Last updated: 2020-02-21

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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Rmd 6788234 brimittleman 2020-02-21 add PM res
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Rmd d2056c1 brimittleman 2020-02-20 add lcls, add coloc package, add 5’ss by decile

Attempt a colocalziation analysis for the eQTLs and the snps in the nuclear QTL data.

mkdir ../data/coloc
wget http://eqtl.uchicago.edu/jointLCL/output_RNAseqGeuvadis_PC14.txt
#install.packages("coloc")
library(snpStats)
Loading required package: survival
Loading required package: Matrix
library(cowplot)
Loading required package: ggplot2

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(data.table)
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()   masks data.table::between()
✖ tidyr::expand()    masks Matrix::expand()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::first()     masks data.table::first()
✖ cowplot::ggsave()  masks ggplot2::ggsave()
✖ dplyr::lag()       masks stats::lag()
✖ dplyr::last()      masks data.table::last()
✖ purrr::transpose() masks data.table::transpose()
library("coloc")

First change the names of the rsid and gene.

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F, header = T)
RSID=read.table("/project2/gilad/briana/li_genotypes/RSID2snploc.txt",header = T, stringsAsFactors = F)
#ed 's/^chr//' output_RNAseqGeuvadis_PC14.txt  > output_RNAseqGeuvadis_PC14.noChr.txt 

eQTLres=read.table("../data/coloc/output_RNAseqGeuvadis_PC14.noChr.txt",header = T, stringsAsFactors = F)  %>% separate(snps,into=c("chr", "pos"), sep="\\.")%>% separate(gene,into=c("gene_id", "extra"), sep="\\.")  %>% mutate(snp=paste(chr,pos, sep=":")) %>% inner_join(RSID,by="snp") %>% inner_join(geneNames, by="gene_id") %>% select(RSID, GeneName,pvalue, beta)

I will be able to seperate these for the coloc analysis. I need the PAS gene pairs.

PASgene=read.table("../data/eQTL_LZ/PasGENEsnpstoUse.txt",stringsAsFactors = F, col.names = c("GeneName", "PAS", "RSID") )

Test if these are in the file above:

eQTLres_genesnp=eQTLres %>% mutate(test=paste(GeneName, RSID, sep="_")) %>% select(test)
PASgene_test= PASgene %>%  mutate(test=paste(GeneName, RSID, sep="_")) %>% select(test)


PASgene_indata= PASgene_test %>% inner_join(eQTLres_genesnp, by="test")

PASgene_indata= PASgene_test %>% anti_join(eQTLres_genesnp, by="test")

181 of the snp gene pairs are in this data.

Test on one first. c10orf88

Need:

p values for each SNP each SNP’s minor allele frequency sample size

C10orf88_rs7904973

PASgene_c10orf88= PASgene %>% filter(GeneName=="C10orf88")
PASgene_c10orf88
  GeneName       PAS      RSID
1 C10orf88 peak19682 rs7904973
PeakData=read.table("../data/eQTL_LZ/NuclearAssoc/peak19682_NuclearResults4LZ.txt",col.names = c("RSID","Pvalue_APA"),stringsAsFactors = F) %>% mutate(sampleSizeAPA=52)

GeneData=eQTLres %>% filter(GeneName=="C10orf88")

Number of overlapping snps:

length(intersect(PeakData$RSID,GeneData$RSID ))
[1] 123
nrow(GeneData)
[1] 123
nrow(PeakData)
[1] 2550
ind=read.table("../data/molPhenos/RNAhead.txt", header = F)
dim(ind)[2]-4
[1] 69
Genenomnames=c("gene", 'snp','dist', 'pval', 'slope')
c10orf88eqtl=read.table("../data/coloc/eQTL_c10orf88.txt",col.names = Genenomnames,stringsAsFactors = F) %>% mutate(SampleSizeE=69) %>% inner_join(RSID, by="snp")

Get freq for the snps in allChrom.dose.filt.vcf. these are the filtered snps from john. not the li snps but should include those i need and they have the same 119 ind.

module load vcftools


vcftools --vcf allChrom.dose.filt.vcf   --freq --out allChrom.dose.filt_freq

snpmaf=fread("/project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt_freq.frq",skip = 1, col.names = c("chr", "loc", "nAlles", "nchr", "allele1", "allele2")) %>% mutate(snp=paste(chr, loc, sep=":")) %>% separate("allele2", into=c("allele", "MAF2"), sep=":") %>%separate("allele1", into=c("allele", "MAF1"), sep=":")  %>% mutate(MAF= ifelse(MAF1 < MAF2, MAF1, MAF2))%>%  inner_join(RSID, by="snp") %>% select(RSID, MAF)
All= PeakData %>% inner_join(snpmaf,by="RSID") %>% inner_join(c10orf88eqtl, by="RSID")
All$Pvalue=as.numeric(as.character(All$Pvalue))
All$MAF=as.numeric(as.character(All$MAF))

Join with eQTL and apa

my.res <- coloc.abf(dataset1=list(pvalues=All$Pvalue,N=52,type="quant"),
                    dataset2=list(pvalues=All$pval,N=69,type="quant"),
                    MAF=All$MAF)
PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf 
   0.5610    0.2080    0.1360    0.0506    0.0440 
[1] "PP abf for shared variant: 4.4%"
C10orf88= as.data.frame(t(my.res$summary ))%>% mutate(gene="C10orf88")
C10orf88
  nsnps PP.H0.abf PP.H1.abf PP.H2.abf  PP.H3.abf  PP.H4.abf     gene
1  2430 0.5607699 0.2084953 0.1361743 0.05058588 0.04397468 C10orf88

This isnt a great result but it works.

I need to do this for all of them.

Write MAF data:

write.table(snpmaf, "../data/coloc/snpmaf.txt", col.names = T, row.names = F, quote = F)
sbatch ColocApAeQTL.sh
cat ../data/coloc/*colocRes.txt > ../data/coloc/AllAssoccolocRes.txt

H0: neither trait has a genetic association in the region H1: only trait 1 has a genetic association in the region H2: only trait 2 has a genetic association in the region H3: both traits are associated, but with different causal variants H4: both traits are associated and share a single causal variant

I think you should use PP4/(PP4+PP3)

and count only when PP4+PP3 > 0.2

ColocRES=read.table("../data/coloc/AllAssoccolocRes.txt", col.names = c("nsnp",'Neither', 'apaQTL', 'eQTL', 'DifferentSnps', 'Coloc','gene'),stringsAsFactors = F) %>% select(-nsnp) %>% mutate(three_four=DifferentSnps+Coloc, Prop=Coloc/three_four)

ColocRES_filt=ColocRES %>% filter(three_four>.2)
ColocRES_filt1=ColocRES %>% filter(three_four>.1)
ColocRES_filt3=ColocRES %>% filter(three_four>.3)
ColocRES_filt4=ColocRES %>% filter(three_four>.4)
all=ggplot(ColocRES,aes(x=Prop)) +geom_histogram(bins=50) + labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("All Genes ", nrow(ColocRES), sep=": "))

one=ggplot(ColocRES_filt1,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .1 : ", nrow(ColocRES_filt1), "genes" , sep=" "))


two=ggplot(ColocRES_filt,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .2 :", nrow(ColocRES_filt), "genes", sep=" "))


three=ggplot(ColocRES_filt3,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .3 : ", nrow(ColocRES_filt3), "genes", sep=" "))

four=ggplot(ColocRES_filt4,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .4 :", nrow(ColocRES_filt4), 'genes', sep=" "))
plot_grid(all, one, two, three, four)

Version Author Date
1ca62a2 brimittleman 2020-02-20

Try with a higher powered eQTL set.

mkdir ../data/GeuvadiseQTL/
cp  /project2/yangili1/zpmu/geuvadis/results/eqtl_new/YRI/nom_YRI_9phenoPC_*.txt.gz ../data/GeuvadiseQTL/
gunzip ../data/GeuvadiseQTL/*
cat ../data/GeuvadiseQTL/nom_YRI* > ../data/GeuvadiseQTL/nom_YRI_9phenoPCALL.txt

MAF for these data:

cp  /project2/yangili1/zpmu/geuvadis/data/new_fastqtl/chr*.dose.r2.nodup.filtered.recode.YRI.frq.gz ../data/GeuvadiseQTL/
  
gunzip ../data/GeuvadiseQTL/*.gz

cat ../data/GeuvadiseQTL/*ose.r2.nodup.filtered.recode.YRI.frq > ../data/GeuvadiseQTL/allMAFRes.txt

mkdir ../data/coloc_PM

sbatch ColocApAeQTL_PM.sh

cat ../data/coloc_PM/*colocRes.txt > ../data/coloc_PM/AllAssoccolocResPM.txt
ColocRESPM=read.table("../data/coloc_PM/AllAssoccolocResPM.txt", col.names = c("nsnp",'Neither', 'apaQTL', 'eQTL', 'DifferentSnps', 'Coloc','gene'),stringsAsFactors = F) %>% select(-nsnp) %>% mutate(three_four=DifferentSnps+Coloc, Prop=Coloc/three_four)

ColocRESPM_filt=ColocRESPM %>% filter(three_four>.2)
ColocRESPM_filt1=ColocRESPM %>% filter(three_four>.1)
ColocRESPM_filt3=ColocRESPM %>% filter(three_four>.3)
ColocRESPM_filt4=ColocRESPM %>% filter(three_four>.4)
allPM=ggplot(ColocRESPM,aes(x=Prop)) +geom_histogram(bins=50) + labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("All Genes ", nrow(ColocRESPM), sep=": "))

onePM=ggplot(ColocRESPM_filt1,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .1 : ", nrow(ColocRESPM_filt1), "genes" , sep=" "))


twoPM=ggplot(ColocRESPM_filt,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .2 :", nrow(ColocRESPM_filt), "genes", sep=" "))


threePM=ggplot(ColocRESPM_filt3,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .3 : ", nrow(ColocRESPM_filt3), "genes", sep=" "))

fourPM=ggplot(ColocRESPM_filt4,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("PP3+PP4 > .4 :", nrow(ColocRESPM_filt4), 'genes', sep=" "))
plot_grid(allPM, onePM, twoPM, threePM, fourPM)

ggplot(ColocRESPM_filt,aes(x=Prop)) +geom_histogram(bins=50)+ labs(y="Number of Genes", x="PP4/(PP3+PP4)", title=paste("Colocalization results for apaQTLs and eQTLs \nPP3+PP4 > .2 :", nrow(ColocRESPM_filt), "genes", sep=" "))

Which genes are these:

ColocRESPM_filt5= ColocRESPM_filt %>% filter(Prop>.5) 
ColocRESPM_filt5$gene
 [1] "AP3M2"     "BLOC1S2"   "CENPQ"     "CHURC1"    "COX11"    
 [6] "ERAP2"     "GSTO1"     "HIBCH"     "HSD17B12"  "KDELR2"   
[11] "LINC00476" "MB21D2"    "MRPL43"    "MTG2"      "PPP2R3C"  
[16] "RAB31"     "SLFN5"     "SMDT1"     "TBC1D4"    "THEM4"    
[21] "TMEM140"   "TMEM80"    "VRK3"      "ZC2HC1A"   "ZNRD1"    
[26] "ZSWIM7"   
length(ColocRESPM_filt5$gene)
[1] 26

Are these unexplained:

UexPe=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt",col.names = "GeneName") %>% filter(GeneName %in% ColocRESPM_filt5$gene)
UexPe
    GeneName
1      THEM4
2    BLOC1S2
3     MRPL43
4     TMEM80
5     ZSWIM7
6      SMDT1
7      ZNRD1
8      CENPQ
9     KDELR2
10   TMEM140
11   ZC2HC1A
12 LINC00476

sessionInfo()
R version 3.5.1 (2018-07-02)
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] coloc_3.2-1       forcats_0.3.0     stringr_1.3.1    
 [4] dplyr_0.8.0.1     purrr_0.3.2       readr_1.3.1      
 [7] tidyr_0.8.3       tibble_2.1.1      tidyverse_1.2.1  
[10] data.table_1.12.0 cowplot_0.9.4     ggplot2_3.1.1    
[13] snpStats_1.32.0   Matrix_1.2-15     survival_2.43-1  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2          mvtnorm_1.0-8       lubridate_1.7.4    
 [4] lattice_0.20-38     assertthat_0.2.0    rprojroot_1.3-2    
 [7] digest_0.6.18       R6_2.3.0            cellranger_1.1.0   
[10] plyr_1.8.4          backports_1.1.2     pcaPP_1.9-73       
[13] stats4_3.5.1        evaluate_0.12       httr_1.3.1         
[16] pillar_1.3.1        zlibbioc_1.28.0     rlang_0.4.0        
[19] lazyeval_0.2.1      readxl_1.1.0        rstudioapi_0.10    
[22] whisker_0.3-2       rmarkdown_1.10      labeling_0.3       
[25] splines_3.5.1       munsell_0.5.0       broom_0.5.1        
[28] compiler_3.5.1      httpuv_1.4.5        modelr_0.1.2       
[31] pkgconfig_2.0.2     BiocGenerics_0.28.0 htmltools_0.3.6    
[34] tidyselect_0.2.5    workflowr_1.6.0     reshape_0.8.8      
[37] BMA_3.18.11         rrcov_1.4-7         crayon_1.3.4       
[40] withr_2.1.2         later_0.7.5         leaps_3.1          
[43] grid_3.5.1          nlme_3.1-137        jsonlite_1.6       
[46] gtable_0.2.0        git2r_0.26.1        magrittr_1.5       
[49] scales_1.0.0        cli_1.1.0           stringi_1.2.4      
[52] fs_1.3.1            promises_1.0.1      robustbase_0.93-3  
[55] xml2_1.2.0          generics_0.0.2      tools_3.5.1        
[58] glue_1.3.0          DEoptimR_1.0-8      hms_0.4.2          
[61] parallel_3.5.1      yaml_2.2.0          inline_0.3.15      
[64] colorspace_1.3-2    cluster_2.0.7-1     rvest_0.3.2        
[67] knitr_1.20          haven_1.1.2