2024-04-26_eQTL_subtypePlieotropy
2024-04-26
Last updated: 2024-04-29
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ChromatinSplicingQTLs/analysis/
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pleiotropy of u-sQTLs/eQTLs vs hQTL/eQTLs
I predict that u-sQTL/eQTLs will be eQTLs less genes than hQTL/eQTLs…
About that horizontal pleiotropy of transcription based eQTLs vs splicing based eQTLs… Here I used the same red/purple groups of eQTL SNPs that I used for the manuscript where we looked at effect sizes across GTEx tissues. In this plot I looked for associations in GTEx LCL data, and counted the number of protein coding genes (100kb window from the SNP, among the 14000 genes we considered for many of our other analyses throughout the paper) with a nominally significant association (P<0.01) with the SNP.
library(tidyverse)
GTEx.dat <- read_tsv("../code/scratch/Tidy.SummaryStats.eQTLsQTLhQTL_AllGenes.tsv.gz")
GTEx.dat %>% distinct(sQTL_or_hQTL, BensClassification)# A tibble: 3 × 2
sQTL_or_hQTL BensClassification
<chr> <chr>
1 sQTL sQTL
2 hQTL hQTL_Distal
3 hQTL hQTL_Proximal GTEx.dat %>%
group_by(TopSNP, sQTL_or_hQTL, gene) %>%
summarise(IsSigInAnyTissue = any(eQTL_nomP < 0.01)) %>%
ungroup() %>%
count(TopSNP, sQTL_or_hQTL) %>%
ggplot(aes(x=n, color=sQTL_or_hQTL)) +
stat_ecdf()
GTEx.dat %>%
distinct(tissue)# A tibble: 54 × 1
tissue
<chr>
1 ovary
2 brain_cerebellar_hemisphere
3 nerve_tibial
4 breast_mammary_tissue
5 testis
6 colon_transverse
7 fallopian_tube
8 brain_caudate_basal_ganglia
9 lung
10 esophagus_muscularis
# … with 44 more rowsGTEx.dat %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
# mutate(sQTL_or_hQTL = BensClassification) %>%
count(nom_pval < 0.01, TopSNP, sQTL_or_hQTL) %>%
filter(`nom_pval < 0.01`) %>%
add_count(sQTL_or_hQTL) %>%
mutate(sQTL_or_hQTL = recode(sQTL_or_hQTL, "sQTL"="u-sQTL/eQTL", "hQTL"="hQTL/eQTL")) %>%
mutate(sQTL_or_hQTL = str_glue("{sQTL_or_hQTL}; n={nn}")) %>%
ggplot(aes(x=n, color=sQTL_or_hQTL)) +
stat_ecdf() +
scale_color_manual(values=c("#e31a1c", "#6a3d9a"), name=NULL) +
coord_cartesian(xlim=c(1,10)) +
scale_x_continuous(breaks=seq(0, 10, 2)) +
labs(y="ecdf", x="horizontal pleiotropy\n[Number eGenes per SNP\n in validation dataset]", caption="P=2.2E-16") +
theme(
legend.position = c(1, .5),
legend.justification = c("right", "top"),
legend.box.just = "right",
legend.margin = margin(6, 6, 6, 6)
)
ggsave("../code/scratch/sQTL_hQTL_Pleiotropy.pdf", width=3.5, height=3.5)
GTEx.dat %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
count(nom_pval < 0.01, TopSNP, sQTL_or_hQTL) %>%
filter(`nom_pval < 0.01`) %>%
wilcox.test(n~sQTL_or_hQTL, data=.)
Wilcoxon rank sum test with continuity correction
data: n by sQTL_or_hQTL
W = 79914, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0What’s up with the 25% or so of sQTLs apparently with more than one eQTL?
Genes <- read_tsv("../code/GTEx/QTLs/cells_ebv-transformed_lymphocytes/qqnorm.sorted.bed.gz") %>%
dplyr::select(1:6)
GTEx.dat %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
filter(sQTL_or_hQTL == "sQTL") %>%
filter(nom_pval < 0.01) %>%
add_count(TopSNP) %>%
inner_join(Genes, by=c("gene"="gid")) %>%
mutate(IsPleitropic = n > 1) %>%
dplyr::select(`#Chr`:pid, nom_pval, strand, IsPleitropic) %>%
group_by(IsPleitropic) %>%
group_walk(~ write_tsv(.x, paste0("../code/scratch/sQTLs_IsPleitropic_", .y$IsPleitropic, ".bed"), col_names = F))Let’s repeat some of the above analysis but after I recalled QTLtools use use 500kb cis windows…
dat.500kbwindow <- Sys.glob("../code/GTEx/BenSubsetSNPs_QTLs/*/NominalPass.qqnorm.txt.tabix.gz") %>%
setNames(str_replace(., "../code/GTEx/BenSubsetSNPs_QTLs/(.+?)/NominalPass.qqnorm.txt.tabix.gz", "\\1")) %>%
lapply(read_tsv) %>%
bind_rows(.id="tissue") %>%
inner_join(GTEx.dat %>%
dplyr::select(-c(1:5)) %>%
distinct() %>%
separate(TopSNP, into=c("SNP_chrom", "var_from", "SNP_ref", "SNP_alt"), sep=":", remove=F, convert=T) %>%
mutate(var_chr = paste0("chr", SNP_chrom)),
by=c("var_chr", "var_from"))
dat.500kbwindow %>%
distinct(TopSNP)# A tibble: 913 × 1
TopSNP
<chr>
1 1:1661169:C:A
2 1:2556224:C:A
3 1:6205308:A:G
4 1:9943262:G:A
5 1:10211630:C:G
6 1:11635653:T:G
7 1:13769535:C:A
8 1:15586903:A:T
9 1:16206527:C:T
10 1:19801518:G:T
# … with 903 more rowsGTEx.dat %>%
distinct(TopSNP)# A tibble: 913 × 1
TopSNP
<chr>
1 2:75660970:G:A
2 7:30500783:G:T
3 19:41586462:A:T
4 12:6321571:G:A
5 12:4607406:C:A
6 12:6184753:G:A
7 1:41105645:A:T
8 4:6609511:TG:T
9 19:54962842:G:A
10 6:155314147:G:GTTC
# … with 903 more rowsdat.500kbwindow %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
mutate(sQTL_or_hQTL = recode(sQTL_or_hQTL, "sQTL"="u-sQTL/eQTL", "hQTL"="hQTL/eQTL")) %>%
mutate(IsDiscovery.eGene = if_else(`#phe_id` == eGene, "Original discovery gene", "Other cis-genes")) %>%
group_by(sQTL_or_hQTL, IsDiscovery.eGene) %>%
mutate(expected_p = percent_rank(nom_pval)) %>%
ggplot(aes(x=-log10(expected_p), y=-log10(nom_pval), color=sQTL_or_hQTL)) +
geom_point() +
geom_abline(slope=1, color='red') +
scale_color_manual(values=c("#e31a1c", "#6a3d9a")) +
facet_wrap(~IsDiscovery.eGene) +
geom_hline(yintercept =c(2, 5, 8), linetype='dashed')
dat.500kbwindow %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
mutate(`1E-2`=nom_pval<0.01,
`1E-5`=nom_pval<1E-5,
`1E-8`=nom_pval<1E-8,
) %>%
dplyr::select(sQTL_or_hQTL, TopSNP, `1E-2`:`1E-8`) %>%
pivot_longer(names_to = "Threshold", values_to = "PassThreshold", `1E-2`:`1E-8`) %>%
group_by(sQTL_or_hQTL, TopSNP, Threshold) %>%
summarise(Num_eGenes = sum(PassThreshold)) %>%
ungroup() %>%
add_count(sQTL_or_hQTL, Threshold) %>%
mutate(sQTL_or_hQTL = recode(sQTL_or_hQTL, "sQTL"="u-sQTL/eQTL", "hQTL"="hQTL/eQTL")) %>%
mutate(sQTL_or_hQTL = str_glue("{sQTL_or_hQTL}; n={n}")) %>%
ggplot() +
stat_ecdf(aes(x=Num_eGenes, color=sQTL_or_hQTL)) +
scale_color_manual(values=c("#e31a1c", "#6a3d9a")) +
coord_cartesian(xlim=c(0,10)) +
facet_wrap(~Threshold) +
geom_text( data = . %>%
group_by(Threshold) %>%
summarize(results = format.pval(wilcox.test(Num_eGenes ~ sQTL_or_hQTL)$p.value, 2)),
aes(label=str_glue("P: {results}"), x=Inf, y=-Inf, vjust=0, hjust=1)) +
labs(y="ecdf", x="horizontal pleiotropy\n(Number eGenes per SNP in GTEx LCLs)", title="facets are nominal Pval threshold")
dat.500kbwindow %>%
filter(tissue == "cells_ebv-transformed_lymphocytes") %>%
mutate(`1E-2`=nom_pval<0.01,
`1E-5`=nom_pval<1E-5,
`1E-8`=nom_pval<1E-8,
) %>%
dplyr::select(sQTL_or_hQTL, TopSNP, `1E-2`:`1E-8`) %>%
pivot_longer(names_to = "Threshold", values_to = "PassThreshold", `1E-2`:`1E-8`) %>%
group_by(sQTL_or_hQTL, TopSNP, Threshold) %>%
summarise(Num_eGenes = sum(PassThreshold)) %>%
ungroup() %>%
group_by(Threshold) %>%
summarize(results = wilcox.test(Num_eGenes ~ sQTL_or_hQTL)$p.value)# A tibble: 3 × 2
Threshold results
<chr> <dbl>
1 1E-2 3.22e-17
2 1E-5 1.08e-11
3 1E-8 4.96e- 8
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
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[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
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