Example intronic APA qtls and eQTLs
Briana Mittleman
5/21/2020
Last updated: 2020-05-21
Checks: 7 0
Knit directory: apaQTL/analysis/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a67b63a | brimittleman | 2020-05-21 | verify dir |
library(tidyverse)── Attaching packages ──────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.2.1 ✔ purrr 0.3.2
✔ tibble 2.1.3 ✔ dplyr 0.8.3
✔ tidyr 0.8.3 ✔ stringr 1.4.0
✔ readr 1.3.1 ✔ forcats 0.4.0── Conflicts ─────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()Check to make sure effect size are the same direction. Check top examples from the eQTL vs intronic apaQTL plot.
eQTLeffect=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName_snploc.txt", stringsAsFactors = F, col.names = c("gene","snp","dist", "pval", "eQTL_es")) %>% select(gene, snp, eQTL_es)nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))
nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>% mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))
alleQTLS_nuclear=bind_rows(nuclearapaUnexplained,nuclearapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))ggplot(alleQTLS_nuclear,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm") + geom_text(aes(label=gene), nudge_y = .1)
Number of PAS per gene:
PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.bed",col.names = c("chr", "start", "end", "name", "score", "strand"), stringsAsFactors = F) %>% separate(name, into=c('num', 'gene', 'loc'),sep=":" )
PASscore= PAS %>% select(num, score) %>% mutate(PASnum=paste("peak", num, sep=""))
PASgene=PAS %>% group_by(gene) %>% summarise(nPAS=n())alleQTLS_nuclear_genenum= alleQTLS_nuclear %>% inner_join(PASgene, by="gene")
alleQTLS_nuclear_score= alleQTLS_nuclear %>% inner_join(PASscore, by="PASnum")ggplot(alleQTLS_nuclear_genenum,aes(x=eQTL_es, y=slope,col=nPAS)) + geom_point() + geom_smooth(method = "lm") + geom_text(aes(label=gene), nudge_y = .1) 
ggplot(alleQTLS_nuclear_score,aes(x=eQTL_es, y=slope,col=score)) + geom_point() + geom_smooth(method = "lm") + geom_text(aes(label=gene), nudge_y = .1) 
PAS %>% filter(gene=="MTHFSD") chr start end num gene loc score strand
1 16 86563782 86563783 52507 MTHFSD utr3 0.16923077 -
2 16 86564455 86564456 52509 MTHFSD utr3 0.31730769 -
3 16 86566995 86566996 52510 MTHFSD intron 0.18134615 -
4 16 86572985 86572986 52511 MTHFSD intron 0.05865385 -
5 16 86586779 86586780 52514 MTHFSD intron 0.05211538 -
6 16 86588585 86588586 52515 MTHFSD intron 0.20423077 -PAS %>% filter(gene=="TMEM156") chr start end num gene loc score strand
1 4 38967631 38967632 96732 TMEM156 end 0.10269231 -
2 4 38968368 38968369 96733 TMEM156 utr3 0.13307692 -
3 4 38985526 38985527 96738 TMEM156 intron 0.07557692 -
4 4 39029992 39029993 96746 TMEM156 intron 0.50500000 -plot expression and apa:
genohead=as.data.frame(read.table("../data/ExampleQTLPlots/genotypeHeader.txt", stringsAsFactors = F, header = F)[,10:128] %>% t()) %>% rename("Ind"=V1)
genotype=as.data.frame(read.table("../data/ExampleQTLPlots/TMEM156_NuclearPeaksGenotype.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())ENSG00000121895 TMEM156
rs2711981
ref = c t=alt
RNAhead=as.data.frame(read.table("../data/molPhenos/RNAhead.txt", stringsAsFactors = F, header = F)[,5:73] %>% t())
RNApheno=as.data.frame(read.table("../data/molPhenos/RNA_TMEM156.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())full_geno=bind_cols(Ind=genohead$Ind, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "CC", ifelse(numdose==1, "CT", "TT")))
full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)
allRNA=full_geno %>% inner_join(full_pheno, by="Ind")Warning: Column `Ind` joining factors with different levels, coercing to
character vectorallRNA$genotype=as.factor(allRNA$genotype)
ggplot(allRNA, aes(x=genotype, y=Expression,group=genotype, fill=genotype)) + geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "Dark2") + labs(title="") + theme(legend.position = "bottom")
summary(lm(data=allRNA, Expression~genotype))
Call:
lm(formula = Expression ~ genotype, data = allRNA)
Residuals:
Min 1Q Median 3Q Max
-2.57048 -0.72008 -0.05794 0.70675 2.52445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.531 1.044 2.423 0.0181 *
genotypeCT -2.303 1.070 -2.152 0.0350 *
genotypeTT -2.744 1.055 -2.601 0.0115 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.044 on 66 degrees of freedom
Multiple R-squared: 0.1164, Adjusted R-squared: 0.08964
F-statistic: 4.348 on 2 and 66 DF, p-value: 0.01683KCTD21 11 rs138463614 peak24232
ENSG00000188997
C is ref T is alt
genotype=as.data.frame(read.table("../data/ExampleQTLPlots/KCTD21_NuclearPeaksGenotype.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())
RNApheno=as.data.frame(read.table("../data/molPhenos/RNA_KCTD21.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())
full_geno=bind_cols(Ind=genohead$Ind, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "CC", ifelse(numdose==1, "CT", "TT")))
full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)
allRNA=full_geno %>% inner_join(full_pheno, by="Ind")Warning: Column `Ind` joining factors with different levels, coercing to
character vectorallRNA$genotype=as.factor(allRNA$genotype)
ggplot(allRNA, aes(x=genotype, y=Expression,group=genotype, fill=genotype)) + geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "Dark2") + labs(title="") + theme(legend.position = "bottom")
summary(lm(data=allRNA, Expression~genotype))
Call:
lm(formula = Expression ~ genotype, data = allRNA)
Residuals:
Min 1Q Median 3Q Max
-2.84260 -0.77776 0.08783 0.73800 2.20026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.07669 0.16303 -0.470 0.640
genotypeCT 0.41155 0.30500 1.349 0.182
genotypeTT 0.18620 0.47530 0.392 0.697
Residual standard error: 1.094 on 66 degrees of freedom
Multiple R-squared: 0.02716, Adjusted R-squared: -0.002324
F-statistic: 0.9212 on 2 and 66 DF, p-value: 0.4031not a great example. expression effect size isnt strong.. but the diection is in the correct direction. increased expression with T.
filter to find another example:
alleQTLS_nuclear_scorefilt= alleQTLS_nuclear_score %>% filter(eQTL_es <1)
ggplot(alleQTLS_nuclear_scorefilt,aes(x=eQTL_es, y=slope,col=score)) + geom_point() + geom_text(aes(label=gene), nudge_y = .1) 
C10orf88 (ex in paper) peak 19682
rs7904973
ref =g alt = t
ENSG00000119965
genotype=as.data.frame(read.table("../data/ExampleQTLPlots/KCTD21_NuclearPeaksGenotype.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())
RNApheno=as.data.frame(read.table("../data/molPhenos/RNA_c10orf88.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())
full_geno=bind_cols(Ind=genohead$Ind, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "GG", ifelse(numdose==1, "GT", "TT")))
full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)
allRNA=full_geno %>% inner_join(full_pheno, by="Ind")Warning: Column `Ind` joining factors with different levels, coercing to
character vectorallRNA$genotype=as.factor(allRNA$genotype)
ggplot(allRNA, aes(x=genotype, y=Expression,group=genotype, fill=genotype)) + geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "Dark2") + labs(title="") + theme(legend.position = "bottom")
summary(lm(data=allRNA, Expression~genotype))
Call:
lm(formula = Expression ~ genotype, data = allRNA)
Residuals:
Min 1Q Median 3Q Max
-2.86310 -0.52048 0.09394 0.52691 2.42987
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.10807 0.15391 0.702 0.485
genotypeGT -0.36784 0.28794 -1.277 0.206
genotypeTT -0.07503 0.44873 -0.167 0.868
Residual standard error: 1.032 on 66 degrees of freedom
Multiple R-squared: 0.0242, Adjusted R-squared: -0.005373
F-statistic: 0.8183 on 2 and 66 DF, p-value: 0.4456same situation with not a great effect size effect but again we see the correct direction.
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.8 haven_2.1.1
[4] lattice_0.20-38 colorspace_1.4-1 generics_0.0.2
[7] vctrs_0.2.0 htmltools_0.3.6 yaml_2.2.0
[10] rlang_0.4.0 later_0.8.0 pillar_1.4.2
[13] withr_2.1.2 glue_1.3.1 RColorBrewer_1.1-2
[16] modelr_0.1.4 readxl_1.3.1 lifecycle_0.1.0
[19] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.0
[22] cellranger_1.1.0 rvest_0.3.4 evaluate_0.14
[25] labeling_0.3 knitr_1.23 httpuv_1.5.1
[28] broom_0.5.2 Rcpp_1.0.3 promises_1.0.1
[31] backports_1.1.4 scales_1.1.0 jsonlite_1.6
[34] farver_2.0.1 fs_1.3.1 hms_0.5.0
[37] digest_0.6.20 stringi_1.4.3 grid_3.6.1
[40] rprojroot_1.3-2 cli_1.1.0 tools_3.6.1
[43] magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4
[46] whisker_0.3-2 pkgconfig_2.0.2 zeallot_0.1.0
[49] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.1
[52] rmarkdown_1.13 httr_1.4.0 rstudioapi_0.10
[55] R6_2.4.0 nlme_3.1-140 git2r_0.26.1
[58] compiler_3.6.1