/
characterize_apaQTLs.Rmd
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characterize_apaQTLs.Rmd
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---
title: "Characterize apaQTLs"
author: "Briana Mittleman"
date: "2/16/2019"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(workflowr)
library(tidyverse)
library(qvalue)
library(reshape2)
library(cowplot)
```
In this analysis I will look at the apaQTLs to draw biological insight. To do this I will run the following analysis:
* Look at chromatin regions for QTLs (chromHMM)
* Overlap apaQTLs between fractions
* Overlap apaQTLs with GWAS
* QTL examples
##Chromatin regions for QTLs
This will start with the sig QTL bed files:
* /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed
* /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed
Creare a python script using pybedtools:
ChromHMM.QTLs.py
```{bash,eval=F}
import pybedtools
sigNuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed')
sigTot=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed')
hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
#map hmm to snps
Tot_overlapHMM=sigTot.map(hmm, c=4)
Nuc_overlapHMM=sigNuc.map(hmm,c=4)
#save results
Tot_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Tot_overlapHMM.bed")
Nuc_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuc_overlapHMM.bed")
```
Run in three-prime-env
```{r}
chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)
TotalOverlapHMM=read.table("../data/ApaQTLs/Tot_overlapHMM.bed", col.names=c("chrom", "start", "end", "pid", "significance", "strand", "number"))
TotalOverlapHMM_names=TotalOverlapHMM %>% left_join(chromHmm, by="number")
NuclearOverlapHMM=read.table("../data/ApaQTLs/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "pid", "significance", "strand", "number"))
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")
ggplot(TotalOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Total APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
```
Group them to put on plot together
```{r}
NuclearOverlapHMM_names_byname= NuclearOverlapHMM_names %>% group_by(name) %>% summarise(Nuclear=n())
TotalOverlapHMM_names_byname= TotalOverlapHMM_names %>% group_by(name) %>% summarise(Total=n())
bothFracHMM= TotalOverlapHMM_names_byname %>% full_join(NuclearOverlapHMM_names_byname, by="name")
bothFracHMM$Nuclear= bothFracHMM$Nuclear %>% replace_na(0)
```
melt and plot
```{r}
bothFracHMM_melt= melt(bothFracHMM,id.vars="name")
colnames(bothFracHMM_melt)=c("Region", "Fraction", "NQTLs")
apaQTLsChromregion=ggplot(bothFracHMM_melt, aes(x=Region, y=NQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3")) + labs(title="apaQTLs by chromatin region")
apaQTLsChromregion
ggsave(apaQTLsChromregion, file="../output/plots/apaQTLsbyChromHMM.png", width = 7, height = 5)
```
```{r}
bothFracHMM_prop= bothFracHMM %>% mutate(Total_prop=Total/291) %>% mutate(Nuclear_prop=Nuclear/615) %>% select(-Total,-Nuclear)
bothFracHMM_prop_melt= melt(bothFracHMM_prop,id.vars="name")
colnames(bothFracHMM_prop_melt)=c("Region", "Fraction", "PropQTLs")
propapaQTLsChromregion=ggplot(bothFracHMM_prop_melt, aes(x=Region, y=PropQTLs, by=Fraction, fill=Fraction)) + geom_bar(stat="identity", position = "dodge")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) +scale_fill_manual(values=c("darkviolet","deepskyblue3")) + labs(y="Proportion of apaQTLs", title="Proportion of apaQTLs by chromatin region")
propapaQTLsChromregion
ggsave(propapaQTLsChromregion, file="../output/plots/proportionapaQTLsbyChromHMM.png", width = 7, height = 5)
```
I will need to get matched snps to look for enrichment in these values.
##Overlap apaQTLs between fractions.
Goal: Find the nominal pvalue for the significant snp peak pair in oposite fraction. I can make a dictionary with the total and nuclear QTLs then run through the nominal files to get the pvalues:
Start with apa QTLs:
* /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed
* /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed
NomResFromOppFrac.py
```{bash,eval=F}
nucQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed"
totQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed"
nucNom="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"
totNom="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"
outnuc="/project2/gilad/briana/threeprimeseq/data/QTL_Overlap/TotQTLs_inNucFractionRes.txt"
outtot="/project2/gilad/briana/threeprimeseq/data/QTL_Overlap/NucQTLs_inTotFractionRes.txt"
def oppFract(inRes, inQTL, out):
fout=open(out, "w")
qtl_dic={}
#SNP is key, peak is value
for ln in open(inQTL,"r"):
snp=ln.split()[2]
chrom=ln.split()[0]
peak=ln.split()[3].split(":")[0]
qtl=str(chrom) + ":" + str(snp)
if qtl not in qtl_dic.keys():
qtl_dic[qtl]=[peak]
else:
qtl_dic[qtl].append(peak)
#print(qtl_dic)
for ln in open(inRes, "r"):
pval=ln.split()[3]
snp=ln.split()[1]
peak=ln.split()[0].split(":")[3].split("_")[-1]
if snp in qtl_dic.keys():
if peak in qtl_dic[snp]:
fout.write("%s\t%s\t%s\n"%(snp, peak, pval))
fout.close()
oppFract(nucNom, totQTLs,outnuc)
oppFract(totNom, nucQTLs, outtot)
```
Run in bash:
run_NomResFromOppFrac.sh
```{bash,eval=F}
#!/bin/bash
#SBATCH --job-name=NomResFromOppFrac
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=NomResFromOppFrac.out
#SBATCH --error=NomResFromOppFrac.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
python NomResFromOppFrac.py
```
```{r}
names=c("SNP", "peak", "pval")
NucQTLinTot=read.table("../data/QTL_overlap/NucQTLs_inTotFractionRes.txt", stringsAsFactors = F, col.names = names)
TotQTLinNuc=read.table("../data/QTL_overlap/TotQTLs_inNucFractionRes.txt", stringsAsFactors = F, col.names = names)
```
Get pi values:
```{r}
qvalTot= pi0est(NucQTLinTot$pval, pi0.method = "bootstrap")
1-qvalTot$pi0
```
```{r}
qvalNuc= pi0est(TotQTLinNuc$pval, pi0.method = "bootstrap")
1-qvalNuc$pi0
```
plots:
```{r}
par(mfrow=c(1,2))
hist(NucQTLinTot$pval,xlab="Total apaQTL pvalue", main="Nuclear apaQTLs \nin Total Fraction")
text(.6,200, paste("pi_1=", round((1-qvalTot$pi0), digit=3), sep=" "))
hist(TotQTLinNuc$pval,xlab="Nuclear apaQTL pvalue", main="Total apaQTLs \nin Nuclear Fraction")
text(.6,125, paste("pi_1=", round((1-qvalNuc$pi0), digit=3), sep=" "))
```
```{r}
png("../output/plots/apaFractionOverlapPi1.png", width=1000, height = 500)
par(mfrow=c(1,2))
hist(NucQTLinTot$pval,xlab="Total apaQTL pvalue", main="Nuclear apaQTLs \nin Total Fraction")
text(.6,200, paste("pi_1=", round((1-qvalTot$pi0), digit=3), sep=" "))
hist(TotQTLinNuc$pval,xlab="Nuclear apaQTL pvalue", main="Total apaQTLs \nin Nuclear Fraction")
text(.6,125, paste("pi_1=", round((1-qvalNuc$pi0), digit=3), sep=" "))
dev.off()
```
This provides evidence for high degree of QTL sharing with increased sharing total to nuclear. This demonstrates to me that there are nuclear QTLs that do not persist in the total fraction. I will want to learn more about these.
##Overlap with GWAS catelog
I did this analysis with the QTLs in the preprocessed 39 individual analysis. I will follow a similar pipeline here. I will find all of the snps in LD with the QTLs then test for these in the GWAS catelog. The pipeline I used to get the LD for all of the snp is shown [here](apaQTLoverlapGWAS.Rmd). The plink files are in /project2/gilad/briana/threeprimeseq/data/GWAS_overlap/. There are both map and ped files.
I can now adapt the subset_plink4QTLs.py file to take the current QTLs list. The file just has the QTLs with the chromosome and position. I can make this and put it in:
/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed
The 50mb QTLs are in /project2/gilad/briana/threeprimeseq/data/ApaQTLs.
* NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt
* TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt
The QTL snps are in the 6th column.
```{bash,eval=F}
cut -f6 -d" " /project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt | uniq > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb.txt
cut -f6 -d" " /project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt | uniq > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb.txt
```
I can convert these the the way they are in GEU snp files tony made (snp_num_pos)
QTLs2GeuSnps_proc.py
```{bash,eval=F}
tot_in=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb.txt", "r")
nuc_in=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb.txt", "r")
tot_out=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb_GEU.txt", "w")
nuc_out=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb_GEU.txt", "w")
def fix_file(fin, fout):
for ln in fin:
chrom, pos = ln.split(":")
fout.write("snp_%s_%s"%(chrom,pos))
fout.close()
fix_file(tot_in, tot_out)
fix_file(nuc_in, nuc_out)
```
subset_plink4QTLs_proc.py
```{bash,eval=F}
def main(genFile, qtlFile, outFile):
#convert snp file to a list:
def file_to_list(file):
snp_list=[]
for ln in file:
snp=ln.strip()
snp_list.append(snp)
return(snp_list)
gen=open(genFile,"r")
fout=open(outFile, "w")
qtls=open(qtlFile, "r")
qtl_list=file_to_list(qtls)
for ln in gen:
snp=ln.split()[2]
if snp in qtl_list:
fout.write(ln)
fout.close()
if __name__ == "__main__":
import sys
chrom=sys.argv[1]
fraction=sys.argv[2]
genFile = "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap/geu_plinkYRI_LDchr%s.ld"%(chrom)
outFile= "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/%sApaQTL_LD/chr%s.%sQTL.LD.geno.ld"%(fraction,chrom,fraction)
qtlFile= "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sQTLs_uniq_50mb_GEU.txt"%(fraction)
main(genFile, qtlFile, outFile)
```
run_subset_plink4QTLs_proc.sh
```{bash,eval=F}
#!/bin/bash
#SBATCH --job-name= run_subset_plink4QTLs_proc
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=run_subset_plink4QTLs_proc.out
#SBATCH --error=run_subset_plink4QTLs_proc.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
for i in {1..22};
do
python subset_plink4QTLs_proc.py ${i} "Total"
done
for i in {1..22};
do
python subset_plink4QTLs_proc.py ${i} "Nuclear"
done
```
This added 2446 total snps and 6258 nuclear snps.
Cat and remove indels:
```{bash,eval=F}
cat chr* > allChr.TotalQTL.LD.gene.ld
grep -v indel allChr.TotalQTL.LD.gene.ld > allChr.TotalQTL.LD.gene.ld_noIndel
cat chr* > allChr.NuclearQTL.LD.gene.ld
grep -v indel allChr.NuclearQTL.LD.gene.ld > allChr.NuclearQTL.LD.gene.ld_noIndel
```
Make these bed files:
makeAlloverlapbed_proc.py
```{bash,eval=F}
#load files:
QTL_total=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/TotalQTLs_uniq_50mb_GEU.txt", "r")
QTL_nuclear=open("/project2/gilad/briana/threeprimeseq/data/ApaQTLs/NuclearQTLs_uniq_50mb_GEU.txt", "r")
LD_total=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/TotalApaQTL_LD/allChr.TotalQTL.LD.gene.ld_noIndel", "r")
LD_nuclear=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/NuclearApaQTL_LD/allChr.NuclearQTL.LD.gene.ld_noIndel", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.bed", "w")
#function for qtl to bed format
def qtl2bed(fqtl, fraction, fout=outFile):
for ln in fqtl:
snp, chrom, pos = ln.split("_")
start=int(pos)-1
end= int(pos)
fout.write("%s\t%d\t%d\tQTL_%s\n"%(chrom, start, end,fraction))
#function for ld to bed format
def ld2bed(fLD, fraction, fout=outFile):
for ln in fLD:
snpID=ln.split()[5]
snp, chrom, pos= snpID.split("_")
start=int(pos)-1
end=int(pos)
fout.write("%s\t%d\t%d\tLD_%s\n"%(chrom, start, end,fraction))
#I will run each of these for both fractions to get all of the snps in the out file.
qtl2bed(QTL_nuclear, "Nuclear")
qtl2bed(QTL_total, "Total")
ld2bed(LD_nuclear, "Nuclear")
ld2bed(LD_total, "Total")
outFile.close()
```
Sort this:
```{bash,eval=F}
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.bed > /project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.sort.bed
```
Overlap with GWAS
I can use the overlapSNPsGWAS.py file I created in the previous rendition of this analysis but run it with these files.
run_overlapSNPsGWAS_proc.sh
```{bash,eval=F}
#!/bin/bash
#SBATCH --job-name=run_overlapSNPsGWAS_proc
#SBATCH --account=pi-yangili1
#SBATCH --time=5:00:00
#SBATCH --output=run_overlapSNPsGWAS_proc.out
#SBATCH --error=run_overlapSNPsGWAS_proc.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
python overlapSNPsGWAS.py "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllOverlapSnps.sort.bed" "/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped.txt"
```
Total QTLs overlap:
rs3117582 6:31620520
Total LD overlap:
* rs2282301 1:155868625
* rs3596 12:95696420
* rs2277862 20:34152782
* rs2517713 6:29918099
Nuclear QTL overlap:
rs7206971 17:45425115
Nucelar LD overlapL
* rs10889353 1:63118196
* rs2282301 1:155868625
* rs10859871 12:95711876
* rs10133111 14:103377321
* rs17382723 2:242053546
* rs2277862 20:34152782
* rs2298428 22:21982892
* rs13160562 5:96111371
* rs29784 5:172595308
* rs2517713 6:29918099
* rs3077 6:33033022
Are these eQTLs?
Pull these snps from the eQTLs nominal pvalues:
Process the AllSnps_GWASoverlapped.txt file to have the RS id, the snp, and where it was found.
formatGWASOverlap.py
```{bash,eval=F}
inFile="/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped.txt"
outFile=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped_format.txt","w")
for i in open(inFile, "r"):
chrom=i.split()[0]
loc=i.split()[2]
snp=chrom + ":" + loc
found=i.split()[3]
rs=i.split()[7].split(":")[0]
outFile.write("%s\t%s\t%s\n"%(snp, rs, found))
outFile.close()
```
This is 14 uniq snps.
/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out
I need to go throguh this file and check for the snps. I can do this by making a dictionary of the uniq snps in the overlap then testing the lines ib the nominal file. I want to keep the gene, snp, and pvalue for these associations
getexpPvalOverlap.py
```{bash,eval=F}
overlapSnps="/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/AllSnps_GWASoverlapped_format.txt"
outF=open("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap_processed/eQTL_pval_GWASOverlap.txt","w")
nomRes="/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out"
snps_dic={}
for ln in open(overlapSnps, "r"):
snp=ln.split()[0]
if snp not in snps_dic.keys():
snps_dic[snp]=""
for ln in open(nomRes,"r"):
snp=ln.split()[1]
if snp in snps_dic.keys():
gene=ln.split()[0].split(".")[0]
pval=ln.split()[3]
outF.write("%s\t%s\t%s\n"%(snp,gene,pval))
outF.close()
```
I can pull this in and get the lowest pval for each one
```{r}
eQTLpvalOverlapGWAS=read.table("../data/ApaQTLs/eQTL_pval_GWASOverlap.txt", stringsAsFactors = F, col.names = c("snp", "gene", "pval"))
```
```{r}
eQTLpvalOverlapGWAS_min= eQTLpvalOverlapGWAS %>% group_by(snp) %>% summarise(pvalM=min(pval)) %>% mutate(noSig=ifelse(pvalM<.05, "yes", "no"))
```
FIlter by the non sig ones to look at as exmaples:
```{r}
eQTLpvalOverlapGWAS_NotSig= eQTLpvalOverlapGWAS_min %>% filter(noSig=="no")
eQTLpvalOverlapGWAS_NotSig
```
1:63118196 in LD with nuclear QTL (find which it is LD with )
```{bash,eval=F}
grep snp_1_63118196 chr1.NuclearQTL.LD.geno.ld
#snp_1_63018852
#r2 .91
```
12:95696420 LD with total QTL
```{bash,eval=F}
grep snp_12_95696420 TotalApaQTL_LD/chr12.TotalQTL.LD.geno.ld
#snp_12_95672642
#r2 =1
```
12:95711876 LD_Nuclear
```{bash,eval=F}
grep snp_12_95711876 NuclearApaQTL_LD/chr12.NuclearQTL.LD.geno.ld
#snp_12_95688540
#r2 =1
```
2:242053546 LD_Nuclear
```{bash,eval=F}
grep snp_2_242053546 NuclearApaQTL_LD/chr2.NuclearQTL.LD.geno.ld
#snp_2_242018994
#r2 = .94
```
5:172595308 LD Nuclear
```{bash,eval=F}
grep snp_5_172595308 NuclearApaQTL_LD/chr5.NuclearQTL.LD.geno.ld
#snp_5_172580291
#r2 = .938
```
##Exampls plots:
In [this analysis file](swarmPlots_QTLs.html) I made boxplots for example QTLs. Here I can look at these.
###1:63018852
First example 1:63018852 (nuclear QTL) DOCK7 peak5329 (2 GWAS hits for this snp and gene)
```{bash,eval=F}
grep DOCK7 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000116641
```
createQTLsnpAPAPhenTable_proc.py
```{bash,eval=F}
def main(PhenFile, GenFile, outFile, snp, peak):
fout=open(outFile, "w")
#Phen=open(PhenFile, "r")
Gen=open(GenFile, "r")
#get ind and pheno info
def get_pheno():
Phen=open(PhenFile, "r")
for num, ln in enumerate(Phen):
if num == 0:
indiv= ln.split()[4:]
else:
id=ln.split()[3].split(":")[3]
peakID=id.split("_")[2]
if peakID == peak:
pheno_list=ln.split()[4:]
pheno_data=list(zip(indiv,pheno_list))
#print(pheno_data)
return(pheno_data)
def get_geno():
for num, lnG in enumerate(Gen):
if num == 13:
Ind_geno=lnG.split()[9:]
if num >= 14:
sid= lnG.split()[2]
if sid == snp:
gen_list=lnG.split()[9:]
allele1=[]
allele2=[]
for i in gen_list:
genotype=i.split(":")[0]
allele1.append(genotype.split("|")[0])
allele2.append(genotype.split("|")[1])
#now i have my indiv., phen, allele 1, alle 2
geno_data=list(zip(Ind_geno, allele1, allele2))
#print(geno_data)
return(geno_data)
phenotype=get_pheno()
pheno_df=pd.DataFrame(data=phenotype,columns=["Ind", "Pheno"])
#print(pheno_df)
genotype=get_geno()
geno_df=pd.DataFrame(data=genotype, columns=["Ind", "Allele1", "Allele2"])
#print(geno_df)
full_df=pd.merge(geno_df, pheno_df, how="inner", on="Ind")
full_df.to_csv(fout, sep="\t", encoding='utf-8', index=False)
fout.close()
if __name__ == "__main__":
import sys
import pandas as pd
chrom=sys.argv[1]
snp = sys.argv[2]
peak = sys.argv[3]
fraction=sys.argv[4]
PhenFile = "/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.%s.fixed.pheno_5perc.fc.gz.phen_chr%s"%(fraction, chrom)
GenFile= "/project2/gilad/briana/YRI_geno_hg19/chr%s.dose.filt.vcf"%(chrom)
outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTL_proc_examples/qtlSNP_PeakAPA%s.%s%s.txt"%(fraction, snp, peak)
main(PhenFile, GenFile, outFile, snp, peak)
```
**createQTLsnpMolPhenTable.py changed the output dir**
```{bash,eval=F}
python createQTLsnpAPAPhenTable_proc.py 1 1:63018852 peak5329 Total
python createQTLsnpAPAPhenTable_proc.py 1 1:63018852 peak5329 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "1" "1:63018852" "ENSG00000116641"
```
Function to make plots:
```{r}
plotQTL_func= function(SNP, peak, gene){
apaN_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
apaT_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPATotal.", SNP, peak, ".txt", sep = "" ), header=T)
su30_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_4su_30_", SNP, gene, ".txt", sep=""), header = T)
su60_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_4su_60_", SNP, gene, ".txt", sep=""), header=T)
RNA_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_RNAseq_", SNP, gene, ".txt", sep=""),header=T)
RNAg_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_RNAseqGeuvadis_", SNP, gene, ".txt", sep=""), header = T)
ribo_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_ribo_", SNP, gene, ".txt", sep=""),header=T)
prot_file=read.table(paste("../data/apaExamp_proc/qtlSNP_Peak_prot.", SNP, gene, ".txt", sep=""), header=T)
ggplot_func= function(file, molPhen,GENE){
file = file %>% mutate(genotype=Allele1 + Allele2)
file$genotype= as.factor(as.character(file$genotype))
plot=ggplot(file, aes(y=Pheno, x=genotype, by=genotype, fill=genotype)) + geom_boxplot(width=.25) + geom_jitter() + labs(y="Phenotpye",title=paste(molPhen, GENE, sep=": ")) + scale_fill_brewer(palette="Paired")
return(plot)
}
apaNplot=ggplot_func(apaN_file, "Apa Nuclear", gene)
apaTplot=ggplot_func(apaT_file, "Apa Total", gene)
su30plot=ggplot_func(su30_file, "4su30",gene)
su60plot=ggplot_func(su60_file, "4su60",gene)
RNAplot=ggplot_func(RNA_file, "RNA Seq",gene)
RNAgPlot=ggplot_func(RNAg_file, "RNA Seq Geuvadis",gene)
riboPlot= ggplot_func(ribo_file, "Ribo Seq",gene)
protplot=ggplot_func(prot_file, "Protein",gene)
full_plot= plot_grid(apaNplot,apaTplot, RNAplot, protplot,nrow=2)
return (full_plot)
}
```
```{r}
DOC7_boxplots=plotQTL_func(SNP="1:63018852", peak="peak5329", gene="ENSG00000116641")
DOC7_boxplots
ggsave(DOC7_boxplots, file="../output/plots/DOC7_boxplots.png")
```
Only ApaPlot:
```{r}
plotAPAQTL_func= function(SNP, peak, gene){
apaN_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
apaT_file=read.table(paste("../data/apaExamp_proc/qtlSNP_PeakAPATotal.", SNP, peak, ".txt", sep = "" ), header=T)
ggplot_func= function(file, molPhen,GENE){
file = file %>% mutate(genotype=Allele1 + Allele2)
file$genotype= as.factor(as.character(file$genotype))
plot=ggplot(file, aes(y=Pheno, x=genotype, by=genotype, fill=genotype)) + geom_boxplot(width=.25) + geom_jitter() + labs(y="Phenotpye",title=paste(molPhen, GENE, sep=": ")) + scale_fill_brewer(palette="Paired")
return(plot)
}
apaNplot=ggplot_func(apaN_file, "Apa Nuclear", gene)
apaTplot=ggplot_func(apaT_file, "Apa Total", gene)
full_plot= plot_grid(apaNplot,apaTplot,nrow=1)
return (full_plot)
}
```
```{r}
plotAPAQTL_func(SNP="1:63018852", peak="peak5329", gene="DOCK7")
```
FInd this in the catelog:
/project2/gilad/briana/genome_anotation_data/hg19GwasCatalog.sort.bed
1 63118195 63118196 rs10889353:19060911:DOCK7 0.32
In GWAS associated with LDL cholesteral and similar phenotypes
####Locus zoom for this plot
```{bash,eval=F}
grep DOCK7 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000116641
grep peak5329 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/NuclearAPA.peak5329.DOCK7.nomNuc.txt
grep peak5329 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/TotalAPA.peak5329.DOCK7.nomTotal.txt
grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/RNA.DOCK7.nomTotal.txt
grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/Prot.DOCK7.nomTotal.txt
grep ENSG00000116641 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/Ribo.DOCK7.nomTotal.txt
```
```{r}
APATotal_DOCK7_LZ=read.table("../data/LocusZoom_proc/TotalAPA.peak5329.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(APATotal_DOCK7_LZ,"../data/LocusZoom_proc/apaTotalDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
APANuclear_DOCK7_LZ=read.table("../data/LocusZoom_proc/NuclearAPA.peak5329.DOCK7.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(APANuclear_DOCK7_LZ,"../data/LocusZoom_proc/apaNuclearDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
prot_DOCK7_LZ=read.table("../data/LocusZoom_proc/Prot.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(prot_DOCK7_LZ,"../data/LocusZoom_proc/ProtDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
#sed -e 's/^/Chr/'
RNA_DOCK7_LZ=read.table("../data/LocusZoom_proc/RNA.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(RNA_DOCK7_LZ,"../data/LocusZoom_proc/RNADOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
#sed -e 's/^/Chr/'
ribo_DOCK7_LZ=read.table("../data/LocusZoom_proc/Ribo.DOCK7.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(ribo_DOCK7_LZ,"../data/LocusZoom_proc/RiboDOCK7_LZ.txt", col.names = T, row.names = F, quote = F)
#sed -e 's/^/Chr/'
```
Put these on the locus zoom site.
###12:95672642
peak39092 VEZT 12:95672642
```{bash,eval=F}
grep VEZT /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000028203
gunzip /project2/gilad/briana/YRI_geno_hg19/chr12.dose.filt.vcf.gz
python createQTLsnpAPAPhenTable_proc.py 12 12:95672642 peak39092 Total
python createQTLsnpAPAPhenTable_proc.py 12 12:95672642 peak39092 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "12" "12:95672642" "ENSG00000028203"
```
No protein for this gene
```{r}
plotQTL_func(SNP="12:95672642", peak="peak39092", gene="ENSG00000028203")
```
###12:95688540
Nuclear QTL
VEZT peak39092
Same gene peak
###2:242018994
Nuclear QTL
MTERF4 peak96962 2:242018994
```{bash,eval=F}
grep MTERF4 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000122085
gunzip /project2/gilad/briana/YRI_geno_hg19/chr2.dose.filt.vcf.gz
python createQTLsnpAPAPhenTable_proc.py 2 2:242018994 peak96962 Total
python createQTLsnpAPAPhenTable_proc.py 2 2:242018994 peak96962 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "2" "2:242018994" "ENSG00000122085"
```
No protein for this gene
```{r}
plotQTL_func(SNP="2:242018994", peak="peak96962", gene="ENSG00000122085")
```
No protein data
###5:172580291
BNIP1 _peak135178 5:172580291
Nuclear QTL
```{bash,eval=F}
grep BNIP1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000113734
gunzip /project2/gilad/briana/YRI_geno_hg19/chr5.dose.filt.vcf.gz
python createQTLsnpAPAPhenTable_proc.py 5 5:172580291 peak135178 Total
python createQTLsnpAPAPhenTable_proc.py 5 5:172580291 peak135178 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "5" "5:172580291" "ENSG00000113734"
```
```{r}
plotQTL_func(SNP="5:172580291", peak="peak135178", gene="ENSG00000113734")
```
Not sure whats up with total
###EIF2A
3:150302010 rs14434 peak114357
```{bash,eval=F}
grep EIF2A /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000144895
gunzip /project2/gilad/briana/YRI_geno_hg19/chr3.dose.filt.vcf.gz
python createQTLsnpAPAPhenTable_proc.py 3 3:150302010 peak114357 Total
python createQTLsnpAPAPhenTable_proc.py 3 3:150302010 peak114357 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "3" "3:150302010" "ENSG00000144895"
```
```{r}
EIF2a_plot=plotQTL_func(SNP="3:150302010", peak="peak114357", gene="ENSG00000144895")
EIF2a_plot
ggsave(EIF2a_plot, file="../output/plots/EIF2a_boxplots.png")
```
#### Locus zoom for this one
(only have to redo the APA plots)
```{bash,eval=F}
grep EIF2A /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000144895
grep peak114357 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/NuclearAPA.peak114357.EIF2A.nomNuc.txt
grep peak114357 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom_proc/TotalAPA.peak114357.EIF2A.nomTotal.txt
```
```{r}
APATotal_EIF2A_LZ=read.table("../data/LocusZoom_proc/TotalAPA.peak114357.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(APATotal_EIF2A_LZ,"../data/LocusZoom_proc/apaTotalEIF2A_LZ.txt", col.names = T, row.names = F, quote = F)
APANuclear_EIF2A_LZ=read.table("../data/LocusZoom_proc/NuclearAPA.peak114357.EIF2A.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SNP", "Dist", "P","slope")) %>% select( SNP, P)
write.table(APANuclear_EIF2A_LZ,"../data/LocusZoom_proc/apaNuclearEIF2A_LZ.txt", col.names = T, row.names = F, quote = F)
```
###Churc1
14:65389250 peak48989 TotalQTL rs10131002
```{bash,eval=F}
grep CHURC1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ENSG00000258289
gunzip /project2/gilad/briana/YRI_geno_hg19/chr14.dose.filt.vcf.gz
python createQTLsnpAPAPhenTable_proc.py 14 14:65389250 peak48989 Total
python createQTLsnpAPAPhenTable_proc.py 14 14:65389250 peak48989 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "14" "14:65389250" "ENSG00000258289"
```
```{r}
plotQTL_func(SNP="14:65389250", peak="peak48989", gene="ENSG00000258289")
```
No protein but this is a good example of one that is an eQTL
##Matched SNP for enrichment
Use https://data.broadinstitute.org/mpg/snpsnap/match_snps.html to get matched snps. Give it the total and nuclear QTLs in chr:pos format
/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/ApaQTLs
```{bash,eval=F}
cut -f6 -d" " NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt > NuclearapaQTLs_SNPonly.txt
cut -f6 -d" " TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt > TotalapaQTLs_SNPonly.txt
```
I am asking for 1 snp per input. I am asking for annotation and specifying west african population. I have not exclued teh HLA snps.
I will need to make a bed file from the matches to run the overlap with the HMM regions. This will be simplt bed with just chrom, pos-1, pos.
Move to Midway
/project2/gilad/briana/threeprimeseq/data/MatchedSnp
matched2bed.py
```{bash,eval=F}
nucSnp="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps.txt"
totSnp="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps.txt"
nucBed="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Nuclear_matched_snps.bed"
totBed="/project2/gilad/briana/threeprimeseq/data/MatchedSnp/Total_matched_snps.bed"
def toBed(Fin,Fout):
fout=open(Fout, "w")
fin= open(Fin, "r")
for i, ln in enumerate(fin):
if i >0:
qtl, match = ln.split()
chrom=match.split(":")[0]
pos=match.split(":")[1]
start=int(pos) -1
end=int(pos)
fout.write("%s\t%s\t%s\n"%(chrom, start, end))
fout.close()
toBed(nucSnp,nucBed)
toBed(totSnp,totBed)
```