Last updated: 2018-06-06

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Despite high average levels of sharing of eQTLs among tissues, mash also identifies eQTLs that are relatively “tissue-specific”. Here we count the number of “tissue-specific” eQTLs in each tissue.

Compare the bar chart at the bottom of this page against Supplementary Figure 5 in the manuscript.

Set up environment

First, we load some functions defined for mash analyses.


This is the threshold used to determine which genes have at least one significant effect across tissues.

thresh <- 0.05

Load data and mash results

Load some GTEx summary statistics, as well as some of the results generated from the mash analysis of the GTEx data.

out <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")
maxb           <- out$test.b
maxz           <- out$test.z
standard.error <- out$test.s
out <- readRDS(paste("../output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3",
                     "lite.single.expanded.V1.posterior.rds",sep = "."))
pm.mash      <- out$posterior.means
lfsr.mash    <- out$lfsr
pm.mash.beta <- pm.mash * standard.error

To create the bar chart below, we use the colours that are conventionally used to represent the GTEx tissues in plots.

missing.tissues <- c(7,8,19,20,24,25,31,34,37)
color.gtex      <- read.table("../data/GTExColors.txt",sep = '\t',
                              comment.char = '')[-missing.tissues,]
col = as.character(color.gtex[,2])

Compute number of “tissue-specific” effects for each tissue.

We define “tissue-specific” to mean that the effect is at least 2-fold larger in one tissue than in any other tissue.

nsig                <- rowSums(lfsr.mash < thresh)
pm.mash.beta.norm   <- het.norm(effectsize = pm.mash.beta)
pm.mash.beta.norm   <- pm.mash.beta.norm[nsig > 0,]
lfsr.mash           <- as.matrix(lfsr.mash[nsig > 0,])
colnames(lfsr.mash) <- colnames(maxz)
a         <- which(rowSums(pm.mash.beta.norm > 0.5) == 1)
lfsr.fold <- as.matrix(lfsr.mash[a,])
pm        <- as.matrix(pm.mash.beta.norm[a,])
tspec     <- NULL
for(i in 1:ncol(pm))
  tspec[i] <- sum(pm[,i] > 0.5)
tspec           <- as.matrix(tspec)
rownames(tspec) <- colnames(maxz)

Plot number of “tissue-specific” effects for each tissue

par(mfrow = c(2,1))
barplot(as.numeric(t(tspec)),las = 2,cex.names = 0.75,col = col,
        names = colnames(lfsr.fold))

Testis stands out as the tissue with the most tissue-specific effects. Other tissues showing stronger-than-average tissue specificity include skeletal muscle, thyroid and transformed cell lines (fibroblasts and LCLs).

Session information

# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.4
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# loaded via a namespace (and not attached):
#  [1] workflowr_1.0.1.9000 Rcpp_0.12.16         digest_0.6.15       
#  [4] rprojroot_1.3-2      R.methodsS3_1.7.1    backports_1.1.2     
#  [7] git2r_0.21.0         magrittr_1.5         evaluate_0.10.1     
# [10] stringi_1.1.7        whisker_0.3-2        R.oo_1.21.0         
# [13] R.utils_2.6.0        rmarkdown_1.9        tools_3.4.3         
# [16] stringr_1.3.0        yaml_2.1.18          compiler_3.4.3      
# [19] htmltools_0.3.6      knitr_1.20

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