Last updated: 2018-11-15

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Unstaged changes:
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    File Version Author Date Message
    Rmd e79d21a Briana Mittleman 2018-11-15 add LD color to plot
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    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. 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

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)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(VennDiagram)
Loading required package: grid
Loading required package: futile.logger
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
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library(ggpubr)
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library(cowplot)

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    ggsave
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)

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

allPheno=APA %>% inner_join(Prot, 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.

allPhen_filt_APA=allPhen_filt_melt %>% filter(variable=="APAPval")
allPhen_filt_Prot=allPhen_filt_melt %>% filter(variable=="ProtPval")
allPhen_filt_RNA=allPhen_filt_melt %>% filter(variable=="RnaPval")

Plot each seperatly then use cow plot

apa=ggplot(allPhen_filt_APA, aes(x=Location, y= -log10(value))) + geom_point()+ geom_vline(xintercept=150302010, linetype="dashed", color = "red") 

prot=ggplot(allPhen_filt_Prot, aes(x=Location, y= -log10(value))) + geom_point()+ geom_vline(xintercept=150302010, linetype="dashed", color = "red") 

rna=ggplot(allPhen_filt_RNA, aes(x=Location, y= -log10(value))) + geom_point()+ geom_vline(xintercept=150302010, linetype="dashed", color = "red") 
plot_grid(apa,prot,rna, align = "v", ncol=1)

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
813a500 Briana Mittleman 2018-11-15

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)

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

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