Response_Point_09-2
Last updated: 2020-09-23
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Knit directory: Comparative_eQTL/analysis/
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Intro
I performed the cell type QTL analysis, using code in the snakemake. This was a GWAS with all GTEX left-venticle samples, (~300 individuals) using qq-normalized cell type phenotypes (the first PC of the CIBERSORT cell type composition estimates), with the same genotyping related PCs used by GTEX for eQTL mapping as covariates in a simple additive linear model. Here are the results…
GWAS_Manhattan
Nothing seems to reach genome wide significance. Nonetheless, there may be true signal buried in here that we can answer with directed hypothesis tests. For example, it still seems reasonable to me to check if eQTL SNPs have inflated P-values for this cell type QTL analysis.
I checked if eQTL SNPs (top SNPs for GTEX heart left ventricle eGenes (FDR<0.01) ) have generally smaller P-values than a same sized random sample of non eQTL SNPs, using a QQ-plot:
.
My interpretation of this is that eQTLs generally are not driven by cell type QTLs. Another thing I could ask is if the top cell type QTLs (some which may be false positives), are closer to highly dispersed genes compared to lowly dispersed genes.
Despite no SNPs exceeding standard GWAS significance (P<5E-8) I have taken the 98 most significant loci (lumped by clustering into the best SNP within 250000 of any locus than contains a SNP with FDR (Storey’s Q) less than 0.5) and found the closest gene for which I estimated dispersion (SNPs further than 100kb from any of these genes were discarded). I have done the same for a set of random test SNPs for comparison.
Let’s plot the dispersion for these closest gene SNPs, with the closest genes to the random control SNPs as a comparison.
library(tidyverse)
GWAS.Snps <- read.table("../output/CellProportionGWAS.RandomControlloci.closestGenes.txt", sep = ' ', col.names = c("gene", "dist"))
Control.Snps <- read.table("../output/CellProportionGWAS.Testloci.closestGenes.txt", sep = ' ', col.names = c("gene", "dist"))
AllClosestGenes <- dplyr::bind_rows(list(CellTypeAssociatedSNPs=GWAS.Snps, RandomTestSNPs=Control.Snps), .id = 'source') %>%
mutate(gene=str_remove(gene, "\\.\\d+$"))
head(AllClosestGenes) source gene dist
1 CellTypeAssociatedSNPs ENSG00000171960 78877
2 CellTypeAssociatedSNPs ENSG00000066557 61156
3 CellTypeAssociatedSNPs ENSG00000138185 8280
4 CellTypeAssociatedSNPs ENSG00000095587 63564
5 CellTypeAssociatedSNPs ENSG00000188385 55631
6 CellTypeAssociatedSNPs ENSG00000175356 65094Dispersion <- read.delim('../output/OverdispersionEstimatesFromChimp.txt')
TestResults <-AllClosestGenes %>%
left_join(Dispersion, by="gene") %>%
wilcox.test(Human.Residual~source, data=., alternative="greater")
TestResults
Wilcoxon rank sum test
data: Human.Residual by source
W = 932, p-value = 0.2583
alternative hypothesis: true location shift is greater than 0lb1 = paste0('P==', format.pval(TestResults$p.value, 2))
AllClosestGenes %>%
left_join(Dispersion, by="gene") %>%
ggplot(aes(x=Human.Residual, color=source)) +
stat_ecdf() +
xlab("Dispersion (Human)") +
ylab("ecdf") +
annotate("text",x=Inf,y=-Inf, label=lb1, hjust=1.5, vjust=-0.5, parse=TRUE) +
scale_color_manual(name=NULL, values=c("red", "blue"), labels=c("Closest genes to loci\nassociated with cell type\n(FDR<0.5, n=40)", "Closest genes to\nrandom test SNPs")) +
theme_bw() +
theme(legend.position="bottom")
And write out the plot…
ggsave("../figures/OriginalArt/ResponseToReviewers.GWAS.ClosestGenes.pdf", height=3, width=3.65)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[5] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[9] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.11 haven_2.2.0 lattice_0.20-38
[5] colorspace_1.4-1 vctrs_0.2.0 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.0 rlang_0.4.1 later_1.0.0 pillar_1.4.2
[13] withr_2.1.2 glue_1.3.1 DBI_1.0.0 dbplyr_1.4.2
[17] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[21] gtable_0.3.0 workflowr_1.5.0 cellranger_1.1.0 rvest_0.3.5
[25] evaluate_0.14 labeling_0.3 knitr_1.26 httpuv_1.5.2
[29] fansi_0.4.0 broom_0.5.2 Rcpp_1.0.5 promises_1.1.0
[33] backports_1.1.5 scales_1.1.0 jsonlite_1.6 farver_2.0.1
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[41] grid_3.6.1 rprojroot_1.3-2 cli_2.0.0 tools_3.6.1
[45] magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4 whisker_0.4
[49] pkgconfig_2.0.3 zeallot_0.1.0 xml2_1.2.2 reprex_0.3.0
[53] lubridate_1.7.4 rstudioapi_0.10 assertthat_0.2.1 rmarkdown_1.18
[57] httr_1.4.1 R6_2.4.1 nlme_3.1-143 git2r_0.26.1
[61] compiler_3.6.1