Explore Unexplained QTLs

Last updated: 2019-02-25

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Knit directory: threeprimeseq/analysis/

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File	Version	Author	Date	Message
Rmd	d3dfe95	Briana Mittleman	2019-02-25	look at pi1
html	b74a969	Briana Mittleman	2019-02-25	Build site.
Rmd	2336f87	Briana Mittleman	2019-02-25	add unexplained QTL analysis

library(qvalue)
library(tidyverse)

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library(cowplot)


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    ggsave

library(workflowr)

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Run ?workflowr for help getting started

One original goal for this project was too see if APA qtls could explain a number of the unexplianed eQTLs Yang found in the integrated molQTL science paper. He has provided me a list of explained eQTLs (chromatin associatated) and unexplained eQTLs. As a first pass, I want to look at the loci/gene associations in my QTL data. If there is significant sharing I expect lower pvalues for the apa associatiations at these loci. I will start with all peaks in the e genes.

These data have 1163 explained loci and 801 unexplained loci.

I want to make a python script that can take either of these and the nominal results for my total or nuclear apaQTLs. It will extract any association for a peak in one of these genes.

First sort these. They are chr, pos, gene,

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/explained_FDR10.txt > /project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/explained_FDR10.sort.txt

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/unexplained_FDR10.txt > /project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/unexplained_FDR10.sort.txt

Look for sharing in associations

Take some of this code from this analysis

APApval4eQTL.py

def main(eQTL,apaQTL, outF):  
    fout=open(outF,"w")
    geneNames=open("/project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt","r")
    #gene name dictionary  
    geneDic={}
    geneDicOpp={}
    for i, ln in enumerate(geneNames):
        if i >0:
            ID=ln.split()[0]
            gene=ln.split()[1]
            if gene not in geneDic.keys():
                geneDic[gene]=[ID]
            else: 
                geneDic[gene].append(ID)
            geneDicOpp[ID]=gene
    qtl_dic={}
    for ln in open(eQTL,"r"):
        chrom=ln.split()[0][3:]
        pos=ln.split()[1]
        snp=chrom + ":" + pos
        gene=ln.split()[2]
        if gene not in geneDicOpp.keys():
            continue
        geneName=geneDicOpp[gene]
        qtl_dic[snp]=geneName
    for ln in open(apaQTL, "r"):
        snp=ln.split()[1]
        gene=ln.split()[0].split(":")[-1].split("_")[0]
        peak=ln.split()[0].split(":")[-1].split("_")[-1]
        pval=ln.split()[3]
        if snp in qtl_dic.keys():
            if qtl_dic[snp]==gene:
                fout.write("%s\t%s\t%s\t%s\n"%(snp, gene, peak, pval))
    fout.close()
            
    
if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    eqtl = sys.argv[2]
    inQTL="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.%s.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"%(fraction)
    eQTLin="/project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/%s_FDR10.sort.txt"%(eQTL)
    outFile="/project2/gilad/briana/threeprimeseq/data/ExplaineQTLS/NomPval_%sApaQTLs_for%seQTLs.txt"%(fraction, eQTL)
    main(eQTLin,inQTL,outFile)

Run this overall combinations:
runAPApval4eQTL.sh

#!/bin/bash


#SBATCH --job-name=runAPApval4eQTL
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=runAPApval4eQTL.out
#SBATCH --error=runAPApval4eQTL.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


python APApval4eQTL.py Total explained
python APApval4eQTL.py Total unexplained

python APApval4eQTL.py Nuclear explained
python APApval4eQTL.py Nuclear unexplained

Genes not in the switch gene name file:

geneNoName=read.table("../data/eQTLs_Lietal/genesNoName_uniq.txt", stringsAsFactors = F, col.names = c("GeneID"))

Upload results:

resNames=c("SNP", "gene", "peak", "pval")
totUn=read.table("../data/eQTLs_Lietal/NomPval_TotalApaQTLs_forunexplainedeQTLs.txt", stringsAsFactors = F, col.names = resNames)
totEx=read.table("../data/eQTLs_Lietal/NomPval_TotalApaQTLs_forexplainedeQTLs.txt", stringsAsFactors = F, col.names = resNames)

nucUn=read.table("../data/eQTLs_Lietal/NomPval_NuclearApaQTLs_forunexplainedeQTLs.txt", stringsAsFactors = F, col.names = resNames)
nucEx=read.table("../data/eQTLs_Lietal/NomPval_NuclearApaQTLs_forexplainedeQTLs.txt", stringsAsFactors = F, col.names = resNames)

ggplot(totUn, aes(x=pval)) + geom_density(fill="blue", alpha=.5) + geom_density(data=totEx,aes(x=pval), fill="red", alpha=.5 ) + labs(title="Total APA association pval for explained and unexplained eQTLs \n red=explained, blue=unexplained")

ggplot(nucUn, aes(x=pval)) + geom_density(fill="blue", alpha=.5) + geom_density(data=nucEx,aes(x=pval), fill="red", alpha=.5 ) + labs(title="Nuclear APA association pval for explained and unexplained eQTLs \n red=explained, blue=unexplained")

Pi1 values:

Tot Explained:

TotExPi=pi0est(totEx$pval, pi0.method = "bootstrap")
1-TotExPi$pi0

[1] 0.1386882

Tot unexplained:

TotUnPi=pi0est(totUn$pval, pi0.method = "bootstrap")
1-TotUnPi$pi0

[1] 0.1043331

Nuc Explained:

NucExPi=pi0est(nucEx$pval, pi0.method = "bootstrap")
1-NucExPi$pi0

[1] 0.08966862

Nuc unexplained:

NucUnPi=pi0est(nucUn$pval, pi0.method = "bootstrap")
1-NucUnPi$pi0

[1] 0.1117647

This is the naive version, I need to accont for the multiple peaks in the same gene.

totUn_fix=totUn %>% group_by(gene) %>% mutate(nPeaks=n()) %>% ungroup()

totEx_fix=totEx %>% group_by(gene) %>% mutate(nPeaks=n()) %>% ungroup()

nucUn_fix=nucUn %>% group_by(gene) %>% mutate(nPeaks=n()) %>% ungroup()

nucEx_fix=nucEx %>% group_by(gene) %>% mutate(nPeaks=n()) %>% ungroup()

Direct overlap

I can use a similar LD anaylsis I used in the GWAS overlap. I will get all of the snps in LD with the eQTLs then look for overlap with my apaQTLs.

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] workflowr_1.2.0 cowplot_0.9.3   forcats_0.3.0   stringr_1.4.0  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1 qvalue_2.12.0  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4 reshape2_1.4.3   splines_3.5.1    haven_1.1.2     
 [5] lattice_0.20-35  colorspace_1.3-2 htmltools_0.3.6  yaml_2.2.0      
 [9] rlang_0.2.2      pillar_1.3.0     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     bindrcpp_0.2.2   bindr_0.1.1     
[17] plyr_1.8.4       munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0
[21] rvest_0.3.2      evaluate_0.13    labeling_0.3     knitr_1.20      
[25] broom_0.5.0      Rcpp_0.12.19     scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.2.6         hms_0.4.2        digest_0.6.17   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.0.1       
[37] tools_3.5.1      magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.11   httr_1.3.1       rstudioapi_0.9.0
[49] R6_2.3.0         nlme_3.1-137     git2r_0.24.0     compiler_3.5.1

Explore Unexplained QTLs

Briana Mittleman

2/25/2019

Look for sharing in associations

Direct overlap