New QTL heatmap

Last updated: 2019-04-29

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File	Version	Author	Date	Message
Rmd	39a6572	brimittleman	2019-04-29	add correlation genotype heatmap

library(gdata)

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library(workflowr)

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library(gplots)


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Compare QTLs to those found with previous batch data

I have about double the QTLs hear compared to before resequencing batch 4. I will look at the new QTL to see if there is evidence for them being false positives. I am going to see if there is structure in the genotypes for these QTLs.

The old QTLs are from the threeprimeseq repository.

Total

Import old QTLs

oldtot=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].

OldTotQTLs= oldtot %>% filter(-log10(bh)>=1)
nrow(OldTotQTLs)

[1] 291

Import new QTLs:

newTotQTLs=read.table("../data/apaQTLs/Total_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newTotQTLs)

[1] 502

Filter out those matching from the old:

UniqueNewTot=newTotQTLs %>% semi_join(OldTotQTLs, by="sid")

There are only 105 new snps This makes sense because 1 sno associates with multiple peaks.

Write these out to fetch the genotypes:

write.table(UniqueNewTot, file="../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)

Nuclear

oldnuc=read.table("../../threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", header=T,stringsAsFactors = F) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Strand","Peak"), sep="_")

Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].

OldNucQTLs= oldnuc %>% filter(-log10(bh)>=1)
nrow(OldNucQTLs)

[1] 615

Import new QTLs:

newNucQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", stringsAsFactors = F, header = T)
nrow(newNucQTLs)

[1] 1070

Filter out those matching from the old:

UniqueNewNuc=newNucQTLs %>% semi_join(OldNucQTLs, by="sid")

There are 200 new snps in this set.

write.table(UniqueNewNuc, file="../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt", quote = F, col.names = F, row.names = F)

Extract genotypes:

I wrote a script to pull the doses from the vcf file. Run it with:

 python extractGenotypes.py ../data/apaQTLs/Nuclear_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt
 
  python extractGenotypes.py ../data/apaQTLs/Total_apaQTLs_5fdr_NewUnique.txt ../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt

I also need the header from the VCF to have the individuals:

head -n14 /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf | tail -n1  > ../data/QTLGenotypes/vcfheader.txt

#manually remove # and unneaded columns, keep snp and ind.

vcfhead=read.table("../data/QTLGenotypes/vcfheader.txt", header = T)

input sample list:

samples=read.table("../data/phenotype/SAMPLE.txt")
samplist=as.vector(samples$V1)

Total:

totgeno=read.table("../data/QTLGenotypes/Genotypes_TotalapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()

Correlation:

totgeneCorr=round(cor(totgeno),2)

heatmap.2(as.matrix(totgeneCorr),trace="none", dendrogram =c("none"), main="Genotype correlation\n for new Total QTL snps")

###Nuclear

nucgeno=read.table("../data/QTLGenotypes/Genotypes_NuclearapaQTLS_newunique.txt", col.names = colnames(vcfhead)) %>% select(samplist) %>% t()