LocusZoom Plot

Last updated: 2018-11-16

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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	4fb0d81	Briana Mittleman	2018-11-16	add more examples
html	617a3b7	Briana Mittleman	2018-11-15	Build site.
Rmd	e79d21a	Briana Mittleman	2018-11-15	add LD color to plot
html	813a500	Briana Mittleman	2018-11-15	Build site.
Rmd	23c62c9	Briana Mittleman	2018-11-15	add locus zoom initial analysis

In this analysis I will create locus zoom plots for the example QTLs that look to be associated in APA and protein but not in RNA.

EIF2A

I will first do this for the EIF2A totalAPA example. peak228606, 3:150302010.

To run this analysis, I will need the nominal pvalues for this peak/gene. I can then plot the snp location against the pvalue. After I have this working, I can add the r2 values.

EIF2A==ENSG00000144895

grep EIF2A /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt

grep peak228606 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak228606.EIF2A.nomTotal.txt


grep ENSG00000144895 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/RNA.EIF2A.nomTotal.txt

grep ENSG00000144895 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Prot.EIF2A.nomTotal.txt

 grep ENSG00000144895 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Ribo.EIF2A.nomTotal.txt

FastQTL results for nominal: * phenoID

SID
Distance
Nominal Pval
Slope

Librarys

library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

library(reshape2)
library(tidyverse)

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library(VennDiagram)

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library(data.table)


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library(cowplot)


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APA=read.table("../data/LocusZoom/TotalAPA.peak228606.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APAPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APAPval)
APA$Location=as.integer(APA$Location)
Prot=read.table("../data/LocusZoom/Prot.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "ProtPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, ProtPval)
Prot$Location=as.integer(Prot$Location)
RNA=read.table("../data/LocusZoom/RNA.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RnaPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RnaPval)
RNA$Location=as.integer(RNA$Location)
Ribo=read.table("../data/LocusZoom/Ribo.EIF2A.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RiboPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RiboPval)
Ribo$Location=as.integer(Ribo$Location)

I can join these by the snps that are tested for all three.

allPheno=APA %>% inner_join(Prot, by="Location") %>% inner_join(Ribo, by="Location") %>% inner_join(RNA, by="Location")

First I can just plot these as is and see what happens:

allPhen_melt= melt(allPheno, id.vars="Location")

ggplot(allPhen_melt,aes(x=Location, y=value)) + geom_point() + facet_grid( rows=vars(variable))

Expand here to see past versions of unnamed-chunk-6-1.png:

Version	Author	Date
813a500	Briana Mittleman	2018-11-15

I need to zoom in around my locus 150302010

allPheno_filt=allPheno %>% filter(Location> 150297010 & Location < 150307010)

allPhen_filt_melt= melt(allPheno_filt, id.vars="Location")

ggplot(allPhen_filt_melt,aes(x=Location, y=-log10(value))) + geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=150302010, linetype="dashed", color = "red") + theme(axis.line=element_line()) + theme(panel.grid.major = element_line("lightgray",0.25), panel.grid.minor = element_line("lightgray",0.25)) + labs(x="Chromosome 3 Location", y="-Log 10 Pvalue", title="Locus Zoom for EIF2A:peak228606")

Expand here to see past versions of unnamed-chunk-7-1.png:

Version	Author	Date
813a500	Briana Mittleman	2018-11-15

Plot each seperatly because power is different.

ggplot(allPhen_filt_melt,aes(x=Location, y=-log10(value))) + geom_point() + facet_grid( rows=vars(variable),scales="free") + geom_vline(xintercept=150302010, linetype="dashed", color = "red") + theme(axis.line=element_line()) + theme(panel.grid.major = element_line("lightgray",0.25), panel.grid.minor = element_line("lightgray",0.25)) + labs(x="Chromosome 3 Location", y="-Log 10 Pvalue", title="Locus Zoom for EIF2A:peak228606")

The next step is to add the LD structure. I can do this with PLINK and the files I made for the GWAS overlap.

RunPlink_EIF2A.sh

#!/bin/bash

#SBATCH --job-name=RunPlink_EIF2A
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=RunPlink_EIF2A.out
#SBATCH --error=RunPlink_EIF2A.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load plink


plink --ped /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr3.ped  --map /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr3.map --r2  --ld-snp 3:150302010 --ld-window-kb 1000 --ld-window 99999  --out /project2/gilad/briana/threeprimeseq/data/LocusZoom/EIF2A_leadsnp.txt

LD_structure=read.table("../data/LocusZoom/EIF2A_leadsnp.txt.ld", header=T) %>% select(BP_B, R2) 
colnames(LD_structure)=c("Location", "R2")

allPheno_filt2=allPheno %>% filter(Location> 150292010 & Location < 150312010)
allPheno_filt_LD=allPheno_filt2 %>% inner_join(LD_structure, by="Location")


allPheno_filt_LD_melt=melt(allPheno_filt_LD, id.vars=c("Location", "R2"))

lockedscale=ggplot(allPheno_filt_LD_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=150302010, linetype="dashed", color = "red") +  theme_linedraw()


freescale=ggplot(allPheno_filt_LD_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable), scales = "free") + geom_vline(xintercept=150302010, linetype="dashed", color = "red") +  theme_linedraw()

plot_grid(lockedscale,freescale, align = "v", ncol=1)

Try on the same plot:

ggplot(allPheno_filt_LD_melt, aes(x=Location, y=-log10(value), col=variable, by =variable)) +  geom_point() + geom_vline(xintercept=150302010, linetype="dashed", color = "red") +  theme_linedraw()

rs14434 https://www.ncbi.nlm.nih.gov/variation/view/?q=rs14434&assm=GCF_000001405.33

RINT1

RINT1 is a nuclear QTL. peak303436 7:105155320 ENSG00000135249

grep peak303436  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak303436.RINT1.nomNuc.txt

grep peak303436  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak303436.RINT1.nomTotal.txt

grep ENSG00000135249 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/RNA.RINT1.nomTotal.txt

grep ENSG00000135249 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Prot.RINT1.nomTotal.txt

 grep ENSG00000135249 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Ribo.RINT1.nomTotal.txt

RunPlink_RINT1.sh

#!/bin/bash

#SBATCH --job-name=RunPlink_RINT1
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=RunPlink_RINT1.out
#SBATCH --error=RunPlink_RINT1.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load plink


plink --ped /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr7.ped  --map /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr7.map --r2  --ld-snp 7:105155320 --ld-window-kb 1000 --ld-window 99999  --out /project2/gilad/briana/threeprimeseq/data/LocusZoom/RINT1_leadsnp

APA_Total_RINT1=read.table("../data/LocusZoom/TotalAPA.peak303436.RINT1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_TotalPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_TotalPval)
APA_Total_RINT1$Location=as.integer(APA_Total_RINT1$Location)

APA_Nuclear_RINT1=read.table("../data/LocusZoom/TotalAPA.peak303436.RINT1.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_NuclearPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_NuclearPval)
APA_Nuclear_RINT1$Location=as.integer(APA_Nuclear_RINT1$Location)

Prot_RINT1=read.table("../data/LocusZoom/Prot.RINT1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "ProtPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, ProtPval)
Prot_RINT1$Location=as.integer(Prot_RINT1$Location)
RNA_RINT1=read.table("../data/LocusZoom/RNA.RINT1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RnaPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RnaPval)
RNA_RINT1$Location=as.integer(RNA_RINT1$Location)
Ribo_RINT1=read.table("../data/LocusZoom/Ribo.RINT1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RiboPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RiboPval)
Ribo_RINT1$Location=as.integer(Ribo_RINT1$Location)

LD_structure_RINT1=read.table("../data/LocusZoom/RINT1_leadsnp.ld", header=T) %>% select(BP_B, R2) 
colnames(LD_structure_RINT1)=c("Location", "R2")

I can join these by the snps that are tested for all three. Filter 1kb up and downstream

allPheno_RINT1=APA_Total_RINT1 %>% inner_join(APA_Nuclear_RINT1, by="Location") %>% inner_join(Prot_RINT1, by="Location") %>% inner_join(Ribo_RINT1, by="Location") %>% inner_join(RNA_RINT1, by="Location") %>% inner_join(LD_structure_RINT1, by="Location") %>% filter(Location> 105154320 & Location < 105156320)

allPheno_RINT1_melt=melt(allPheno_RINT1, id.vars=c("Location", "R2"))


lockedscale_RINT1=ggplot(allPheno_RINT1_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=105155320, linetype="dashed", color = "red") +  theme_linedraw()


freescale_RINT1=ggplot(allPheno_RINT1_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable), scales = "free") + geom_vline(xintercept=105155320, linetype="dashed", color = "red") +  theme_linedraw()


plot_grid(lockedscale_RINT1,freescale_RINT1, align = "v", ncol=1)

rs2463632 (7:105155320): it is an intronic variant in PUS7

PUS7 chr7:105,080,108-105,162,714 RINT1 chr7:105,172,532-105,208,124

This snp is in the intron on the gene directly upstream of RINT1.

LYAR

This is a nuclear QTL as well. peak235215 4:4196045 ENSG00000145220

RunLocusZoom_LYAR.sh

#!/bin/bash

#SBATCH --job-name=RunLocusZoom_LYAR
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=RunLocusZoom_LYAR.out
#SBATCH --error=RunLocusZoom_LYAR.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load plink


grep peak235215  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/NuclearAPA.peak303436.LYAR.nomNuc.txt

grep peak235215  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak303436.LYAR.nomTotal.txt

grep ENSG00000145220 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/RNA.LYAR.nomTotal.txt

grep ENSG00000145220 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Prot.LYAR.nomTotal.txt

 grep ENSG00000145220 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Ribo.LYAR.nomTotal.txt


plink --ped /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr4.ped  --map /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr4.map --r2  --ld-snp 4:4196045 --ld-window-kb 1000 --ld-window 99999  --out /project2/gilad/briana/threeprimeseq/data/LocusZoom/LYAR_leadsnp.txt

Move to my computer:

APA_Total_LYAR=read.table("../data/LocusZoom/TotalAPA.peak303436.LYAR.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_TotalPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_TotalPval)
APA_Total_LYAR$Location=as.integer(APA_Total_LYAR$Location)

APA_Nuclear_LYAR=read.table("../data/LocusZoom/NuclearAPA.peak303436.LYAR.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_NuclearPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_NuclearPval)
APA_Nuclear_LYAR$Location=as.integer(APA_Nuclear_LYAR$Location)

Prot_LYAR=read.table("../data/LocusZoom/Prot.LYAR.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "ProtPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, ProtPval)
Prot_LYAR$Location=as.integer(Prot_LYAR$Location)
RNA_LYAR=read.table("../data/LocusZoom/RNA.LYAR.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RnaPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RnaPval)
RNA_LYAR$Location=as.integer(RNA_LYAR$Location)
Ribo_LYAR=read.table("../data/LocusZoom/Ribo.LYAR.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RiboPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RiboPval)
Ribo_LYAR$Location=as.integer(Ribo_LYAR$Location)

LD_structure_LYAR=read.table("../data/LocusZoom/LYAR_leadsnp.txt.ld", header=T) %>% select(BP_B, R2) 
colnames(LD_structure_LYAR)=c("Location", "R2")


allPheno_LYAR=APA_Total_LYAR %>% inner_join(APA_Nuclear_LYAR, by="Location") %>% inner_join(Prot_LYAR, by="Location") %>% inner_join(Ribo_LYAR, by="Location") %>% inner_join(RNA_LYAR, by="Location") %>% inner_join(LD_structure_LYAR, by="Location") %>% filter(Location> 4191045 & Location < 4201045)

allPheno_LYAR_melt=melt(allPheno_LYAR, id.vars=c("Location", "R2"))


lockedscale_LYAR=ggplot(allPheno_LYAR_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=4196045, linetype="dashed", color = "red") +  theme_linedraw()


freescale_LYAR=ggplot(allPheno_LYAR_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable), scales = "free") + geom_vline(xintercept=4196045, linetype="dashed", color = "red") +  theme_linedraw()


plot_grid(lockedscale_LYAR,freescale_LYAR, align = "v", ncol=1)

Snp is in an intron OTOP1 gene 2 genes upstream. rs7682186

PSMF1

Total QTL peak193648 20:1131308 ENSG00000125818

RunLocusZoom_PSMF1.sh

#!/bin/bash

#SBATCH --job-name=RunLocusZoom_PSMF1
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=RunLocusZoom_PSMF1.out
#SBATCH --error=RunLocusZoom_PSMF1.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load plink


grep peak193648  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/NuclearAPA.peak193648.PSMF1.nomNuc.txt

grep peak193648  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak193648.PSMF1.nomTotal.txt

grep ENSG00000125818 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/RNA.PSMF1.nomTotal.txt

grep ENSG00000125818 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Prot.PSMF1.nomTotal.txt

 grep ENSG00000125818 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Ribo.PSMF1.nomTotal.txt


plink --ped /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr20.ped  --map /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr20.map --r2  --ld-snp 20:1131308 --ld-window-kb 1000 --ld-window 99999  --out /project2/gilad/briana/threeprimeseq/data/LocusZoom/PSMF1_leadsnp.txt

Move to computer

APA_Total_PSMF1=read.table("../data/LocusZoom/TotalAPA.peak193648.PSMF1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_TotalPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_TotalPval)
APA_Total_PSMF1$Location=as.integer(APA_Total_PSMF1$Location)

APA_Nuclear_PSMF1=read.table("../data/LocusZoom/NuclearAPA.peak193648.PSMF1.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_NuclearPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_NuclearPval)
APA_Nuclear_PSMF1$Location=as.integer(APA_Nuclear_PSMF1$Location)

Prot_PSMF1=read.table("../data/LocusZoom/Prot.PSMF1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "ProtPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, ProtPval)
Prot_PSMF1$Location=as.integer(Prot_PSMF1$Location)
RNA_PSMF1=read.table("../data/LocusZoom/RNA.PSMF1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RnaPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RnaPval)
RNA_PSMF1$Location=as.integer(RNA_PSMF1$Location)
Ribo_PSMF1=read.table("../data/LocusZoom/Ribo.PSMF1.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RiboPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RiboPval)
Ribo_PSMF1$Location=as.integer(Ribo_PSMF1$Location)

LD_structure_PSMF1=read.table("../data/LocusZoom/PSMF1_leadsnp.txt.ld", header=T) %>% select(BP_B, R2) 
colnames(LD_structure_PSMF1)=c("Location", "R2")


allPheno_PSMF1=APA_Total_PSMF1 %>% inner_join(APA_Nuclear_PSMF1, by="Location") %>% inner_join(Prot_PSMF1, by="Location") %>% inner_join(Ribo_PSMF1, by="Location") %>% inner_join(RNA_PSMF1, by="Location") %>%  inner_join(LD_structure_PSMF1, by="Location") %>% filter(Location> 1121308 & Location < 1181308)
allPheno_PSMF1_melt=melt(allPheno_PSMF1, id.vars=c("Location", "R2"))


lockedscale_PSMF1=ggplot(allPheno_PSMF1_melt, aes(x=Location, y=-log10(value),col=R2)) +  geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=1131308, linetype="dashed", color = "red") +  theme_linedraw()


freescale_PSMF1=ggplot(allPheno_PSMF1_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable), scales = "free") + geom_vline(xintercept=1131308, linetype="dashed", color = "red") +  theme_linedraw()


plot_grid(lockedscale_PSMF1,freescale_PSMF1, align = "v", ncol=1)

This varriant is in an intron of the PSMF1 gene. rs56398212

EBI3

This is a total and a nuclear QTL peak152751, ENSG00000105246 19:4236475

RunLocusZoom_EBI3.sh

#!/bin/bash

#SBATCH --job-name=RunLocusZoom_EBI3
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=RunLocusZoom_EBI3.out
#SBATCH --error=RunLocusZoom_EBI3.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load plink


grep peak152751  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/NuclearAPA.peak152751.EBI3.nomNuc.txt

grep peak152751  /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/LocusZoom/TotalAPA.peak152751.EBI3.nomTotal.txt

grep ENSG00000105246 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/RNA.EBI3.nomTotal.txt

grep ENSG00000105246 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Prot.EBI3.nomTotal.txt

 grep ENSG00000105246 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out > /project2/gilad/briana/threeprimeseq/data/LocusZoom/Ribo.EBI3.nomTotal.txt


plink --ped /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr19.ped  --map /project2/gilad/briana/YRI_geno_hg19/plinkYRIgeno_chr19.map --r2  --ld-snp 19:4236475 --ld-window-kb 1000 --ld-window 99999  --out /project2/gilad/briana/threeprimeseq/data/LocusZoom/EBI3_leadsnp.txt

Move to comp

APA_Total_EBI3=read.table("../data/LocusZoom/TotalAPA.peak152751.EBI3.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_TotalPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_TotalPval)
APA_Total_EBI3$Location=as.integer(APA_Total_EBI3$Location)

APA_Nuclear_EBI3=read.table("../data/LocusZoom/NuclearAPA.peak152751.EBI3.nomNuc.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "APA_NuclearPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":") %>% select( Location, APA_NuclearPval)
APA_Nuclear_EBI3$Location=as.integer(APA_Nuclear_EBI3$Location)

Prot_EBI3=read.table("../data/LocusZoom/Prot.EBI3.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "ProtPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, ProtPval)
Prot_EBI3$Location=as.integer(Prot_EBI3$Location)
RNA_EBI3=read.table("../data/LocusZoom/RNA.EBI3.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RnaPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RnaPval)
RNA_EBI3$Location=as.integer(RNA_EBI3$Location)
Ribo_EBI3=read.table("../data/LocusZoom/Ribo.EBI3.nomTotal.txt", stringsAsFactors = F, col.names = c("PeakID", "SID", "Dist", "RiboPval","slope")) %>% separate(SID, into=c("Chrom", "Location"), sep=":")%>% select( Location, RiboPval)
Ribo_EBI3$Location=as.integer(Ribo_EBI3$Location)

LD_structure_EBI3=read.table("../data/LocusZoom/EBI3_leadsnp.txt.ld", header=T) %>% select(BP_B, R2) 
colnames(LD_structure_EBI3)=c("Location", "R2")


allPheno_EBI3=APA_Total_EBI3 %>% inner_join(APA_Nuclear_EBI3, by="Location") %>% inner_join(Prot_EBI3, by="Location") %>% inner_join(Ribo_EBI3, by="Location") %>% inner_join(RNA_EBI3, by="Location") %>%  inner_join(LD_structure_EBI3, by="Location") %>% filter(Location> 4231475 & Location < 4241475)
allPheno_EBI3_melt=melt(allPheno_EBI3, id.vars=c("Location", "R2"))


lockedscale_EBI3=ggplot(allPheno_EBI3_melt, aes(x=Location, y=-log10(value),col=R2)) +  geom_point() + facet_grid( rows=vars(variable)) + geom_vline(xintercept=4236475, linetype="dashed", color = "red") +  theme_linedraw()


freescale_EBI3=ggplot(allPheno_EBI3_melt, aes(x=Location, y=-log10(value), col=R2)) +  geom_point() + facet_grid( rows=vars(variable), scales = "free") + geom_vline(xintercept=4236475, linetype="dashed", color = "red") +  theme_linedraw()


plot_grid(lockedscale_EBI3,freescale_EBI3, align = "v", ncol=1)

Snp is in the last intron of EBI3. It looks like the lead protien snp is the one directly upstream. rs353704. The region is CCCCAC. The preceeding SNP is rs353705. The relevent peak is 19:4236433:4236517. The snp is in the peak. This is interesting because the alternative allele decreases usage of this peak and the protein.

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2      cowplot_0.9.3       ggpubr_0.1.8       
 [4] magrittr_1.5        data.table_1.11.8   VennDiagram_1.6.20 
 [7] futile.logger_1.4.3 forcats_0.3.0       stringr_1.3.1      
[10] dplyr_0.7.6         purrr_0.2.5         readr_1.1.1        
[13] tidyr_0.8.1         tibble_1.4.2        ggplot2_3.0.0      
[16] tidyverse_1.2.1     reshape2_1.4.3      workflowr_1.1.1    

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4     haven_1.1.2          lattice_0.20-35     
 [4] colorspace_1.3-2     htmltools_0.3.6      yaml_2.2.0          
 [7] rlang_0.2.2          R.oo_1.22.0          pillar_1.3.0        
[10] glue_1.3.0           withr_2.1.2          R.utils_2.7.0       
[13] lambda.r_1.2.3       modelr_0.1.2         readxl_1.1.0        
[16] bindr_0.1.1          plyr_1.8.4           munsell_0.5.0       
[19] gtable_0.2.0         cellranger_1.1.0     rvest_0.3.2         
[22] R.methodsS3_1.7.1    evaluate_0.11        labeling_0.3        
[25] knitr_1.20           broom_0.5.0          Rcpp_0.12.19        
[28] formatR_1.5          backports_1.1.2      scales_1.0.0        
[31] jsonlite_1.5         hms_0.4.2            digest_0.6.17       
[34] stringi_1.2.4        rprojroot_1.3-2      cli_1.0.1           
[37] tools_3.5.1          lazyeval_0.2.1       futile.options_1.0.1
[40] crayon_1.3.4         whisker_0.3-2        pkgconfig_2.0.2     
[43] xml2_1.2.0           lubridate_1.7.4      assertthat_0.2.0    
[46] rmarkdown_1.10       httr_1.3.1           rstudioapi_0.8      
[49] R6_2.3.0             nlme_3.1-137         git2r_0.23.0        
[52] compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1