Last updated: 2020-05-25
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 4111a48 | brimittleman | 2020-05-25 | which eQTL |
I have noticed that the intronic apaQTL relationship with eQTLs are in the opposite direction from the interspecies relationship between delta PAS and expression effect size.
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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First step:
-get the human specific and chimp allele for apaQTLs and eQTLs
bcftools annotate
lift human vcf
Before I get too far. I need to make sure the apaQTL PAS are tested in this study.
interect the QTL pas with the new ones. Then see if the QTL PAS are in this set.
mkdir ../data/QTLPASoverlap
apaQTLPAS=read.table("../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.sort.bed", col.names = c("chr", "start", "end","apaName", "score","strand"))
write.table(apaQTLPAS, "../data/QTLPASoverlap/allPASfromapaQTL.bed", sep="\t", col.names = F, row.names = F, quote = F)
CompPAS=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr","start", "end", "name", "score", "strand"))
bedtools intersect -sorted -s -wa -wb -a ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b ../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.sort.bed > ../data/QTLPASoverlap/OverlappingPASbothprojects.txt
Overlap=read.table("../data/QTLPASoverlap/OverlappingPASbothprojects.txt", col.names = c(colnames(CompPAS),colnames(apaQTLPAS) ))%>% select(name, score,apaName ) %>% separate(apaName,into=c("num", "geneloc"),sep=":") %>% mutate(peak=paste("peak", num,sep="")) %>% separate(geneloc,into=c("gene", "loc"), sep="_")
Overlap %>% filter(gene=="TMEM156")
name score num gene loc peak
1 human255238 0.020 96732 TMEM156 end peak96732
2 human255253 0.130 96738 TMEM156 intron peak96738
3 human255254 0.084 96738 TMEM156 intron peak96738
4 human255282 0.128 96746 TMEM156 intron peak96746
PAS with apaQTL
apaQTL= read.table("../../apaQTL/data/apaQTLs/NuclearQTLs_PeakSNP.txt",stringsAsFactors = F, col.names = c("peak", "snp", "gene"))
Overlap_qtl=Overlap %>% inner_join(apaQTL,by=c("peak","gene"))
Overlap_qtl %>% select(gene) %>% unique() %>% nrow()
[1] 310
Overlap_qtl %>% group_by(loc) %>% summarise(n())
# A tibble: 5 x 2
loc `n()`
<chr> <int>
1 cds 15
2 end 32
3 intron 98
4 utr3 279
5 utr5 5
I will be able to compare 98 intronic and 279 3’ UTR sites if the genotype info exists.
310 QTLs
do these genes have eQTL info?
Look in the nominal for the expression relationships at these sites:
overlapgenesnp= Overlap_qtl %>% select(gene, snp) %>% unique()
eQTL=read.table("../../apaQTL/data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName.txt", col.names = c("gene", "snp", "dist", "pval", "slope"),stringsAsFactors = F) %>% inner_join(overlapgenesnp, by=c("gene","snp"))
nrow(eQTL)
[1] 323
323 of these relationships have been tested in eQTL:
are any of these significant.
eQTL_sig= eQTL %>% filter(pval <0.05)
nrow(eQTL_sig)
[1] 64
eQTL_sig %>% select(gene) %>% unique() %>% nrow()
[1] 54
Are any of these intronic:
eQTL_sig_apaloc= eQTL_sig %>% inner_join(Overlap_qtl,by=c('gene', 'snp' ))
eQTL_sig_apaloc_intron=eQTL_sig_apaloc %>% filter(loc=="intron")
nrow(eQTL_sig_apaloc_intron)
[1] 13
eQTL_sig_apaloc_intron
gene snp dist pval slope name score
1 C10orf88 rs7904973 3167 6.81696e-04 -0.511360 human51879 0.096
2 MTHFSD rs115018779 17711 9.05013e-09 -2.171840 human137593 0.048
3 YES1 rs78139339 14529 7.79311e-03 -0.368041 human153201 0.054
4 YES1 rs78139339 14529 7.79311e-03 -0.368041 human153202 0.130
5 PHACTR4 rs74064856 99467 1.14867e-03 -0.527008 human4946 0.074
6 ZNF264 rs4801440 3509 1.37992e-04 -0.501385 human171312 0.048
7 HIBCH rs11542 15227 2.27510e-03 -0.296270 human197292 0.172
8 LIPH rs62291862 37477 1.07623e-02 -0.627503 human248946 0.230
9 NR2C1 rs139153325 50064 2.31758e-02 -0.465992 human84280 0.054
10 TMEM156 rs2711981 70892 8.25279e-04 -0.825601 human255282 0.128
11 SLC35B3 rs17143705 8203 4.41774e-03 -0.582024 human294988 0.022
12 TBC1D22B rs57137816 3614 1.81026e-02 -0.571407 human299689 0.074
13 NOM1 rs4716445 -10180 2.76954e-04 -0.637932 human336792 0.072
num loc peak
1 19682 intron peak19682
2 52510 intron peak52510
3 59589 intron peak59589
4 59589 intron peak59589
5 2127 intron peak2127
6 67214 intron peak67214
7 76393 intron peak76393
8 94458 intron peak94458
9 31713 intron peak31713
10 96746 intron peak96746
11 110709 intron peak110709
12 113003 intron peak113003
13 126312 intron peak126312
12 gene snp pairs we could even test…
lift vcf
java -jar $PICARD CreateSequenceDictionary REFERENCE=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa OUTPUT=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.dict
Try to lift the VCF file to chimp.
DO this interactivly with the loaded module.
#!/bin/bash
#SBATCH --job-name=liftVCF
#SBATCH --output=liftVCF.out
#SBATCH --error=lliftVCF.err
#SBATCH --time=10:00:00
#SBATCH --partition=gilad
#SBATCH --nodelist=midway-l16b-31
#SBATCH --mem=550G
#SBATCH --mail-type=END
module load picard
#test chrom 4
java -jar $PICARD LiftoverVcf I=/project2/gilad/briana/li_genotypes/genotypesYRI.gen.proc.5MAF.chr4_test.vcf O=../data/QTLPASoverlap/Ch4_geno2pantro.vcf CHAIN=../data/chainFiles/hg19ToPanTro6.over.chain.gz REJECT=../data/QTLPASoverlap/Ch4_rejected_variants.vcf R=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa
VCF version, for input source: file:///project2/gilad/briana/li_genotypes/genotypesYRI.gen.proc.5MAF.chr4_test.vcf
had to add ##fileformat=VCFv4.2 to the vcf head
To get help, see http://broadinstitute.github.io/picard/index.html#GettingHelp Exception in thread “main” java.lang.IllegalStateException: Key INFO found in VariantContext field INFO at 4:11961 but this key isn’t defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
I know the chimp allele for the TMEM156- rs2711981 QTL is T.
look at this gene using example plot boxplot:
human have increased usage of the PAS. this is opposite of the chimp allele in the APA.
Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end'))
DiffIso %>% filter(PAS=="human255282") %>% select(PAS, Human, Chimp,deltaPAU)
PAS Human Chimp deltaPAU
1 human255282 0.2881221 0.03638857 0.2517335
apaQTL info:
pid nvar shape1 shape2 dummy sid dist npval slope ppval bpval bh 4:39029993:39030080:TMEM156_intron_+_peak96746 157 0.887098 26.4876 44.6684 rs2711981 9264 3.17459e-09 1.49328 0.000999001 1.22893e-06 0.000368808094594595
permRes=read.table("/project2/gilad/briana/apaQTL/data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T)
1.560980
../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr$i.gz
../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr4.gz
How many of the intronic apaQTLs are sig in this work
Overlap_qtl_int=Overlap_qtl %>% filter(loc=="intron") %>% rename("PAS"=name)
DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")
DiffIsoSigOverlapint= Overlap_qtl_int %>% inner_join(DiffIsoSig, by=c("PAS"))
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
DiffIsoSigOverlapint
PAS score num gene.x loc.x peak snp chr
1 chimp71069 0.056 28480 BICD1 intron peak28480 rs1673864 chr12
2 human85038 0.176 31979 DRAM1 intron peak31979 rs2138257 chr12
3 human146639 0.220 57020 KPNB1 intron peak57020 rs8071832 chr17
4 human153202 0.130 59589 YES1 intron peak59589 rs78139339 chr18
5 human167137 0.312 65165 ZNF146 intron peak65165 rs11882933 chr19
6 human215583 0.914 82852 IFNGR2 intron peak82852 rs9974603 chr21
7 human255282 0.128 96746 TMEM156 intron peak96746 rs2711981 chr4
8 human260442 0.694 98569 AFF1 intron peak98569 rs7677039 chr4
9 human294636 0.224 110580 LY86-AS1 intron peak110580 rs115728940 chr6
10 human326781 0.054 122625 CDK6 intron peak122625 rs6954290 chr7
11 human367209 0.082 137569 ABL1 intron peak137569 rs2855195 chr9
start end gene.y logef Chimp Human
1 32129860 32130060 BICD1 1.1574666 0.107015575 0.3074286
2 101901369 101901569 DRAM1 -0.6646054 0.560896771 0.3183421
3 47663804 47664004 KPNB1 4.1745016 0.002241554 0.2931332
4 727582 727782 YES1 3.2502413 0.008852500 0.3137472
5 36230417 36230617 ZNF146 1.2125051 0.127344309 0.3701575
6 33404298 33404498 IFNGR2 7.3012431 0.079748414 0.9145805
7 39028361 39028561 TMEM156 1.8975291 0.036388571 0.2881221
8 87071835 87072035 LOC105377320 1.2883239 0.522502000 0.7754384
9 6562291 6562491 LY86-AS1 0.9426266 0.291261376 0.5247326
10 92653768 92653968 CDK6 1.9718073 0.046790926 0.4049052
11 130881699 130881899 ABL1 2.6052641 0.001477165 0.2132200
deltaPAU p.adjust SigPAU2 loc.y
1 0.2004130 3.393258e-03 Yes intron
2 -0.2425547 7.408353e-05 Yes cds
3 0.2908917 1.241188e-06 Yes intron
4 0.3048947 3.675671e-09 Yes intron
5 0.2428132 1.578414e-06 Yes intron
6 0.8348321 8.876451e-14 Yes intron
7 0.2517335 3.261011e-05 Yes utr5
8 0.2529364 1.999108e-05 Yes intron
9 0.2334712 2.632432e-05 Yes intron
10 0.3581143 5.071769e-08 Yes intron
11 0.2117428 3.903033e-07 Yes intron
checking BICD1 example:
rs1673864 - chimp is a T allele.
This is in the correct direction.
DRAM1 rs2138257 chimp allele is T
KPNB1_intron
rs8071832 chimp allele is C
YES1
rs78139339- cant find snp.
ZNF146 rs11882933
chimp is C allele (wrong dir)
rs9974603 IFNGR2
chimp is C allele (correct dir.)
AFF1 - no comp apa plot
rs7677039
LY86-AS1
rs115728940- chimp is an A wrong dir
CDK6- no QTL plot
ABL1-no qtl plot
Made figures in illustrator on comp.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[9] rlang_0.4.0 later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 workflowr_1.6.0 cellranger_1.1.0
[21] rvest_0.3.2 evaluate_0.12 knitr_1.20 httpuv_1.4.5
[25] fansi_0.4.0 broom_0.5.1 Rcpp_1.0.4.6 promises_1.0.1
[29] scales_1.0.0 backports_1.1.2 jsonlite_1.6 fs_1.3.1
[33] hms_0.4.2 digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1 magrittr_1.5
[41] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[45] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[53] git2r_0.26.1 compiler_3.5.1