2
+PNMA1 Pnma1
+PNMA2 Pnma2
+PNMA3 Pnma3
+PNMA5 Pnma5
+PNMAL1 Pnmal1
+PNMAL2 Pnmal2
+PNMT Pnmt
+PNN Pnn
+PNO1 Pno1
+PNOC Pnoc
+PNPLA1 Pnpla1
+PNPLA2 Pnpla2
+PNPLA3 Pnpla3
+PNPLA5 Pnpla5
+PNPLA6 Pnpla6
+PNPLA7 Pnpla7
+PNPLA8 Pnpla8
+PNPO Pnpo
+PNP Pnp
+PNPT1 Pnpt1
+PNRC1 Pnrc1
+PNRC2 Pnrc2
+POC1A Poc1a
+POC1B Poc1b
+POC5 Poc5
+PODNL1 Podnl1
+PODN Podn
+PODXL2 Podxl2
+PODXL Podxl
+POF1B Pof1b
+POFUT1 Pofut1
+POFUT2 Pofut2
+POGK Pogk
+POGLUT1 Poglut1
+POGZ Pogz
+POLA1 Pola1
+POLA2 Pola2
+POLB Polb
+POLD1 Pold1
+POLD2 Pold2
+POLD3 Pold3
+POLD4 Pold4
+POLDIP2 Poldip2
+POLDIP3 Poldip3
+POLE2 Pole2
+POLE3 Pole3
+POLE4 Pole4
+POLE Pole
+POLG2 Polg2
+POLG Polg
+POLH Polh
+POLI Poli
+POLK Polk
+POLL Poll
+POLM Polm
+POLN Poln
+POLQ Polq
+POLR1A Polr1a
+POLR1B Polr1b
+POLR1C Polr1c
+POLR1D Polr1d
+POLR1E Polr1e
+POLR2A Polr2a
+POLR2B Polr2b
+POLR2C Polr2c
+POLR2D Polr2d
+POLR2E Polr2e
+POLR2F Polr2f
+POLR2G Polr2g
+POLR2H Polr2h
+POLR2I Polr2i
+POLR2J Polr2j
+POLR2K Gm14567
+POLR2L Polr2l
+POLR2M Polr2m
+POLR3A Polr3a
+POLR3B Polr3b
+POLR3C Polr3c
+POLR3D Polr3d
+POLR3E Polr3e
+POLR3F Polr3f
+POLR3GL Polr3gl
+POLR3G Polr3g
+POLR3H Polr3h
+POLR3K Polr3k
+POLRMT Polrmt
+POLS Pols
+POM121L12 Pom121l12
+POM121L2 Gm24
+POM121 Pom121
+POMC Pomc
+POMGNT1 Pomgnt1
+POMP Pomp
+POMT1 Pomt1
+POMT2 Pomt2
+PON1 Pon1
+PON2 Pon2
+PON3 Pon3
+POP1 Pop1
+POP4 Pop4
+POP5 Pop5
+POP7 Pop7
+POPDC2 Popdc2
+POPDC3 Popdc3
+PORCN Porcn
+POR Por
+POSTN Postn
+POT1 Pot1a
+POTEF Gm12715
+POTEH Poteg
+POU1F1 Pou1f1
+POU2AF1 Pou2af1
+POU2F1 Pou2f1
+POU2F2 Pou2f2
+POU2F3 Pou2f3
+POU3F1 Pou3f1
+POU3F2 Pou3f2
+POU3F3 Pou3f3
+POU3F4 Pou3f4
+POU4F1 Pou4f1
+POU4F2 Pou4f2
+POU4F3 Pou4f3
+POU5F1P3 Gm5712
+POU5F1 Pou5f1
+POU5F2 Pou5f2
+POU6F1 Pou6f1
+POU6F2 Pou6f2
+PP2D1 4921523A10Rik
+PPA1 Ppa1
+PPA2 Ppa2
+PPAN Ppan
+PPAP2A Ppap2a
+PPAP2B Ppap2b
+PPAP2C Ppap2c
+PPAPDC1A Ppapdc1a
+PPAPDC1B Ppapdc1
+PPAPDC2 Ppapdc2
+PPAPDC3 Ppapdc3
+PPARA Ppara
+PPARBP Pparbp
+PPARD Ppard
+PPARGC1A Ppargc1a
+PPARGC1B Ppargc1b
+PPARG Pparg
+PPAT Ppat
+PPBP Ppbp
+PPCDC Ppcdc
+PPCS Ppcs
+PPDPF Ppdpf
+PPEF1 Ppef1
+PPEF2 Ppef2
+PPFIA1 Ppfia1
+PPFIA2 Ppfia2
+PPFIA3 2410127E16Rik
+PPFIA4 Ppfia4
+PPFIBP1 Ppfibp1
+PPFIBP2 Ppfibp2
+PPGB Ppgb
+PPHLN1 Pphln1
+PPIA Ppia
+PPIB Ppib
+PPIC Ppic
+PPID Ppid
+PPIE Ppie
+PPIF Ppif
+PPIG Ppig
+PPIH Ppih
+PPIL1 Ppil1
+PPIL2 Ppil2
+PPIL3 Ppil3
+PPIL4 Ppil4
+PPIL5 Ppil5
+PPIL6 Ppil6
+PPIP5K1 Ppip5k1
+PPIP5K2 Ppip5k2
+PPL Ppl
+PPM1A Ppm1a
+PPM1B Ppm1b
+PPM1D Ppm1d
+PPM1E Ppm1e
+PPM1F Ppm1f
+PPM1G Ppm1g
+PPM1H Ppm1h
+PPM1J Ppm1j
+PPM1K Ppm1k
+PPM1L Ppm1l
+PPM1M Ppm1m
+PPM1N Ppm1n
+PPM2C Ppm2c
+PPME1 Ppme1
+PPOX Ppox
+PPP1CA Ppp1ca
+PPP1CB Ppp1cb
+PPP1CC Ppp1cc
+PPP1R10 Ppp1r10
+PPP1R11 Ppp1r11
+PPP1R12A Ppp1r12a
+PPP1R12B Ppp1r12b
+PPP1R12C Ppp1r12c
+PPP1R13B Ppp1r13b
+PPP1R13L Ppp1r13l
+PPP1R14A Ppp1r14a
+PPP1R14B Ppp1r14b
+PPP1R14C Ppp1r14c
+PPP1R14D Ppp1r14d
+PPP1R15A Myd116
+PPP1R15B Ppp1r15b
+PPP1R16A Ppp1r16a
+PPP1R16B Ppp1r16b
+PPP1R17 Ppp1r17
+PPP1R18 Ppp1r18
+PPP1R1A Ppp1r1a
+PPP1R1B Ppp1r1b
+PPP1R1C Ppp1r1c
+PPP1R21 Klraq1
+PPP1R26 Ppp1r26
+PPP1R27 Ppp1r27
+PPP1R2P3 Ppp1r2-ps3
+PPP1R2P9 Ppp1r2-ps9
+PPP1R2 Ppp1r2
+PPP1R32 Ppp1r32
+PPP1R35 Ppp1r35
+PPP1R36 Ppp1r36
+PPP1R37 Ppp1r37
+PPP1R3A Ppp1r3a
+PPP1R3B Ppp1r3b
+PPP1R3C Ppp1r3c
+PPP1R3D Ppp1r3d
+PPP1R3E Ppp1r3e
+PPP1R3F Ppp1r3f
+PPP1R3G Ppp1r3g
+PPP1R42 Ppp1r42
+PPP1R7 Ppp1r7
+PPP1R8 Ppp1r8
+PPP1R9A Ppp1r9a
+PPP1R9B Ppp1r9b
+PPP2CA Ppp2ca
+PPP2CB Ppp2cb
+PPP2R1A Ppp2r1a
+PPP2R1B Ppp2r1b
+PPP2R2A Ppp2r2a
+PPP2R2B Ppp2r2b
+PPP2R2C Ppp2r2c
+PPP2R2D Ppp2r2d
+PPP2R3A Ppp2r3a
+PPP2R3C Ppp2r3c
+PPP2R4 Ppp2r4
+PPP2R5A Ppp2r5a
+PPP2R5B Ppp2r5b
+PPP2R5C Ppp2r5c
+PPP2R5D Ppp2r5d
+PPP2R5E Ppp2r5e
+PPP3CA Ppp3ca
+PPP3CB Ppp3cb
+PPP3CC Ppp3cc
+PPP3R1 Ppp3r1
+PPP3R2 Ppp3r2
+PPP4C Ppp4c
+PPP4R1 Ppp4r1
+PPP4R2 Ppp4r2
+PPP4R4 Ppp4r4
+PPP5C Ppp5c
+PPP6C Ppp6c
+PPP6R1 Ppp6r1
+PPP6R2 Ppp6r2
+PPP6R3 Ppp6r3
+PPRC1 Pprc1
+PPT1 Ppt1
+PPT2 Ppt2
+PPTC7 Pptc7
+PPWD1 Ppwd1
+PPY Ppy
+PPYR1 Ppyr1
+PQBP1 Pqbp1
+PQLC1 Pqlc1
+PQLC2 Pqlc2
+PQLC3 Pqlc3
+PRADC1 1700040I03Rik
+PRAF2 Praf2
+PRAM1 mCT168149
+PRAMEF10 4732496O08Rik
+PRAMEF12 Pramef6
+PRAMEF20 Gm3147
+PRAMEF25 Gm13125
+PRAMEF2 Gm13102
+PRAMEF8 Pramef8
+PRAMEL Pramel
+PRAME mCT118583
+PRAP1 Prap1
+PRB1 MMT00014721
+PRB4 Prp15
+PRC1 Prc1
+PRCC Prcc
+PRCD Gm11744
+PRCP Prcp
+PRDM10 Prdm10
+PRDM11 Prdm11
+PRDM12 Prdm12
+PRDM13 Prdm13
+PRDM14 Prdm14
+PRDM15 Prdm15
+PRDM16 Prdm16
+PRDM1 Prdm1
+PRDM2 Prdm2
+PRDM4 Prdm4
+PRDM5 Prdm5
+PRDM6 Prdm6
+PRDM8 Prdm8
+PRDM9 Prdm9
+PRDX1 Prdx1
+PRDX2 Prdx2
+PRDX3 Prdx3
+PRDX4 Prdx4
+PRDX5 Prdx5
+PRDX6 AI326214
+PREB Preb
+PREI3 Prei3
+PRELID1 Prelid1
+PRELID2 Prelid2
+PRELP Prelp
+PREPL Prepl
+PREP Prep
+PREX1 Prex1
+PREX2 Prex2
+PRF1 Prf1
+PRG1 Prg1
+PRG2 Prg2
+PRG3 Prg3
+PRG4 Prg4
+PRH1 NM_001004160
+PRHOXNB Prhoxnb
+PRIC285 BC006779
+PRICKLE1 Prickle1
+PRICKLE2 Prickle2
+PRICKLE3 Prickle3
+PRICKLE4 Prickle4
+PRIM1 Prim1
+PRIM2A Prim2
+PRIMA1 Prima1
+PRKAA1 Prkaa1
+PRKAA2 Prkaa2
+PRKAB1 Prkab1
+PRKAB2 Prkab2
+PRKACA Prkaca
+PRKACB Prkacb
+PRKAG1 Prkag1
+PRKAG2 Prkag2
+PRKAG3 Prkag3
+PRKAR1A Prkar1a
+PRKAR1B Prkar1b
+PRKAR2A Prkar2a
+PRKAR2B Prkar2b
+PRKCA Prkca
+PRKCB1 Prkcb1
+PRKCBP1 Prkcbp1
+PRKCB Prkcb
+PRKCDBP Prkcdbp
+PRKCD Prkcd
+PRKCE Prkce
+PRKCG Prkcc
+PRKCH Prkch
+PRKCI Prkci
+PRKCQ Prkcq
+PRKCSH Prkcsh
+PRKCZ Prkcz
+PRKD1 Prkcm
+PRKD2 Prkd2
+PRKD3 Prkcn
+PRKDC Prkdc
+PRKG1 Prkg1
+PRKG2 Prkg2
+PRKRA Prkra
+PRKRIP1 Prkrip1
+PRKRIR Prkrir
+PRKX Prkx
+PRLH MMT00044992
+PRLHR Prlhr
+PRL Prl
+PRLR Prlr
+PRM1 Prm1
+PRM2 Prm2
+PRM3 Prm3
+PRMT10 Prmt10
+PRMT1 Prmt1
+PRMT2 Prmt2
+PRMT3 Prmt3
+PRMT5 Prmt5
+PRMT6 Prmt6
+PRMT7 Prmt7
+PRMT8 Prmt8
+PRND Prnd
+PRNPIP Prnpip1
+PRNP Prnp
+PROCA1 Proca1
+PROC Proc
+PROCR Procr
+PRODH2 Prodh2
+PRODH Prodh
+PROK1 Prok1
+PROK2 Prok2
+PROKR1 Prokr1
+PROKR2 Prokr2
+PROL1 MMT00040149
+PROM1 Prom1
+PROM2 Prom2
+PROP1 Prop1
+PRORSD1P Prorsd1
+PROS1 Pros1
+ProSAPiP1 Prosapip1
+PROSC Prosc
+PROSER1 2810046L04Rik
+PROX1 Prox1
+PROX2 Prox2
+PROZ Proz
+PRPF18 Prpf18
+PRPF19 Prpf19
+PRPF31 Prpf31
+PRPF38A Prpf38a
+PRPF38B Prpf38b
+PRPF39 Prpf39
+PRPF3 Prpf3
+PRPF40A Prpf40a
+PRPF40B Prpf40b
+PRPF4B Prpf4b
+PRPF4 Prpf4
+PRPF6 Prpf6
+PRPF8 Prpf8
+PRPH2 Prph2
+PRPH Prph
+PRPS1L1 Prps1l1
+PRPS1 Prps1
+PRPS2 Prps2
+PRPSAP1 Prpsap1
+PRPSAP2 Prpsap2
+PRR11 Prr11
+PRR12 Prr12
+PRR13 Prr13
+PRR14L C330019G07Rik
+PRR14 Prr14
+PRR15L Prr15l
+PRR15 Prr15
+PRR16 Prr16
+PRR18 Prr18
+PRR19 Prr19
+PRR22 Prr22
+PRR23C Prr23a
+PRR24 Prr24
+PRR3 Prr3
+PRR5 Arhgap8
+PRR5L Prr5l
+PRR5 Prr5
+PRR6 Prr6
+PRR7 Prr7
+PRR8 3200001K10Rik
+PRR9 Prr9
+PRRC1 Prrc1
+PRRC2A Prrc2a
+PRRC2B Prrc2b
+PRRC2C Prrc2c
+PRRG1 2010007L08Rik
+PRRG2 Prrg2
+PRRG3 Prrg3
+PRRG4 Prrg4
+PRRT1 Prrt1
+PRRT2 1500031I19Rik
+PRRT3 Prrt3
+PRRT4 Prrt4
+PRRX1 Prrx1
+PRRX2 Prrx2
+PRRXL1 Prrxl1
+PRSS12 Prss12
+PRSS16 Prss16
+PRSS1 Prss1
+PRSS21 Prss21
+PRSS22 Prss22
+PRSS23 Prss23
+PRSS27 Prss27
+PRSS29P Prss29
+PRSS2 Prss2
+PRSS30P Prss30
+PRSS33 Prss33
+PRSS35 Prss35
+PRSS36 Prss36
+PRSS37 Prss37
+PRSS38 Prss38
+PRSS3 mCT171504
+PRSS41 Prss41
+PRSS42 Prss42
+PRSS43 Prss43
+PRSS45 Prss45
+PRSS46 Prss46
+PRSS47 Prss47
+PRSS48 Prss48
+PRSS50 Prss50
+PRSS53 Prss53
+PRSS54 Prss54
+PRSS55 Prss55
+PRSS56 Prss56
+PRSS57 Prss57
+PRSS58 Prss58
+PRSS7 Prss7
+PRSS8 Prss8
+PRSSL1 Prssl1
+PRTFDC1 MMT00038006
+PRTG Prtg
+PRTN3 Prtn3
+PRUNE2 Prune2
+PRUNE Prune
+PRX Prx
+PSAPL1 Psapl1
+PSAP Psap
+PSAT1 Psat1
+PSCA Psca
+PSCD1 Pscd1
+PSCD2 Pscd2
+PSCD3 Pscd3
+PSCD4 Pscd4
+PSCDBP Pscdbp
+PSD2 Psd2
+PSD3 Psd3
+PSD4 Psd4
+PSD Psd
+PSEN1 Psen1
+PSEN2 Psen2
+PSENEN Psenen
+PSG4 mCT3269
+PSIP1 Psip1
+PSKH1 Pskh1
+PSMA1 Psma1
+PSMA2 Psma2
+PSMA3 Psma3
+PSMA4 Psma4
+PSMA5 Psma5
+PSMA6 Psma6
+PSMA7 Psma7
+PSMA8 Psma8
+PSMB10 Psmb10
+PSMB11 Psmb11
+PSMB1 Psmb1
+PSMB2 Psmb2
+PSMB3 Psmb3
+PSMB4 Psmb4
+PSMB5 Psmb5
+PSMB6 Psmb6
+PSMB7 Psmb7
+PSMB8 Psmb8
+PSMB9 Psmb9
+PSMC1 Psmc1
+PSMC2 Psmc2
+PSMC3IP Psmc3ip
+PSMC3 Psmc3
+PSMC4 Psmc4
+PSMC5 Psmc5
+PSMC6 Psmc6
+PSMD10 Psmd10
+PSMD11 Psmd11
+PSMD12 Psmd12
+PSMD13 Psmd13
+PSMD14 Psmd14
+PSMD1 Psmd1
+PSMD2 Psmd2
+PSMD3 Psmd3
+PSMD4 Psmd4
+PSMD5 Psmd5
+PSMD6 Psmd6
+PSMD7 Psmd7
+PSMD8 Psmd8
+PSMD9 Psmd9
+PSME1 Psme1
+PSME2 Psme2
+PSME3 Psme3
+PSME4 Psme4
+PSMF1 Psmf1
+PSMG1 Psmg1
+PSMG2 Psmg2
+PSMG3 Psmg3
+PSMG4 Psmg4
+PSORS1C2 Psors1c2
+PSPC1 Pspc1
+PSPH Psph
+PSPN Pspn
+PSRC1 Psrc1
+PSTK Pstk
+PSTPIP1 Pstpip1
+PSTPIP2 Pstpip2
+PTAFR Ptafr
+PTAR1 Ptar1
+PTBP1 Ptbp1
+PTBP2 Ptbp2
+PTBP3 Rod1
+PTCD2 Ptcd2
+PTCD3 Ptcd3
+PTCH1 Ptch1
+PTCH2 Ptch2
+PTCHD1 Ptchd1
+PTCHD2 Ptchd2
+PTCHD3 Ptchd3
+PTCHD4 3110082D06Rik
+PTCRA Ptcra
+PTDSR Ptdsr
+PTDSS1 Ptdss1
+PTDSS2 Ptdss2
+PTEN Pten
+PTER Pter
+PTF1A AK053418
+PTGDR2 Gpr44
+PTGDR Ptgdr
+PTGDS Ptgds
+PTGER1 Ptger1
+PTGER2 Ptger2
+PTGER3 Ptger3
+PTGER4 Ptger4
+PTGES2 Ptges2
+PTGES3 Ptges3
+PTGES Ptges
+PTGFRN Ptgfrn
+PTGFR Ptgfr
+PTGIR Ptgir
+PTGIS Ptgis
+PTGR1 Ptgr1
+PTGR2 Ptgr2
+PTGS1 Ptgs1
+PTGS2 Ptgs2
+PTH1R Pth1r
+PTH2 Pth2
+PTH2R Pth2r
+PTHLH Pthlh
+PTH Pth
+PTHR1 Pthr1
+PTK2B Ptk2b
+PTK2 Ptk2
+PTK6 Ptk6
+PTK7 Ptk7
+PTK9L Ptk9l
+PTK9 MMT00002888
+PTMA Ptma
+PTMS Ptms
+PTN Ptn
+PTOV1 Ptov1
+PTP4A1 Ptp4a1
+PTP4A2 Ptp4a2
+PTP4A3 Ptp4a3
+PTPDC1 AW456874
+PTPLAD1 Ptplad1
+PTPLAD2 Ptplad2
+PTPLA Ptpla
+PTPLB Ptplb
+PTPMT1 Ptpmt1
+PTPN11 Ptpn11
+PTPN12 Ptpn12
+PTPN13 Ptpn13
+PTPN14 Ptpn14
+PTPN18 Ptpn18
+PTPN1 Ptpn1
+PTPN20B Ptpn20
+PTPN21 Ptpn21
+PTPN22 Ptpn22
+PTPN23 Ptpn23
+PTPN2 Ptpn2
+PTPN3 Ptpn3
+PTPN4 Ptpn4
+PTPN5 Ptpn5
+PTPN6 Ptpn6
+PTPN7 Ptpn7
+PTPN9 Ptpn9
+PTPRA Ptpra
+PTPRB Ptprb
+PTPRCAP Ptprcap
+PTPRC Ptprc
+PTPRD Ptprd
+PTPRE Ptpre
+PTPRF Ptprf
+PTPRG Ptprg
+PTPRH Ptprh
+PTPRJ Ptprj
+PTPRK Ptprk
+PTPRM Ptprm
+PTPRN2 Ptprn2
+PTPRN Ptprn
+PTPRO Ptpro
+PTPRQ Ptprq
+PTPRR Ptprr
+PTPRS Ptprs
+PTPRT Ptprt
+PTPRU Ptpru
+PTPRVP Ptprv
+PTPRZ1 Ptprz1
+PTRF Ptrf
+PTRH1 Ptrh1
+PTRH2 Ptrh2
+PTRHD1 2410017P09Rik
+PTS Pts
+PTTG1IP Pttg1ip
+PTTG1 Pttg1
+PTX3 Ptx3
+PTX4 Ptx4
+PUF60 Puf60
+PUM1 Pum1
+PUM2 Pum2
+PUNC Punc
+PURA Pura
+PURB Purb
+PURG Purg
+PUS10 Pus10
+PUS1 Pus1
+PUS3 Pus3
+PUS7L Pus7l
+PUS7 Pus7
+PUSL1 Pusl1
+PVALB Pvalb
+PVRIG EG620147
+PVRL1 Pvrl1
+PVRL2 Pvrl2
+PVRL3 Pvrl3
+PVRL4 Pvrl4
+PVR Pvr
+PVT1 Pvt1
+PWCR1 Snord116
+PWP1 Pwp1
+PWP2H Pwp2h
+PWP2 Pwp2
+PWWP2A Pwwp2a
+PWWP2B Pwwp2b
+PXDC1 1300014I06Rik
+PXDN Pxdn
+PXK Pxk
+PXMP2 Pxmp2
+PXMP3 Pxmp3
+PXMP4 Pxmp4
+PXN Pxn
+PXT1 Pxt1
+PYCARD Pycard
+PYCR1 Pycr1
+PYCR2 Pycr2
+PYCRL Pycrl
+PYGB Pygb
+PYGL Pygl
+PYGM Pygm
+PYGO1 Pygo1
+PYGO2 Pygo2
+PYROXD1 Pyroxd1
+PYROXD2 Pyroxd2
+PYY Pyy
+PZP Pzp
+QARS Qars
+QDPR Qdpr
+QKI Qk
+QPCTL Qpctl
+QPCT Qpct
+QPRT Qprt
+QRFP Qrfp
+QRFPR Qrfpr
+QRICH1 2610028H07Rik
+QRICH2 Qrich2
+QRSL1 Qrsl1
+QSCN6L1 Qscn6l1
+QSCN6 Qscn6
+QSER1 Qser1
+QSOX1 Qsox1
+QSOX2 Qsox2
+QTRT1 Qtrt1
+QTRTD1 Qtrtd1
+R3HCC1L D19Ertd386e
+R3HCC1 R3hcc1
+R3HDM1 R3hdm1
+R3HDM2 R3hdm2
+R3HDM4 C030046I01Rik
+R3HDML MMT00009592
+R46311 Gm879
+RAB10 Rab10
+RAB11A Rab11a
+RAB11B Rab11b
+RAB11FIP1 Rab11fip1
+RAB11FIP2 Rab11fip2
+RAB11FIP3 Rab11fip3
+RAB11FIP4 Rab11fip4
+RAB11FIP5 Rab11fip5
+RAB12 Rab12
+RAB13 Rab13
+RAB14 Rab14
+RAB15 Rab15
+RAB17 Rab17
+RAB18 Rab18
+RAB19 Rab19
+RAB1A Rab1
+RAB1B Rab1b
+RAB20 Rab20
+RAB21 Rab21
+RAB22A Rab22a
+RAB23 Rab23
+RAB24 Rab24
+RAB25 Rab25
+RAB26 Rab26
+RAB27A Rab27a
+RAB27B Rab27b
+RAB28 Rab28
+RAB2A Rab2a
+RAB2B Rab2b
+RAB2 Rab2
+RAB30 Rab30
+RAB31 Rab31
+RAB32 Rab32
+RAB33A Rab33a
+RAB33B Rab33b
+RAB34 Rab34
+RAB35 Rab35
+RAB36 Rab36
+RAB37 Rab37
+RAB38 Rab38
+RAB39A Rab39
+RAB39B Rab39b
+RAB39 Rab39
+RAB3A Rab3a
+RAB3B Rab3b
+RAB3C Rab3c
+RAB3D Rab3d
+RAB3GAP1 Rab3gap1
+RAB3GAP2 Rab3gap2
+RAB3IL1 Rab3il1
+RAB3IP Rab3ip
+RAB40B Rab40b
+RAB40C Rab40c
+RAB42 Rab42
+RAB43 1810048P08Rik
+RAB44 Rab44
+RAB4A Rab4a
+RAB4B Rab4b
+RAB5A Rab5a
+RAB5B Rab5b
+RAB5C Rab5c
+RAB6A Rab6
+RAB6B Rab6b
+RAB6IP1 Rab6ip1
+RAB7A Rab7
+RAB7B 5430435G22Rik
+RAB7L1 Rab7l1
+RAB7 Rab7
+RAB8A Rab8a
+RAB8B Rab8b
+RAB9A Rab9
+RAB9BP1 Rab9b-ps1
+RAB9B Rab9b
+RAB9 Rab9
+RABAC1 Rabac1
+RABEP1 Rabep1
+RABEP2 Rabep2
+RABEPK Rabepk
+RABGAP1L Rabgap1l
+RABGAP1 Rabgap1
+RABGEF1 Rabgef1
+RABGGTA Rabggta
+RABGGTB Rabggtb
+RABIF Rabif
+RABL2A Rabl2
+RABL2B Rabl2a
+RABL3 Rabl3
+RABL4 Rabl4
+RABL5 Rabl5
+RABL6 Rabl6
+RAC1 Rac1
+RAC2 Rac2
+RAC3 Rac3
+RACGAP1 Racgap1
+RAD17 Rad17
+RAD18 Rad18
+RAD1 Rad1
+RAD21L1 Rad21l
+RAD21 Rad21
+RAD23A Rad23a
+RAD23B Rad23b
+RAD50 Rad50
+RAD51AP1 Rad51ap1
+RAD51AP2 Rad51ap2
+RAD51B Rad51l1
+RAD51C Rad51c
+RAD51D Rad51l3
+RAD51L1 Rad51l1
+RAD51L3 Rad51l3
+RAD51 Rad51
+RAD52 Rad52
+RAD54B E130016E03Rik
+RAD54L2 Rad54l2
+RAD54L Rad54l
+RAD9A Rad9
+RAD9B Rad9b
+RADIL Radil
+RAE1 Rae1
+RAET1E Raet1e
+RAF1 Raf1
+RAG1AP1 Rag1ap1
+RAG1 Rag1
+RAG2 Rag2
+RAGE Rage
+RAI14 Rai14
+RAI16 Rai16
+RAI1 Rai1
+RAI2 Rai2
+RALA Rala
+RALBP1 Ralbp1
+RALB Ralb
+RALGAPA1 Ralgapa1
+RALGAPA2 Ralgapa2
+RALGAPB Ralgapb
+RALGDS Ralgds
+RALGPS1 Ralgps1
+RALGPS2 Ralgps2
+RALYL Ralyl
+RALY Raly
+RAMP1 Ramp1
+RAMP2 Ramp2
+RAMP3 Ramp3
+RANBP10 Ranbp10
+RANBP17 Ranbp17
+RANBP1 Ranbp1
+RANBP2 Ranbp2
+RANBP3L Ranbp3l
+RANBP3 Ranbp3
+RANBP5 Ranbp5
+RANBP6 Ranbp6
+RANBP9 Ranbp9
+RANGAP1 Rangap1
+RANGRF Rangrf
+RAN Ran
+RAP1A Rap1a
+RAP1BL Gm9392
+RAP1B Rap1b
+RAP1GAP2 Rap1gap2
+RAP1GAP Rap1gap
+RAP1GDS1 Rap1gds1
+RAP2A Rap2a
+RAP2B Rap2b
+RAP2C Rap2c
+RAPGEF1 Rapgef1
+RAPGEF2 Rapgef2
+RAPGEF3 Rapgef3
+RAPGEF4 Rapgef4
+RAPGEF5 Rapgef5
+RAPGEF6 Rapgef6
+RAPGEFL1 Rapgefl1
+RAPSN Rapsn
+RARA Rara
+RARB Rarb
+RARG Rarg
+RARRES1 Rarres1
+RARRES2 Rarres2
+RARS2 Rars2
+RARSL Rarsl
+RARS Rars
+RASA1 Rasa1
+RASA2 Rasa2
+RASA3 Rasa3
+RASA4 Rasa4
+RASAL1 Rasal1
+RASAL2 Rasal2
+RASAL3 Rasal3
+RASD1 Rasd1
+RASD2 Rasd2
+RASEF Rasef
+RASGEF1A Rasgef1a
+RASGEF1B Rasgef1b
+RASGEF1C Rasgef1c
+RASGRF1 Rasgrf1
+RASGRF2 Rasgrf2
+RASGRP1 Rasgrp1
+RASGRP2 Rasgrp2
+RASGRP3 Rasgrp3
+RASGRP4 Rasgrp4
+RASIP1 Rasip1
+RASL10A Rasl10a
+RASL10B Rasl10b
+RASL11A Rasl11a
+RASL11B Rasl11b
+RASL12 Rasl12
+RASSF10 Rassf10
+RASSF1 Rassf1
+RASSF2 Rassf2
+RASSF3 Rassf3
+RASSF4 Rassf4
+RASSF5 Rassf5
+RASSF6 Rassf6
+RASSF7 Rassf7
+RASSF8 Rassf8
+RASSF9 Rassf9
+RAVER1 Raver1
+RAVER2 Raver2
+RAX Rax
+RB1CC1 Rb1cc1
+RB1 Rb1
+RBAK Rbak
+RBBP4 Rbbp4
+RBBP5 Rbbp5
+RBBP6 Rbbp6
+RBBP7 Rbbp7
+RBBP8 Rbbp8
+RBBP9 Rbbp9
+RBCK1 Rbck1
+RBED1 Rbed1
+RBFA Rbfa
+RBFOX1 Rbfox1
+RBFOX2 Rbfox2
+RBFOX3 Rbfox3
+RBKS Rbks
+RBL1 Rbl1
+RBL2 Rbl2
+RBM10 Rbm10
+RBM11 Rbm11
+RBM12B Rbm12b2
+RBM12 Rbm12
+RBM13 Rbm13
+RBM14 Rbm14
+RBM15B Rbm15b
+RBM15 Rbm15
+RBM16 Rbm16
+RBM17 Rbm17
+RBM18 Rbm18
+RBM19 Rbm19
+RBM20 Rbm20
+RBM22 Rbm22
+RBM24 MMS010091173
+RBM25 2600011C06Rik
+RBM26 Rbm26
+RBM27 Rbm27
+RBM28 Rbm28
+RBM33 Rbm33
+RBM34 Rbm34
+RBM35A Rbm35a
+RBM35B Rbm35b
+RBM38 Rbm38
+RBM39 Rbm39
+RBM3 Rbm3
+RBM41 Rbm41
+RBM42 Rbm42
+RBM43 Rbm43
+RBM44 Rbm44
+RBM45 Rbm45
+RBM46 Rbm46
+RBM47 Rbm47
+RBM48 C030048B08Rik
+RBM4B Rbm4b
+RBM4 Rbm4
+RBM5 Rbm5
+RBM6 Rbm6
+RBM7 Rbm7
+RBM8A Rbm8a
+RBM9 Rbm9
+RBMS1 Rbms1
+RBMS2 Rbms2
+RBMS3 Rbms3
+RBMX2 Rbmx2
+RBMXL1 Rbmxl1
+RBMXL2 Rbmxl2
+RBMX Rbmx
+RBMY1A1 Rbmy1a1
+RBP1 Rbp1
+RBP2 Rbp2
+RBP3 Rbp3
+RBP4 Rbp4
+RBP7 Rbp7
+RBPJL Rbpjl
+RBPJ Rbpj
+RBPMS2 Rbpms2
+RBPMS Rbpms
+RBPSUHL Rbpsuhl
+RBPSUH Rbpsuh
+RBX1 Rbx1
+RC3H1 Rc3h1
+RC3H2 Rc3h2
+RCAN1 Rcan1
+RCAN2 Rcan2
+RCAN3 Rcan3
+RCBTB1 Rcbtb1
+RCBTB2 Rcbtb2
+RCC1 Rcc1
+RCC2 Rcc2
+RCCD1 Rccd1
+RCE1 Rce1
+RCHY1 Rchy1
+RCL1 Rcl1
+RCN1 Rcn1
+RCN2 Rcn2
+RCN3 Rcn3
+RCOR1 Rcor1
+RCOR2 Rcor2
+RCOR3 Rcor3
+RCSD1 Rcsd1
+RCVRN Rcvrn
+RD3 Rd3
+RDBP Rdbp
+RDH10 Rdh10
+RDH11 Rdh11
+RDH12 Rdh12
+RDH13 Rdh13
+RDH14 Rdh14
+RDH16 Rdh16
+RDH5 Rdh5
+RDH8 Rdh8
+RDM1 Rdm1
+RDS Rds
+RDX Rdx
+REC8L1 Rec8L1
+REC8 Rec8
+RECK Reck
+RECQL4 Recql4
+RECQL5 Recql5
+RECQL Recql
+REEP1 Reep1
+REEP2 Reep2
+REEP3 Reep3
+REEP4 Reep4
+REEP5 Reep5
+REEP6 Reep6
+REG1A Reg1
+REG1B Reg2
+REG3A Pap
+REG3G Reg3a
+REG4 Reg4
+RELA Rela
+RELB Relb
+RELL1 Rell1
+RELL2 Rell2
+RELN Reln
+REL Rel
+RELT Relt
+REM1 Rem1
+REM2 Rem2
+RENBP Renbp
+REN Ren1
+REP15 Rep15
+REPIN1 Repin1
+REPS1 Reps1
+REPS2 Reps2
+RER1 Rer1
+RERE Rere
+RERGL Rergl
+RERG Rerg
+RESP18 Resp18
+REST Rest
+RETNLB Retnlb
+RETN Retn
+RET Ret
+RETSAT Retsat
+REV1L Rev1l
+REV1 Rev1
+REV3L Rev3l
+REXO1 Rexo1
+REXO2 Rexo2
+REXO4 Rexo4
+RFC1 Rfc1
+RFC2 Rfc2
+RFC3 Rfc3
+RFC4 Rfc4
+RFC5 Rfc5
+RFESD Rfesd
+RFFL Rffl
+RFK Rfk
+RFNG Rfng
+RFPL4A Rfpl4
+RFT1 Rft1
+RFTN1 Rftn1
+RFTN2 Rftn2
+RFWD2 Rfwd2
+RFWD3 Rfwd3
+RFX1 Rfx1
+RFX2 Rfx2
+RFX3 Rfx3
+RFX4 Rfx4
+RFX5 Rfx5
+RFX6 Rfx6
+RFX7 Rfx7
+RFX8 Rfx8
+RFXANK Rfxank
+RFXAP Rfxap
+RFXDC1 Rfxdc1
+RG9MTD1 Rg9mtd1
+RG9MTD2 Rg9mtd2
+RG9MTD3 Rg9mtd3
+RGAG1 Rgag1
+RGAG4 Rgag4
+RGCC 1190002H23Rik
+RGL1 Rgl1
+RGL2 Rgl2
+RGL3 Rgl3
+RGMA Rgma
+RGMB Rgmb
+RGNEF Rgnef
+RGN Rgn
+RGP1 Rgp1
+RGR Rgr
+RGS10 Rgs10
+RGS11 Rgs11
+RGS12 Rgs12
+RGS13 Rgs13
+RGS14 Rgs14
+RGS16 Rgs16
+RGS17 Rgs17
+RGS18 Rgs18
+RGS19 Rgs19
+RGS1 Rgs1
+RGS20 Rgs20
+RGS21 MMT00048679
+RGS22 Rgs22
+RGS2 Rgs2
+RGS3 Rgs3
+RGS4 Rgs4
+RGS5 Rgs5
+RGS6 Rgs6
+RGS7BP Rgs7bp
+RGS7 Rgs7
+RGS8 Rgs8
+RGS9BP Rgs9bp
+RGS9 Rgs9
+RGSL1 Rgsl1
+RGSL2 mCT19432
+RHAG Rhag
+RHBDD1 Rhbdd1
+RHBDD2 Rhbdd2
+RHBDD3 Rhbdd3
+RHBDF1 Rhbdf1
+RHBDF2 Rhbdf2
+RHBDL1 Rhbdl1
+RHBDL2 MMT00002427
+RHBDL3 Rhbdl3
+RHBG Rhbg
+RHCE Rhd
+RHCG Rhcg
+RHD Rhd
+RHEBL1 Rhebl1
+RHEB Rheb
+RHOA Rhoa
+RHOB Rhob
+RHOBTB1 Rhobtb1
+RHOBTB2 Rhobtb2
+RHOBTB3 Rhobtb3
+RHOC Rhoc
+RHOD Rhod
+RHOF Rhof
+RHOG Rhog
+RHOH Rhoh
+RHOJ Rhoj
+RHOQ Rhoq
+RHO Rho
+RHOT1 Rhot1
+RHOT2 Rhot2
+RHOU Rhou
+RHOV Rhov
+RHPN1 Rhpn1
+RHPN2 Rhpn2
+RIBC1 Ribc1
+RIBC2 Ribc2
+RIC3 Ric3
+RIC8A Ric8
+RIC8B Ric8b
+RICTOR Rictor
+RIF1 Rif1
+RIIAD1 Riiad1
+RILPL1 Rilpl1
+RILPL2 Rilpl2
+RILP Rilp
+RIMBP2 Rimbp2
+RIMBP3C Rimbp3
+RIMKLA Rimkla
+RIMKLB Rimklb
+RIMS1 Rims1
+RIMS2 Rims2
+RIMS3 Rims3
+RIMS4 Rims4
+RIN1 Rin1
+RIN2 Rin2
+RIN3 Rin3
+RING1 Ring1
+RINL Rinl
+RINT1 Rint1
+RIOK1 Riok1
+RIOK2 Riok2
+RIOK3 Riok3
+RIPK1 Ripk1
+RIPK2 Ripk2
+RIPK3 Ripk3
+RIPK4 Ripk4
+RIPK5 Ripk5
+RIPPLY1 Ripply1
+RIPPLY2 Ripply2
+RIT1 Rit1
+RIT2 Rit2
+RKHD2 Rkhd2
+RKHD3 Rkhd3
+RLBP1 Rlbp1
+RLF Rlf
+RLIM Rlim
+RLN1 Rln1
+RLN3 Rln3
+RMI1 Rmi1
+RMI2 A630055G03Rik
+RMND1 Rmnd1
+RMND5A Rmnd5a
+RMND5B Rmnd5b
+RMRP Rmrp
+RN5S1@ Rn5s
+RN7SK Rn7sk
+RNASE10 Rnase10
+RNASE11 Rnase11
+RNASE12 Rnase12
+RNASE13 Rnase13
+RNASE1 Rnase1
+RNASE2 Ear11
+RNASE3 Ear1
+RNASE4 Rnase4
+RNASE6 Rnase6
+RNASE9 Rnase9
+RNASEH1 Rnaseh1
+RNASEH2A Rnaseh2a
+RNASEH2B Rnaseh2b
+RNASEH2C Rnaseh2c
+RNASEK Rnasek
+RNASEL Rnasel
+RNASEN Rnasen
+RNASET2 Rnaset2a
+RND1 Rnd1
+RND2 Rnd2
+RND3 Rnd3
+RNF103 Rnf103
+RNF10 Rnf10
+RNF111 Rnf111
+RNF112 Rnf112
+RNF113A Rnf113a1
+RNF114 Rnf114
+RNF115 Rnf115
+RNF11 Rnf11
+RNF121 Rnf121
+RNF122 Rnf122
+RNF123 Rnf123
+RNF125 Rnf125
+RNF126 Rnf126
+RNF128 Rnf128
+RNF12 Rnf12
+RNF130 Rnf130
+RNF133 Rnf133
+RNF135 Rnf135
+RNF138 Rnf138
+RNF139 Rnf139
+RNF13 Rnf13
+RNF141 Rnf141
+RNF144A Rnf144a
+RNF144B Rnf144b
+RNF144 Rnf144
+RNF145 Rnf145
+RNF146 Rnf146
+RNF148 Rnf148
+RNF149 Rnf149
+RNF14 Rnf14
+RNF150 Rnf150
+RNF151 Rnf151
+RNF152 Rnf152
+RNF157 Rnf157
+RNF165 Gm96
+RNF166 Rnf166
+RNF167 Rnf167
+RNF168 Rnf168
+RNF169 2900057K09Rik
+RNF170 Rnf170
+RNF175 MMT00048370
+RNF17 Rnf17
+RNF180 Rnf180
+RNF181 Rnf181
+RNF182 Rnf182
+RNF183 Rnf183
+RNF185 Rnf185
+RNF186 Rnf186
+RNF187 Rnf187
+RNF190 Rnf190
+RNF19A Rnf19a
+RNF19B Rnf19b
+RNF19 Rnf19
+RNF207 Rnf207
+RNF208 Rnf208
+RNF20 Rnf20
+RNF212 Rnf212
+RNF213 Rnf213
+RNF214 Rnf214
+RNF215 Rnf215
+RNF216 Rnf216
+RNF217 Rnf217
+RNF219 Rnf219
+RNF220 Rnf220
+RNF222 Rnf222
+RNF223 9930005F22Rik
+RNF224 Gm757
+RNF24 Rnf24
+RNF25 Rnf25
+RNF26 Rnf26
+RNF2 Rnf2
+RNF31 Rnf31
+RNF32 Rnf32
+RNF34 Rnf34
+RNF38 Rnf38
+RNF39 Rnf39
+RNF40 Rnf40
+RNF41 Rnf41
+RNF43 Rnf43
+RNF44 Rnf44
+RNF4 Rnf4
+RNF5 Rnf5
+RNF6 Rnf6
+RNF7 Rnf7
+RNF8 Rnf8
+RNFT1 Rnft1
+RNFT2 Rnft2
+RNGTT Rngtt
+RNH1 Rnh1
+RNLS Rnls
+RNMTL1 Rnmtl1
+RNMT Rnmt
+RNPC3 Rnpc3
+RNPEPL1 Rnpepl1
+RNPEP Rnpep
+RNPS1 MMS010007186
+RNR1 mt-Rnr1
+RNU11 Rnu11
+RNU12-2P Rnu12
+RNU1-4 Rnu1a1
+RNUXA Rnuxa
+RNY1 Rny1
+RNY3 Rny3
+ROBO1 Robo1
+ROBO2 Robo2
+ROBO3 Robo3
+ROBO4 Robo4
+ROCK1 Rock1
+ROCK2 Rock2
+ROD1 Rod1
+ROGDI Rogdi
+ROM1 Rom1
+ROMO1 Romo1
+ROPN1B Ropn1
+ROPN1L Ropn1l
+ROPN1 Ropn1
+ROR1 Ror1
+ROR2 Ror2
+RORA Rora
+RORB Rorb
+RORC Rorc
+ROS1 Ros1
+RP11-11C5.2 2410129H14Rik
+RP11-125A7.3 1300010F03Rik
+RP11-130N24.1 C77370
+RP11-139H14.4 4921509B22Rik
+RP11-145H9.1 EG238564
+RP11-162I21.1 mCT173539
+RP11-169K16.3 BC025833
+RP11-217H1.1 2610529C04Rik
+RP11-218C14.6 Cst13
+RP11-301I17.1 D230005D02Rik
+RP11-311P8.3 Gm774
+RP11-321G1.2 MMT00054272
+RP11-35N6.1 E130309F12Rik
+RP11-393H10.2 2900002G04Rik
+RP11-444E17.2 Tcte1
+RP11-450P7.3 Gm388
+RP11-45J16.2 4831428F09Rik
+RP1-14N1.3 XM_143396
+RP11-506K6.3 2310047M15Rik
+RP11-50D16.3 8030451K01Rik
+RP11-529I10.4 5330431N19Rik
+RP11-54H7.1 C230040D10Rik
+RP11-56A21.1 D6Wsu116e
+RP11-93B10.1 9630042H07Rik
+RP1-199H16.1 4933432B09Rik
+RP13-102H20.1 1100001E04Rik
+RP1-93H18.5 A630077B13Rik
+RP1L1 Rp1hl1
+RP1 Rp1
+RP2 Rp2h
+RP3-340N1.3 Pla2g2c
+RP3-366L4.2 AW049829
+RP3-402G11.5 1300018J18Rik
+RP3-402H5.2 mCT56262
+RP4-691N24.1 4930519N13Rik
+RP4-742C19.3 Apobec3
+RP5-1022P6.2 Prei4
+RP5-1054A22.3 D630003M21Rik
+RP5-1077B9.4 D4Wsu114e
+RP5-821D11.2 Mei1
+RP5-860F19.3 Ebf4
+RP6-213H19.1 2610018G03Rik
+RP9 Rp9
+RPA1 Rpa1
+RPA2 Rpa2
+RPA3 Rpa3
+RPAIN Rpain
+RPAP1 Rpap1
+RPAP2 Rpap2
+RPAP3 Rpap3
+RPE65 Rpe65
+RPE Rpe
+RPF1 Rpf1
+RPF2 Rpf2
+RPGRIP1L Rpgrip1l
+RPGRIP1 Rpgrip1
+RPGR Rpgr
+RPH3AL Rph3al
+RPH3A Rph3a
+RPIA Rpia
+RPL10AP3 Rpl10a-ps2
+RPL10A Rpl10a
+RPL10L Rpl10l
+RPL10P16 Rpl10-ps3
+RPL10 Rpl10
+RPL11 Rpl11
+RPL12 Rpl12
+RPL13A Rpl13a
+RPL13P12 Gm12918
+RPL13 Rpl13
+RPL14 Rpl14
+RPL15 Rpl15
+RPL17 Rpl17
+RPL18A Rpl18a
+RPL18 Rpl18
+RPL19 Rpl19
+RPL21 Rpl21
+RPL22L1 Rpl22l1
+RPL22 Rpl22
+RPL23AP32 Gm3362
+RPL23 Rpl23
+RPL24 Rpl24
+RPL26 MMS010061863
+RPL27A Rpl27a
+RPL27 Rpl27
+RPL28 Rpl28
+RPL29 Rpl29
+RPL30 Rpl30
+RPL31P5 Gm5670
+RPL31 Rpl31
+RPL32 Rpl32
+RPL34 Gm6404
+RPL35A Rpl35a
+RPL35 Rpl35
+RPL36AL Rpl36al
+RPL36A Rpl36a
+RPL36P8 Gm5614
+RPL36 Rpl36
+RPL37A Rpl37a
+RPL37 Rpl37
+RPL38 Rpl38
+RPL39 4930517K11Rik
+RPL3L Rpl3l
+RPL3 Rpl3
+RPL41 Rpl41
+RPL4 Rpl4
+RPL5 Rpl5
+RPL6 Rpl6
+RPL7A Rpl7a
+RPL7L1 Rpl7l1
+RPL7 Rpl7
+RPL8 Rpl8
+RPL9 Rpl9
+RPLP0 Arbp
+RPLP1 Rplp1
+RPLP2 Rplp2
+RPN1 Rpn1
+RPN2 Rpn2
+RPP14 Rpp14
+RPP21 Rpp21
+RPP25L 2810432D09Rik
+RPP25 Rpp25
+RPP30 Rpp30
+RPP38 Rpp38
+RPP40 Rpp40
+RPPH1 Rpph1
+RPRC1 Rprc1
+RPRD1A Rprd1a
+RPRD1B Rprd1b
+RPRD2 Rprd2
+RPRML Rprml
+RPRM Rprm
+RPS10 Rps10
+RPS11 Rps11
+RPS12P4 Gm6646
+RPS12 Rps12
+RPS13 Rps13
+RPS14 Rps14
+RPS15A Rps15a
+RPS15 Rps15
+RPS16 Rps16
+RPS17 Rps17
+RPS18 Rps18
+RPS19BP1 Rps19bp1
+RPS19 Rps19
+RPS20 Rps20
+RPS21 Rps21
+RPS23 Rps23
+RPS24 Rps24
+RPS25 Rps25
+RPS26 Rps26
+RPS27A Rps27a
+RPS27L Rps27l
+RPS27 Rps27
+RPS28 MMT00000788
+RPS29 Rps29
+RPS2 Rps2
+RPS3AP6 Gm10119
+RPS3A Rps3a
+RPS3 Rps3
+RPS4XP16 Gm13680
+RPS4XP6 Gm8729
+RPS4X Rps4x
+RPS5 Rps5
+RPS6KA1 Rps6ka1
+RPS6KA2 Rps6ka2
+RPS6KA3 Rps6ka3
+RPS6KA4 Rps6ka4
+RPS6KA5 Rps6ka5
+RPS6KA6 Rps6ka6
+RPS6KB1 Rps6kb1
+RPS6KB2 Rps6kb2
+RPS6KC1 Rps6kc1
+RPS6KL1 Rps6kl1
+RPS6 Rps6-ps4
+RPS7 Rps7
+RPS8 Rps8
+RPSAP52 Rpsa-ps9
+RPSA Rpsa
+RPTN Rptn
+RPTOR Rptor
+RPUSD1 Rpusd1
+RPUSD2 Rpusd2
+RPUSD3 Rpusd3
+RPUSD4 Rpusd4
+RQCD1 Rqcd1
+RRAD Rrad
+RRAGA Rraga
+RRAGB Rragb
+RRAGC Rragc
+RRAGD Rragd
+RRAS2 Rras2
+RRAS Rras
+RRBP1 Rrbp1
+RREB1 Rreb1
+RRH Rrh
+RRM1 Rrm1
+RRM2B Rrm2b
+RRM2 Rrm2
+RRN3 Rrn3
+RRNAD1 Rrnad1
+RRP12 Rrp12
+RRP15 Rrp15
+RRP1B Rrp1b
+RRP1 Rrp1
+RRP36 Rrp36
+RRP7A Rrp7a
+RRP8 Rrp8
+RRP9 Rnu3ip2
+RRS1 Rrs1
+RS1 Rs1
+RSAD1 Rsad1
+RSAD2 Rsad2
+RSBN1L Rsbn1l
+RSBN1 Rsbn1
+RSC1A1 Rsc1a1
+RSE_00000246951 Olfr748
+RSE_00000273080 MMT00007609
+RSE_00000336386 Olfr481
+RSE_00000567221 5730521E12Rik
+RSE_00000613195 4732495G21Rik
+RSE_00000616896 Gm949
+RSE_00000868336 Olfr1406
+RSE_00000869609 Tas1r3
+RSF1 Rsf1
+RSG1 6330545A04Rik
+RSHL1 Rshl1
+RSHL2 Rshl2a
+RSHL3 Rshl3
+RSL1D1 Rsl1d1
+RSL24D1 Rsl24d1
+RSNL2 Rsnl2
+RSN Rsn
+RSPH10B2 Rsph10b2
+RSPH1 Rsph1
+RSPH3 Rsph3a
+RSPH4A Rsph4a
+RSPH6A Rsph6a
+RSPH9 Rsph9
+RSPO1 Rspo1
+RSPO2 Rspo2
+RSPO3 Rspo3
+RSPO4 Rspo4
+RSPRY1 Rspry1
+RSRC1 Rsrc1
+RSRC2 Rsrc2
+RSU1 Rsu1
+RTBDN Rtbdn
+RTCA Rtcd1
+RTDR1 Rtdr1
+RTEL1 Rtel1
+RTF1 Rtf1
+RTKN2 Rtkn2
+RTKN Rtkn
+RTL1 Rtl1
+RTN1 Rtn1
+RTN2 Rtn2
+RTN3 Rtn3
+RTN4IP1 Rtn4ip1
+RTN4RL1 Rtn4rl1
+RTN4RL2 Rtn4rl2
+RTN4R Rtn4r
+RTN4 Rtn4
+RTP1 Rtp1
+RTP2 Rtp2
+RTP3 Rtp3
+RTP4 Rtp4
+RTTN Rttn
+RUFY1 Rufy1
+RUFY2 Rufy2
+RUFY3 Rufy3
+RUFY4 Rufy4
+RUNDC1 MMT00006239
+RUNDC3A Rundc3a
+RUNDC3B Rundc3b
+RUNX1 Runx1
+RUNX1T1 Runx1t1
+RUNX2 Runx2
+RUNX3 Runx3
+RUSC1 Rusc1
+RUSC2 Rusc2
+RUTBC1 Rutbc1
+RUTBC2 Rutbc2
+RUTBC3 Rutbc3
+RUVBL1 Ruvbl1
+RUVBL2 Ruvbl2
+RWDD1 Rwdd1
+RWDD2A Rwdd2a
+RWDD2B Rwdd2b
+RWDD2 Rwdd2
+RWDD3 Rwdd3
+RWDD4A Rwdd4a
+RXFP1 Rxfp1
+RXFP2 Rxfp2
+RXFP3 Rxfp3
+RXFP4 Rxfp4
+RXRA Rxra
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+TSSK1B Tssk1
+TSSK2 Tssk2
+TSSK3 Tssk3
+TSSK4 Tssk4
+TSSK6 Tssk6
+TSTA3 Tsta3
+TSTD1 Tstd1
+TSTD2 Tstd2
+TSTD3 2610029I01Rik
+TST Tst
+T T
+TTBK1 Ttbk1
+TTBK2 Ttbk2
+TTC12 Ttc12
+TTC13 Ttc13
+TTC14 Ttc14
+TTC15 Ttc15
+TTC16 Ttc16
+TTC17 Ttc17
+TTC18 Ttc18
+TTC19 Ttc19
+TTC1 Ttc1
+TTC21A Ttc21a
+TTC21B Ttc21b
+TTC22 Ttc22
+TTC23L Ttc23l
+TTC23 Ttc23
+TTC24 A430025D11Rik
+TTC25 Ttc25
+TTC26 Ttc26
+TTC27 Ttc27
+TTC28 Ttc28
+TTC29 Ttc29
+TTC30A 4930506L13Rik
+TTC30B Ttc30b
+TTC32 Ttc32
+TTC33 Ttc33
+TTC34 Ttc34
+TTC36 Ttc36
+TTC37 Ttc37
+TTC38 Ttc38
+TTC39A Ttc39a
+TTC39B Ttc39b
+TTC39C Ttc39c
+TTC39DP Ttc39d
+TTC3 Ttc3
+TTC4 Ttc4
+TTC5 Ttc5
+TTC6 EG639426
+TTC7A Ttc7
+TTC7B Ttc7b
+TTC8 Ttc8
+TTC9B Ttc9b
+TTC9C Ttc9c
+TTC9 Ttc9
+TTF1 Ttf1
+TTF2 Ttf2
+TTI1 Tti1
+TTI2 Tti2
+TTK Ttk
+TTLL10 Ttll10
+TTLL11 Ttll11
+TTLL12 Ttll12
+TTLL13 Ttll13
+TTLL1 Ttll1
+TTLL2 Ttll2
+TTLL3 Ttll3
+TTLL4 Ttll4
+TTLL5 Ttll5
+TTLL6 Ttll6
+TTLL7 4921517B04Rik
+TTLL8 Ttll8
+TTLL9 Ttll9
+TTL Ttl
+TTN Ttn
+TTPAL Ttpal
+TTPA Ttpa
+TTRAP Ttrap
+TTR Ttr
+TTYH1 Ttyh1
+TTYH2 Ttyh2
+TTYH3 Ttyh3
+TUBA1A Tuba1a
+TUBA1B Tuba1b
+TUBA1C Tuba1c
+TUBA1 Tuba4
+TUBA2 Tuba7
+TUBA3E Tuba3a
+TUBA4A Tuba4a
+TUBA8 Tuba8
+TUBAL3 Tubal3
+TUBB1 Tubb1
+TUBB2A Tubb2a
+TUBB2B Tubb2b
+TUBB3 Tubb3
+TUBB4A Tubb4a
+TUBB4B Tubb4b
+TUBB4 Tubb4
+TUBB6 Tubb6
+TUBB Tubb5
+TUBD1 Tubd1
+TUBE1 Tube1
+TUBG1 Tubg1
+TUBG2 Tubg2
+TUBGCP2 Tubgcp2
+TUBGCP3 Tubgcp3
+TUBGCP4 Tubgcp4
+TUBGCP5 Tubgcp5
+TUBGCP6 Tubgcp6
+TUB Tub
+TUFM Tufm
+TUFT1 Tuft1
+TUG1 Tug1
+TULP1 Tulp1
+TULP2 Tulp2
+TULP3 Tulp3
+TULP4 Tulp4
+TUSC1 Tusc1
+TUSC2 Tusc2
+TUSC3 Tusc3
+TUSC4 Tusc4
+TUSC5 Tusc5
+TUT1 Tut1
+TWF1 Twf1
+TWF2 Twf2
+TWIST1 Twist1
+TWIST2 MMT00053624
+TWISTNB Twistnb
+TWSG1 Twsg1
+TXK Txk
+TXLNA Txlna
+TXLNB Txlnb
+TXLNG Txlng
+TXN2 Txn2
+TXNDC10 Txndc10
+TXNDC11 Txndc11
+TXNDC12 Txndc12
+TXNDC13 Txndc13
+TXNDC14 Txndc14
+TXNDC15 Txndc15
+TXNDC16 Txndc16
+TXNDC17 Txndc17
+TXNDC1 Txndc1
+TXNDC2 Txndc2
+TXNDC3 Txndc3
+TXNDC4 Txndc4
+TXNDC5 Txndc5
+TXNDC6 1600029I14Rik
+TXNDC8 Txndc8
+TXNDC9 Txndc9
+TXNIP Txnip
+TXNL1 Txnl1
+TXNL2 Txnl2
+TXNL4A Txnl4
+TXNL4B Txnl4b
+TXNL6 Txnl6
+TXNRD1 Txnrd1
+TXNRD2 Txnrd2
+TXNRD3 Txnrd3
+TXN Txn1
+TYK2 Tyk2
+TYMP Tymp
+TYMS Tyms
+TYRO3 Tyro3
+TYROBP Tyrobp
+TYRP1 Tyrp1
+TYR Tyr
+TYSND1 Tysnd1
+TYW1 Tyw1
+TYW3 Tyw3
+TYW5 1110034B05Rik
+U2AF1L4 U2af1l4
+U2AF1 U2af1
+U2AF2 U2af2
+U2SURP 2610101N10Rik
+U79260 NM_011936
+U79293 MMT00027771
+UACA Uaca
+UAP1L1 Uap1l1
+UAP1 Uap1
+UBA1 Uba1
+UBA2 Uba2
+UBA3 Uba3
+UBA52 Uba52
+UBA5 Uba5
+UBA6 Uba6
+UBA7 Uba7
+UBAC1 Ubac1
+UBAC2 Ubac2
+UBADC1 Ubadc1
+UBAP1L Gm514
+UBAP1 Ubap1
+UBAP2L Ubap2l
+UBAP2 Ubap2
+UBASH3A Ubash3a
+UBASH3B Ubash3b
+UBB Ubb
+UBC Ubc
+UBD Ubd
+UBE1C Ube1c
+UBE1DC1 MMS010236757
+UBE1L2 Ube1l2
+UBE1L Ube1l
+UBE1 Ube1x
+UBE2A Ube2a
+UBE2B Ube2b
+UBE2C Ube2c
+UBE2D1 Ube2d1
+UBE2D2 Ube2d2
+UBE2D3 Ube2d3
+UBE2D4 EG546638
+UBE2DNL Ube2dnl1
+UBE2E1 Ube2e1
+UBE2E2 Ube2e2
+UBE2E3 Ube2e3
+UBE2F Ube2f
+UBE2G1 Ube2g1
+UBE2G2 Ube2g2
+UBE2H Ube2h
+UBE2I Ube2i
+UBE2J1 Ube2j1
+UBE2J2 Ube2j2
+UBE2K Ube2k
+UBE2L3 Ube2l3
+UBE2L6 Ube2l6
+UBE2M Ube2m
+UBE2N Ube2n
+UBE2O Ube2o
+UBE2Q1 Ube2q1
+UBE2Q2 Ube2q2
+UBE2QL1 Ube2ql1
+UBE2R2 Ube2r2
+UBE2S Ube2s
+UBE2T Ube2t
+UBE2U Ube2u
+UBE2V1 Ube2v1
+UBE2V2 Ube2v2
+UBE2W Ube2w
+UBE2Z Ube2z
+UBE3A Ube3a
+UBE3B Ube3b
+UBE3C Ube3c
+UBE3D Ube2cbp
+UBE4A Ube4a
+UBE4B Ube4b
+UBFD1 Ubfd1
+UBIAD1 Ubiad1
+UBL3 Ubl3
+UBL4A Ubl4
+UBL4B Ubl4b
+UBL5 Ubl5
+UBL7 Ubl7
+UBLCP1 Ublcp1
+UBN1 Ubn1
+UBN2 Ubn2
+UBOX5 Ubox5
+UBP1 Ubp1
+UBQLN1 Ubqln1
+UBQLN2 Ubqln2
+UBQLN3 Ubqln3
+UBQLN4 Ubqln4
+UBQLNL Ubqlnl
+UBR1 Ubr1
+UBR2 Ubr2
+UBR3 Ubr3
+UBR4 Ubr4
+UBR5 Ubr5
+UBR7 Ubr7
+UBTD1 Ubtd1
+UBTD2 Ubtd2
+UBTF Ubtf
+UBXD1 Ubxd1
+UBXD2 Ubxd2
+UBXD3 Ubxd3
+UBXD4 Ubxd4
+UBXD5 Ubxd5
+UBXD6 Ubxd6
+UBXD8 Ubxd8
+UBXN10 Ubxn10
+UBXN11 Ubxn11
+UBXN1 Ubxn1
+UBXN2A Ubxn2a
+UBXN2B Ubxn2b
+UBXN4 Ubxn4
+UBXN6 Ubxn6
+UBXN7 Ubxn7
+UBXN8 Ubxn8
+UCHL1 Uchl1
+UCHL3 Uchl3
+UCHL5 Uchl5
+UCK1 Uck1
+UCK2 Uck2
+UCKL1-AS1 Gm16119
+UCKL1 Uckl1
+UCMA Ucma
+UCN2 Ucn2
+UCN3 Ucn3
+UCN Ucn
+UCP1 Ucp1
+UCP2 Ucp2
+UCP3 Ucp3
+UEVLD Uevld
+UFC1 Ufc1
+UFD1L Ufd1l
+UFL1 1810074P20Rik
+UFM1 Ufm1
+UFSP1 Ufsp1
+UFSP2 Ufsp2
+UGCGL1 Ugcgl1
+UGCG Ugcg
+UGDH Ugdh
+UGGT1 Uggt1
+UGGT2 Uggt2
+UGP2 Ugp2
+UGT1A1 Ugt1a1
+UGT1A3 Ugt1a2
+UGT1A4 Ugt1a5
+UGT1A6 Ugt1a6a
+UGT1A8 Ugt1a7c
+UGT1A9 Ugt1a9
+UGT2A1 Ugt2a1
+UGT2A2 Ugt2a2
+UGT2A3 Ugt2a3
+UGT2B10 Ugt2b34
+UGT2B15 Ugt2b38
+UGT2B28 Ugt2b1
+UGT2B7 Ugt2b35
+UGT3A1 Ugt3a2
+UGT8 Ugt8a
+UHMK1 Uhmk1
+UHRF1BP1L Uhrf1bp1l
+UHRF1BP1 Uhrf1bp1
+UHRF1 Uhrf1
+UHRF2 Uhrf2
+UIMC1 Uimc1
+ULBP1 Ulbp1
+ULK1 Ulk1
+ULK2 Ulk2
+ULK3 Ulk3
+ULK4 Ulk4
+UMODL1 Umodl1
+UMOD Umod
+UMPS Umps
+UNC119B Unc119b
+UNC119 Unc119
+UNC13A Unc13a
+UNC13B Unc13b
+UNC13C Unc13c
+UNC13D Unc13d
+UNC45A Unc45a
+UNC45B Unc45b
+UNC50 Unc50
+UNC5A Unc5a
+UNC5B Unc5b
+UNC5CL Unc5cl
+UNC5C Unc5c
+UNC5D Unc5d
+UNC79 Unc79
+UNC84A Unc84a
+UNC84B Unc84b
+UNC93A Unc93a
+UNC93B1 Unc93b1
+UNCX Uncx
+UNG2 Ung2
+UNG Ung
+UNKL Unkl
+UNK Unk
+UNQ1887 Sppl3
+UNQ338 3830408D24Rik
+UNQ755 Cbln3
+UOX Uox
+UPB1 Upb1
+UPF1 Upf1
+UPF2 Upf2
+UPF3A Upf3a
+UPF3B Upf3b
+UPK1A Upk1a
+UPK1B Upk1b
+UPK2 Upk2
+UPK3A Upk3a
+UPK3BL Upk3bl
+UPK3B Upk3b
+UPP1 Upp1
+UPP2 Upp2
+UPRT Uprt
+UQCC Uqcc
+UQCR10 Uqcr10
+UQCR11 Uqcr11
+UQCRB Uqcrb
+UQCRC1 Uqcrc1
+UQCRC2 Uqcrc2
+UQCRFS1 Uqcrfs1
+UQCRH Uqcrh
+UQCRQ Uqcrq
+UQCR Uqcr
+URB1 Urb1
+URB2 Urb2
+URGCP Urgcp
+URI1 C80913
+URM1 Urm1
+UROC1 Uroc1
+UROD Urod
+UROS Uros
+USE1 Use1
+USF1 Usf1
+USF2 Usf2
+USH1C Ush1c
+USH1G Ush1g
+USH2A Ush2a
+USH3A Ush3a
+USHBP1 Ushbp1
+USMG5 Usmg5
+USO1 Uso1
+USP10 Usp10
+USP11 Usp11
+USP12 Usp12
+USP13 Usp13
+USP14 Usp14
+USP15 Usp15
+USP16 Usp16
+USP18 Usp18
+USP19 Usp19
+USP1 MMT00001293
+USP20 Usp20
+USP21 Usp21
+USP22 Usp22
+USP24 Usp24
+USP25 Usp25
+USP26 Usp26
+USP27X Usp27x
+USP28 Usp28
+USP29 Usp29
+USP2 Usp2
+USP30 Usp30
+USP31 Usp31
+USP32 Usp32
+USP33 Usp33
+USP34 Usp34
+USP35 Usp35
+USP36 Usp36
+USP37 Usp37
+USP38 Usp38
+USP39 Usp39
+USP3 Usp3
+USP40 Usp40
+USP42 Usp42
+USP43 Usp43
+USP44 Usp44
+USP45 Usp45
+USP46 Usp46
+USP47 Usp47
+USP48 Usp48
+USP49 Usp49
+USP4 Usp4
+USP50 Usp50
+USP51 Usp51
+USP52 Usp52
+USP53 Usp53
+USP54 C030002J06Rik
+USP5 Usp5
+USP6NL Usp6nl
+USP7 Usp7
+USP8 Usp8
+USP9X Usp9x
+USP9Y Usp9y
+USPL1 Uspl1
+UST Ust
+UTF1 Utf1
+UTP11L Utp11l
+UTP14A Utp14a
+UTP15 Utp15
+UTP18 Utp18
+UTP20 Utp20
+UTP23 Utp23
+UTP3 Utp3
+UTP6 Utp6
+UTRN Utrn
+UTS2D Uts2d
+UTS2R Uts2r
+UTS2 Uts2
+UTX Utx
+UTY Uty
+UVRAG Uvrag
+UVSSA 4933407H18Rik
+UXS1 Uxs1
+UXT Uxt
+VAC14 Vac14
+VAMP1 Vamp1
+VAMP2 Vamp2
+VAMP3 Vamp3
+VAMP4 Vamp4
+VAMP5 Vamp5
+VAMP7 Vamp7
+VAMP8 Vamp8
+VANGL1 Vangl1
+VANGL2 Vangl2
+VAPA Vapa
+VAPB Vapb
+VARS2 Vars2
+VARS Vars
+VASH1 Vash1
+VASH2 Vash2
+VASN Vasn
+VASP Vasp
+VAT1L Vat1l
+VAT1 Vat1
+VAV1 Vav1
+VAV2 Vav2
+VAV3 Vav3
+VAX1 Vax1
+VAX2 Vax2
+VBP1 Vbp1
+VCAM1 Vcam1
+VCAN Vcan
+VCL Vcl
+VCPIP1 Vcpip1
+VCP Vcp
+VDAC1 Vdac1
+VDAC2 Vdac2
+VDAC3 Vdac3
+VDR Vdr
+VEGFA Vegfa
+VEGFB Vegfb
+VEGFC Vegfc
+VEGF Vegfa
+VEPH1 Veph1
+VEZF1 Vezf1
+VEZT Vezt
+VGF Vgf
+VGLL1 Vgll1
+VGLL2 Vgll2
+VGLL3 1700110N18Rik
+VGLL4 Vgll4
+VHL Vhl
+VIL1 Vil1
+VIL2 Vil2
+VILL Vill
+VIMP H47
+VIM Vim
+VIPAS39 Vipas39
+VIPR1 Vipr1
+VIPR2 Vipr2
+VIP Vip
+VIS1 Vis1
+VIT Vit
+VKORC1L1 Vkorc1l1
+VKORC1 Vkorc1
+VLDLR Vldlr
+VMA21 Vma21
+VMAC Vmac
+VMO1 Vmo1
+VMP1 Vmp1
+VN1R1 V1rd12
+VN1R2 Vmn1r224
+VN1R3 V1rg12
+VN1R4 V1re8
+VN2R17P Gm14496
+VN2R1P Vmn2r1
+VNN1 Vnn1
+VNN3 Vnn3
+VOPP1 Vopp1
+VPRBP B930007L02Rik
+VPREB1 Vpreb1
+VPREB3 Vpreb3
+VPS11 Vps11
+VPS13A Vps13a
+VPS13B Vps13b
+VPS13C Vps13c
+VPS13D Vps13d
+VPS16 Vps16
+VPS18 Vps18
+VPS25 Vps25
+VPS26A Vps26a
+VPS26B Vps26b
+VPS28 Vps28
+VPS29 Vps29
+VPS33A Vps33a
+VPS33B Vps33b
+VPS35 Vps35
+VPS36 Vps36
+VPS37A Vps37a
+VPS37B Vps37b
+VPS37C Vps37c
+VPS37D Vps37d
+VPS39 Vps39
+VPS41 Vps41
+VPS45A Vps45
+VPS4A Vps4a
+VPS4B Vps4b
+VPS51 1110014N23Rik
+VPS52 Vps52
+VPS53 Vps53
+VPS54 Vps54
+VPS72 Vps72
+VPS8 Vps8
+VRK1 Vrk1
+VRK2 Vrk2
+VRK3 Vrk3
+VRTN Vrtn
+VSIG10L Vsig10l
+VSIG10 Vsig10
+VSIG1 Vsig1
+VSIG2 Vsig2
+VSIG4 Vsig4
+VSIG8 Vsig8
+VSIG9 XM_358998
+VSNL1 Vsnl1
+VSTM2A Vstm2a
+VSTM2B Vstm2b
+VSTM2L Vstm2l
+VSTM2 Vstm2
+VSTM4 E130203B14Rik
+VSTM5 2200002K05Rik
+VSX1 Vsx1
+VSX2 Vsx2
+VTA1 Vta1
+VTCN1 Vtcn1
+VTI1A Vti1a
+VTI1B Vti1b
+VTN Vtn
+VWA1 Vwa1
+VWA2 Vwa2
+VWA3A Vwa3a
+VWA3B Vwa3b
+VWA5A Vwa5a
+VWA5B1 Vwa5b1
+VWA5B2 Vwa5b2
+VWA7 D17H6S56E-3
+VWA8 1300010F03Rik
+VWC2L Vwc2l
+VWC2 Vwc2
+VWCE Vwce
+VWDE Vwde
+VWF Vwf
+WAC Wac
+WAPAL Wapal
+WARS2 Wars2
+WARS Wars
+WASF1 Wasf1
+WASF2 Wasf2
+WASF3 Wasf3
+WASH2P Wash
+WASL Wasl
+WAS Was
+WBP11 Wbp11
+WBP1L D19Wsu162e
+WBP1 Wbp1
+WBP2NL Wbp2nl
+WBP2 Wbp2
+WBP4 Wbp4
+WBP5 Wbp5
+WBSCR16 Wbscr16
+WBSCR17 Wbscr17
+WBSCR18 Wbscr18
+WBSCR22 Wbscr22
+WBSCR26 2010001M06Rik
+WBSCR27 Wbscr27
+WBSCR28 Wbscr28
+WDFY1 Wdfy1
+WDFY2 Wdfy2
+WDFY3 Wdfy3
+WDFY4 Wdfy4
+WDHD1 Wdhd1
+WDPCP Wdpcp
+WDR11 Wdr11
+WDR12 Wdr12
+WDR13 Wdr13
+WDR16 Wdr16
+WDR17 Wdr17
+WDR18 Wdr18
+WDR19 Wdr19
+WDR1 Wdr1
+WDR20 Wdr20a
+WDR21A Wdr21
+WDR22 Wdr22
+WDR23 Wdr23
+WDR24 Wdr24
+WDR25 Wdr25
+WDR26 Wdr26
+WDR27 Wdr27
+WDR31 Wdr31
+WDR32 Wdr32
+WDR33 Wdr33
+WDR34 Wdr34
+WDR35 Wdr35
+WDR36 Wdr36
+WDR37 Wdr37
+WDR38 Wdr38
+WDR39 Wdr39
+WDR3 Wdr3
+WDR40A Wdr40a
+WDR40C Wdr40c
+WDR41 Wdr41
+WDR42A Wdr42a
+WDR43 Wdr43
+WDR44 Wdr44
+WDR45L Wdr45l
+WDR45 Wdr45
+WDR46 Wdr46
+WDR47 Wdr47
+WDR48 Wdr48
+WDR49 MMT00007851
+WDR4 Wdr4
+WDR51A Wdr51a
+WDR51B Wdr51b
+WDR52 Wdr52
+WDR53 Wdr53
+WDR54 Wdr54
+WDR55 Wdr55
+WDR57 Wdr57
+WDR59 Wdr59
+WDR5B Wdr5b
+WDR5 Wdr5
+WDR60 D430033N04Rik
+WDR61 Wdr61
+WDR62 Wdr62
+WDR63 Wdr63
+WDR64 Wdr64
+WDR65 Wdr65
+WDR66 Wdr66
+WDR67 Wdr67
+WDR68 Wdr68
+WDR69 4930563E19Rik
+WDR6 Wdr6
+WDR70 Wdr70
+WDR72 Wdr72
+WDR73 Wdr73
+WDR74 Wdr74
+WDR75 Wdr75
+WDR76 Wdr76
+WDR77 Wdr77
+WDR78 Wdr78
+WDR79 Wdr79
+WDR7 Wdr7
+WDR81 Wdr81
+WDR82 Wdr82
+WDR83OS BC056474
+WDR83 Wdr83
+WDR85 Wdr85
+WDR86 Wdr86
+WDR88 Wdr88
+WDR89 2600001A11Rik
+WDR8 Wdr8
+WDR90 Wdr90
+WDR91 Wdr91
+WDR92 Wdr92
+WDR93 Wdr93
+WDSOF1 Wdsof1
+WDSUB1 Wdsub1
+WDTC1 Wdtc1
+WDYHV1 Wdyhv1
+WEE1 Wee1
+WEE2 BC052883
+WFDC11 Wfdc11
+WFDC12 Wfdc12
+WFDC13 Wfdc13
+WFDC1 Wfdc1
+WFDC2 Wfdc2
+WFDC3 Wfdc3
+WFDC5 Wfdc5
+WFDC6 Wfdc6b
+WFDC8 Wfdc8
+WFDC9 MMT00044435
+WFIKKN1 Wfikkn1
+WFIKKN2 Wfikkn2
+WFS1 Wfs1
+WHAMM Whamm
+WHSC1L1 Whsc1l1
+WHSC1 Whsc1
+WHSC2 Whsc2
+WIBG Wibg
+WIF1 Wif1
+WIPF1 Waspip
+WIPF2 5730509C05Rik
+WIPF3 Wipf3
+WIPI1 Wipi1
+WIPI2 Wipi2
+WISP1 Wisp1
+WISP2 Wisp2
+WISP3 Wisp3
+WIT1 MMT00061772
+WIZ Wiz
+WLS Wls
+WNK1 Wnk1
+WNK2 Wnk2
+WNK3 Wnk3-ps
+WNK4 Wnk4
+WNT10A Wnt10a
+WNT10B Wnt10b
+WNT11 Wnt11
+WNT16 Wnt16
+WNT1 Wnt1
+WNT2B Wnt2b
+WNT2 Wnt2
+WNT3A Wnt3a
+WNT3 Wnt3
+WNT4 Wnt4
+WNT5A Wnt5a
+WNT5B Wnt5b
+WNT6 Wnt6
+WNT7A Wnt7a
+WNT7B Wnt7b
+WNT8A Wnt8a
+WNT8B Wnt8b
+WNT9A Wnt9a
+WNT9B Wnt9b
+WRAP53 Wrap53
+WRAP73 Wdr8
+WRB Wrb
+WRNIP1 Wrnip1
+WRN Wrn
+WSB1 Wsb1
+WSB2 Wsb2
+WSCD1 Wscd1
+WSCD2 Wscd2
+WT1 Wt1
+WTAP Wtap
+WTIP Wtip
+WWC1 Wwc1
+WWC2 Wwc2
+WWOX Wwox
+WWP1 Wwp1
+WWP2 Wwp2
+WWTR1 Wwtr1
+XAB1 Xab1
+XAB2 Xab2
+XAF1 Xaf1
+XBP1 Xbp1
+XCL1 Xcl1
+XCR1 Xcr1
+XDH Xdh
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diff --git a/inst/unitTests/test_kda.analyze.R b/inst/unitTests/test_kda.analyze.R
new file mode 100644
index 0000000..1ea97cc
--- /dev/null
+++ b/inst/unitTests/test_kda.analyze.R
@@ -0,0 +1,38 @@
+test_kda.analyze <- function() {
+ library(RUnit)
+ library(Mergeomics)
+
+ job.kda <- list()
+ job.kda$label<-"HDLC"
+ ## parent folder for results
+ job.kda$folder<-"Results"
+ ## Input a network
+ ## columns: TAIL HEAD WEIGHT
+ job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+ package="Mergeomics")
+ ## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+ ## NODE, delimited by tab
+ job.kda$modfile<- system.file("extdata","mergedModules.txt",
+ package="Mergeomics")
+ ## "0" means we do not consider edge weights while 1 is opposite.
+ job.kda$edgefactor<-0.0
+ ## The searching depth for the KDA
+ job.kda$depth<-1
+ ## 0 means we do not consider the directions of the regulatory
+ ## interactions; while 1 is opposite.
+ job.kda$direction <- 1
+ job.kda$nperm <- 100 # the default value is 2000, use 100 for unit tests
+ ## Let's run KDA!
+ job.kda <- kda.configure(job.kda)
+ job.kda <- kda.start(job.kda)
+ job.kda <- kda.prepare(job.kda)
+ job.kda <- kda.analyze(job.kda)
+
+ ## compare the pvals with the expected ones:
+ ## since we set the seed for random # generation, we know the exact
+ ## results for our input sets:
+
+ ## check Pvalues of the top KD
+ checkEqualsNumeric(as.numeric(sort(job.kda$results$P))[1],
+ 1.610e-68, tolerance=1.0e-4)
+}
diff --git a/inst/unitTests/test_ssea.analyze.R b/inst/unitTests/test_ssea.analyze.R
new file mode 100644
index 0000000..061c1d0
--- /dev/null
+++ b/inst/unitTests/test_ssea.analyze.R
@@ -0,0 +1,29 @@
+test_ssea.analyze <- function() {
+ library(RUnit)
+ library(Mergeomics)
+ job.msea <- list()
+ job.msea$label <- "hdlc"
+ job.msea$folder <- "Results"
+ job.msea$genfile <- system.file("extdata",
+ "genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+ job.msea$marfile <- system.file("extdata",
+ "marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+ job.msea$modfile <- system.file("extdata",
+ "modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+ job.msea$inffile <- system.file("extdata",
+ "coexpr.info.txt", package="Mergeomics")
+ job.msea$nperm <- 100 ## default value is 20000
+
+ ## run ssea.start()
+ job.msea <- ssea.start(job.msea)
+ job.msea <- ssea.prepare(job.msea)
+ job.msea <- ssea.control(job.msea)
+ job.msea <- ssea.analyze(job.msea)
+
+ ## compare the pvals with the expected ones:
+ ## since we set the seed for random # generation, we know the exact
+ ## results for our input sets:
+ checkEqualsNumeric(sort(as.numeric(job.msea$results$P))[1],
+ 3.67e-33, tolerance=1.0e-4)
+
+}
diff --git a/man/MSEA.KDA.onestep.Rd b/man/MSEA.KDA.onestep.Rd
new file mode 100644
index 0000000..158778d
--- /dev/null
+++ b/man/MSEA.KDA.onestep.Rd
@@ -0,0 +1,94 @@
+\name{MSEA.KDA.onestep}
+\alias{MSEA.KDA.onestep}
+\title{
+Run MSEA and/or KDA in one step
+}
+
+\description{
+\code{\link{MSEA.KDA.onestep}} performs Marker Set Enrichment Analysis (MSEA)
+and/or Key Driver Anlaysis (KDA) processes in one step.
+}
+\usage{
+MSEA.KDA.onestep(plan, apply.MSEA=TRUE, apply.KDA=FALSE,
+maxoverlap.genesets=0.33, symbol.transfer.needed=FALSE,
+sym.from=c("HUMAN", "MOUSE"), sym.to=c("HUMAN", "MOUSE"))
+}
+\arguments{
+\item{plan}{a data list including file and parameter settings for MSEA and/or
+KDA processes: \preformatted{
+label: unique identifier for the analysis
+folder: output folder for results
+modfile: path to module file (cols: MODULE GENE)
+genfile: path to gene file (cols: GENE LOCUS) (MSEA-specific)
+marfile: path to marker file (cols: MARKER VALUE) (MSEA-specific)
+inffile: path to module info file (cols: MODULE DESCR)
+seed: seed for random number generator
+permtype: gene for gene-level, locus for marker-level
+nperm: max number of random permutations
+mingenes: min number of genes per module (after merging)
+maxgenes: max number of genes per module
+quantiles: cutoffs for test statistic
+maxoverlap: max overlap allowed between genes
+netfile: path to network file (TAIL HEAD WEIGHT) (KDA-specific)
+}
+}
+\item{apply.MSEA}{determines whether MSEA will be performed to the given
+set. Default value is TRUE.}
+\item{apply.KDA}{determines whether KDA will be performed to the given
+set. Default value is FALSE.}
+\item{maxoverlap.genesets}{maximum overlapping ratio for the genesets. This is
+applicable if KDA is performed following the MSEA process in one-step running.
+Default value is 0.33.}
+\item{symbol.transfer.needed}{determines whether gene symbols in the
+gene sets are needed to be transformed between different species.
+Default value is FALSE.}
+\item{sym.from}{defines the species, whose gene symbols will be converted
+to the gene symbols of \code{sym.to} species. It can be either HUMAN or MOUSE.
+It is applicable if \code{symbol.transfer.needed} is TRUE.}
+\item{sym.to}{defines the species, whose gene symbols will be converted
+from the gene symbols of \code{sym.from} species. It can be either HUMAN or
+MOUSE. It is applicable if \code{symbol.transfer.needed} is TRUE.}
+}
+\details{
+\code{\link{MSEA.KDA.onestep}} performs MSEA and/or KDA operations in one
+built-in function. Users can run both MSEA and KDA sequentially, or they can
+run either MSEA or KDA in one step with the same function.
+If MSEA and KDA will be applied sequentially, significantly enriched gene sets
+(having FDR < 0.25), coming from MSEA results, will be merged if their
+overlapping ratios are larger than a given threshold, i.e.
+\code{maxoverlap.genesets}, to proceed the next step with relatively indepent
+gene sets. Then, KDA is applied to this relatively independent gene sets.
+}
+\value{
+\item{plan }{the updated data frame after performing MSEA and/or KDA.
+If MSEA is performed, results will include standard MSEA results (see
+\code{\link{ssea.analyze}} for details); if KDA is applied, results will
+include standard KDA results (see \code{\link{kda.analyze}} for details).}
+}
+\examples{
+plan <- list()
+plan$label <- "hdlc"
+plan$folder <- "Results"
+plan$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+plan$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+plan$nperm <- 100 ## default value is 20000
+\donttest{plan <- MSEA.KDA.onestep(plan, apply.MSEA=TRUE)}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{ssea.analyze}}, \code{\link{kda.analyze}}
+}
diff --git a/man/Mergeomics-package.Rd b/man/Mergeomics-package.Rd
new file mode 100644
index 0000000..095d661
--- /dev/null
+++ b/man/Mergeomics-package.Rd
@@ -0,0 +1,70 @@
+\name{Mergeomics-package}
+\alias{Mergeomics}
+\alias{Mergeomics-package}
+\docType{package}
+\title{
+Integrative network analysis of omics data
+}
+\description{
+The Mergeomics pipeline serves as a flexible framework for integrating
+multidimensional omics-disease associations, functional genomics,
+canonical pathways and gene-gene interaction networks to generate
+mechanistic hypotheses. It includes two main parts,
+1) Marker set enrichment analysis (MSEA);
+2) Weighted Key Driver Analysis (wKDA).
+
+}
+\details{
+\tabular{ll}{
+Package: \tab Mergeomics\cr
+Type: \tab Package\cr
+Version: \tab 1.1.10\cr
+Date: \tab 2016-01-04\cr
+License: \tab GPL (>= 2)\cr
+Depends: R (>= 3.0.1)\cr
+URL: \tab http://mergeomics.research.idre.ucla.edu/\cr
+}
+
+Mergeomics amalgamates disease association information derived from
+multidimensional omics data (e.g., genome, epigenome, transcriptome,
+metablome) with functional genomics (e.g., eQTLs, ENCODE), canonical
+pathways (e.g., KEGG, Reactome), and molecular networks (e.g., gene
+regulatory networks, protein-protein interaction networks).
+Two main steps of the pipeline are: Marker set enrichment analysis
+(MSEA) and weighted key driver analysis (wKDA). MSEA takes the following
+data as input: i) disease association data (GWAS, EWAS, TWAS...),
+ii) functional genomics (eQTLs and/or ENCODE information), and iii)
+functionally related genes information extracted from knowledge-based
+biological pathways or data-driven network modules (e.g., coexpressed
+genes in a given tissue relevant to a disease of interest). These datasets
+are integrated via MSEA to return gene sets that are significantly enriched
+for markers showing low p value associations with a given disease. Then,
+the disease related gene sets are examined to detect the key drivers by
+using the wKDA step of the pipeline, which requires pre-defined directional
+networks such as tissue-specific Bayesian networks, protein-protein
+interaction networks, etc. wKDA maps the disease related gene sets to the
+pre-defined directional networks to identify key driver genes that are more
+likely regulators of the disease gene sets based on their central positions
+in the gene networks. The key drivers and their local network topology can
+be viewed and downloaded after the completion of the analysis via
+Visualization step. Our pipeline provides users to perform MSEA and wKDA
+together or separately using either their own input data or selecting
+preloaded sample datasets.
+The details of the functions and parameter settings are described in the
+Manual of the package.
+}
+\author{
+Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang
+Maintainer: <zeyneb at ucla.edu>
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+
+\keyword{
+Integrative Genomics; Multidimensional Data Integration; Gene Networks;
+Key Drivers
+}
diff --git a/man/job.kda.Rd b/man/job.kda.Rd
new file mode 100644
index 0000000..271737a
--- /dev/null
+++ b/man/job.kda.Rd
@@ -0,0 +1,19 @@
+\name{job.kda}
+\alias{job.kda}
+\docType{data}
+\title{
+Key Driver Analyzing results
+}
+\description{
+Key Driver Analysis (KDA) applied data object.
+"modules.mousecoexpr.liver.human.txt" including the coexpression modules
+and "network.mouseliver.mouse.txt" including the network (graph)
+information files (under the extdata folder) were used for the KDA.
+}
+\examples{
+data(job.kda)
+}
+\format{
+The format is: list
+}
+\keyword{datasets}
diff --git a/man/kda.analyze.Rd b/man/kda.analyze.Rd
new file mode 100644
index 0000000..5a8ecac
--- /dev/null
+++ b/man/kda.analyze.Rd
@@ -0,0 +1,97 @@
+\name{kda.analyze}
+\alias{kda.analyze}
+\title{
+Weighted key driver analysis (wKDA) main function
+}
+\description{
+Finds the statistics (enrichment score, p-value, FDR, etc.) of the key
+driver (hub) genes belonging to the specified modules based on the graph
+topology. The enrichment score of a hub node based on the shared nodes
+between this hub's neighbor nodes in the graph and the member nodes of the
+hub's module. The hub node enrichment P-values reflect the degree of
+observed enrichment of the hub, when compared to the null distribution of
+randomly expected enrichment of this hub within graph's nodes. Permutation
+test is used to obtain these statistics.
+}
+\usage{
+kda.analyze(job)
+}
+\arguments{
+\item{job}{
+The data list that will be subjected to KDA. It involves the modules,
+member genes belonging to each module, graph (network) topology, hubs
+of the graph, and sub-graph around each hub (hubnets of the graph).
+}
+}
+\details{
+\code{\link{kda.analyze}} analyzes each module individually and determines
+the p-values and FDRs of hub nodes of each module via permutation test.
+It returns the hit hub (key driver) gene name and member list of each module.
+}
+\value{
+\item{job}{
+The KDA applied data list. It involves the modules, hub gene and
+member genes belonging to each module, and False Discovery Rate (FDR)
+adjusted p-values of hub nodes for each module.
+}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+## Let's run KDA!
+\donttest{
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+}
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze.exec}}, \code{\link{kda.analyze.simulate}},
+\code{\link{kda.analyze.test}}
+}
diff --git a/man/kda.analyze.exec.Rd b/man/kda.analyze.exec.Rd
new file mode 100644
index 0000000..1f5441c
--- /dev/null
+++ b/man/kda.analyze.exec.Rd
@@ -0,0 +1,102 @@
+\name{kda.analyze.exec}
+\alias{kda.analyze.exec}
+\title{
+Auxiliary function for weight key driver analysis (wKDA)
+}
+\description{
+Obtains the enrichment scores (and p-values of these scores) of the hub
+nodes by the module member genes for a given module.
+The hub node enrichment P-values reflect the degree of enrichment of hub's
+neighbor nodes within the member genes of the module, to whom this hub
+belongs to, when compared to the null distribution of randomly expected
+enrichment of hub within graph's nodes.
+}
+\usage{
+kda.analyze.exec(memb, graph, nsim)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+\item{memb}{Member nodes of the given module.}
+\item{graph}{Entire graph (network) of the dataset.}
+\item{nsim}{Number of the simulations for the permutation test to obtain
+p-values of the enrichment scores belonging to the hub nodes for a given
+module.}
+}
+\details{
+\code{\link{kda.analyze.exec}} obtains the p-values of the enrichment scores
+belonging to the hub nodes for a given module. Enrichment score of a hub node
+for a given module is obtained by the overlapped (shared) nodes between this
+hub's neighbor nodes and the member nodes of the given module. If a hub node
+does not have at least a particular number of neighbors, its enrichment score
+is assigned as 0.0.
+}
+\value{
+\item{pvals }{
+P-values of the enrichment scores belonging to the hub nodes for the given
+module.}
+}
+\examples{
+## This auxiliary function is called by kda.analyze(),
+## see this main function for more details
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction<-1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+## Let's prepare KDA object for KDA:
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+set.seed(job.kda$seed)
+i = 1 ## index of the module, whose p-val is calculated:
+memb <- job.kda$module2nodes[[i]]
+graph <- job.kda$graph ## we need to import a network
+nsim <- job.kda$nperm ## number of simulations
+## calculate p-vals of KDs for the specified module:
+# p <- kda.analyze.exec(memb, graph, nsim) ## see kda.analyze() for details
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.analyze.simulate}},
+\code{\link{kda.analyze.test}}
+}
diff --git a/man/kda.analyze.simulate.Rd b/man/kda.analyze.simulate.Rd
new file mode 100644
index 0000000..18b1922
--- /dev/null
+++ b/man/kda.analyze.simulate.Rd
@@ -0,0 +1,138 @@
+\name{kda.analyze.simulate}
+\alias{kda.analyze.simulate}
+\title{
+Weighted key driver analysis (wKDA) simulation
+}
+\description{
+Generates simulations for permutation test, which is performed to obtain
+the p-value for the enrichment score of a given hub for a specified module
+during the wKDA process.
+}
+\usage{
+kda.analyze.simulate(o, g, nmemb, nnodes, nsim)
+}
+\arguments{
+\item{o}{
+Observed enrichment score of a hub node assigned for a given module.
+}
+\item{g}{
+Sub-graph of a given hub and its neighbors (hubnet).
+}
+\item{nmemb}{
+Number of the members included in a given module.
+}
+\item{nnodes}{
+Number of the nodes in the whole graph (network) of the dataset.
+}
+\item{nsim}{
+Number of the iterations (simulations) performed for the permutation
+test.
+}
+}
+\details{
+\code{\link{kda.analyze.simulate}} performs permutation tests to obtain
+p-values for the enrichment score of a given hub node for a given module.
+It takes the observed enrichment score of the given hub, hubnet (subgraph
+of the hub and its neighbors), number of the members of the given module,
+total number of the nodes in the entire graph of the dataset, and number of
+the simulations for the permutation test.
+In each iteration (simulation), it samples \code{nmemb} nodes randomly
+among the entire nodes of the graph. Then, it tests the overlapped nodes
+among the randomly chosen nodes and the given node's neigborhood. At the
+end, it obtains an enrichment score for each simulation and evaluates these
+permuted enrichment scores with respect to the observed enrichment score of
+the hub. Among \code{nsim} random simulations; maximally, enrichment scores
+of 10 iterations are allowed to be greater than the observed (actual)
+enrichment score of the hub. If this limitation is exceeded, simulation
+will be finalized at that point and the enrichment score list of the
+iterations will be returned.
+}
+\value{
+\item{x}{A list containing enrichment scores of the simulation's
+iterations}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction<-1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+## Let's prepare KDA object for KDA:
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+set.seed(job.kda$seed)
+i = 1 ## index of the module, whose p-val is calculated:
+memb <- job.kda$module2nodes[[i]]
+graph <- job.kda$graph ## we need to import a network
+nsim <- job.kda$nperm ## number of simulations
+## This auxiliary function is called by kda.analyze.exec(), which is called
+## by kda.analyze() main function, see this main function for more details
+
+hubs <- graph$hubs
+hubnets <- graph$hubnets
+nhubs <- length(hubs)
+nnodes <- length(graph$nodes)
+nmemb <- length(memb)
+
+## Observed enrichment scores.
+# obs <- rep(NA, nhubs)
+# k <- 1 ## actual using: for(k in 1:nhubs){}, for unit test, use the 1st hub
+# g <- hubnets[[hubs[k]]]
+# obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)
+
+## Estimate P-values.
+# pvals <- rep(NA, nhubs)
+# for(k in which(obs > 0)) {
+# g <- hubnets[[hubs[k]]]
+## First pass:
+# x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
+## Then, use x to estimate preliminary and final P-values.
+## See kda.analyze() for more detail
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+# } ## finishing for loop
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.analyze.exec}},
+\code{\link{kda.analyze.test}}
+}
diff --git a/man/kda.analyze.test.Rd b/man/kda.analyze.test.Rd
new file mode 100644
index 0000000..49c0414
--- /dev/null
+++ b/man/kda.analyze.test.Rd
@@ -0,0 +1,127 @@
+\name{kda.analyze.test}
+\alias{kda.analyze.test}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Calculate enrichment score for wKDA
+}
+\description{
+Obtains the enrichment score of a given hub (center node) belonging to a
+specified module. Enrichment score of a center node depends on the shared
+node number between the neighbor nodes of this center node (derived from
+the provided graph topology) and member nodes of this center node's module.
+The more a center node has neighbors in the graph among the member genes
+belonging to the module of this center node, the greater enrichment score
+it has.
+}
+\usage{
+kda.analyze.test(neigh, w, members, nnodes)
+}
+
+\arguments{
+\item{neigh}{
+Neighbor nodes of the given hub node (i.e. nodes in the hubnet)
+}
+\item{w}{
+Weigths of the given hub node based on its in-degree and out-degree
+edge density in the hubnet
+}
+\item{members}{
+Node indices -within the entire graph- of the member genes of given
+hub's module.
+}
+\item{nnodes}{
+Number of the nodes in the entire graph of the dataset.
+}
+}
+\details{
+\code{\link{kda.analyze.test}} takes a hub node's neigbor list and weight
+list; additionally, it takes the member node list of relevant module. It
+searches the masses of the shared nodes between hubnet and the given module
+(gene set).
+The shared edge mass is normalized with respect to number of the expected
+match ratio between hubnet and the given node list. This normalized ratio
+is assigned as the observed enrichment score of the hubnet according to
+the given member node list.
+}
+\value{
+\item{z}{Calculated enrichment score}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<- "Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction<-1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+## Let's prepare KDA object for KDA:
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+set.seed(job.kda$seed)
+i = 1 ## index of the module, whose p-val is calculated:
+memb <- job.kda$module2nodes[[i]]
+graph <- job.kda$graph ## we need to import a network
+nsim <- job.kda$nperm ## number of simulations
+## This auxiliary function is called by kda.analyze.exec(), which is called
+## by kda.analyze() main function, see this main function for more details
+
+hubs <- graph$hubs
+hubnets <- graph$hubnets
+nhubs <- length(hubs)
+nnodes <- length(graph$nodes)
+nmemb <- length(memb)
+
+## Observed enrichment scores for the hubs of the given module.
+obs <- rep(NA, nhubs)
+k <- 1 ## actual using: for(k in 1:nhubs){}, for test, use only the 1st hub
+g <- hubnets[[hubs[k]]]
+obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)
+
+## Then, estimate preliminary and final P-values by kda.analyze.simulate()
+## See kda.analyze() for more details
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.analyze.exec}},
+\code{\link{kda.analyze.simulate}}
+}
+
diff --git a/man/kda.configure.Rd b/man/kda.configure.Rd
new file mode 100644
index 0000000..6c505cc
--- /dev/null
+++ b/man/kda.configure.Rd
@@ -0,0 +1,98 @@
+\name{kda.configure}
+\alias{kda.configure}
+\title{
+Set parameters for weighted key driver analysis (wKDA)
+}
+\description{
+takes the configuration (plan) parameter for wKDA process as input and
+assigns default values if needed.
+The fields of this parameter are listed in the arguements section in
+detail.
+}
+\usage{
+kda.configure(plan)
+}
+\arguments{
+\item{plan}{
+a parameter including fields about the details of the wKDA process:
+\preformatted{
+label: unique identifier for the analysis
+folder: parent folder for results
+netfile: path to network file (TAIL HEAD WEIGHT)
+modfile: path to module file (MODULE GENE)
+inffile: path to module info file
+nodfile: path to node selection file
+depthsearch: depth for subgraph search
+direction: 0 for undirected, negative for downstream and
+positive for upstream
+maxoverlap: maximum allowed overlap between two key driver
+neighborhoods
+minsize: minimum module size
+mindegreeminimum: node degree to qualify as a hub
+maxdegreemaximum: node degree to include
+edgefactor: influence of node strengths: 0.0 no influence,
+1.0 full influence
+seed: seed for random number generator
+}
+}
+}
+\details{
+\code{\link{kda.configure}} prepares the environment for wKDA process,
+checks the fields of the input plan parameter (that includes paths of
+required input files and output folder, min module size, etc.), and
+assigns the default values to these fields if they are not specified.
+}
+\value{
+\item{plan}{configured and -if needed updated- plan parameter to be used
+in wKDA process. }
+}
+\examples{
+## for KDA the essential parameters should be assigned by user is as follows:
+plan <- list()
+## assign job label:
+plan$label<-"HDLC"
+## specify parent folder for results:
+plan$folder<-"Results"
+## Get an input network (columns: TAIL HEAD WEIGHT)
+plan$netfile <-"network.mouseliver.mouse.txt"
+## Get the gene sets derived from ModuleMerge, containing two columns,
+## MODULE and NODE, delimited by tab
+plan$modfile<- "moddata.txt"
+## If above parameters are not assigned by users, code will stop with error:
+if(is.null(plan$folder)) stop("No parent folder.")
+if(is.null(plan$label)) stop("No job label.")
+if(is.null(plan$netfile)) stop("No network file.")
+if(is.null(plan$modfile)) stop("No module file.")
+
+## other parameters are optional, if they are not specified by user,
+## kda.configure assigns their default values:
+## graph search depth parameter:
+if(is.null(plan$depth)) plan$depth <- 1
+## edge directionality in the network: O means undirected
+if(is.null(plan$direction)) plan$direction <- 0
+## max overlap allowed between two modules
+if(is.null(plan$maxoverlap)) plan$maxoverlap <- 0.33
+## min size of the modules
+if(is.null(plan$minsize)) plan$minsize <- 20
+## min and max hub degree to be included:
+if(is.null(plan$mindegree)) plan$mindegree <- "automatic"
+if(is.null(plan$maxdegree)) plan$maxdegree <- "automatic"
+## number of simulations for permutation test:
+if(is.null(plan$nperm)) plan$nperm <- 2000
+## seed for random number generator:
+if(is.null(plan$seed)) plan$seed <- 1
+## these are the main parameters needed to be assigned default values.
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}
+}
+
diff --git a/man/kda.finish.Rd b/man/kda.finish.Rd
new file mode 100644
index 0000000..683d0d0
--- /dev/null
+++ b/man/kda.finish.Rd
@@ -0,0 +1,78 @@
+\name{kda.finish}
+\alias{kda.finish}
+\title{
+Organize and save results
+}
+\description{
+After wKDA process is accomplished, \code{\link{kda.finish.estimate}} sums
+up the results and log them to the relevant files and folders. Besides,
+return them within the given job parameter.
+}
+\usage{
+kda.finish(job)
+}
+\arguments{
+\item{job}{
+the data list including label and folder fields to specify a unique
+identifier for the wKDA process and the output folder for the obtained
+results, respectively.
+}
+}
+\details{
+\code{\link{kda.finish.estimate}} estimates additional measures if needed,
+saves results into relevant files, trims numbers to provide a simpler file
+for viewing, and stores a summary file of top hits after the wKDA prcess is
+accomplished. It also obtains the overlaps of the modules with hub
+neighborhoods, finds co-hubs information, determines the top key driver
+for each module and saves the updated and sorted p-values belonging to
+them.
+}
+\value{
+\item{job}{
+updated information including the overlapping hub neighborhoods,
+co-hubs information, top driver of each module, and their updated and
+sorted p-values.
+}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## finish the KDA process
+job.kda <- kda.finish(job.kda)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.finish.estimate}}, \code{\link{kda.finish.save}},
+\code{\link{kda.finish.summarize}}, \code{\link{kda.finish.trim}}
+}
diff --git a/man/kda.finish.estimate.Rd b/man/kda.finish.estimate.Rd
new file mode 100644
index 0000000..85de67a
--- /dev/null
+++ b/man/kda.finish.estimate.Rd
@@ -0,0 +1,54 @@
+\name{kda.finish.estimate}
+\alias{kda.finish.estimate}
+\title{
+Estimate measures for accomplished wKDA results
+}
+\description{
+Estimates additional measures based on overlapping of module member nodes
+with hub neighbor nodes in the graph.
+}
+\usage{
+kda.finish.estimate(job)
+}
+\arguments{
+\item{job}{
+The data list that was subjected to wKDA. It involves the modules,
+member genes belonging to each module, graph information of the
+dataset, hubs and hubnets of the graph.
+}
+}
+\details{
+\code{\link{kda.finish.save}} determines the overlaps of modules with hub
+neighborhoods, obtains graph measures based on the ratio of the observed
+overlap amounts to the expected overlap amount, and returns the values of
+this measure.
+}
+\value{
+\item{res }{Returns the overlapping ratio of the modules with hubnets. }
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## finish the KDA process by estimating additional measures for the modules
+## such as module sizes, overlaps with hub neighborhoods, etc.
+# job.kda <- kda.finish(job.kda)
+# if (nrow(job.kda$results)==0){
+# cat("No Key Driver Found!!!!")
+# } else{
+## Estimate additional measures - see kda.analyze and kda.finish for details
+# res <- kda.finish.estimate(job.kda)
+# }
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.finish}}, \code{\link{kda.finish.save}},
+\code{\link{kda.finish.summarize}}, \code{\link{kda.finish.trim}}
+}
diff --git a/man/kda.finish.save.Rd b/man/kda.finish.save.Rd
new file mode 100644
index 0000000..b47c56a
--- /dev/null
+++ b/man/kda.finish.save.Rd
@@ -0,0 +1,61 @@
+\name{kda.finish.save}
+\alias{kda.finish.save}
+\title{
+Save full wKDA results
+}
+\description{
+\code{\link{kda.finish.save}} sorts (according to KD p-values) and saves
+the wKDA results into specified files and folders.
+}
+\usage{
+kda.finish.save(res, job)
+}
+\arguments{
+\item{res}{
+the results obtained from \code{\link{kda.finish.estimate}}. They
+will be stored into specified folder.
+}
+\item{job}{
+information including the entire graph, nodes, modules, co-hubs,
+top key driver of each module, and their updated and sorted p-values.
+All the information included \code{job} will be stored into relevant
+files.
+}
+}
+\value{
+\item{res }{
+the results obtained from \code{\link{kda.finish.estimate}} are merged
+with the module and graph nodes information gained from \code{job}
+data frame. At the end, merged information is both written to file and
+returned to the user.
+}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## finish the KDA process by estimating additional measures for the modules
+## such as module sizes, overlaps with hub neighborhoods, etc.
+# job.kda <- kda.finish(job.kda)
+# if (nrow(job.kda$results)==0){
+# cat("No Key Driver Found!!!!")
+# } else{
+## Estimate additional measures - see kda.analyze and kda.finish for details
+# res <- kda.finish.estimate(job.kda)
+## Save full results about modules such as co-hub, nodes, P-values info etc.
+# res <- kda.finish.save(res, job.kda)
+# }
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.finish}}, \code{\link{kda.finish.estimate}},
+\code{\link{kda.finish.summarize}}, \code{\link{kda.finish.trim}}
+}
+
diff --git a/man/kda.finish.summarize.Rd b/man/kda.finish.summarize.Rd
new file mode 100644
index 0000000..5849c6c
--- /dev/null
+++ b/man/kda.finish.summarize.Rd
@@ -0,0 +1,65 @@
+\name{kda.finish.summarize}
+\alias{kda.finish.summarize}
+\title{
+Summarize the wKDA results
+}
+\description{
+Create a summary file of top key drivers. The file includes the key driver
+of each block of the dataset and their p-values.}
+\usage{
+kda.finish.summarize(res, job)
+}
+\arguments{
+\item{res}{
+the data frame including the p-values, false discovery rates, and fold
+scores of the nodes obtained from \code{\link{kda.finish.trim}}
+}
+\item{job}{
+the data frame including the path of output file which will briefly
+contain top key drivers of the blocks and ranked p-values of those top
+key drivers
+}
+}
+\details{
+\code{\link{kda.finish.summarize}} determines the ranking scores of blocks,
+finds the top node for each block, selects and saves top key drivers, and
+stores P-values into file. top drovers of the blocks are also returned to
+the user.
+}
+\value{
+\item{res}{data frame including top node for each block}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## finish the KDA process by estimating additional measures for the modules
+## such as module sizes, overlaps with hub neighborhoods, etc.
+# job.kda <- kda.finish(job.kda)
+# if (nrow(job.kda$results)==0){
+# cat("No Key Driver Found!!!!")
+# } else{
+## Estimate additional measures - see kda.analyze and kda.finish for details
+# res <- kda.finish.estimate(job.kda)
+## Save full results about modules such as co-hub, nodes, P-values info etc.
+# res <- kda.finish.save(res, job.kda)
+## Create a simpler file for viewing by trimming floating numbers
+# res <- kda.finish.trim(res, job.kda)
+## Create a summary file of top hit KDs.
+# res <- kda.finish.summarize(res, job.kda)
+# }
+## See kda.analyze() and kda.finish() for details
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.finish}}, \code{\link{kda.finish.estimate}},
+\code{\link{kda.finish.save}}, \code{\link{kda.finish.trim}}
+}
+
diff --git a/man/kda.finish.trim.Rd b/man/kda.finish.trim.Rd
new file mode 100644
index 0000000..7b103c5
--- /dev/null
+++ b/man/kda.finish.trim.Rd
@@ -0,0 +1,58 @@
+\name{kda.finish.trim}
+\alias{kda.finish.trim}
+\title{
+Trim numbers before save
+}
+\description{
+\code{\link{kda.finish.trim}} trims p-values, false discovery rates, and
+fold scores to make them nicer to look at before saving the file. It also
+returns trimmed results to the user.
+}
+\usage{
+kda.finish.trim(res, job)
+}
+\arguments{
+\item{res}{
+includes p-values, false discovery rates, and fold scores of the nodes
+}
+\item{job}{
+data frame including output folder path to store trimmed results
+}
+}
+\value{
+\item{res }{
+Trimmed and formatted p-values, false discovery rates, and fold
+scores of the nodes
+}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## finish the KDA process by estimating additional measures for the modules
+## such as module sizes, overlaps with hub neighborhoods, etc.
+# job.kda <- kda.finish(job.kda)
+# if (nrow(job.kda$results)==0){
+# cat("No Key Driver Found!!!!")
+# } else{
+## Estimate additional measures - see kda.analyze and kda.finish for details
+# res <- kda.finish.estimate(job.kda)
+## Save full results about modules such as co-hub, nodes, P-values info etc.
+# res <- kda.finish.save(res, job.kda)
+## Create a simpler file for viewing by trimming floating numbers
+# res <- kda.finish.trim(res, job.kda)
+# }
+## See kda.analyze() and kda.finish() for details
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.finish}}, \code{\link{kda.finish.estimate}},
+\code{\link{kda.finish.save}}, \code{\link{kda.finish.summarize}}
+}
diff --git a/man/kda.prepare.Rd b/man/kda.prepare.Rd
new file mode 100644
index 0000000..45922f0
--- /dev/null
+++ b/man/kda.prepare.Rd
@@ -0,0 +1,105 @@
+\name{kda.prepare}
+\alias{kda.prepare}
+\title{
+Prepare graph topology for weighted key driver analysis
+}
+\description{
+\code{\link{kda.prepare}} gets graph topology required by wKDA process,
+then provides the information including hub list, hubnets, and overlapping
+co-hubs.
+}
+\usage{
+kda.prepare(job)
+}
+\arguments{
+\item{job}{
+a parameter including restirictions while determining the graph topology
+information (such as hubs, hubnets, co-hubs, etc.), which is required by
+the wKDA process: \preformatted{
+graph: graph of the dataset
+depth: search depth for subgraph search
+direction: use 0 for undirected, negative for downstream
+and positive for upstream
+maxoverlap: maximum allowed overlap between two key driver
+neighborhoods
+mindegree: minimum hub degree to include
+edgefactor: influence of node strengths; 0.0 no influence,
+1.0 full influence
+}
+}
+}
+\details{
+\code{\link{kda.prepare}} determines minimum hub degree if it is not
+specified by the user, finds hubs and their neighborhoods (hubnets),
+extracts overlapping co-hubs, returns this information to user, and prints
+it to the screen.
+}
+\value{
+\item{job }{Updated data frame including information about the graph
+topology in terms of hubs, hubnets, and overlapping co-hubs:
+\preformatted{
+hubs: hub nodes list
+hubnets: neighborhoods of hubs (hubnets)
+cohubsets: overlapping hubs (co-hubs)
+}
+}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<- "Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+## Configure the parameters for KDA:
+job.kda <- kda.configure(job.kda)
+## Create the object properly
+job.kda <- kda.start(job.kda)
+## Find the hubs, co-hubs, and hub neighborhoods (hubnets), etc.:
+job.kda <- kda.prepare(job.kda)
+## After that, we need to call kda.analyze() and kda.finish()
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.prepare.overlap}},
+\code{\link{kda.prepare.screen}}
+}
diff --git a/man/kda.prepare.overlap.Rd b/man/kda.prepare.overlap.Rd
new file mode 100644
index 0000000..9b91bde
--- /dev/null
+++ b/man/kda.prepare.overlap.Rd
@@ -0,0 +1,86 @@
+\name{kda.prepare.overlap}
+\alias{kda.prepare.overlap}
+\title{
+Extract overlapping co-hubs
+}
+\description{
+\code{\link{kda.prepare.overlap}} finds overlapping co-hubs of the given
+graph.
+}
+\usage{
+kda.prepare.overlap(graph, direction, rmax)
+}
+\arguments{
+\item{graph}{
+entire graph, whose overlapping co-hubs will be found
+}
+\item{direction}{
+the direction of the interactions among graph components. 0 for
+undirected, negative for downstream, and positive for upstream
+}
+\item{rmax}{
+maximum allowed overlap between two key driver neighborhoods
+}
+}
+\value{
+\item{graph}{
+Updated graph including overlapping co-hubs: \preformatted{
+cohubsetsco-hubs of the given graph}
+}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<- "Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-"network.mouseliver.mouse.txt"
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- "mergedModules.txt"
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Configure the parameters for KDA:
+# job.kda <- kda.configure(job.kda)
+## Create the object properly
+# job.kda <- kda.start(job.kda)
+
+## Find the hubs, co-hubs, and hub neighborhoods (hubnets) by kda.prepare()
+## and its auxiliary functions kda.prepare.screen and kda.prepare.overlap
+## First, determine the minimum and maximum hub degrees:
+# nnodes <- length(job.kda$graph$nodes)
+# if (job.kda$mindegree == "automatic") {
+# dmin <- as.numeric(quantile(job.kda$graph$stats$DEGREE,0.75))
+# job.kda$mindegree <- dmin
+# }
+# if (job.kda$maxdegree == "automatic") {
+# dmax <- as.numeric(quantile(job.kda$graph$stats$DEGREE,1))
+# job.kda$maxdegree <- dmax
+# }
+## Collect neighbors.
+# job.kda$graph <- kda.prepare.screen(job.kda$graph, job.kda$depth,
+# job.kda$direction, job.kda$edgefactor, job.kda$mindegree, job.kda$maxdegree)
+
+## Then, extract overlapping co-hubs by kda.prepare.overlap():
+## Collect overlapping co-hubs.
+# job.kda$graph <- kda.prepare.overlap(job.kda$graph, job.kda$direction,
+# job.kda$maxoverlap)
+
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.prepare}}
+}
diff --git a/man/kda.prepare.screen.Rd b/man/kda.prepare.screen.Rd
new file mode 100644
index 0000000..0af1d76
--- /dev/null
+++ b/man/kda.prepare.screen.Rd
@@ -0,0 +1,94 @@
+\name{kda.prepare.screen}
+\alias{kda.prepare.screen}
+\title{
+Prepare hubs and hubnets
+}
+\description{
+\code{\link{kda.prepare.screen}} finds hubs and their neighborhoods
+(hubnets) from the given graph.
+}
+\usage{
+kda.prepare.screen(graph, depth, direction, efactor, dmin, dmax)
+}
+\arguments{
+\item{graph}{
+entire graph, whose hubs and hubnets will be obtained
+}
+\item{depth}{
+search depth for subgraph search
+}
+\item{direction}{
+the direction of the interactions among graph components. 0 for
+undirected, negative for downstream, and positive for upstream
+}
+\item{efactor}{
+influence of node strengths (weights): 0.0 no influence, 1.0 full
+influence
+}
+\item{dmin}{
+minimum hub degree to include
+}
+\item{dmax}{
+maximum hub degree to include
+}
+}
+\value{
+\item{graph}{Updated graph including obtained hubs and hubnets:
+\preformatted{
+hubs: hub nodes list
+hubnets: neighborhoods of hubs (hubnets)
+}
+}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<- "Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-"network.mouseliver.mouse.txt"
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- "mergedModules.txt"
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Configure the parameters for KDA:
+# job.kda <- kda.configure(job.kda)
+## Create the object properly
+# job.kda <- kda.start(job.kda)
+
+## Find the hubs, co-hubs, and hub neighborhoods (hubnets) by kda.prepare()
+## and its auxiliary functions kda.prepare.screen and kda.prepare.overlap
+## First, determine the minimum and maximum hub degrees:
+# nnodes <- length(job.kda$graph$nodes)
+# if (job.kda$mindegree == "automatic") {
+# dmin <- as.numeric(quantile(job.kda$graph$stats$DEGREE,0.75))
+# job.kda$mindegree <- dmin
+# }
+# if (job.kda$maxdegree == "automatic") {
+# dmax <- as.numeric(quantile(job.kda$graph$stats$DEGREE,1))
+# job.kda$maxdegree <- dmax
+# }
+## Collect neighbors.
+# job.kda$graph <- kda.prepare.screen(job.kda$graph, job.kda$depth,
+# job.kda$direction, job.kda$edgefactor, job.kda$mindegree, job.kda$maxdegree)
+
+## Then, extract overlapping co-hubs by kda.prepare.overlap()
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.prepare}}
+}
diff --git a/man/kda.start.Rd b/man/kda.start.Rd
new file mode 100644
index 0000000..de7d76c
--- /dev/null
+++ b/man/kda.start.Rd
@@ -0,0 +1,96 @@
+\name{kda.start}
+\alias{kda.start}
+\title{
+Import data for weighted key driver analysis
+}
+\description{
+\code{\link{kda.start}} converts identities (such as module descriptions,
+module identifiers, and module nodes) to indices. It prepares graph
+topology and module information for wKDA process.
+}
+\usage{
+kda.start(job)
+}
+\arguments{
+\item{job}{
+a data frame including fields for edges and nodes information of the
+graph (TAIL, HEAD, WEIGHT). It also involves path of input files
+including module descriptions and module-gene lists.
+}
+}
+\details{
+\code{\link{kda.start}} imports graph and relevant module descriptor input
+files, creates an indexed graph structure, and converts identities to
+indices from module descriptions and module-gene lists. Hence, it concludes
+with a graph structure and a module set involving member gene IDs for
+each module.
+}
+\value{
+\item{job }{Updated data frame including indexed graph topology, modules,
+and nodes information: \preformatted{
+graph: indexed topology
+modules: module identities
+modinfo: module descriptions (indexed)
+moddata: module data (indexed)
+module2nodes: lists of node indices for each module
+modulesizes: module sizes
+}
+}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+job.kda <- kda.configure(job.kda)
+## Import data for weighted key driver analysis:
+job.kda <- kda.start(job.kda)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}},
+\code{\link{kda.prepare}}, \code{\link{kda.start.edges}},
+\code{\link{kda.start.identify}}, \code{\link{kda.start.modules}}
+}
+
diff --git a/man/kda.start.edges.Rd b/man/kda.start.edges.Rd
new file mode 100644
index 0000000..8d0309a
--- /dev/null
+++ b/man/kda.start.edges.Rd
@@ -0,0 +1,68 @@
+\name{kda.start.edges}
+\alias{kda.start.edges}
+\title{
+Import nodes and edges of graph topology
+}
+\description{
+\code{\link{kda.start.edges}} imports network file, gets edge data
+(in TAIL, HEAD, WEIGHT format), eliminates the nodes -whose degree is
+smaller than the maximum allowed node degree-, and returns the edges of
+remaining nodes.
+}
+\usage{
+kda.start.edges(job)
+}
+\arguments{
+\item{job}{a data frame including information such as network file name,
+maximum allowed node degree, edge direction (\code{job$netfile},
+\code{job$maxdegree},
+\code{job$direction}, and so on.) }
+}
+\value{
+\item{edgdata }{filtered edge list, i.e. edges of the nodes, whose degree
+is smaller than the maximum allowed node degree}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+job.kda <- kda.configure(job.kda)
+
+## Import topology of the graph for KDA
+## This is already had been done in the kda.start() main function, due to
+## the time constraint while running examples, we did not run it again.
+# edgdata <- kda.start.edges(job.kda)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}},
+\code{\link{kda.prepare}}, \code{\link{kda.start}}
+}
diff --git a/man/kda.start.identify.Rd b/man/kda.start.identify.Rd
new file mode 100644
index 0000000..3ef2bc3
--- /dev/null
+++ b/man/kda.start.identify.Rd
@@ -0,0 +1,54 @@
+\name{kda.start.identify}
+\alias{kda.start.identify}
+\title{
+Convert identities to indices for wKDA
+}
+\description{
+\code{\link{kda.start.identify}} searches the members of \code{dat} among
+the members of \code{labels} with respect to the \code{varname} attribute,
+returns the matching rows of the \code{dat}.
+}
+\usage{
+kda.start.identify(dat, varname, labels)
+}
+\arguments{
+\item{dat}{
+data list of the identities that will be searched
+}
+\item{varname}{
+search will be performed with respect to which attribute (MODULE or NODE)
+}
+\item{labels}{
+the place, where data list (i.e. \code{dat}) will be searched
+}
+}
+\value{
+\item{res}{matched rows of \code{dat} among the members of \code{labels}
+list according to the \code{varname} attribute}
+}
+\examples{
+## Converts identities (either module names or gene names) to the indices
+aa<- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+NODE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+aa
+bb <- kda.start.identify(aa, "MODULE", c("Mod1"))
+bb
+cc <- kda.start.identify(aa, "MODULE", c("Mod1", "Mod3"))
+cc
+dd <- kda.start.identify(aa, "NODE", c("GeneA"))
+dd
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}},
+\code{\link{kda.prepare}}, \code{\link{kda.start}}
+}
+
diff --git a/man/kda.start.modules.Rd b/man/kda.start.modules.Rd
new file mode 100644
index 0000000..ffa5ff6
--- /dev/null
+++ b/man/kda.start.modules.Rd
@@ -0,0 +1,70 @@
+\name{kda.start.modules}
+\alias{kda.start.modules}
+\title{
+Import module descriptions
+}
+\description{
+\code{\link{kda.start.modules}} searches the whole nodes of the modules
+within the nodes of \code{edgdata} edgelist, filters out the nodes that
+does not exist in the nodes of \code{edgdata}, and deletes the modules,
+which does not have enough nodes.
+}
+\usage{
+kda.start.modules(job, edgdata)
+}
+
+\arguments{
+\item{job}{a data frame including information such as module data file
+name, edge direction, minimum acceptable module size (\code{job$modfile},
+\code{job$direction}, \code{job$minsize}, and so on.) }
+\item{edgdata}{edge list data obtained from \code{\link{kda.start.edges}} }
+}
+\value{
+\item{moddata }{module descriptions and their member node lists}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+job.kda <- kda.configure(job.kda)
+
+## Import topology of the graph for KDA, then find the module statistics
+## This is already had been done in the kda.start() main function, due to
+## the time constraint while running examples, we did not run it again.
+# edgdata <- kda.start.edges(job.kda)
+## Find module memberships of the graph nodes and obtain module statistics:
+# moddata <- kda.start.modules(job.kda, edgdata)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}},
+\code{\link{kda.prepare}}, \code{\link{kda.start}}
+}
diff --git a/man/kda2cytoscape.Rd b/man/kda2cytoscape.Rd
new file mode 100644
index 0000000..7fdaf0f
--- /dev/null
+++ b/man/kda2cytoscape.Rd
@@ -0,0 +1,101 @@
+\name{kda2cytoscape}
+\alias{kda2cytoscape}
+\title{
+Generate input files for Cytoscape
+}
+\description{
+\code{kda2cytoscape} generates input files for Cytoscape to visualize the
+graph and hubnets after the wKDA process finished. The network
+visualization is a streamlined depiction of the module enrichment in hub
+neighborhoods.
+}
+\usage{
+kda2cytoscape(job, node.list = NULL, modules = NULL, ndrivers = 5,
+depth = 1)
+}
+\arguments{
+\item{job}{
+wKDA result data list as returned by \code{\link{kda.finish}}
+}
+\item{node.list}{
+array of node/gene names to be visualized with their neighbor node.
+if this is not specified top \code{ndrivers} of each module and their
+neighborhoods will be illustrated.
+}
+\item{modules}{
+array of module names to be visualized
+}
+\item{ndrivers}{
+maximum number of drivers per module
+}
+\item{depth}{
+depth for neighborhood search in the graph
+}
+}
+\details{
+\code{\link{kda2cytoscape}} first, selects top scoring key drivers for
+each module; then, assigns a colormap to modules, processes each module
+separately, finds key nodes' neighborhoods within a particular search
+depth, and saves the edge and node lists of the modules to the specified
+output folder. Besides, it returns this configuration data to the user.
+Created file list for Cytoscape are given below:
+\preformatted{
+kda2cytoscape.top.kds.txt: top key drivers of the modules are
+listed in this file. Number of the key drivers can be set by
+user with ndrivers parameter.
+kda2cytoscape.edges.txt: edge lists of the integrated graph
+that includes the subnetworks of all modules.
+kda2cytoscape.nodes.txt: node lists of the integrated graph
+that includes the subnetworks of all modules.
+module.color.mapping.txt: color mapping for the modules,
+i.e. one color is assigned to each module.
+}
+}
+\value{
+\item{job }{updated data list including the node and edge information of
+the modules converted to Cytoscape format}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## finish the KDA process
+job.kda <- kda.finish(job.kda)
+
+## prepare the cytoscape-ready files:
+job.kda <- kda2cytoscape(job.kda)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}}
+}
diff --git a/man/kda2cytoscape.colorize.Rd b/man/kda2cytoscape.colorize.Rd
new file mode 100644
index 0000000..515720a
--- /dev/null
+++ b/man/kda2cytoscape.colorize.Rd
@@ -0,0 +1,58 @@
+\name{kda2cytoscape.colorize}
+\alias{kda2cytoscape.colorize}
+\title{
+Trace module memberships of genes
+}
+\description{
+\code{kda2cytoscape.colorize} assigns color to each node of the given
+module. If a node belongs to more than one module, different colors will
+be assigned to that node, as each color representing one module (shared
+nodes are illustrated as pie charts in the graph).
+}
+\usage{
+kda2cytoscape.colorize(noddata, moddata, modpool, palette)
+}
+\arguments{
+\item{noddata}{
+node information of the entire graph
+}
+\item{moddata}{
+module data including node (member gene) list
+}
+\item{modpool}{
+unique module list including significant key drivers
+}
+\item{palette}{
+assigned unique color map for all modules
+}
+}
+\value{
+\item{res }{data frame including the assigned color labels for the nodes of
+the given module. If a node is concurrently member of many modules, multiple
+colors will be assigned to that node (one color for each of these modules)
+}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\examples{
+## Trace module memberships for each KD and its neighbors
+## If a KD (and its neigbor nodes) is member of multiple modules, assign
+## multiple colors to these multi-member nodes.
+## We need to know data of all possible modules and all possible module ids
+## to assign multiple colors to a shared node (between modules) when needed
+if(exists("valdata"))
+cat("Marker pvalues will be used to determine node sizes
+in the network illustration")
+# noddata <- kda2cytoscape.colorize(neighs, job.kda$moddata, modpool, palette)
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2cytoscape.colormap.Rd b/man/kda2cytoscape.colormap.Rd
new file mode 100644
index 0000000..b3f9908
--- /dev/null
+++ b/man/kda2cytoscape.colormap.Rd
@@ -0,0 +1,37 @@
+\name{kda2cytoscape.colormap}
+\alias{kda2cytoscape.colormap}
+\title{
+Assign one color to each unique module
+}
+\description{
+\code{kda2cytoscape.colormap} takes number of the modules and assigns a
+particular color to each module. Returns the color list (palette).
+}
+\usage{
+kda2cytoscape.colormap(ncolors)
+}
+\arguments{
+\item{ncolors}{
+number of the unique modules
+}
+}
+\value{
+\item{palette }{color list: one color is assigned to each module}
+}
+\examples{
+color.number = 5 ## let us assume we have 5 modules, assign 1 color to each:
+palette <- kda2cytoscape.colormap(color.number)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2cytoscape.drivers.Rd b/man/kda2cytoscape.drivers.Rd
new file mode 100644
index 0000000..21487e8
--- /dev/null
+++ b/man/kda2cytoscape.drivers.Rd
@@ -0,0 +1,75 @@
+\name{kda2cytoscape.drivers}
+\alias{kda2cytoscape.drivers}
+\title{
+Select top key drivers for each module
+}
+\description{
+\code{kda2cytoscape.drivers} finds maximally top \code{ndriv} key drivers
+for each module with respect to the significance level of the drivers.
+}
+\usage{
+kda2cytoscape.drivers(data, modules, ndriv)
+}
+\arguments{
+\item{data}{
+data frame including information of the modules (key driver list,
+p-values, node list, false discovery rates (fdr), and so on.)
+}
+\item{modules}{
+top scoring modules among KDA results
+}
+\item{ndriv}{
+maximum number of drivers that can be chosen for per module
+}
+}
+\value{
+\item{data }{top key drivers (maximally \code{ndriv} drivers for each
+module) for top modules}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+## Select top key drivers from each module.
+## First, take module names from kda results
+modules <- unique(job.kda$results$MODULE)
+## Take top 2 KDs:
+drivers <- kda2cytoscape.drivers(job.kda$results, modules, ndriv=2)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2cytoscape.edges.Rd b/man/kda2cytoscape.edges.Rd
new file mode 100644
index 0000000..87bd269
--- /dev/null
+++ b/man/kda2cytoscape.edges.Rd
@@ -0,0 +1,79 @@
+\name{kda2cytoscape.edges}
+\alias{kda2cytoscape.edges}
+\title{
+Find edges of a given node with a specified depth
+}
+\description{
+\code{kda2cytoscape.edges} finds the sub-graph (node and edge lists) of a
+central node and its neighborhood at a particular search depth. The central
+node is a member of a module, which is defined at
+\code{\link{kda2cytoscape.exec}}.
+}
+\usage{
+kda2cytoscape.edges(graph, center, depth, direction)
+}
+\arguments{
+\item{graph}{
+entire graph
+}
+\item{center}{
+the node, whose interactions with neighbors will be searched within
+\code{graph}.
+}
+\item{depth}{
+search depth for graph neighborhood
+}
+\item{direction}{
+edge direction. 0 for undirected, negative for downstream and positive
+for upstream
+}
+}
+\value{
+\item{g }{the sub-graph including TAIL, HEAD, WEIGHT information of the
+central node, which belongs to the specified module.}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+## Select a center node to seek its neighbors in the graph:
+edges.of.center.node <- kda2cytoscape.edges(job.kda$graph, 1,
+job.kda$depth, job.kda$direction)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2cytoscape.exec.Rd b/man/kda2cytoscape.exec.Rd
new file mode 100644
index 0000000..c566d2f
--- /dev/null
+++ b/man/kda2cytoscape.exec.Rd
@@ -0,0 +1,103 @@
+\name{kda2cytoscape.exec}
+\alias{kda2cytoscape.exec}
+\title{
+Evaluate each module separately for visualization
+}
+\description{
+\code{kda2cytoscape.exec} deals with the modules individually; takes a
+particular amount of top key drivers of the given module in company with
+the top key driver lists and colormap of all modules; traces module
+memberships and produces colormap, it finds the edge and node lists for
+the top key drivers and their neighborhood for a given module.
+}
+\usage{
+kda2cytoscape.exec(job, drivers, modpool, palette, graph.depth = 1)
+}
+\arguments{
+\item{job}{
+data list including entire graph, nodes, modules information
+}
+\item{drivers}{
+top key drivers of the specified module
+}
+\item{modpool}{
+unique key driver list for all modules
+}
+\item{palette}{
+assigned unique color map for all modules
+}
+\item{graph.depth}{
+search depth for graph neighborhood
+}
+}
+\value{
+\item{res }{uniquely identified node and edge lists of the members
+belonging to the given module}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+## Select top key drivers from each module.
+## First, take module names from kda results
+modules <- unique(job.kda$results$MODULE)
+## Take top 2 KDs:
+drivers <- kda2cytoscape.drivers(job.kda$results, modules, ndriv=2)
+drivers <- as.data.frame(drivers)
+colnames(drivers) <- c("MODULE" , "NODE")
+
+mods <- unique(drivers$MODULE)
+modnames <- job.kda$modules[mods]
+modnames[which(mods == 0)] <- "NON.MODULE"
+palette <- kda2cytoscape.colormap(length(mods))
+palette[,which(mods == 0)] <- c(90,90,90)
+drivers$MODNAMES <- modnames[match(drivers$MODULE, mods)]
+drivers$NODNAMES <- job.kda$graph$nodes[drivers$NODE]
+for(i in 1:nrow(drivers))
+drivers$COLOR[i] <- paste(palette[1, match(drivers$MODULE[i], mods)],
+palette[2, match(drivers$MODULE[i], mods)],
+palette[3, match(drivers$MODULE[i], mods)], sep=" ")
+## Process each module separately. Just perform for the 1st module:
+i <- 1
+rows <- which(drivers$MODULE == mods[i])
+if(length(rows) > 0)
+tmp <- kda2cytoscape.exec(job.kda, drivers[rows,], mods, palette,
+job.kda$depth)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2cytoscape.identify.Rd b/man/kda2cytoscape.identify.Rd
new file mode 100644
index 0000000..05aa05e
--- /dev/null
+++ b/man/kda2cytoscape.identify.Rd
@@ -0,0 +1,59 @@
+\name{kda2cytoscape.identify}
+\alias{kda2cytoscape.identify}
+\title{
+Match identities with respect to given variable name
+}
+\description{
+\code{kda2cytoscape.identify} searches the given data list \code{dat}
+within the \code{labels} according to the specified attribute (variable
+name). It returns the matched rows. Hence, it finds identifier numbers
+for the searched data list \code{dat}.
+}
+\usage{
+kda2cytoscape.identify(dat, varname, labels)
+}
+\arguments{
+\item{dat}{
+node ID list whose symbols or names will be collected from network
+node name (or symbol) list.
+}
+\item{varname}{
+specifies that \code{dat} will be searched among \code{labels}
+according to which variable (attribute). Here, gene symbols whose IDs
+are given, will be searched in the causal network node list according
+to the NODE attribute.
+}
+\item{labels}{
+the data list possibly including names or symbols corresponding to
+the given IDs in the \code{dat} data list.
+}
+}
+\value{
+\item{res }{the matching rows of \code{labels} with the identifiers of
+given data list \code{dat} }
+}
+\examples{
+## Converts identities (either module names or gene names) to the indices
+aa<- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+NODE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+aa
+bb <- kda2cytoscape.identify(aa, "MODULE", c("Mod1"))
+bb
+cc <- kda2cytoscape.identify(aa, "MODULE", c("Mod1", "Mod3"))
+cc
+dd <- kda2cytoscape.identify(aa, "NODE", c("GeneA"))
+dd
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Zeyneb Kurt
+}
+\seealso{
+\code{\link{kda2cytoscape}}
+}
+
diff --git a/man/kda2himmeli.Rd b/man/kda2himmeli.Rd
new file mode 100644
index 0000000..cae9d65
--- /dev/null
+++ b/man/kda2himmeli.Rd
@@ -0,0 +1,84 @@
+\name{kda2himmeli}
+\alias{kda2himmeli}
+\title{
+Generate input files for Himmeli
+}
+\description{
+\code{kda2himmeli} generates input files for Himmeli to visualize the
+graph and hubnets after the wKDA process finished. The network visualization
+is a streamlined depiction of the module enrichment in hub neighborhoods.
+}
+\usage{
+kda2himmeli(job, modules = NULL, ndrivers = 5)
+}
+\arguments{
+\item{job}{
+KDA result data list as returned by \code{\link{kda.finish}}
+}
+\item{modules}{
+array of module names to be visualized
+}
+\item{ndrivers}{
+maximum number of drivers per module
+}
+}
+\details{
+\code{\link{kda2himmeli}} first, selects top scoring key drivers for each
+module; then, assigns a colormap to modules, processes each module
+separately, finds key nodes' neighborhoods, and saves the edge and node
+lists of the modules to the specified output folder. Besides, it returns
+this configuration data to the user.
+}
+\value{
+\item{job }{updated data list including the node and edge information of
+the modules converted to Himmeli format}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+job.kda$nodfile <- system.file("extdata","msea2kda.nodes.txt",
+package="Mergeomics")
+
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## finish the KDA process
+job.kda <- kda.finish(job.kda)
+
+## prepare the cytoscape-ready files:
+job.kda <- kda2himmeli(job.kda)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda.analyze}}, \code{\link{kda.finish}}
+}
+
diff --git a/man/kda2himmeli.colorize.Rd b/man/kda2himmeli.colorize.Rd
new file mode 100644
index 0000000..c3522bc
--- /dev/null
+++ b/man/kda2himmeli.colorize.Rd
@@ -0,0 +1,59 @@
+\name{kda2himmeli.colorize}
+\alias{kda2himmeli.colorize}
+\title{
+Trace module memberships of genes
+}
+\description{
+\code{kda2himmeli.colorize} assigns color to each node of the given
+module. If a node belongs to more than one module, different colors will
+be assigned to that node, as each color representing one module (shared
+nodes are illustrated as pie charts in the graph).
+}
+\usage{
+kda2himmeli.colorize(noddata, moddata, modpool, palette)
+}
+\arguments{
+\item{noddata}{
+node information of the entire graph
+}
+\item{moddata}{
+module data including node (member gene) list
+}
+\item{modpool}{
+unique module list including significant key drivers
+}
+\item{palette}{
+assigned unique color map for all modules
+}
+}
+\value{
+\item{res }{data frame including the assigned color labels for the nodes
+of the given module. If a node is concurrently member of many modules,
+many colors will be assigned to that node (one color for each of these
+modules)
+}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\examples{
+## Trace module memberships for each KD
+## If a KD is member of multiple modules, assign multiple colors to it
+## Also consider the locus pval of the top locus of each KD (by valdata)
+## We need to know data of all possible modules and all possible module ids
+## to assign multiple colors(sectors) to a KD when needed
+if(exists("valdata"))
+cat("Marker pvalues will be used to determine node sizes
+in the network illustration")
+# noddata <- kda2himmeli.colorize(valdata, job.kda$moddata, modpool, palette)
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/kda2himmeli.colormap.Rd b/man/kda2himmeli.colormap.Rd
new file mode 100644
index 0000000..bef93e8
--- /dev/null
+++ b/man/kda2himmeli.colormap.Rd
@@ -0,0 +1,37 @@
+\name{kda2himmeli.colormap}
+\alias{kda2himmeli.colormap}
+\title{
+Assign one color to each unique module
+}
+\description{
+\code{kda2himmeli.colormap} takes number of the modules and assigns a
+particular color to each module. Returns the color list (palette).
+}
+\usage{
+kda2himmeli.colormap(ncolors)
+}
+\arguments{
+\item{ncolors}{
+number of the unique modules
+}
+}
+\value{
+\item{palette }{color list: one color is assigned to each module}
+}
+\examples{
+color.number = 5 ## let us assume we have 5 modules, assign 1 color to each:
+palette <- kda2himmeli.colormap(color.number)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/kda2himmeli.drivers.Rd b/man/kda2himmeli.drivers.Rd
new file mode 100644
index 0000000..ae5c402
--- /dev/null
+++ b/man/kda2himmeli.drivers.Rd
@@ -0,0 +1,77 @@
+\name{kda2himmeli.drivers}
+\alias{kda2himmeli.drivers}
+\title{
+Select top key drivers for each module
+}
+\description{
+\code{kda2himmeli.drivers} finds maximally top \code{ndriv} key drivers
+for each module with respect to the significance level of the drivers.
+}
+\usage{
+kda2himmeli.drivers(data, modules, ndriv)
+}
+\arguments{
+\item{data}{
+data frame including information of the modules (key driver list,
+p-values, node list, false discovery rates (fdr), and so on.)
+}
+\item{modules}{
+top scoring modules among KDA results
+}
+\item{ndriv}{
+maximum number of drivers that can be chosen for per module
+}
+}
+\value{
+\item{data }{top key drivers (maximally \code{ndriv} drivers for each
+module) for top modules (if module significance levels are given)}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+job.kda$nodfile <- system.file("extdata","msea2kda.nodes.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+## Select top key drivers from each module.
+## First, take module names from kda results
+modules <- unique(job.kda$results$MODULE)
+## Take top 2 KDs:
+drivers <- kda2himmeli.drivers(job.kda$results, modules, ndriv=2)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/kda2himmeli.edges.Rd b/man/kda2himmeli.edges.Rd
new file mode 100644
index 0000000..a4e405d
--- /dev/null
+++ b/man/kda2himmeli.edges.Rd
@@ -0,0 +1,81 @@
+\name{kda2himmeli.edges}
+\alias{kda2himmeli.edges}
+\title{
+Find edges of a given node with a specified depth
+}
+\description{
+\code{kda2himmeli.edges} finds the sub-graph (node and edge lists) of a
+central node and its neighborhood at a particular search depth. The
+central node is a member of a module, which is defined at
+\code{\link{kda2himmeli.exec}}.
+}
+\usage{
+kda2himmeli.edges(graph, center, depth, direction)
+}
+\arguments{
+\item{graph}{
+entire graph
+}
+\item{center}{
+the node, whose interactions with neighbors will be searched within
+\code{graph}.
+}
+\item{depth}{
+search depth for graph neighborhood
+}
+\item{direction}{
+edge direction. 0 for undirected, negative for downstream and
+positive for upstream
+}
+}
+\value{
+\item{g }{the sub-graph including TAIL, HEAD, WEIGHT information of the
+central node, which belongs to the specified module.}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+job.kda$nodfile <- system.file("extdata","msea2kda.nodes.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+## Select a center node to seek its neighbors in the graph:
+edges.of.center.node <- kda2himmeli.edges(job.kda$graph, 1,
+job.kda$depth, job.kda$direction)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/kda2himmeli.exec.Rd b/man/kda2himmeli.exec.Rd
new file mode 100644
index 0000000..1ba62d4
--- /dev/null
+++ b/man/kda2himmeli.exec.Rd
@@ -0,0 +1,115 @@
+\name{kda2himmeli.exec}
+\alias{kda2himmeli.exec}
+\title{
+Evaluate each module separately for visualization
+}
+\description{
+\code{kda2himmeli.exec} deals with the modules individually; takes a
+particular amount of top key drivers of the given module in company with
+the top key driver lists and colormap of all modules; traces module
+memberships and produces colormap, it finds the edge and node lists for
+the top key drivers and their neighborhood for a given module.
+}
+\usage{
+kda2himmeli.exec(job, valdata, drivers, modpool, palette)
+}
+\arguments{
+\item{job}{
+data list including entire graph, nodes, modules information
+}
+\item{valdata}{
+GWAS pvalues of top loci of the nodes - if this information is available,
+sizes of the nodes in the figure will be correlated with the p-value of
+the top loci of the nodes -
+}
+\item{drivers}{
+top key drivers of the specified module
+}
+\item{modpool}{
+unique key driver list for all modules
+}
+\item{palette}{
+assigned unique color map for all modules
+}
+}
+\value{
+\item{res }{uniquely identified node and edge lists of the members
+belonging to the given module}
+}
+\examples{
+## get the prepared and KDA applied dataset:(see kda.analyze for details)
+data(job_kda_analyze)
+## set the relevant parameters:
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns, MODULE,
+## NODE, delimited by tab
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+job.kda$nodfile <- system.file("extdata","msea2kda.nodes.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+
+## Finish the KDA process
+job.kda <- kda.finish(job.kda)
+
+## Get valdata including marker pvals
+valdata <- tool.read(job.kda$nodfile)
+z <- as.double(valdata$VALUE)
+z <- (z/quantile(z, 0.95) + rank(z)/length(z))
+valdata$SIZE <- pmin(4.0, z)
+## Select subset of genes.
+valdata <- kda2himmeli.identify(valdata, "NODE", job.kda$graph$nodes)
+
+## Select top key drivers from each module.
+## First, take module names from kda results
+modules <- unique(job.kda$results$MODULE)
+## Take top 2 KDs:
+drivers <- kda2himmeli.drivers(job.kda$results, modules, ndriv=2)
+drivers <- as.data.frame(drivers)
+colnames(drivers) <- c("MODULE" , "NODE")
+
+mods <- unique(drivers$MODULE)
+modnames <- job.kda$modules[mods]
+modnames[which(mods == 0)] <- "NON.MODULE"
+palette <- kda2himmeli.colormap(length(mods))
+palette[,which(mods == 0)] <- c(90,90,90)
+drivers$MODNAMES <- modnames[match(drivers$MODULE, mods)]
+drivers$NODNAMES <- job.kda$graph$nodes[drivers$NODE]
+for(i in 1:nrow(drivers))
+drivers$COLOR[i] <- paste(palette[1, match(drivers$MODULE[i], mods)],
+palette[2, match(drivers$MODULE[i], mods)],
+palette[3, match(drivers$MODULE[i], mods)], collapse=" ")
+## Process each module separately. Just perform for the 1st module:
+i <- 1
+rows <- which(drivers$MODULE == mods[i])
+if(length(rows) > 0)
+tmp <- kda2himmeli.exec(job.kda, valdata, drivers[rows,], mods, palette)
+
+## remove the results folder
+unlink("Results", recursive = TRUE)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/kda2himmeli.identify.Rd b/man/kda2himmeli.identify.Rd
new file mode 100644
index 0000000..e234908
--- /dev/null
+++ b/man/kda2himmeli.identify.Rd
@@ -0,0 +1,59 @@
+\name{kda2himmeli.identify}
+\alias{kda2himmeli.identify}
+\title{
+Match identities with respect to given variable name
+}
+\description{
+\code{kda2himmeli.identify} searches the given data list \code{dat}
+within the \code{labels} according to the specified attribute (variable
+name). It returns the matched rows. Hence, it finds identifier numbers for
+the searched data list \code{dat}.
+}
+\usage{
+kda2himmeli.identify(dat, varname, labels)
+}
+\arguments{
+\item{dat}{
+node ID list whose symbols or names will be collected from network
+node name (or symbol) list.
+}
+\item{varname}{
+specifies that \code{dat} will be searched among \code{labels}
+according to which variable (attribute). Here, gene symbols whose IDs
+are given, will be searched in the causal network node list according
+to the NODE attribute.
+}
+\item{labels}{
+the data list possibly including names or symbols corresponding to the
+given IDs in the \code{dat} data list.
+}
+}
+\value{
+\item{res }{the matching labels or names of \code{labels} with the IDs of
+\code{dat} list }
+}
+\examples{
+## Converts identities (either module names or gene names) to the indices
+aa<- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+NODE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+aa
+bb <- kda2himmeli.identify(aa, "MODULE", c("Mod1"))
+bb
+cc <- kda2himmeli.identify(aa, "MODULE", c("Mod1", "Mod3"))
+cc
+dd <- kda2himmeli.identify(aa, "NODE", c("GeneA"))
+dd
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{kda2himmeli}}
+}
+
diff --git a/man/ssea.analyze.Rd b/man/ssea.analyze.Rd
new file mode 100644
index 0000000..ed48676
--- /dev/null
+++ b/man/ssea.analyze.Rd
@@ -0,0 +1,113 @@
+\name{ssea.analyze}
+\alias{ssea.analyze}
+\title{
+Marker set enrichment analysis (MSEA)
+}
+\description{
+\code{\link{ssea.analyze}} finds the enrichment of the pathways or
+co-expression modules by a marker set (e.g. associated risk variants
+-loci- of a relevant disease).
+Association study by mapping markers (e.g. SNPs) to genes (e.g. via
+expression QTLs).
+Enrichment P-values obtained by the MSEA denote the degree of enrichment
+of significantly disease-associated (high ranking) markers (e.g. eSNPs)
+within these pathways when compared to the null distribution of expected
+uniform distribution of all ranks of the markers.
+MSEA is performed with either gene-level or marker-level permutations
+based on Gaussian distribution.
+}
+\usage{
+ssea.analyze(job)
+}
+\arguments{
+\item{job}{
+the data list including fields: seed for random number generator
+\code{(job$seed)} (to obtain the same results from permutation when
+the same input set is given), random permutation level
+\code{(job$permtype)} (either gene- or marker-level), maximum number
+of permutations \code{(job$nperm)} for the permutation test, and the
+database that uses indexed identities for modules, genes, and markers
+(e.g. loci) \code{(job$database)}.
+}
+}
+\details{
+\code{\link{ssea.analyze}} associates the gene sets (pathways or
+co-expression modules) with relevant disease (e.g. Coronary Artery Disease)
+association data by mapping markers (e.g. SNPs) to genes (e.g. via expression
+QTLs).
+It performs the MSEA by using observed and estimated enrichment scores.
+First, the observed enrichment scores of the pathways by markers (e.g.
+loci) are calculated. Then, a Gaussian distribution based simulation is
+performed, by using the statistics of the observed scores (mean, std.dev.,
+etc.), to obtain the estimated enrichment scores, enrichment frequencies,
+and other statistics e.g. p-values for the pathways.
+}
+\value{
+\item{job }{the updated data frame including results: indexed module
+identity, enrichment P-values, raw frequencies (raw frequency of a gene
+set defines the number of the estimated enrichment scores that are larger
+than this gene set's enrichment score under the null distribution based
+on Gaussian function)}.
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.control}}, \code{\link{ssea.finish}},
+\code{\link{ssea.prepare}}, \code{\link{ssea.start}},
+\code{\link{ssea2kda}}
+}
diff --git a/man/ssea.analyze.observe.Rd b/man/ssea.analyze.observe.Rd
new file mode 100644
index 0000000..14b7587
--- /dev/null
+++ b/man/ssea.analyze.observe.Rd
@@ -0,0 +1,101 @@
+\name{ssea.analyze.observe}
+\alias{ssea.analyze.observe}
+\title{
+Collect enrichment score statistics for MSEA
+}
+\description{
+\code{\link{ssea.analyze.observe}} obtains the observed enrichment scores
+of the pathways or modules by a given marker set (e.g. GWAS loci data of
+a disease) depending on the observation frequencies of this markers in
+the pathways.
+}
+\usage{
+ssea.analyze.observe(db)
+}
+\arguments{
+\item{db}{
+database including the indexed identities for modules, genes and marker:
+\preformatted{
+modulesizes: gene counts for modules.
+modulelengths: distinct marker counts for modules.
+moduledensities: ratio between distinct and non-distinct
+markers.
+genesizes: marker count for each gene.
+module2genes: gene lists for each module.
+gene2loci: marker lists for each gene.
+locus2row: row indices in the marker data frame for each
+marker.
+observed: matrix of observed counts of values that exceed
+each quantile point for each marker.
+expected: 1.0 - quantile points.
+}
+}
+}
+\value{
+\item{scores }{enrichment scores}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+
+## Observed enrichment scores.
+db <- job.msea$database
+scores <- ssea.analyze.observe(db)
+nmods <- length(scores)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}
+}
diff --git a/man/ssea.analyze.randgenes.Rd b/man/ssea.analyze.randgenes.Rd
new file mode 100644
index 0000000..2d9c05a
--- /dev/null
+++ b/man/ssea.analyze.randgenes.Rd
@@ -0,0 +1,118 @@
+\name{ssea.analyze.randgenes}
+\alias{ssea.analyze.randgenes}
+\title{
+Estimate enrichment from randomized genes
+}
+\description{
+\code{\link{ssea.analyze.randgenes}} simulates enrichment scores by
+randomizing the genes from all modules (from database - db)
+}
+\usage{
+ssea.analyze.randgenes(db, targets)
+}
+\arguments{
+\item{db}{
+database including the indexed identities for modules, genes and markers:
+\preformatted{
+modulesizes: gene counts for modules.
+modulelengths: distinct marker counts for modules.
+moduledensities: ratio between distinct and non-distinct
+markers.
+genesizes: marker count for each gene.
+module2genes: gene lists for each module.
+gene2loci: marker lists for each gene .
+locus2row: row indices in the marker data frame for each
+marker.
+observed: matrix of observed counts of values that exceed
+each quantile point for each marker.
+expected: 1.0 - quantile points.
+}
+}
+\item{targets}{all modules}
+}
+\value{
+\item{scores }{randomly simulated enrichment scores}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+
+## Observed enrichment scores.
+db <- job.msea$database
+scores <- ssea.analyze.observe(db)
+nmods <- length(scores)
+
+## Simulated scores.
+nperm <- job.msea$nperm
+observ <- scores
+## Include only non-empty modules for simulation.
+nmods <- length(db$modulesizes)
+targets <- which(db$modulesizes > 0)
+hits <- rep(NA, nmods)
+hits[targets] <- 0
+
+## Prepare data structures to hold null samples.
+keys <- rep(0, nperm)
+scores <- rep(NA, nperm)
+scoresets <- list()
+for(i in 1:nmods) scoresets[[i]] <- double()
+## Simulate random scores.
+## within a for loop: check capacity, find new statistics, update snull
+## distribution (simulated null distr.) by permuting genes
+snull <- ssea.analyze.randgenes(db, targets)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}
+}
diff --git a/man/ssea.analyze.randloci.Rd b/man/ssea.analyze.randloci.Rd
new file mode 100644
index 0000000..3504d38
--- /dev/null
+++ b/man/ssea.analyze.randloci.Rd
@@ -0,0 +1,119 @@
+\name{ssea.analyze.randloci}
+\alias{ssea.analyze.randloci}
+\title{
+Estimate enrichment from randomized marker
+}
+\description{
+\code{\link{ssea.analyze.randloci}} simulates enrichment scores by
+randomizing the marker that mapped to genes from all modules
+(from database, db)
+}
+\usage{
+ssea.analyze.randloci(db, targets)
+}
+\arguments{
+\item{db}{
+database including the indexed identities for modules, genes and markers:
+\preformatted{
+modulesizes: gene counts for modules.
+modulelengths: distinct marker counts for modules.
+moduledensities: ratio between distinct and non-distinct
+markers.
+genesizes: marker count for each gene.
+module2genes: gene lists for each module.
+gene2loci: marker lists for each gene .
+locus2row: row indices in the marker data frame for each
+marker.
+observed: matrix of observed counts of values that exceed
+each quantile point for each marker.
+expected: 1.0 - quantile points.
+}
+}
+\item{targets}{all modules }
+}
+\value{
+\item{scores}{randomly simulated enrichment scores}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+
+## Observed enrichment scores.
+db <- job.msea$database
+scores <- ssea.analyze.observe(db)
+nmods <- length(scores)
+
+## Simulated scores.
+nperm <- job.msea$nperm
+observ <- scores
+## Include only non-empty modules for simulation.
+nmods <- length(db$modulesizes)
+targets <- which(db$modulesizes > 0)
+hits <- rep(NA, nmods)
+hits[targets] <- 0
+
+## Prepare data structures to hold null samples.
+keys <- rep(0, nperm)
+scores <- rep(NA, nperm)
+scoresets <- list()
+for(i in 1:nmods) scoresets[[i]] <- double()
+## Simulate random scores.
+## within a for loop: check capacity, find new statistics, update snull
+## distribution (simulated null distr.) by permuting loci
+snull <- ssea.analyze.randloci(db, targets)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}
+}
diff --git a/man/ssea.analyze.simulate.Rd b/man/ssea.analyze.simulate.Rd
new file mode 100644
index 0000000..87addb9
--- /dev/null
+++ b/man/ssea.analyze.simulate.Rd
@@ -0,0 +1,113 @@
+\name{ssea.analyze.simulate}
+\alias{ssea.analyze.simulate}
+\title{
+Simulate scores for MSEA
+}
+\description{
+\code{\link{ssea.analyze.simulate}} simulates enrichment scores by
+randomly permuting database with respect to the specified permutation
+type (either gene-level or marker-level).
+}
+\usage{
+ssea.analyze.simulate(db, observ, nperm, permtype)
+}
+\arguments{
+\item{db}{
+database including the indexed identities for modules, genes and markers
+(e.g. loci): \preformatted{
+modulesizes: gene counts for modules.
+modulelengths: distinct marker counts for modules.
+moduledensities: ratio between distinct and
+non-distinct markers.
+genesizes: marker count for each gene.
+module2genes: gene lists for each module.
+gene2loci: marker lists for each gene.
+locus2row: row indices in the marker data frame for
+each marker.
+observed: matrix of observed counts of values that
+exceed each quantile point for each marker.
+expected: 1.0 - quantile points.
+}
+}
+\item{observ}{
+observed enrichment scores
+}
+\item{nperm}{
+maximum nubmer of permutations (for simulation)
+}
+\item{permtype}{
+permutation type (either gene or locus)
+}
+}
+\value{
+\item{scoresets }{simulated score lists for the statistically significant
+modules}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+
+## Observed enrichment scores.
+db <- job.msea$database
+scores <- ssea.analyze.observe(db)
+nmods <- length(scores)
+
+## Simulated scores.
+nperm <- job.msea$nperm
+nullsets <- ssea.analyze.simulate(db, scores, nperm, job.msea$permtype)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}
+}
diff --git a/man/ssea.analyze.statistic.Rd b/man/ssea.analyze.statistic.Rd
new file mode 100644
index 0000000..bc64531
--- /dev/null
+++ b/man/ssea.analyze.statistic.Rd
@@ -0,0 +1,45 @@
+\name{ssea.analyze.statistic}
+\alias{ssea.analyze.statistic}
+\title{
+MSEA statistics for enrichment score
+}
+\description{
+\code{\link{ssea.analyze.statistic}} estimates the enrichment score based
+on observed and expected ones.
+}
+\usage{
+ssea.analyze.statistic(o, e)
+}
+\arguments{
+\item{o}{
+observed enrichment score
+}
+\item{e}{
+expected enrichment score
+}
+}
+\value{
+\item{score }{estimated enrichment score based on observed and expected
+scores}
+}
+\examples{
+## O and E the observed and expected counts of positive findings
+## (enrichment scores) at a given cutoff:
+set.seed(1)
+o <- rnorm(1)
+e <- rnorm(1)
+## find the final enrichment score from the observed and estimated scores:
+z <- ssea.analyze.statistic(o, e)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}
+}
diff --git a/man/ssea.control.Rd b/man/ssea.control.Rd
new file mode 100644
index 0000000..69b05b0
--- /dev/null
+++ b/man/ssea.control.Rd
@@ -0,0 +1,97 @@
+\name{ssea.control}
+\alias{ssea.control}
+\title{
+Add internal positive control modules for MSEA
+}
+\description{
+\code{\link{ssea.control}} adds positive control modules that includes the
+top-scored genes based on the marker scores of these genes.
+The database structure, including identities of the variables, is updated
+properly.
+}
+\usage{
+ssea.control(job)
+}
+
+\arguments{
+\item{job}{
+data list including module and gene identities as characters; also
+including database that has indexed identities for MSEA:
+\preformatted{
+modules: module identities as characters.
+genes: gene identities as characters.
+moddata: preprocessed module data (indexed identities).
+database: database including indexed identities for
+modules, genes, and markers.
+}
+}
+}
+\value{
+\item{job }{data list including augmented internal control modules:
+\preformatted{
+modules: augmented module names
+moddata: augmented module data
+database: augmented database
+}
+}
+}
+\examples{
+## Check the slots for control module;
+## if it cannot find any control module, function throws an error,
+## if can find control slots, updates the database identities (modules,
+## genes, markers) properly:
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/ssea.finish.Rd b/man/ssea.finish.Rd
new file mode 100644
index 0000000..4fcf00c
--- /dev/null
+++ b/man/ssea.finish.Rd
@@ -0,0 +1,112 @@
+\name{ssea.finish}
+\alias{ssea.finish}
+\title{
+Organize and save MSEA results
+}
+\description{
+\code{ssea.finish} organizes and stores the MSEA results into relevant
+output files.
+}
+\usage{
+ssea.finish(job)
+}
+\arguments{
+\item{job}{
+data list including the results of MSEA process. \code{job} will be
+saved after getting organized: \preformatted{
+label: unique identifier for the analysis.
+folder: output folder for results.
+resultsdata: frame including indexed module identities
+(MODULE) and enrichment P-values (P).
+database: database including indexed identities for
+modules, genes, and markers.
+}
+}
+}
+\details{
+\code{ssea.finish} obtains module statistics (member genes, size, length,
+density, enrichment scores, false discovery rates), finds the top marker
+within genes, updates the gene scores and gene sizes (i.e. number of markers
+for each gene), and saves the organized results regarding the modules and
+genes into the relevant files.
+}
+\value{
+\item{job }{data list including the organized results of MSEA process:
+\preformatted{
+results: updated information of modules:
+number of distinct member genes (NGENES),
+number of distinct member markers (NLOCI),
+ratio of distinct to non-distinct markers (DENSITY),
+false discovery rates (FDR).
+generesults: updated gene-specific information including:
+indexed gene identity (GENE),
+gene size (NLOCI),
+unadjusted enrichment score (SCORE),
+marker with maximum value (LOCUS),
+marker value (VALUE).
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}, \code{\link{ssea.control}},
+\code{\link{ssea.prepare}}, \code{\link{ssea.start}},
+\code{\link{ssea2kda}}
+}
diff --git a/man/ssea.finish.details.Rd b/man/ssea.finish.details.Rd
new file mode 100644
index 0000000..c997486
--- /dev/null
+++ b/man/ssea.finish.details.Rd
@@ -0,0 +1,102 @@
+\name{ssea.finish.details}
+\alias{ssea.finish.details}
+\title{
+Organize and save module, gene, top locus, Ps of MSEA results
+}
+\description{
+\code{ssea.finish.details} finds significant modules and their gene lists,
+and top marker (with GWAS -log10 transformed p-vals) of these genes,
+merge results of markers, genes and module statistics, sort results
+according to first, module enrichment score, then marker P-value,
+and saves these sorted results into the relevant files.
+}
+\usage{
+ssea.finish.details(job)
+}
+\arguments{
+\item{job}{
+data list including the results of MSEA process:
+\preformatted{
+label: unique identifier for the analysis.
+folder: output folder for results.
+modinfo: descriptions of the modules.
+resultsdata: frame including indexed module identities
+(MODULE) and enrichment P-values (P).
+database: database including indexed identities for
+modules, genes, and markers.
+}
+}
+}
+\value{
+None.
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+## Estimate mod FDR values, sort according to significance, save full results:
+job.msea <- ssea.finish.fdr(job.msea)
+## Collect top markers(e.g.loci) within genes, save genes with top marker Pval
+job.msea <- ssea.finish.genes(job.msea)
+## Find signficant modules, collect gene members of top modules,
+## Merge gene results (with top marker info),
+## Sort and save details according to enrichment and marker value:
+job.msea <- ssea.finish.details(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.finish}}
+}
diff --git a/man/ssea.finish.fdr.Rd b/man/ssea.finish.fdr.Rd
new file mode 100644
index 0000000..4ada3a6
--- /dev/null
+++ b/man/ssea.finish.fdr.Rd
@@ -0,0 +1,102 @@
+\name{ssea.finish.fdr}
+\alias{ssea.finish.fdr}
+\title{
+Organize and save FDR results of the MSEA
+}
+\description{
+\code{ssea.finish.fdr} estimates the FDR values of the enrichment P-values
+belonging to the modules. It also gets the other module information such as
+size (gene number), length (marker number), density, etc., sorts the modules
+according to P-values, saves this information into relevant files.
+}
+\usage{
+ssea.finish.fdr(job)
+}
+\arguments{
+\item{job}{
+data list including module-realted results of MSEA process:
+\preformatted{
+folder: output folder for results.
+modules: module names.
+results: data frame including indexed module identities
+(MODULE) and enrichment P-values (P).
+database: database including indexed identities for
+modules, genes, and markers.
+}
+}
+}
+\value{
+\item{job }{data list including the organized module-related results of
+MSEA process: \preformatted{
+results: updated information of modules:
+number of distinct member genes (NGENES),
+number of distinct member markers (NLOCI),
+ratio of distinct to non-distinct markers (DENSITY),
+false discovery rates (FDR).
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+## Estimate mod FDR values, sort according to significance, save full results:
+job.msea <- ssea.finish.fdr(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.finish}}
+}
diff --git a/man/ssea.finish.genes.Rd b/man/ssea.finish.genes.Rd
new file mode 100644
index 0000000..1a92742
--- /dev/null
+++ b/man/ssea.finish.genes.Rd
@@ -0,0 +1,102 @@
+\name{ssea.finish.genes}
+\alias{ssea.finish.genes}
+\title{
+Organize and save gene-realted MSEA results
+}
+\description{
+\code{ssea.finish.genes} organizes and stores the gene-related MSEA
+results into relevant output file. It finds the top markers within genes,
+update gene scores and gene sizes, and save the results.
+}
+\usage{
+ssea.finish.genes(job)
+}
+
+\arguments{
+\item{job}{
+data list including the information about the MSEA process:
+\preformatted{
+folder: output folder for results.
+database: database including indexed identities for
+modules, genes, and markers.
+}
+}
+}
+\value{
+\item{job }{data list including the organized gene-related results of
+MSEA process: \preformatted{
+generesults: updated gene-specific information;
+indexed gene identity (GENE),
+gene size (NLOCI),
+unadjusted enrichment score (SCORE),
+marker with maximum value (LOCUS),
+marker value (VALUE).
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+## Estimate mod FDR values, sort according to significance, save full results:
+job.msea <- ssea.finish.fdr(job.msea)
+## Collect top markers(e.g.loci) within genes, save genes with top marker Pval
+job.msea <- ssea.finish.genes(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.finish}}
+}
diff --git a/man/ssea.meta.Rd b/man/ssea.meta.Rd
new file mode 100644
index 0000000..2f03dbb
--- /dev/null
+++ b/man/ssea.meta.Rd
@@ -0,0 +1,98 @@
+\name{ssea.meta}
+\alias{ssea.meta}
+\title{
+Merge multiple MSEA results into meta MSEA
+}
+\description{
+\code{ssea.meta} merges MSEA results of modules, genes, and markers,
+constructs hierarchical representation of genes and modules, calculates
+meta P-values of the modules (based on z-scores), and save all statistics
+results.
+}
+\usage{
+ssea.meta(jobs, label, folder)
+}
+\arguments{
+\item{jobs}{
+data list including information and statistics about genes, markers,
+and modules
+}
+\item{label}{
+label (unique identifier) for meta job
+}
+\item{folder}{
+parent folder for meta job
+}
+}
+\value{
+\item{meta }{data list including meta-analyzing results for the modules,
+which enables analyzing the multiple MSEA results for the modules.}
+}
+\examples{
+## Create an object for multiple MSEAs:
+job.multiple.msea <- list()
+set.seed(1)
+for(i in 1:3){
+## make 3 trials, each time pick 10 random modules among the first 20 modules
+mod.indices <- sample(20, 10)
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 30 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[mod.indices]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+job.multiple.msea[[i]] <- job.msea
+}
+
+meta.results <- ssea.meta(job.multiple.msea, job.multiple.msea[[1]]$label,
+job.multiple.msea[[1]]$folder)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/ssea.prepare.Rd b/man/ssea.prepare.Rd
new file mode 100644
index 0000000..c418a49
--- /dev/null
+++ b/man/ssea.prepare.Rd
@@ -0,0 +1,118 @@
+\name{ssea.prepare}
+\alias{ssea.prepare}
+\title{
+Prepare an indexed database for MSEA
+}
+\description{
+\code{ssea.prepare} prepares a database that includes hierarchical for
+modules, i.e. it collects gene list and unique marker list of the modules
+for MSEA process
+}
+\usage{
+ssea.prepare(job)
+}
+\arguments{
+\item{job}{a data list with the following components: \preformatted{
+modules: module identities as characters.
+genesgene: identities as characters.
+loci: marker identities as characters.
+moddata: preprocessed module data (indexed identities).
+gendata: preprocessed mapping data (indexed identities).
+locdata: preprocessed marker data (indexed identities).
+mingenes: minimum module size allowed.
+maxgenes: maximum module size allowed.
+maxoverlap: maximum module overlap allowed (1.0 to skip).
+quantiles: quantile points for test statistic.
+}
+}
+}
+\details{
+\code{ssea.prepare} removes extreme-sized modules, constructs a
+hierarchical representation of genes and modules, obtains hit counts for
+markers, and returns the finalized module, genes, markers, database
+information.
+}
+\value{
+\item{job }{an updated data list with the following components:
+\preformatted{
+modules: finalized module names.
+moddata: finalized module data.
+gendata: finalized mapping data.
+locdata: finalized marker data.
+quantiles: verified quantile points.
+database$modulesizes: gene counts for modules.
+database$modulelengths: distinct markers counts for
+modules.
+database$moduledensities: ratio between distinct and
+non-distinct markers.
+database$genesizeslocus: count for each gene.
+database$module2genes: gene lists for each module.
+database$gene2locilocus: lists for each gene.
+database$locus2row: indices in the marker data frame
+for each marker.
+database$observed: matrix of observed counts of values
+that exceed each quantile point for each marker.
+database$expected: 1.0 - quantile points.
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}, \code{\link{ssea.control}},
+\code{\link{ssea.finish}}, \code{\link{ssea.start}},
+\code{\link{ssea2kda}}
+}
diff --git a/man/ssea.prepare.counts.Rd b/man/ssea.prepare.counts.Rd
new file mode 100644
index 0000000..7f5df03
--- /dev/null
+++ b/man/ssea.prepare.counts.Rd
@@ -0,0 +1,107 @@
+\name{ssea.prepare.counts}
+\alias{ssea.prepare.counts}
+\title{
+Calculate hit counts up to a given quantile
+}
+\description{
+Counts unique loci in a module, maps the marker data of a module
+to the all available markers by creating a bit matrix for values above the
+given quantiles. Created bit matrix contains either TRUE (above quantiles)
+or FALSE (below or equals to quantiles) values as a resuls of these
+comparisons. It returns the results (marker mapping and bit matrix)
+}
+\usage{
+ssea.prepare.counts(locdata, nloci, quantiles)
+}
+\arguments{
+\item{locdata}{marker data}
+\item{nloci}{number of elements in markers list}
+\item{quantiles}{quantile points for test statistic}
+}
+\value{
+\item{res }{a data list with the following components: \preformatted{
+locus2row: mapped marker information
+observed: bit matrix that involves TRUEs and FALSEs
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+
+## Remove extremely big or small modules:
+st <- tool.aggregate(job.msea$moddata$MODULE)
+mask <- which((st$lengths >= job.msea$mingenes) &
+(st$lengths <= job.msea$maxgenes))
+pos <- match(job.msea$moddata$MODULE, st$labels[mask])
+job.msea$moddata <- job.msea$moddata[which(pos > 0),]
+
+## Construct hierarchical representation for modules, genes, and markers:
+ngens <- length(job.msea$genes)
+nmods <- length(job.msea$modules)
+db <- ssea.prepare.structure(job.msea$moddata, job.msea$gendata,
+nmods, ngens)
+## Determine test cutoffs:
+if(is.null(job.msea$quantiles)) {
+lengths <- db$modulelengths
+mu <- median(lengths[which(lengths > 0)])
+job.msea$quantiles <- seq(0.5, (1.0 - 1.0/mu), length.out=10)
+}
+## Calculate hit counts:
+nloci <- length(job.msea$loci)
+hits <- ssea.prepare.counts(job.msea$locdata, nloci, job.msea$quantiles)
+db <- c(db, hits)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.prepare}}
+}
diff --git a/man/ssea.prepare.structure.Rd b/man/ssea.prepare.structure.Rd
new file mode 100644
index 0000000..b4e486d
--- /dev/null
+++ b/man/ssea.prepare.structure.Rd
@@ -0,0 +1,105 @@
+\name{ssea.prepare.structure}
+\alias{ssea.prepare.structure}
+\title{
+Construct hierarchical representation of components
+}
+\description{
+\code{ssea.prepare.structure} represents modules, genes, and markers in a
+hierarchical structure.
+}
+\usage{
+ssea.prepare.structure(moddata, gendata, nmods, ngens)
+}
+\arguments{
+\item{moddata}{module data (indexed identities)}
+\item{gendata}{mapping data (indexed identities)}
+\item{nmods}{number of modules}
+\item{ngens}{number of all genes}
+}
+\details{
+\code{ssea.prepare.structure} finds member genes of modules and marker
+lists of genes; counts distinct markers within each module and obtains
+module's density from this count; at the end, it returns hierarchically
+structured results.
+}
+\value{
+\item{res }{a data list with the following components: \preformatted{
+modulesizes: module size
+modulelengths: module length
+moduledensities: module densities
+genesizes: gene sizes of module
+module2genesgene: list of module
+gene2loci: markers lists of genes
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+
+## Remove extremely big or small modules:
+st <- tool.aggregate(job.msea$moddata$MODULE)
+mask <- which((st$lengths >= job.msea$mingenes) &
+(st$lengths <= job.msea$maxgenes))
+pos <- match(job.msea$moddata$MODULE, st$labels[mask])
+job.msea$moddata <- job.msea$moddata[which(pos > 0),]
+
+## Construct hierarchical representation for modules, genes, and markers:
+ngens <- length(job.msea$genes)
+nmods <- length(job.msea$modules)
+db <- ssea.prepare.structure(job.msea$moddata, job.msea$gendata,
+nmods, ngens)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.prepare}}
+}
diff --git a/man/ssea.start.Rd b/man/ssea.start.Rd
new file mode 100644
index 0000000..9093f83
--- /dev/null
+++ b/man/ssea.start.Rd
@@ -0,0 +1,110 @@
+\name{ssea.start}
+\alias{ssea.start}
+\title{
+Create a job for MSEA
+}
+\description{
+Creates identities (for modules, member genes, and loci) to start MSEA
+process.
+}
+\usage{
+ssea.start(plan)
+}
+\arguments{
+\item{plan}{a data list with the following components: \preformatted{
+label: unique identifier for the analysis
+folder: output folder for results
+modfile: path to module file (cols: MODULE GENE)
+marfile: path to marker file (cols: MARKER VALUE)
+genfile: path to gene file (cols: GENE LOCUS)
+inffile: path to module info file (cols: MODULE DESCR)
+seed: seed for random number generator
+permtype: gene for gene-level, locus for marker-level
+nperm: max number of random permutations
+mingenes: min number of genes per module (after merging)
+maxgenes: max number of genes per module
+quantiles: cutoffs for test statistic
+maxoverlap: max overlap allowed between genes
+}
+}
+}
+\details{
+\code{ssea.start} imports modules, genes-locus mapping, and locus values;
+removes the genes with no locus values from the list, find identities for
+modules, genes, loci components, and excludes missing data and factorize
+identities for these components.
+}
+\value{
+\item{job }{a data list with the following components: \preformatted{
+modules: module identities as characters.
+genes: gene identities as characters.
+loci: marker identities as characters.
+moddata: preprocessed module data (indexed identities)
+modinfo: description of the modules.
+gendata: preprocessed mapping data between genes and
+markers (indexed identities).
+locdata: preprocessed marker data (indexed identities)
+geneclusters: genes with shared markers.
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() for this small set:(due to the huge runtime we did not use
+## full sets of modules, genes, and markers)
+job.msea <- ssea.start(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}, \code{\link{ssea.control}},
+\code{\link{ssea.finish}}, \code{\link{ssea.prepare}},
+\code{\link{ssea2kda}}
+}
diff --git a/man/ssea.start.configure.Rd b/man/ssea.start.configure.Rd
new file mode 100644
index 0000000..06da67d
--- /dev/null
+++ b/man/ssea.start.configure.Rd
@@ -0,0 +1,108 @@
+\name{ssea.start.configure}
+\alias{ssea.start.configure}
+\title{
+Check parameters before MSEA
+}
+\description{
+\code{ssea.start.configure} checks the input parameter before MSEA process
+starts and assigns default values for non-exist fields of the input data
+object.
+}
+\usage{
+ssea.start.configure(plan)
+}
+\arguments{
+\item{plan}{a data list with the following components: \preformatted{
+label: unique identifier for the analysis
+folder: output folder for results
+modfile: path to module file (cols: MODULE GENE)
+marfile: path to marker file (cols: MARKER VALUE)
+genfile: path to gene file (cols: GENE MARKER)
+inffile: path to module info file (cols: MODULE DESCR)
+seed: seed for random number generator
+permtype: gene for gene-level, marker for marker-level
+nperm: max number of random permutations
+mingenes: min number of genes per module (after merging)
+maxgenes: max number of genes per module
+quantiles: cutoffs for test statistic
+maxoverlap: max overlap allowed between genes
+}
+}
+}
+\value{
+\item{plan}{a data list including checked and assigned values (to
+non-existing fields) of the input parameter: \preformatted{
+label: unique identifier for the analysis
+folder: output folder for results
+modfile: path to module file (cols: MODULE GENE)
+marfile: path to marker file (cols: MARKER VALUE)
+genfile: path to gene file (cols: GENE MARKER)
+inffile: path to module info file (cols: MODULE DESCR)
+seed: seed for random number generator
+permtype: gene for gene-level, marker for marker-level
+nperm: max number of random permutations
+mingenes: min number of genes per module (after merging)
+maxgenes: max number of genes per module
+quantiles: cutoffs for test statistic
+maxoverlap: max overlap allowed between genes
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() for this small set:(due to the huge runtime we did not use
+## full sets of modules, genes, and markers)
+job.msea <- ssea.start.configure(job.msea)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.start}}
+}
diff --git a/man/ssea.start.identify.Rd b/man/ssea.start.identify.Rd
new file mode 100644
index 0000000..f4ff70b
--- /dev/null
+++ b/man/ssea.start.identify.Rd
@@ -0,0 +1,51 @@
+\name{ssea.start.identify}
+\alias{ssea.start.identify}
+\title{
+Convert identities to indices for MSEA
+}
+\description{
+\code{\link{ssea.start.identify}} finds matching identities for the given
+variable name. It searches the members of \code{dat} among the members of
+\code{labels} with respect to the \code{varname} attribute, returns the
+matching rows of the \code{dat}.
+}
+\usage{
+ssea.start.identify(dat, varname, labels)
+}
+\arguments{
+\item{dat}{data list (source) of the identities that will be searched.
+e.g. the information after merging of overlapped genes (containing shared
+markers) }
+\item{varname}{search and match will be performed with respect to
+which attribute (MODULE or NODE or MARKER) }
+\item{labels}{the place, where the identities of \code{dat} will be
+searched and matched. }
+}
+\value{
+\item{res}{matched rows of \code{dat} among the members of \code{labels}
+list according to the \code{varname} attribute}
+}
+\examples{
+## Converts identities (either module names or gene names) to the indices
+aa<- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+NODE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+aa
+bb <- ssea.start.identify(aa, "MODULE", c("Mod1"))
+bb
+cc <- ssea.start.identify(aa, "MODULE", c("Mod1", "Mod3"))
+cc
+dd <- ssea.start.identify(aa, "NODE", c("GeneA"))
+dd
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.start}}
+}
diff --git a/man/ssea.start.relabel.Rd b/man/ssea.start.relabel.Rd
new file mode 100644
index 0000000..57dd51e
--- /dev/null
+++ b/man/ssea.start.relabel.Rd
@@ -0,0 +1,114 @@
+\name{ssea.start.relabel}
+\alias{ssea.start.relabel}
+\title{
+Update gene symbols after merging overlapped markers
+}
+\description{
+\code{ssea.start.relabel} updates gene symbols within the modules after
+merging overlapping genes that contain shared markers
+}
+\usage{
+ssea.start.relabel(dat, grp)
+}
+\arguments{
+\item{dat}{
+module data corresponding gene sets
+}
+\item{grp}{
+gene data that is needed to be relabeled after the merging process
+of the overlapping markers
+}
+}
+\value{
+\item{dat}{relabeled module data of \code{grp}}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() for this small set:(due to the huge runtime we did not use
+## full sets of modules, genes, and markers)
+job.msea <- ssea.start.configure(job.msea)
+
+## Import moddata:
+moddata <- tool.read(job.msea$modfile, c("MODULE", "GENE"))
+moddata <- unique(na.omit(moddata))
+
+## Import marker (e.g. locus) values:
+locdata <- tool.read(job.msea$locfile, c("LOCUS", "VALUE"))
+locdata$VALUE <- as.double(locdata$VALUE)
+rows <- which(0*(locdata$VALUE) == 0)
+locdata <- unique(na.omit(locdata[rows,]))
+locdata_ex <- locdata
+names(locdata_ex) <- c("MARKER","VALUE")
+## Import mapping data between genes and markers:
+gendata <- tool.read(job.msea$genfile, c("GENE", "LOCUS"))
+gendata <- unique(na.omit(gendata))
+gendata_ex <- gendata
+names(gendata_ex) <- c("GENE","MARKER")
+
+## Remove genes with no marker values:
+pos <- match(gendata$LOCUS, locdata$LOCUS)
+gendata <- gendata[which(pos > 0),]
+
+## Merge overlapping genes:
+gendata <- tool.coalesce(items=gendata$LOCUS, groups=gendata$GENE,
+rcutoff=job.msea$maxoverlap)
+job.msea$geneclusters <- gendata[,c("CLUSTER","GROUPS")]
+job.msea$geneclusters <- unique(job.msea$geneclusters)
+
+## Update gene symbols after merging the overlapping ones:
+moddata <- ssea.start.relabel(moddata, gendata)
+gendata <- unique(gendata[,c("GROUPS", "ITEM")])
+names(gendata) <- c("GENE", "LOCUS")
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.start}}
+}
diff --git a/man/ssea2kda.Rd b/man/ssea2kda.Rd
new file mode 100644
index 0000000..95be42b
--- /dev/null
+++ b/man/ssea2kda.Rd
@@ -0,0 +1,133 @@
+\name{ssea2kda}
+\alias{ssea2kda}
+\title{
+Generate inputs for wKDA
+}
+\description{
+\code{ssea2kda} forwards MSEA results to weighted key driver analysis (wKDA)
+from the first MSEA results, merges the overlapped modules according to
+a given overlapping ratio to obtain a relatively independent module set,
+apply a second MSEA on the merged modules (supersets), updates and saves
+the second MSEA results properly for wKDA process.
+}
+\usage{
+ssea2kda(job, symbols = NULL, rmax = NULL, min.module.count=NULL)
+}
+\arguments{
+\item{job}{data list including the organized results of MSEA process. It
+has following components:\preformatted{
+results: updated information of modules including:
+number of distinct member genes (NGENES),
+number of distinct member markers (NLOCI),
+ratio of distinct to non-distinct markers (DENSITY),
+false discovery rates (FDR).
+generesults: updated gene-specific information including:
+indexed gene identity (GENE),
+gene size (NLOCI),
+unadjusted enrichment score (SCORE),
+marker with max value (LOCUS),
+marker value (VALUE).
+}
+}
+\item{symbols}{
+dataframe for translating gene symbols
+}
+\item{rmax}{
+maximum allowed overlap ratio between gene sets
+}
+\item{min.module.count}{
+minimum number of the pathways to be taken from the MSEA results to merge.
+Default value is NULL. If it is not specified, all the pathways having MSEA-FDR
+value less than 0.25 will be considered for merging if they are overlapping
+with the given ratio \code{rmax}.
+}
+}
+\details{
+\code{ssea2kda} gets genes and top markers from input files, selects
+significant modules with respect to ordered p-values, gets identities of
+modules and genes, merges and trims the overlapping modules (either having FDR
+less than 0.25 or top \code{min.module.count} modules when ranked up to the
+P-values), obtains enrichment scores for merged modules, translates the gene
+symbols (between species) if needed, and finally saves the module, gene, node,
+and marker information into relevant output files.
+}
+\value{
+\item{plan }{an updated data list with the following components:
+\preformatted{
+label: unique identifier for the analysis.
+parent: parent folder for results.
+modfile: path of module file (columns: MODULE NODE).
+inffile: path of module information file
+(columns: MODULE DESCR).
+nodfile: path of node selection file (columns: NODE).
+}
+}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+############### Create intermediary datasets for KDA ##################
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+job.ssea2kda <- ssea2kda(job.msea, symbols=syms)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea.analyze}}, \code{\link{kda.analyze}}
+}
diff --git a/man/ssea2kda.analyze.Rd b/man/ssea2kda.analyze.Rd
new file mode 100644
index 0000000..d9ddf10
--- /dev/null
+++ b/man/ssea2kda.analyze.Rd
@@ -0,0 +1,133 @@
+\name{ssea2kda.analyze}
+\alias{ssea2kda.analyze}
+\title{
+Apply second MSEA after merging the modules
+}
+\description{
+\code{ssea2kda.analyze} performs a second MSEA for the updated modules
+after merging the highly overlapped modules (according to a specified
+overlapping ratio)
+}
+\usage{
+ssea2kda.analyze(job, moddata)
+}
+\arguments{
+\item{job}{the data list including the information of modules, genes, and
+markers, and also involving the database that uses indexed identities for
+modules, genes, and markers \code{(job$database)}.
+}
+\item{moddata}{
+merged modules including MODULE, GENE, and OVERLAP information
+}
+}
+\details{
+\code{ssea2kda.analyze} constructs new gene lists for merged modules and
+updates module database including module sizes, lengths, densities (based
+on marker sizes), and gene list. Then, it runs a second MSEA and returns
+the enrichment scores of the updated module database.
+}
+\value{
+\item{res }{data list including updated information (after merge) such as,
+enrichment scores of merged modules}
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+############### Create intermediary datasets for KDA ##################
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+## Collect genes and top markers from original files.
+noddata <- ssea2kda.import(job.msea$genfile, job.msea$locfile)
+
+## Select candidate modules (significant ones according to FDRs)
+res <- job.msea$results
+res <- res[order(res$P),]
+rows <- which(res$FDR < 0.25)
+res <- res[rows,]
+
+## Collect member genes.
+moddata <- job.msea$moddata
+pos <- match(moddata$MODULE, res$MODULE)
+moddata <- moddata[which(pos > 0),]
+
+## Restore original identities.
+modinfo <- job.msea$modinfo
+modinfo$MODULE <- job.msea$modules[modinfo$MODULE]
+moddata$MODULE <- job.msea$modules[moddata$MODULE]
+moddata$GENE <- job.msea$genes[moddata$GENE]
+
+## Merge and trim overlapping modules.
+moddata$OVERLAP <- moddata$MODULE
+rmax <- 0.33
+moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+rcutoff=rmax)
+moddata$MODULE <- moddata$CLUSTER
+moddata$GENE <- moddata$ITEM
+moddata$OVERLAP <- moddata$GROUPS
+moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+moddata <- unique(moddata)
+
+## Calculate enrichment scores for merged modules.
+tmp <- unique(moddata[,c("MODULE","OVERLAP")])
+res <- ssea2kda.analyze(job.msea, moddata)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea2kda}}
+}
diff --git a/man/ssea2kda.import.Rd b/man/ssea2kda.import.Rd
new file mode 100644
index 0000000..31ef35a
--- /dev/null
+++ b/man/ssea2kda.import.Rd
@@ -0,0 +1,93 @@
+\name{ssea2kda.import}
+\alias{ssea2kda.import}
+\title{
+Import genes and top markers from original files}
+\description{
+\code{ssea2kda.import} gets marker values from marker information file and
+mapping data (between genes and markers) from gene file, merges the imported
+information, and returns the merged data for top significant markers.
+}
+\usage{
+ssea2kda.import(genfile, locfile)
+}
+\arguments{
+\item{genfile}{
+gene information file
+}
+\item{locfile}{
+marker information file
+}
+}
+\value{
+\item{data }{merged gene and corresponding marker data for top significant
+markers }
+}
+\examples{
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000
+
+## ssea.start() process takes long time while merging the genes sharing high
+## amounts of markers (e.g. loci). it is performed with full module list in
+## the vignettes. Here, we used a very subset of the module list (1st 10 mods
+## from the original module file) and we collected the corresponding genes
+## and markers belonging to these modules:
+moddata <- tool.read(job.msea$modfile)
+gendata <- tool.read(job.msea$genfile)
+mardata <- tool.read(job.msea$marfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+gendata <- gendata[which(!is.na(match(gendata$GENE,
+unique(moddata$GENE)))),]
+mardata <- mardata[which(!is.na(match(mardata$MARKER,
+unique(gendata$MARKER)))),]
+
+## save this to a temporary file and set its path as new job.msea$modfile:
+tool.save(moddata, "subsetof.coexpr.modules.txt")
+tool.save(gendata, "subsetof.genfile.txt")
+tool.save(mardata, "subsetof.marfile.txt")
+job.msea$modfile <- "subsetof.coexpr.modules.txt"
+job.msea$genfile <- "subsetof.genfile.txt"
+job.msea$marfile <- "subsetof.marfile.txt"
+## run ssea.start() and prepare for this small set: (due to the huge runtime)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+
+############### Create intermediary datasets for KDA ##################
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+## Collect genes and top markers from original files.
+noddata <- ssea2kda.import(job.msea$genfile, job.msea$locfile)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.coexpr.modules.txt")
+file.remove("subsetof.genfile.txt")
+file.remove("subsetof.marfile.txt")
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{ssea2kda}}
+}
diff --git a/man/tool.aggregate.Rd b/man/tool.aggregate.Rd
new file mode 100644
index 0000000..bff8110
--- /dev/null
+++ b/man/tool.aggregate.Rd
@@ -0,0 +1,50 @@
+\name{tool.aggregate}
+\alias{tool.aggregate}
+\title{
+Aggregate the entries
+}
+\description{
+\code{tool.aggregate} aggregates the entries with respect to the given
+feature. It first finds raw indices (either genes or markers), then sorts
+them, and finds the blocks (segments) of identical entries.
+}
+\usage{
+tool.aggregate(entries, limit = 1)
+}
+\arguments{
+\item{entries}{
+an array that will be sorted and aggregated within blocks
+}
+\item{limit}{
+minimum block size to be included
+}
+}
+
+\value{
+\item{res }{a data list with the following components: \preformatted{
+labels: shared values within blocks
+lengths: numbers of entries in blocks
+blocks: integer arrays of entry positions within blocks
+ranks: entry positions included in blocks
+}
+}
+}
+\examples{
+aa <- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+GENE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+## aggregate according to the module names:
+bb <- tool.aggregate(aa$MODULE)
+bb
+## aggregate according to the gene names:
+cc <- tool.aggregate(aa$GENE)
+cc
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
\ No newline at end of file
diff --git a/man/tool.cluster.Rd b/man/tool.cluster.Rd
new file mode 100644
index 0000000..f555153
--- /dev/null
+++ b/man/tool.cluster.Rd
@@ -0,0 +1,59 @@
+\name{tool.cluster}
+\alias{tool.cluster}
+\title{
+Hierarchical clustering of nodes
+}
+\description{
+\code{tool.cluster} performs agglomerative hierarchical clustering for
+nodes (genes)
+}
+\usage{
+tool.cluster(edges, cutoff = NULL)
+}
+\arguments{
+\item{edges}{
+edge (weight) list among two group, whose overlapping information
+(overlapping ratio based on shared entries of two groups, number of
+members in both group) had been assesed previously
+}
+\item{cutoff}{cutting level of dendrogram for hierarchical clustering}
+}
+\details{
+\code{tool.cluster} takes overlapping information between two groups,
+produces distance matrix based on 1-strength(overlap) ratio between
+two groups, and apply agglomerative hierarchical clustering based on the
+distance matrix.
+}
+\value{
+\item{res }{data list including clustering results: \preformatted{
+CLUSTER: cluster label
+NODE: item (node) name
+}
+}
+}
+\examples{
+## read the coexpr module file as an example:
+moddata <- tool.read(system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))
+
+## let us cluster the first 10 modules in the module file:
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## Find clusters.
+rmax = 0.33
+edges <- tool.overlap(items=moddata$GENE, groups=moddata$MODULE)
+clustdat <- tool.cluster(edges, cutoff=rmax)
+nclust <- length(unique(clustdat$CLUSTER))
+nnodes <- length(unique(clustdat$NODE))
+}
+
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.cluster.static.Rd b/man/tool.cluster.static.Rd
new file mode 100644
index 0000000..9048f30
--- /dev/null
+++ b/man/tool.cluster.static.Rd
@@ -0,0 +1,42 @@
+\name{tool.cluster.static}
+\alias{tool.cluster.static}
+\title{
+Static hierarchical clustering
+}
+\description{
+\code{tool.cluster.static} takes dendrogram (clustering tree) and its
+cutting height; then, obtains cluster labels for the nodes of the tree.
+}
+\usage{
+tool.cluster.static(dendro, hlim)
+}
+\arguments{
+\item{dendro}{dendrogram (tree) }
+\item{hlim}{cutting height of the dendrogram}
+}
+\value{
+\item{clusters }{cluster labels of the components after static clustering}
+}
+\examples{
+set.seed(1)
+## assume that we have a dataset including several samples
+## with distinct features
+dataset <- matrix(rnorm(20), ncol=5) ## 4 samples with 5 features
+## Find the distances between each sample pair to cluster them
+d <- dist(dataset, method = "euclidean", upper=TRUE, diag=TRUE)
+tree <- hclust(d)
+## Height cutoff.
+hlim <- max(tree$height)
+## Find clusters.
+clusters <- tool.cluster.static(tree, hlim)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+
diff --git a/man/tool.coalesce.Rd b/man/tool.coalesce.Rd
new file mode 100644
index 0000000..554cc55
--- /dev/null
+++ b/man/tool.coalesce.Rd
@@ -0,0 +1,64 @@
+\name{tool.coalesce}
+\alias{tool.coalesce}
+\title{
+Calculate overlaps between groups (main function)
+}
+\description{
+\code{tool.coalesce} is utilized to merge and trim either overlapping
+modules (containing shared genes) or overlapping genes (containing
+shared markers)
+}
+\usage{
+tool.coalesce(items, groups, rcutoff = 0, ncore = NULL)
+}
+\arguments{
+\item{items}{
+array of item identities
+}
+\item{groups}{
+array of group identities for items
+}
+\item{rcutoff}{
+maximum overlap not coalesced
+}
+\item{ncore}{
+minimum number of items required for trimming
+}
+}
+\value{a data list with the following components:
+\item{CLUSTER }{cluster identities after merging and triming (a subset of
+group identities)}
+\item{ITEM }{item identities}
+\item{GROUPS }{comma separated overlapping group identities}
+}
+\examples{
+## read the coexpr module file as an example:
+moddata <- tool.read(system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))
+
+## let us find the overlapping ratio between first 10 modules in the file:
+## to merge overlapping modules first collect member genes:
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+
+## Merge and trim overlapping modules.(max allowed overlap ratio is 0.33)
+rmax <- 0.33
+moddata$OVERLAP <- moddata$MODULE
+moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+rcutoff=rmax)
+moddata$MODULE <- moddata$CLUSTER
+moddata$GENE <- moddata$ITEM
+moddata$OVERLAP <- moddata$GROUPS
+moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+moddata <- unique(moddata)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.coalesce.exec.Rd b/man/tool.coalesce.exec.Rd
new file mode 100644
index 0000000..bf82e2e
--- /dev/null
+++ b/man/tool.coalesce.exec.Rd
@@ -0,0 +1,55 @@
+\name{tool.coalesce.exec}
+\alias{tool.coalesce.exec}
+\title{
+Find, merge, and trim overlapping clusters
+}
+\description{
+\code{tool.coalesce.exec} searchs overlaps, iteratively merges and trims
+overlapping clusters (by using \code{\link{tool.coalesce.find}} and
+\code{\link{tool.coalesce.merge}}, respectively) until no more overlap is
+available, and assigns representative label for the merged clusters.
+}
+\usage{
+tool.coalesce.exec(items, groups, rcutoff, ncore)
+}
+\arguments{
+\item{items}{array of item identities}
+\item{groups}{array of group identities for items}
+\item{rcutoff}{maximum overlap not coalesced}
+\item{ncore}{minimum number of items required for trimming}
+}
+\value{a data list with the following components:
+\item{CLUSTER }{cluster identities after merging and triming (a subset of
+group identities)}
+\item{GROUPS }{comma separated overlapping group identities}
+}
+\examples{
+## Generate item and group labels for 100 items:
+## Assume that unique gene number (items) is 60:
+members <- 1:100 ## will be updated
+modules <- 1:100 ## will be updated
+set.seed(1)
+for (i in 1:10){
+## each time pick 10 items (genes) from 60 unique item labels
+members[(i*10-9):(i*10)] <- sample(60,10)
+}
+## Assume that unique group labels is 30:
+for (i in 1:10){
+## each time pick 10 items (genes) from 30 unique group labels
+modules[(i*10-9):(i*10)] <- sample(30, 10)
+}
+rcutoff <- 0.33
+ncore <- length(members)
+## Find and trim clusters after iteratively merging the overlapping ones:
+res <- tool.coalesce.exec(members, modules, rcutoff, ncore)
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+
diff --git a/man/tool.coalesce.find.Rd b/man/tool.coalesce.find.Rd
new file mode 100644
index 0000000..d355c17
--- /dev/null
+++ b/man/tool.coalesce.find.Rd
@@ -0,0 +1,58 @@
+\name{tool.coalesce.find}
+\alias{tool.coalesce.find}
+\title{
+Find overlapping clusters
+}
+\description{
+\code{tool.coalesce.find} finds overlapped clusters of the given
+\code{data} according to a given overlapping ratio by using
+\code{\link{tool.overlap}} and \code{\link{tool.cluster}}, respectively. }
+\usage{
+tool.coalesce.find(data, rmax)
+}
+\arguments{
+\item{data}{a list including ITEM identities and their GROUP identities}
+\item{rmax}{maximum overlap not coalesced}
+}
+\value{data list including clustering results and following components:
+\item{CLUSTER }{cluster label}
+\item{NODE }{item (node) name}
+}
+\examples{
+## Generate item and group labels for 100 items:
+## Assume that unique gene number (items) is 60:
+members <- 1:100 ## will be updated
+modules <- 1:100 ## will be updated
+set.seed(1)
+for (i in 1:10){
+## each time pick 10 items (genes) from 60 unique item labels
+members[(i*10-9):(i*10)] <- sample(60,10)
+}
+## Assume that unique group labels is 30:
+for (i in 1:10){
+## each time pick 10 items (genes) from 30 unique group labels
+modules[(i*10-9):(i*10)] <- sample(30, 10)
+}
+rcutoff <- 0.33
+ncore <- length(members)
+## Default output.
+res <- data.frame(CLUSTER=modules, GROUPS=modules, ITEM=members,
+stringsAsFactors=FALSE)
+## Iterative merging and trimming.
+res$COUNT <- 0.0
+while(TRUE) {
+clust <- tool.coalesce.find(res, rcutoff)
+if(is.null(clust)) break
+res <- tool.coalesce.merge(clust, ncore)
+}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
+
diff --git a/man/tool.coalesce.merge.Rd b/man/tool.coalesce.merge.Rd
new file mode 100644
index 0000000..89bfc34
--- /dev/null
+++ b/man/tool.coalesce.merge.Rd
@@ -0,0 +1,62 @@
+\name{tool.coalesce.merge}
+\alias{tool.coalesce.merge}
+\title{
+Merge overlapping clusters
+}
+\description{
+\code{tool.coalesce.merge} determines combinable groups and trims clusters
+by removing rarest items.
+}
+\usage{
+tool.coalesce.merge(data, ncore)
+}
+\arguments{
+\item{data }{data list including following components: \preformatted{
+CLUSTER: cluster label
+NODE: item (node) name
+}
+}
+\item{ncore}{
+minimum number of items required for trimming
+}
+}
+\value{
+\item{res }{data list including GROUPS, ITEMs, and their hit COUNTs}
+}
+\examples{
+## Generate item and group labels for 100 items:
+## Assume that unique gene number (items) is 60:
+members <- 1:100 ## will be updated
+modules <- 1:100 ## will be updated
+set.seed(1)
+for (i in 1:10){
+## each time pick 10 items (genes) from 60 unique item labels
+members[(i*10-9):(i*10)] <- sample(60,10)
+}
+## Assume that unique group labels is 30:
+for (i in 1:10){
+## each time pick 10 items (genes) from 30 unique group labels
+modules[(i*10-9):(i*10)] <- sample(30, 10)
+}
+rcutoff <- 0.33
+ncore <- length(members)
+## Default output.
+res <- data.frame(CLUSTER=modules, GROUPS=modules, ITEM=members,
+stringsAsFactors=FALSE)
+## Iterative merging and trimming.
+res$COUNT <- 0.0
+while(TRUE) {
+clust <- tool.coalesce.find(res, rcutoff)
+if(is.null(clust)) break
+res <- tool.coalesce.merge(clust, ncore)
+}
+}
+\references{
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.fdr.Rd b/man/tool.fdr.Rd
new file mode 100644
index 0000000..ce8ee0b
--- /dev/null
+++ b/man/tool.fdr.Rd
@@ -0,0 +1,36 @@
+\name{tool.fdr}
+\alias{tool.fdr}
+\title{
+Estimate False Discovery Rates (FDR)
+}
+\description{
+\code{tool.fdr} estimates FDRs for modules as another module statistic.
+}
+\usage{
+tool.fdr(p, f = NULL)
+}
+\arguments{
+\item{p}{p-values of modules}
+\item{f}{pre-defined threshold for FDR}
+}
+\details{
+FDRs of modules can be obtained by using either empirical method or
+Benjamini and Hochberg method.
+}
+\value{
+\item{res }{data list including the estimated false discovery rates of
+modules }
+}
+\examples{
+## let us assume we have a set of pvalues
+## and would like to find FDR values:
+set.seed(1)
+p <- abs(rnorm(10))*1e-2
+FDRs <- tool.fdr(p) ## default method is Benjamini Hochberg
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.fdr.empirical}}, \code{\link{tool.fdr.bh}}
+}
diff --git a/man/tool.fdr.bh.Rd b/man/tool.fdr.bh.Rd
new file mode 100644
index 0000000..163da9c
--- /dev/null
+++ b/man/tool.fdr.bh.Rd
@@ -0,0 +1,32 @@
+\name{tool.fdr.bh}
+\alias{tool.fdr.bh}
+\title{
+Benjamini and Hochberg False Discovery Rate
+}
+\description{
+\code{tool.fdr.bh} estimates FDRs of modules by using Benjamini and
+Hochberg method.
+}
+\usage{
+tool.fdr.bh(p)
+}
+\arguments{
+\item{p}{p-values of modules}
+}
+\value{
+\item{res }{data list including the estimated false discovery rates of
+modules}
+}
+\examples{
+## let us assume we have a set of pvalues
+## and would like to find FDR values:
+set.seed(1)
+p <- abs(rnorm(10))*1e-2
+FDRs <- tool.fdr.bh(p) ## the default method is already Benjamini Hochberg
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.fdr}}, \code{\link{tool.fdr.bh}}
+}
diff --git a/man/tool.fdr.empirical.Rd b/man/tool.fdr.empirical.Rd
new file mode 100644
index 0000000..3f2b56e
--- /dev/null
+++ b/man/tool.fdr.empirical.Rd
@@ -0,0 +1,33 @@
+\name{tool.fdr.empirical}
+\alias{tool.fdr.empirical}
+\title{
+Estimate Empirical False Discovery Rates
+}
+\description{
+\code{tool.fdr.empirical} estimates empirical FDR for modules
+}
+\usage{
+tool.fdr.empirical(p, f0)
+}
+\arguments{
+\item{p}{p-values of modules}
+\item{f0}{pre-defined threshold for FDR}
+}
+\value{
+\item{res }{data list including the estimated false discovery rates of
+modules}
+}
+\examples{
+## let us assume we have a set of pvalues
+## and would like to find FDR values:
+set.seed(1)
+p <- abs(rnorm(10))*1e-2
+f = 0.05 ## pre-defined threshold for FDR
+FDRs <- tool.fdr.empirical(p, f)
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.fdr}}, \code{\link{tool.fdr.bh}}
+}
diff --git a/man/tool.graph.Rd b/man/tool.graph.Rd
new file mode 100644
index 0000000..7313cc6
--- /dev/null
+++ b/man/tool.graph.Rd
@@ -0,0 +1,75 @@
+\name{tool.graph}
+\alias{tool.graph}
+\title{
+Convert an edge list to a graph representation
+}
+\description{
+\code{tool.graph} translates an edge list including TAIL, HEAD and WEIGHT
+information into a graph representation-adapted data list. It also
+provides in-degree and out-degree statistics for nodes.
+}
+\usage{
+tool.graph(edges)
+}
+\arguments{
+\item{edges}{a data frame with three columns TAIL, HEAD and WEIGHT}
+}
+\value{a datalist including following components:
+\item{nodes }{N-element array of node names}
+\item{tails }{K-element array of node indices}
+\item{heads}{K-element array of node indices}
+\item{weights}{K-element array of edge weights}
+\item{tail2edge}{N-element list of adjacent edge indices}
+\item{head2edge}{N-element list of adjacent edge indices}
+\item{outstats}{N-row data frame of out-degree node statistics}
+\item{instats}{N-row data frame of in-degree node statistics}
+\item{stats}{N-row data frame of node statistics}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## module file:
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+job.kda <- kda.configure(job.kda)
+## Import data for weighted key driver analysis:
+## Import topology.
+edgdata <- kda.start.edges(job.kda)
+## Create an indexed graph structure.
+job.kda$graph <- tool.graph(edgdata)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.subgraph}}
+}
diff --git a/man/tool.graph.degree.Rd b/man/tool.graph.degree.Rd
new file mode 100644
index 0000000..0704cb4
--- /dev/null
+++ b/man/tool.graph.degree.Rd
@@ -0,0 +1,94 @@
+\name{tool.graph.degree}
+\alias{tool.graph.degree}
+\title{
+Find degrees of the nodes
+}
+\description{
+\code{tool.graph.degree} finds in-degree and out-degree statistics of the
+network by using edge lists of the nodes. It also obtains the strenghts
+of the degrees by using edge weights.
+}
+\usage{
+tool.graph.degree(node2edge, weights)
+}
+\arguments{
+\item{node2edge}{edge list of each node}
+\item{weights}{strengths of the edges}
+}
+\details{
+Degree of a node means number of the neighbors belonging to that node.
+Hence, out-degree statistics are applicable for tail nodes; while
+in-degree statistics are applicable for the heads.
+}
+\value{
+\item{res}{a data list including degree and its strength for each node}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## module file:
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+job.kda <- kda.configure(job.kda)
+
+## Import data for weighted key driver analysis:
+## Import topology.
+edges <- kda.start.edges(job.kda)
+## Create an indexed graph structure.
+tails <- as.character(edges$TAIL)
+heads <- as.character(edges$HEAD)
+wdata <- as.double(edges$WEIGHT)
+
+nedges <- length(tails)
+# Create factorized representation.
+labels <- as.character(c(tails, heads))
+labels <- as.factor(labels)
+labelsT <- as.integer(labels[1:nedges])
+labelsH <- as.integer(labels[(nedges+1):(2*nedges)])
+# Create edge lists.
+nodnames <- levels(labels)
+nnodes <- length(nodnames)
+elistT <- tool.graph.list(labelsT, nnodes)
+elistH <- tool.graph.list(labelsH, nnodes)
+## Collect edge degree stats:
+res <- list()
+res$nodes <- as.character(nodnames)
+res$outstats <- tool.graph.degree(elistT, wdata) ## out degrees
+res$instats <- tool.graph.degree(elistH, wdata) ## in degrees
+res$stats <- (res$outstats + res$instats)
+
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.graph}}
+}
diff --git a/man/tool.graph.list.Rd b/man/tool.graph.list.Rd
new file mode 100644
index 0000000..62a33d7
--- /dev/null
+++ b/man/tool.graph.list.Rd
@@ -0,0 +1,81 @@
+\name{tool.graph.list}
+\alias{tool.graph.list}
+\title{
+Return edge list for each node
+}
+\description{
+\code{tool.graph.list} finds and returns the edge list of each node for
+both tail and head node lists.
+}
+\usage{
+tool.graph.list(entries, nnodes)
+}
+\arguments{
+\item{entries}{either tail nodes list or head nodes list}
+\item{nnodes}{total number of all nodes including both tails and heads}
+}
+\value{
+\item{groups }{a data list including edge list of each node}
+}
+\examples{
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics")
+## module file:
+job.kda$modfile<- system.file("extdata","mergedModules.txt",
+package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is opposite.
+job.kda$edgefactor<-0.0
+## The searching depth for the KDA
+job.kda$depth<-1
+## 0 means we do not consider the directions of the regulatory interactions
+## while 1 is opposite.
+job.kda$direction <- 1
+job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests
+
+## kda.start() process takes long time while seeking hubs in the given net
+## Here, we used a very small subset of the module list (1st 10 mods
+## from the original module file):
+moddata <- tool.read(job.kda$modfile)
+mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
+10)]
+moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
+## save this to a temporary file and set its path as new job.kda$modfile:
+tool.save(moddata, "subsetof.supersets.txt")
+job.kda$modfile <- "subsetof.supersets.txt"
+
+job.kda <- kda.configure(job.kda)
+
+## Import data for weighted key driver analysis:
+## Import topology.
+edges <- kda.start.edges(job.kda)
+## Create an indexed graph structure.
+tails <- as.character(edges$TAIL)
+heads <- as.character(edges$HEAD)
+wdata <- as.double(edges$WEIGHT)
+
+nedges <- length(tails)
+# Create factorized representation.
+labels <- as.character(c(tails, heads))
+labels <- as.factor(labels)
+labelsT <- as.integer(labels[1:nedges])
+labelsH <- as.integer(labels[(nedges+1):(2*nedges)])
+# Create edge lists.
+nodnames <- levels(labels)
+nnodes <- length(nodnames)
+elistT <- tool.graph.list(labelsT, nnodes)
+elistH <- tool.graph.list(labelsH, nnodes)
+## Remove the temporary files used for the test:
+file.remove("subsetof.supersets.txt")
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.graph}}
+}
diff --git a/man/tool.metap.Rd b/man/tool.metap.Rd
new file mode 100644
index 0000000..e5f8285
--- /dev/null
+++ b/man/tool.metap.Rd
@@ -0,0 +1,43 @@
+\name{tool.metap}
+\alias{tool.metap}
+\title{
+Estimate meta P-values
+}
+\description{
+\code{tool.metap} returns the meta p-values of given datasets with
+multiple p-values.
+}
+\usage{
+tool.metap(datasets, idcolumn, pcolumn, weights = NULL)
+}
+\arguments{
+\item{datasets}{data list, whose meta p-values will be obtained}
+\item{idcolumn}{column number of the datasets that includes identities}
+\item{pcolumn}{column number of the datasets that includes p-values}
+\item{weights}{weight list of the data list}
+}
+\value{
+\item{res }{data list including identities and meta p-values of the
+given \code{datasets}}
+}
+\examples{
+set.seed(1)
+## let us assume we have p-values for the coexpr modules obtained from
+## distinct analyses by using different gene-marker mapping sets (e.g. eQTLs
+## from diff tissues) and we would like to make a meta-analysis for
+## these multiple Pvalues of the modules:
+datasets=list()
+## we have 3 datasets and 3 diff result sets
+datasets[[1]] <- data.frame(MODULE=c("Mod1", "Mod2", "Mod3", "Mod4"),
+P=c(rnorm(4)))
+datasets[[2]] <- data.frame(MODULE=c("Mod1", "Mod2", "Mod3", "Mod4"),
+P=c(rnorm(4)))
+datasets[[3]] <- data.frame(MODULE=c("Mod1", "Mod2", "Mod3", "Mod4"),
+P=c(rnorm(4)))
+idcolumn <- "MODULE" ## identifiers of the modules are in the 1st col
+pcolumn <- "P" ## p values of the modules are in the 2nd col
+tool.metap(datasets, idcolumn, pcolumn)
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.normalize.Rd b/man/tool.normalize.Rd
new file mode 100644
index 0000000..457f6b5
--- /dev/null
+++ b/man/tool.normalize.Rd
@@ -0,0 +1,49 @@
+\name{tool.normalize}
+\alias{tool.normalize}
+\title{
+Estimate statistical scores based on Gauss distribution
+}
+\description{
+To estimate the both pre-liminary and final p-values,
+\code{tool.normalize} normalizes the given data, \code{x}, based on
+Gaussian distribution defined by \code{prm} if it is provided. If
+\code{prm} is not provided \code{tool.normalize} utilizes the mean and
+std dev of \code{x}.
+}
+\usage{
+tool.normalize(x, prm = NULL, inverse = FALSE)
+}
+\arguments{
+\item{x}{data that is aimed to be normalized and produced by a simulation
+process}
+\item{prm}{normalization will take place according to the specified
+Gaussian distribution parameters, i.e. mean and std dev. If it is not
+specified, Gaussian statistics of \code{x} will be obtained and utilized}
+\item{inverse}{specifies whether the normalization takes place in reverse
+order}
+}
+\value{
+\item{prm }{transformed (normalized) parameters for either enrichment
+score or p-values}
+}
+\examples{
+set.seed(1)
+## let us assume we have a set of simulated enrichment scores and
+## one observed score
+x <- rnorm(10) ## obtained from 1st permutation test
+obs <- rnorm(1)
+## Estimate preliminary P-value:
+param <- tool.normalize(x)
+z <- tool.normalize(obs, param)
+p <- pnorm(z, lower.tail=FALSE)
+
+## Estimate final P-value.
+y <- rnorm(10) ## obtained from 2nd permutation test
+param <- tool.normalize(c(x, y))
+z <- tool.normalize(obs, param)
+p <- pnorm(z, lower.tail=FALSE)
+p <- max(p, .Machine$double.xmin)
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.normalize.quality.Rd b/man/tool.normalize.quality.Rd
new file mode 100644
index 0000000..5a7a268
--- /dev/null
+++ b/man/tool.normalize.quality.Rd
@@ -0,0 +1,41 @@
+\name{tool.normalize.quality}
+\alias{tool.normalize.quality}
+\title{
+Check normalization quality
+}
+\description{
+\code{tool.normalize.quality} checks transformation quality by using
+Kolmogorov-Smirnov Test. It seeks the best log transform parameter within
+the previously specified upper and lower limits, and applies the log
+transform with the best log parameter.
+}
+\usage{
+tool.normalize.quality(g, z)
+}
+\arguments{
+\item{g}{normalization quality control will take place according to the
+normal distribution parameters defined by \code{g}, e.g. it can be normal
+distribution with 0-mean and std dev 1. }
+\item{z}{transformed data, i.e. either p-value or enrichment score}
+}
+\value{
+\item{res}{statitics of Kolmogorov-Smirnov Test result obtained for
+\code{z} values}
+}
+\examples{
+set.seed(1)
+## let us assume we have a set of normalized scores:
+z <- abs(rnorm(10)) ## it should be positive and at least 10 length-vector
+z <- z/median(z)
+## Find the best log transform.
+gamma <- optim(par=1.0, fn=tool.normalize.quality, gr=NULL, z,
+lower=-9, upper=9, control=list(reltol=1e-3))
+## After finding the best log transform, apply transform:
+z <- log(exp(gamma$par)*z + 1.0)
+}
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.normalize}}
+}
diff --git a/man/tool.overlap.Rd b/man/tool.overlap.Rd
new file mode 100644
index 0000000..c02ca6a
--- /dev/null
+++ b/man/tool.overlap.Rd
@@ -0,0 +1,46 @@
+\name{tool.overlap}
+\alias{tool.overlap}
+\title{
+Calculate overlaps between groups of specified items
+}
+\description{
+\code{tool.overlap} checks each pair of blocks, finds number of shared
+items, and obtains significance values of the sharings for block pairs.
+}
+\usage{
+tool.overlap(items, groups, nbackground = NULL)
+}
+\arguments{
+\item{items}{array of item identities}
+\item{groups}{array of group identities for items}
+\item{nbackground}{total number of items}
+}
+\value{a data list including following components
+\item{A }{group name}
+\item{B }{group name}
+\item{POSa }{group name rank}
+\item{POSb }{group name rank}
+\item{Na }{group A size}
+\item{Nb }{group B size}
+\item{Nab }{shared items}
+\item{R }{overlap ratio}
+\item{F }{fold change to null expectation}
+\item{P }{overlap P-value (Fisher's test)}
+}
+\examples{
+## read the coexpr module file as an example:
+moddata <- tool.read(system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))
+## let us find the overlapping ratio between two modules:
+## pick the first and last modules:
+mod.names <- unique(moddata$MODULE)[c(1,length(unique(moddata$MODULE)))]
+if(length(mod.names) > 0){
+modA.members <- moddata[which(moddata$MODULE == mod.names[1]),]
+modB.members <- moddata[which(moddata$MODULE == mod.names[2]),]
+}
+mod.pool <- rbind(modA.members, modB.members)
+overlap.stats <- tool.overlap(mod.pool[,2], mod.pool[,1])
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.read.Rd b/man/tool.read.Rd
new file mode 100644
index 0000000..837e889
--- /dev/null
+++ b/man/tool.read.Rd
@@ -0,0 +1,35 @@
+\name{tool.read}
+\alias{tool.read}
+\title{
+Read a data frame from a file
+}
+\description{
+\code{tool.read} reads contents of given input file.
+}
+\usage{
+tool.read(file, vars = NULL)
+}
+\arguments{
+\item{file}{file name to be read}
+\item{vars}{if we want to read particular attributes (columns) from the
+input file, we need to specify names of these attributes within list
+\code{vars} (attribute names can be e.g. MODULE, GENE, LOCUS, etc.)}
+}
+\details{
+All lines with NAs are excluded.
+}
+\value{
+\item{dat }{data frame including content of the given file. If \code{vars}
+is specified, only the listed columns inside the \code{vars} list will be
+returned.}
+}
+\examples{
+## read the network file as an example:
+net.info <- tool.read(system.file("extdata","network.mouseliver.mouse.txt",
+package="Mergeomics"))
+dim(net.info)
+names(net.info)
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.save.Rd b/man/tool.save.Rd
new file mode 100644
index 0000000..553f35e
--- /dev/null
+++ b/man/tool.save.Rd
@@ -0,0 +1,34 @@
+\name{tool.save}
+\alias{tool.save}
+\title{
+Save a data frame in tab-delimited file
+}
+\description{
+\code{tool.save} saves a given data frame into a specified file within a
+given directory.
+}
+\usage{
+tool.save(frame, file, directory = NULL, verbose = TRUE, compression = FALSE)
+}
+\arguments{
+\item{frame}{data frame to be saved into file}
+\item{file}{name of the output file to be written}
+\item{directory}{path of the directory for the file}
+\item{verbose}{specifies whether the information about file saving process
+will be displayed to user}
+\item{compression}{specifies whether the file is compressed while saving.
+Applicable for only UNIX-family systems with gzip.}
+}
+\value{
+\item{fname }{returns file name with full path}
+}
+\examples{
+aa<- data.frame(MODULE=c("Mod1", "Mod1", "Mod2", "Mod2", "Mod3"),
+NODE=c("GeneA", "GeneC", "GeneB", "GeneC", "GeneA"))
+tool.save(aa, "aa.save.txt")
+file.remove("aa.save.txt") ## delete the saved file!
+}
+\author{
+Ville-Petteri Makinen
+}
+\note{Compression only works on UNIX-family systems with gzip.}
diff --git a/man/tool.subgraph.Rd b/man/tool.subgraph.Rd
new file mode 100644
index 0000000..17de1dd
--- /dev/null
+++ b/man/tool.subgraph.Rd
@@ -0,0 +1,51 @@
+\name{tool.subgraph}
+\alias{tool.subgraph}
+\title{
+Determine network neighbors for a set of nodes
+}
+\description{
+\code{tool.subgraph} finds the sub-network, i.e. neighborhood, for a given
+seed node list with a specified depth. It also provides graph statistics
+(degrees and strengths) for seed nodes.
+}
+\usage{
+tool.subgraph(graph, seeds, depth = 1, direction = 0)
+}
+\arguments{
+\item{graph}{a datalist including following components: \preformatted{
+nodes: N-element array of node names
+tails: K-element array of node indices
+heads: K-element array of node indices
+weights: K-element array of edge weights
+tail2edge: N-element list of adjacent edge indices
+head2edge: N-element list of adjacent edge indices
+outstats: N-row data frame of out-degree node statistics
+instats: N-row data frame of in-degree node statistics
+stats: N-row data frame of node statistics
+}
+}
+
+\item{seeds}{list of seed node names }
+\item{depth}{the maximum number of links to connect neighbors}
+\item{direction}{sets the directionality: use a negative value for
+dowstream, positive for upstream or zero for undirected}
+}
+\value{a data list including following components:
+\item{RANK}{indices of neighboring nodes (including seeds)}
+\item{LEVEL}{number of edges away from seed }
+\item{STRENG}{sum of adjacent edge weights within neighborhood}
+\item{DEGREE}{number of adjacent edges within neighborhood}
+}
+\examples{
+data(job_kda_analyze)
+## take the first node in the graph as the seed, find its neighborhood:
+center.node = job.kda$graph$nodes[1]
+subnet = tool.subgraph(job.kda$graph, center.node, depth=1, direction=0)
+}
+
+\author{
+Ville-Petteri Makinen
+}
+\seealso{
+\code{\link{tool.subgraph.search}}
+}
diff --git a/man/tool.subgraph.find.Rd b/man/tool.subgraph.find.Rd
new file mode 100644
index 0000000..13f7a14
--- /dev/null
+++ b/man/tool.subgraph.find.Rd
@@ -0,0 +1,53 @@
+\name{tool.subgraph.find}
+\alias{tool.subgraph.find}
+\title{
+Find edges to adjacent nodes
+}
+\description{
+\code{tool.subgraph.find} finds the edge lists between given seed nodes
+and their neighbors
+}
+\usage{
+tool.subgraph.find(seeds, edgemap, heads, visited)
+}
+\arguments{
+\item{seeds}{seed nodes' indices }
+\item{edgemap}{list of adjacent edge information for entire graph.
+\code{edgemap} can belong to either tails or heads.}
+\item{heads}{list of either head (destination) or tail (source) nodes of
+the entire graph}
+\item{visited}{flag holding already visited node indices during
+neighborhood searching}
+}
+\value{
+\item{neighbors }{neighbor edge lists of seed nodes (for either tails or
+heads)}
+}
+\examples{
+data(job_kda_analyze)
+depth <- 1
+direction <- 0
+## Take one or multiple center nodes (seeds) to search the neighborhoods:
+## e.g. take the first node in the graph as the seed, find its neighborhood:
+center.node = job.kda$graph$nodes[1]
+## Convert center node (seed) names to indices:
+nodes <- job.kda$graph$nodes
+ranks <- match(center.node, nodes)
+ranks <- ranks[which(ranks > 0)]
+## we already know that rank is 1, since we took the first node in the graph
+## as an example:
+ranks <- as.integer(ranks)
+## Find edges to adjacent nodes. (both up- and down-stream searches)
+visited <- ranks
+foundT <- tool.subgraph.find(ranks, job.kda$graph$tail2edge,
+job.kda$graph$heads, visited)
+foundH <- tool.subgraph.find(ranks, job.kda$graph$head2edge,
+job.kda$graph$tails, visited)
+}
+\author{
+Ville-Petteri Makinen
+}
+\note{
+Neighbor edge lists of the seed nodes should be obtained separately for
+tail and head nodes.
+}
diff --git a/man/tool.subgraph.search.Rd b/man/tool.subgraph.search.Rd
new file mode 100644
index 0000000..6638e26
--- /dev/null
+++ b/man/tool.subgraph.search.Rd
@@ -0,0 +1,59 @@
+\name{tool.subgraph.search}
+\alias{tool.subgraph.search}
+\title{
+Search neighborhoods for given nodes
+}
+\description{
+\code{tool.subgraph.search} looks for both upstream and downstream
+neighborhoods of given seed node list for a given depth, gets the directed
+edge information among seed nodes and their neighbors, obtains statistics
+(degrees and strengths) for seed nodes.
+}
+\usage{
+tool.subgraph.search(graph, seeds, depth, direction)
+}
+\arguments{
+\item{graph}{a datalist including following components: \preformatted{
+nodes: N-element array of node names
+tails: K-element array of node indices
+heads: K-element array of node indices
+weights: K-element array of edge weights
+tail2edge: N-element list of adjacent edge indices
+head2edge: N-element list of adjacent edge indices
+outstats: N-row data frame of out-degree node statistics
+instats: N-row data frame of in-degree node statistics
+stats: N-row data frame of node statistics
+}
+}
+\item{seeds}{seed nodes' indices }
+\item{depth}{the maximum number of links to connect neighbors}
+\item{direction}{sets the directionality: use a negative value for dowstream,
+positive for upstream or zero for undirected}
+}
+\value{a data list including seed nodes neighborhood information with
+following components:
+\item{RANK}{indices of neighboring nodes (including seeds)}
+\item{LEVEL}{number of edges away from seed }
+\item{STRENG}{sum of adjacent edge weights within neighborhood}
+\item{DEGREE}{number of adjacent edges within neighborhood}
+}
+\examples{
+data(job_kda_analyze)
+depth <- 1
+direction <- 0
+## Take one or multiple center nodes (seeds) to search the neighborhoods:
+## e.g. take the first node in the graph as the seed, find its neighborhood:
+center.node = job.kda$graph$nodes[1]
+## Convert center node (seed) names to indices:
+nodes <- job.kda$graph$nodes
+ranks <- match(center.node, nodes)
+ranks <- ranks[which(ranks > 0)]
+## we already know that rank is 1, since we took the first node in the graph
+## as an example:
+ranks <- as.integer(ranks)
+## Find neighbors.
+res <- tool.subgraph.search(job.kda$graph, ranks, depth, direction)
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.subgraph.stats.Rd b/man/tool.subgraph.stats.Rd
new file mode 100644
index 0000000..d7d7c8f
--- /dev/null
+++ b/man/tool.subgraph.stats.Rd
@@ -0,0 +1,73 @@
+\name{tool.subgraph.stats}
+\alias{tool.subgraph.stats}
+\title{
+Calculate node degrees and strengths
+}
+\description{
+\code{tool.subgraph.stats} graph statistics (degrees and strengths) of the
+seed nodes obtained from their neighborhoods.
+}
+\usage{
+tool.subgraph.stats(frame, edgemap, heads, weights)
+}
+\arguments{
+\item{frame}{a data frame including following components: \preformatted{
+RANK: indices of neighboring nodes (including seeds)
+LEVEL: number of edges away from seed
+STRENG: sum of adjacent edge weights within neighborhood
+DEGREE: number of adjacent edges within neighborhood
+}
+}
+\item{edgemap}{list of adjacent edge information for detected neighborhoods
+of seed nodes. \code{edgemap} can belong to either tails or heads.}
+\item{heads}{list of either head (destination) or tail (source) nodes for
+neighborhoods of the seed nodes}
+\item{weights}{weights of the edges in the entire graph}
+}
+\value{a data list including seed nodes neighborhood information with
+following components:
+\item{RANK}{indices of neighboring nodes (including seeds)}
+\item{LEVEL}{number of edges away from seed }
+\item{STRENG}{sum of adjacent edge weights within neighborhood}
+\item{DEGREE}{number of adjacent edges within neighborhood}
+}
+\examples{
+data(job_kda_analyze)
+depth <- 1
+direction <- 0
+## Take one or multiple center nodes (seeds) to search the neighborhoods:
+## e.g. take the first node in the graph as the seed, find its neighborhood:
+center.node = job.kda$graph$nodes[1]
+## Convert center node (seed) names to indices:
+nodes <- job.kda$graph$nodes
+ranks <- match(center.node, nodes)
+ranks <- ranks[which(ranks > 0)]
+## we already know that rank is 1, since we took the first node in the graph
+## as an example:
+ranks <- as.integer(ranks)
+## Find edges to adjacent nodes. (both up- and down-stream searches)
+visited <- ranks
+levels <- 0*ranks
+for(i in 1:depth) {
+## Find edges to adjacent nodes.
+foundT <- tool.subgraph.find(ranks, job.kda$graph$tail2edge,
+job.kda$graph$heads, visited)
+foundH <- tool.subgraph.find(ranks, job.kda$graph$head2edge,
+job.kda$graph$tails, visited)
+## Expand neighborhood for the further depths of the neighborhood search
+ranks <- unique(c(foundT, foundH))
+visited <- c(visited, ranks)
+levels <- c(levels, (0*ranks + i)) ## level shows the depth
+if(length(ranks) < 1) break
+}
+## Calculate node degrees and strengths.
+res <- data.frame(RANK=visited, LEVEL=levels, DEGREE=0,
+STRENG=0.0, stringsAsFactors=FALSE)
+res <- tool.subgraph.stats(res, job.kda$graph$tail2edge,
+job.kda$graph$heads, job.kda$graph$weights)
+res <- tool.subgraph.stats(res, job.kda$graph$head2edge,
+job.kda$graph$tails, job.kda$graph$weights)
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.translate.Rd b/man/tool.translate.Rd
new file mode 100644
index 0000000..43f09aa
--- /dev/null
+++ b/man/tool.translate.Rd
@@ -0,0 +1,37 @@
+\name{tool.translate}
+\alias{tool.translate}
+\title{
+Translate gene symbols
+}
+\description{
+\code{tool.translate} converts the symbols given in the list \code{from}
+into the list \code{to}. e.g. we can translate human gene symbols into the
+mouse orthologs (or vice versa) if the symbol mapping file is provided.
+}
+\usage{
+tool.translate(words, from, to)
+}
+\arguments{
+\item{words}{translation table including words (i.e. gene symbols) that
+will be translated}
+\item{from}{a list denoting the words will be translated from which symbols}
+\item{to}{a list denoting the words will be translated to which symbols}
+}
+\value{
+\item{words }{translated table (words)}
+}
+\examples{
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+moddata <- tool.read(system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics"))
+moddata$NODE <- moddata$GENE
+moddata$NODE <- tool.translate(words=moddata$NODE, from=syms$FROM,
+to=syms$TO)
+}
+
+\author{
+Ville-Petteri Makinen
+}
diff --git a/man/tool.unify.Rd b/man/tool.unify.Rd
new file mode 100644
index 0000000..5328e1f
--- /dev/null
+++ b/man/tool.unify.Rd
@@ -0,0 +1,29 @@
+\name{tool.unify}
+\alias{tool.unify}
+\title{
+Convert a distribution to uniform ranks
+}
+\description{
+\code{tool.unify} converts a distribution to uniform ranks with respect
+to a background distribution (or self if no background available).}
+\usage{
+tool.unify(xtrait, xnull = NULL)
+}
+\arguments{
+\item{xtrait}{the distribution that will be standardized, i.e. uniformly
+distributed}
+\item{xnull}{background distribution to be used to distribute
+\code{xtrait} uniformly. If \code{xnull} is not specified, \code{xtrait}
+will be used as background distr}
+}
+\value{
+\item{y }{uniformly distributed form of \code{xtrait}}
+}
+\examples{
+x <- rnorm(10)
+y <- tool.unify(x) ## uniformly distributed form of x when null dist is x
+z <- tool.unify(x, y) ## uniformly distributed form of x when null dist is y
+}
+\author{
+Ville-Petteri Makinen
+}
diff --git a/tests/runTests.R b/tests/runTests.R
new file mode 100644
index 0000000..9769af4
--- /dev/null
+++ b/tests/runTests.R
@@ -0,0 +1 @@
+BiocGenerics:::testPackage("Mergeomics")
diff --git a/vignettes/Mergeomics.Rnw b/vignettes/Mergeomics.Rnw
new file mode 100644
index 0000000..294180d
--- /dev/null
+++ b/vignettes/Mergeomics.Rnw
@@ -0,0 +1,642 @@
+%\VignetteIndexEntry{Mergeomics}
+%\VignetteKeywords{Integrative network analysis of omics data}
+%\VignettePackage{Mergeomics}
+
+\documentclass[11pt]{article}
+
+\usepackage{graphicx}
+\usepackage{color}
+\usepackage{multirow}
+\usepackage{longtable}
+\usepackage{pdflscape}
+\usepackage{amsmath}
+\usepackage{amsfonts}
+\usepackage{natbib}
+\usepackage{hyperref}
+
+\begin{document}
+\SweaveOpts{concordance=TRUE}
+\SweaveOpts{echo=FALSE}
+
+\setkeys{Gin}{width=0.99\textwidth}
+
+\title{\bf Mergeomics: Integrative Network Analysis of Omics Data}
+
+\maketitle
+
+\begin{center}
+\author{Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang,
+Xia Yang}\\
+
+Department of Integrative Biology and Physiology, University of California,
+Los Angeles\\
+South Australian Health and Medical Research Institute\\
+Department of Genetics and Genomics Sciences, Mount Sinai School of Medicine\\
+
+\end{center}
+
+\begin{center}
+zhaoyuqi616 at ucla.edu, zeyneb at ucla.edu, icestrike at ucla.edu,
+xyang123 at ucla.edu
+\end{center}
+
+
+
+If you use mergeomics in published research, please cite:
+
+Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B,
+Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics
+resources to identify pathogenic perturbations to biological systems.
+bioRxiv doi: http://dx.doi.org/10.1101/036012
+
+
+\tableofcontents
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Introduction}
+
+The recent revolution in genomic technologies has enabled the generation of
+massive amount of molecular data encompassing genetic, transcriptomic,
+epigenomic, metabolomics, and proteomics, which have become easily accessible
+in public domains and private sectors. It is increasingly recognized that
+analysis of individual types of data separately only reveals a fraction of
+the complex biology and often misses the key players driving diseases, making
+multi-dimensional big data integration an urgent need. Mergeomics package is
+developed as a capable and flexible pipeline to integrate various types of
+disease association data such as genetic association (e.g, GWAS or exome
+sequencing), transcriptome-wide association (e.g., TWAS from microarray or
+RNA sequencing studies), and epigenetic association (e.g., EWAS from methylome
+association studies), functional genomics (such as eQTLs and ENCODE
+annotations), biological pathways, and gene networks to identify
+disease-associated gene sub-networks and key regulatory genes.
+
+In this tutorial, we will assume that you have downloaded the standalone
+Mergeomics package from
+
+http://mergeomics.research.idre.ucla.edu/Download/Package/
+
+Mergeomics.tar.gz and install it using the following commands:
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+install.packages("Mergeomics.tar.gz", repos = NULL,
+type="source")
+## or from bioconductor3.3 release, use following lines:
+source("https://bioconductor.org/biocLite.R")
+biocLite("Mergeomics")
+@
+
+However, if you want, you can also try out these examples with our webserver at
+http://mergeomics.research.idre.ucla.edu. If you have any questions regarding
+this document or the described software, please contact us: Xia Yang
+(xyang123 at ucla.edu).
+
+\section{Tutorials}
+
+The whole Mergeomics pipeline can be divided into two major components:
+1) Marker Set Enrichment Analysis (MSEA), 2) Weighted Key Driver Analysis
+(wKDA). In the following step, we will illustrate how to implement Mergeomics
+using one sample dataset, the descriptions of which are as follows.
+
+\subsection{Input Data Preparation}
+
+In this part we described the datasets used by Mergeomics. Mergeomics is
+designed to integrate summary level data or curated resources, thus light
+data-preprocessing work is expected for user to convert datasets into
+Mergeomics ready format. For the convenience of users, we also deposited a
+rich collection of datasets that can be directly used for analysis in the
+Mergeomics webserver. Of note, Mergeomics is not able to directly process raw
+data from omics studies (e.g., sequencing reads).
+
+Mergeomics calls for five types of datasets. All files should contain tab
+delimited plain text.
+
+\textbf{1. Marker-disease association summary result:} Summary result files
+from association studies such as GWAS, TWAS or EWAS. Only two columns are
+required, MARKER and VALUE. The MARKER column gives the IDs of the markers
+(e.g., reference SNP ID), whereas the VALUE column contains the -log10
+transformed association P values for the markers. The package provides a
+sample summary file from the Genome-wide association studies (GWAS) of
+high-density lipoprotein cholesterol (HDLC) from Global Lipids Genetics
+Consortium [1].
+
+\textbf{2. Gene-marker mapping file: } Data that defines the mapping of genes
+and markers. Two columns are required, GENE and MARKER. Common resources for
+the file is chromosomal distance based mapping, eQTL based mapping and ENCODE
+based mapping. The Mergeomics webserver provides a comprehensive collection
+of above-mentioned mapping files. Experienced users can also generate their
+own mapping files from publicly available resources such as GTEx and ENCODE.
+The package provides a sample mapping file where the GWAS loci were projected
+to genes within 40 Kb distance from gene region.
+
+\textbf{3. Functionally related gene-sets:} Two columns are required, MODULE
+and GENE. The MODULE column defines the grouping of genes, normally by their
+functional relatedness. Gene-sets can be derived from canonical pathways,
+co-expression modules or differential genes under experimental conditions.
+The sample gene-sets in the package is the coexpression networks re-constructed
+ in mouse/human liver tissues, containing 2674 modules[2-6].
+
+\textbf{4. Gene regulatory networks:} Three columns are required, HEAD, TAIL
+and WEIGHT. If the user is using a directional network, then the starting
+nodes should be put in the HEAD column, while the ending nodes should be put
+in the TAIL column. If a non-directional network is used, users have to set
+the corresponding wKDA parameter to ``non-directional'', and are then safe to
+switch the HEAD and TAIL column. The WEIGHT column normally contains the
+confidence score or consensus score (if users merge multiple networks into
+one) of edges. If no weight information is available, simply fix all WEIGHT
+value to 1. The packages provides a sample Bayesian networks generated from
+mouse/human liver tissues [2-6].
+
+\textbf{5. Marker dependency structure file (Optional):} This file is needed
+if users plan to use the MDPrune module. Three columns are required, MARKERa,
+MARKERb and WEIGHT. The WEIGHT column describes the degree of dependency
+(e.g., linkage disequilibrium R2) between MARKERa and MARKERb.
+
+\subsection{MDPrune}
+
+Before taking any further steps, we recommend users to preprocess datasets to
+filter dependency structure among markers (e.g. Linkage disequilibrium),
+as marker dependency may cause artifacts and biases in the downstream analysis.
+We have provided an external C++ program named MDPrune
+(http://mergeomics.research.idre.ucla.edu/Download/MDPrune/), which can remove
+marker dependency while preferentially keeping those with a
+strong statistical association with traits.
+
+The commands are listed as follows:
+
+\begin{center}
+mdprune MARFile MAPFile MDSFile OutPath [Cutoff]
+\end{center}
+
+The file format request for MDPrune is the same as main Mergeomics pipeline,
+where;
+
+MARFile is the association study summary file
+
+MAPFile is the Gene-marker mapping file;
+
+MDSFile is the marker dependency structure file.
+
+Here, we chose a moderate cutoff (R\texttwosuperior \textgreater 0.7) for
+the dataset.
+
+\subsection{One-step Mergeomics}
+
+In this section, we first provide an one-step way to run the whole pipeline.
+One-step running of the pipeline can be accomplished either with a built-in
+function including all function calls in it or by explicitly calling/running
+all the required functions related with the pipeline processes one-by-one
+sequentially.
+
+We demonstrated the either ways of one-step Mergeomics pipeline with the
+code chunks given in this section. Then, the step-by-step processing of
+the pipeline will be described in detail in separate sections.
+
+The first way of one-step pipeline calling is given in the following
+code chunk. It is achieved by one built-in function call (MSEA.KDA.onestep).
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+####### One-step analysis for Mergeomics - 1st way ##
+###########################################################
+## Import library scripts.
+library(Mergeomics)
+
+## first, give the module info file, genefile, marker file, and network file
+## paths for the pipeline
+plan <- list()
+plan$label <- "hdlc"
+plan$folder <- "Results"
+plan$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+plan$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+plan$inffile <- system.file("extdata",
+"coexpr.info.txt", package="Mergeomics")
+plan$netfile <- system.file("extdata",
+"network.mouseliver.mouse.txt", package="Mergeomics")
+
+## second, define pipeline parameters (e.g. permutation #, seed for random #
+## generator, min and max module sizes, max overlapping ratio, etc.)
+plan$permtype <- "gene" ## default setting is gene permutation
+plan$nperm <- 100 ## default value is 20000
+plan$mingenes <- 10 ## default value is 10
+plan$maxgenes <- 500 ## default value is 500
+
+## then, call the one-step pipeline function including both MSEA and KDA steps
+plan <- MSEA.KDA.onestep(plan, apply.MSEA=TRUE, apply.KDA=TRUE,
+maxoverlap.genesets=0.20, symbol.transfer.needed=TRUE,
+sym.from=c("HUMAN", "MOUSE"), sym.to=c("HUMAN", "MOUSE"))
+## NOTE: default value of maxoverlap.genesets=0.33; but in all the examples of
+## this tutorial it is 0.2 (see job.msea$rmax<- 0.2 in the following example)
+## maxoverlap.genesets=0.33 (or job.msea$rmax<- 0.33) is recommended to use.
+@
+
+The second way of one-step pipeline calling is given in the following
+code chunk. Here, we need to explicitly and sequentially call main functions
+of both MSEA and KDA processes.
+
+<<echo=TRUE,print=FALSE,eval=TRUE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+####### One-step analysis for Mergeomics - 2nd way ##
+###########################################################
+## Import library scripts.
+library(Mergeomics)
+################ MSEA (Marker set enrichment analysis) ###
+job.msea <- list()
+job.msea$label <- "hdlc"
+job.msea$folder <- "Results"
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+package="Mergeomics")
+job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+######### Create intermediary datasets for KDA ###########
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+
+## default and recommended rmax=0.33.
+## min.module.count is the number of the pathways to be taken from the MSEA
+## results to merge. If it is not specified (NULL), all the pathways having
+## MSEA-FDR value less than 0.25 will be considered for merging if they are
+## overlapping with the given ratio rmax.
+job.kda <- ssea2kda(job.msea, rmax=0.2, symbols=syms, min.module.count=NULL)
+####### wKDA (Weighted key driver analysis) ##########
+job.kda$netfile <- system.file("extdata",
+"network.mouseliver.mouse.txt", package="Mergeomics")
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+###### Prepare network files for visualization #########
+## Creates the input files for Cytoscape (http://www.cytoscape.org/)
+job.kda <- kda2cytoscape(job.kda)
+@
+
+
+After that, we focused on each pipeline process in the following sections.
+
+\subsection{Marker set enrichment analysis (MSEA)}
+The purpose of the MSEA is to leverage genome/epigenome wide association data,
+function genomics, canonical pathways and/or data-driven gene modules for
+identifying causal subnetworks for disease/traits.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## Import Mergeomics library.
+library("Mergeomics")
+## create an empty list for setting parameters
+job.msea <- list()
+## Next, label your project
+job.msea$label <- "HDLC"
+## The pathway size varies from 1 to a few thousands and will
+## introduce bias to the analysis. We set criteria for the
+## min. (mingenes) and max. (maxgenes) gene size for the pathways.
+job.msea$maxgenes <- 500
+job.msea$mingenes <- 10
+## The permutation setting and number of permutations. We recommend
+## using gene permutation due to its robust performance.
+## The alternative is locus permutation.
+job.msea$permtype <- "gene"
+job.msea$nperm <- 100 ## default value is 20000 (this is recommended)
+## set the output folder
+job.msea$folder <- "./Results"
+## The parameter genfile defines the Marker-to-Gene mapping file
+## It contains two columns, GENE and MARKER, delimited by tab
+job.msea$genfile <- system.file("extdata",
+"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## The parameter marfile defines the Disease association data file
+## It contains two columns, MARKER and VALUE, delimited by tab
+## Here, the marfile comes from the GWAS file after marker
+## dependency pruning, so the VALUE is the minus log10 transformed
+job.msea$marfile <- system.file("extdata",
+"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
+## The modfile defines the pathway information, which could come
+## from knowledge-based databases (such as KEGG, and Reactome)
+## or data-driven data sets (such as co-expression modules).
+## It contains two columns, MOUDLE and GENE, delimited by tab
+job.msea$modfile <- system.file("extdata",
+"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
+## The inffile provides the basic descriptions for the pathways
+## It contains three columns, MODULE, SOURCE, and DESCR, which
+## provide information for pathway IDs corresponding to the
+## pathway names in modfile, the sources of the pathways, and
+## pathway annotations
+job.msea$inffile <- system.file("extdata", "coexpr.info.txt",
+package="Mergeomics")
+## Then, MSEA will run for ~30 minutes to ~2 hours
+job.msea <- ssea.start(job.msea)
+job.msea <- ssea.prepare(job.msea)
+job.msea <- ssea.control(job.msea)
+job.msea <- ssea.analyze(job.msea)
+job.msea <- ssea.finish(job.msea)
+###########################################################
+@
+
+
+\begin{table}
+\centering
+\caption{Summary result of MSEA}
+\label{MSEAres}
+\begin{tabular}{llll}
+\hline \textbf{Pathways} & \textbf{Pvalues} & \textbf{FDRs} &
+\textbf{Descriptions} \\
+\hline 5099 & 3.48E-04 & 6.05\% & 5099:liver \\
+\hline 4588 & 1.05E-03 & 13.64\% & 4588:liver \\
+\hline 4737 & 2.01E-03 & 21.02\% & 4737:liver \\
+\hline 4128 & 4.44E-03 & 30.24\% & 4128:liver \\
+\hline 5117 & 5.22E-03 & 32.03\% & 5117:liver \\
+\hline 6645 & 5.24E-03 & 32.05\% & 6645:liver \\
+\hline 5104 & 5.50E-03 & 32.29\% & 5104:liver \\
+\hline 4094 & 7.65E-03 & 33.93\% & 4094:liver \\
+\hline 4932 & 7.95E-03 & 34.12\% & 4932:liver
+\end{tabular}
+\end{table}
+
+A part of the original results is listed in Table 1. The pathways
+satisfying certain cutoffs (e.g. FDR\textless 30\%) will be used for the next
+step and can be further annotated using diverse tools, such as DAVID
+(http://david.abcc.ncifcrf.gov/).
+
+\subsubsection{ModuleMerge}
+
+Usually, pathways/modules collected from different sources will have certain
+degree of redundancies, or refer to the same processes. In this case, users
+could merge the disease-related pathways into relatively independent gene
+sets, which exceed a given FDR cutoff after running MSEA.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+job <-list()
+job$folder <- c("module_merge")
+## The moddata and modinfo either come from the significant pathways found in
+## any previous MSEA run or these files can be manually curated by the user.
+## moddata is an obligatory input file; while modinfo is an optional input.
+## It is recommended to merge the overlapping pathways among significant
+## ones before applying KDA to proceed with the independent gene sets.
+moddata <- tool.read(system.file("extdata", "Significant_pathways.txt",
+package="Mergeomics"), c("MODULE","GENE"))
+modinfo <- tool.read(system.file("extdata", "Significant_pathways.info.txt",
+package="Mergeomics"),c("MODULE","SOURCE","DESCR"))
+
+syms <- tool.read(system.file("extdata", "symbols.txt",
+package="Mergeomics"))
+syms <- syms[,c("HUMAN", "MOUSE")]
+names(syms) <- c("FROM", "TO")
+moddata$GENE <- tool.translate(words=moddata$GENE, from=syms$FROM,
+to=syms$TO)
+
+## Merge and trim overlapping modules.
+rmax <- 0.2
+moddata$OVERLAP <- moddata$MODULE
+moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
+rcutoff=rmax)
+moddata$MODULE <- moddata$CLUSTER
+moddata$GENE <- moddata$ITEM
+moddata$OVERLAP <- moddata$GROUPS
+moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
+moddata <- unique(moddata)
+modinfo <- modinfo[which(!is.na(match(modinfo[,1], moddata[,1]))), ]
+
+## Mark modules with overlaps.
+for(i in which(moddata$MODULE != moddata$OVERLAP)){
+ modinfo[which(modinfo[,"MODULE"] == moddata[i,"MODULE"]),
+ "MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+ moddata[i,"MODULE"] <- paste(moddata[i,"MODULE"], "..", sep=",")
+}
+## Save merged module data and info for KDA.
+modfile <- "mergedModules.txt"
+modinfofile <- "mergedModules.info.txt"
+moddata$NODE <- moddata$GENE
+tool.save(frame=unique(moddata[,c("MODULE", "GENE", "OVERLAP", "NODE")]),
+file=modfile, directory=job$folder)
+tool.save(frame=unique(modinfo[,c("MODULE","SOURCE","DESCR")]),
+file=modinfofile, directory=job$folder)
+###########################################################
+@
+
+Users are also recommended to analyze the merged genesets with MSEA again
+to confirm that they are still significantly associated with disease.
+
+\subsubsection{Meta-MSEA}
+
+When multiple disease association datasets are available, meta-MSEA can be
+used to conduct meta-analysis at pathway or network level. This function
+allows user to achieve maximal power by combining results from independent
+association studies of different ethnicity, platform or even species, while
+evading the technical difficulties when performing meta-analysis directly
+on the marker-level association data.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## Assume there are three MSEA objects passed down by
+## ssea.finish()
+# job.metamsea = list()
+# job.metamsea$job1 = job.msea1
+# job.metamsea$job2 = job.msea2
+# job.metamsea$job3 = job.msea3
+# job.metamsea = ssea.meta(job.metamsea,"meta_label",
+# "meta_folder")
+###########################################################
+@
+
+\subsection{Weighted key driver analysis (wKDA)}
+
+wKDA aims to pinpoint key regulator genes (or key drivers) of the disease
+related gene sets using gene network topology and edge weight information.
+In specific, wKDA first screen the network for candidate hub genes. Then
+the disease gene-sets are overlaid onto the subnetworks of the candidate
+hubs to identify key drivers whose neighbors are enriched with disease genes.
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+job.kda <- list()
+job.kda$label<-"HDLC"
+## parent folder for results
+job.kda$folder<-"./Results"
+## Input a network
+## columns: TAIL HEAD WEIGHT
+job.kda$netfile <- system.file("extdata", "network.mouseliver.mouse.txt",
+package="Mergeomics")
+## Gene sets derived from ModuleMerge, containing two columns,
+## MODULE, NODE, delimited by tab
+job.kda$modfile<- system.file("extdata",
+"mergedModules.txt", package="Mergeomics")
+## Annotation file for the gene sets
+## if module or pathway annotation is not available, skip this:
+job.kda$inffile<-system.file("extdata",
+"mergedModules.info.txt", package="Mergeomics")
+## "0" means we do not consider edge weights while 1 is
+## opposite.
+job.kda$edgefactor<-0.5 ## default value
+## The searching depth for the KDA
+job.kda$depth<-1 ## default value
+## "0" means we do not consider the directions of the
+## regulatory interactions
+## while 1 is opposite.
+job.kda$direction<-0 ## default value
+## Let us run KDA!
+job.kda <- kda.configure(job.kda)
+job.kda <- kda.start(job.kda)
+job.kda <- kda.prepare(job.kda)
+job.kda <- kda.analyze(job.kda)
+job.kda <- kda.finish(job.kda)
+###########################################################
+@
+
+\begin{table}
+\centering
+\caption{Summary result of wKDA}
+\label{wKDAres}
+\begin{tabular}{lllll}
+\hline \textbf{Key Drivers} & \textbf{MODULES} & \textbf{Pvalues} &
+\textbf{FDRs} & \textbf{Descriptions}\\
+\hline Itih4 & 4932,.. & 2.90E-14 & 0.00\% & 4932:liver \\
+\hline Clstn3 & 136 & 6.92E-14 & 0.00\% & 136:liver \\
+\hline Pmvk & 4407 & 1.40E-13 & 0.00\% & 4407:liver \\
+\hline Acss2 & 4407 & 1.54E-13 & 0.00\% & 4407:liver \\
+\hline Gpam & 4407 & 3.82E-13 & 0.00\% & 4407:liver \\
+\hline Aqp8 & 4407 & 8.26E-13 & 0.00\% & 4407:liver \\
+\hline Itih4 & 5099 & 1.56E-12 & 0.00\% & 5099:liver \\
+\hline Lpin1 & 4084 & 2.21E-12 & 0.00\% & 4084:liver
+\end{tabular}
+\end{table}
+
+\subsection{Network visualization}
+For the current version, we generate Cytoscape-ready
+(http://www.cytoscape.org/) input files to visualize the network of key
+driver (KD) genes. Cytoscape-ready files are generated for illustrating
+either the neighborhoods of top five KDs of each module or the neighborhoods
+of a particular gene (node) list. By default, log files for each module's
+top five KDs' neighborhoods are prepared. Number of the top KDs can be
+specified by the user. Node and edge list files are created separately
+for Cytoscape. Additionally, top key driver name list for each module and
+color map of the modules are logged in the relevant files. kda2cytoscape
+function provides users:
+
+\begin{itemize}
+\item To illustrate top five KD neighborhoods for the modules, whose
+pathway or module name is listed within ``modules'' parameter (all modules
+are selected by default),
+\item To specify a particular number of KDs for each module with ``ndrivers''
+(default value is five),
+\item To search and illustrate the neighborhoods of a given node list with
+``node.list'' parameter (default value is NULL; if this is not specified by
+the user, top 5 KDs of each module and their neighborhoods will be
+illustrated; if ``node.list'' parameter is specified, it will be prioritized
+rather than top 5 KDs illustration),
+\item To examine the KD neighborhoods for a given depth (default depth is 1;
+hence, only the directly connected neighbor nodes of key drivers are taken
+into account).
+\end{itemize}
+
+<<echo=TRUE,print=FALSE,eval=FALSE,message=FALSE,warning=FALSE,results=hide>>=
+###########################################################
+## run following line after finishing the KDA process
+## i.e., after the kda.finish() function concluded
+job.kda <- kda2cytoscape (job.kda, node.list=NULL,
+modules=NULL, ndrivers=5, depth=1)
+###########################################################
+@
+
+Generated file names and their descriptions are given below:
+
+\begin{itemize}
+\item \textbf{kda2cytoscape.top.kds.txt:} Top KDs of the modules are listed
+in this file. Number of the key drivers can be set by user with ``ndrivers''
+parameter.
+\item \textbf{kda2cytoscape.edges.txt:} Edge lists of the generated graph.
+This file should be imported as network within the Cytoscape.
+\item \textbf{kda2cytoscape.nodes.txt:} Node lists of the generated graph.
+This file includes the optional formatting information for the nodes of the
+graph. It should be imported as node table within the Cytoscape.
+Field/attribute list provided for each node is as follows:
+\begin{itemize}
+\item NODE: gene symbol
+\item LABEL: node label (same as above)
+\item COLOR: background color of the node, it also shows the module
+membership of the node. If a node is member of multiple modules/pathways,
+only one color among them will be assigned as background color, but in the
+URL field this multiple module membership will be represented by a pie chart
+(node is divided into several sectors to show the multi-membership of the
+node). If a node is not a member for any of the pathways, the color will
+be ``grey''
+\item SIZE: size of the node. If the node is a KD it will be twice larger
+than ordinary nodes.
+\item SHAPE: shape of the node. If the node is a KD, it will be in
+diamond-shape, otherwise it is illustrated with circle.
+\item URL: pie chart created by Google chart tools. If the node is member of
+multiple modules, node circle will be divided as a pie chart and colored
+by the module colors it belongs to
+\item LABELSIZE: text size of the node. If the node is a KD, its font size
+will be larger than ordinary nodes.
+\end{itemize}
+\item \textbf{module.color.mapping.txt:} Color mapping for the modules, i.e.
+one color is assigned to each module.
+\end{itemize}
+
+An example graph for top 5 KDs\textquoteright neighborhoods of a given pathway
+is illustrated in Figure 1. Different colors represent
+modules/pathways, while the larger nodes are the key driver genes.
+
+Note that: SHAPE feature is not used in the Cytoscape in Fig 1. All the
+listed attributes for nodes are optional.
+
+\begin{figure}
+\centering
+\includegraphics[width=6in]{top5KDs}
+\caption{The key driver gene subnetwork}
+\label{top5KDnets}
+\end{figure}
+
+\section{Citation}
+If you use Mergeomics in published work, please cite: Shu L, Zhao Y,
+Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M,
+Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources
+to identify pathogenic perturbations to biological systems.
+
+bioRxiv doi: http://dx.doi.org/10.1101/036012.
+
+\section{References}
+
+[1] Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, et al. (2013)
+Discovery and refinement of loci associated with lipid levels.
+Nature Genetics 45.
+
+[2] Derry JMJ, Zhong H, Molony C, MacNeil D, GuhaThakurta D, et al. (2010)
+Identification of genes and networks driving cardiovascular and metabolic
+phenotypes in a mouse F2 intercross.
+PLoS ONE 5: e14319. doi:10.1371/journal.pone.0014319.
+
+[3] Wang SS, Schadt EE, Wang H, Wang X, Ingram-Drake L, et al. (2007)
+Identification of pathways for atherosclerosis in mice: integration of
+quantitative trait locus analysis and global gene expression data.
+Circ Res 101: e11-e30. doi:10.1161/CIRCRESAHA.107.152975.
+
+[4] Yang X, Schadt EE, Wang S, Wang H, Arnold AP, et al. (2006)
+Tissue-specific expression and regulation of sexually dimorphic genes
+in mice. Genome Res 16: 995-1004. doi:10.1101/gr.5217506.
+
+[5] Schadt EE, Molony C, Chudin E, Hao K, Yang X, et al. (2008)
+Mapping the genetic architecture of gene expression in human liver.
+PLoS Biol 6: e107. doi:10.1371/journal.pbio.0060107.
+
+[6] Tu ZZ, Keller MPM, Zhang CC, Rabaglia MEM, Greenawalt DMD, et al. (2012)
+Integrative analysis of a cross-loci regulation network identifies App
+as a gene regulating insulin secretion from pancreatic islets.
+PLoS Genet 8: e1003107-e1003107. doi:10.1371/journal.pgen.1003107.
+
+\end{document}
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