Last updated: 2023-04-25
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ChromatinSplicingQTLs/analysis/
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knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(RColorBrewer)
library(data.table)
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
The following object is masked from 'package:purrr':
transpose
library(edgeR)
Loading required package: limma
# Set theme
theme_set(
theme_classic() +
theme(text=element_text(size=16, family="Helvetica")))
# I use layer a lot, to rotate long x-axis labels
Rotate_x_labels <- theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
#test plot
ggplot(mtcars, aes(x=mpg, y=cyl)) +
geom_point()
gwas.traits <- read_tsv("../code/config/gwas_table.tsv") %>%
dplyr::rename(GWAS.accession=gwas, gwas.trait=trait)
hyprcoloc.results <- read_tsv("../code/hyprcoloc/Results/ForGWASColoc/GWASColoc_ChromatinAPAAndRNA/results.txt.gz") %>%
# hyprcoloc.results <- read_tsv("../code/hyprcoloc/Results/ForGWASColoc/GWASColoc_ChromatinAPAAndRNAYRI/results.txt.gz") %>%
dplyr::rename(GWAS.Loci = GWASLeadSnpChrom_Pos_RefAllele_AltAllele_rsID_trait) %>%
separate(GWAS.Loci, into=c("GWAS.LeadSNP.Chrom", "GWAS.LeadSNP.Pos", "GWAS.accession"), sep="_", remove=F) %>%
separate_rows(ColocalizedTraits, sep = ",") %>%
mutate(IsColocalizedWithSomething = !ColocalizedTraits == "None") %>%
mutate(Trait = if_else(IsColocalizedWithSomething, ColocalizedTraits, DroppedTrait)) %>%
dplyr::select(-DroppedTrait, -ColocalizedTraits) %>%
mutate(Trait = str_replace_all(Trait, " ", "")) %>%
mutate(GWAS.Loci = str_replace_all(GWAS.Loci, " ", "")) %>%
mutate(Trait = if_else(Trait == GWAS.Loci, paste("GWAS",GWAS.Loci,sep = ";"),Trait)) %>%
separate(Trait, into=c("PhenotypeClass", "Phenotype"), sep=";", remove=F) %>%
group_by(GWAS.Loci, HyprcolocIteration) %>%
mutate(ColocalizedClusterContainsGWASTrait = any(PhenotypeClass=="GWAS") & IsColocalizedWithSomething) %>%
ungroup() %>%
inner_join(gwas.traits %>%
dplyr::select(1:2))
hyprcoloc.results$PhenotypeClass %>% unique()
[1] "Expression.Splicing" "H3K4ME1"
[3] "H3K4ME3" "polyA.Splicing"
[5] "chRNA.Splicing" "H3K27AC"
[7] "GWAS" "H3K36ME3"
[9] "chRNA.Expression.Splicing" "APA_Nuclear"
[11] "APA_Total" NA
[13] "chr19_47135282_IMSGC2019" "chr1_92687078_IMSGC2019"
[15] "chr1_85264137_IMSGC2019" "chr1_91756532_IMSGC2019"
[17] "chr1_100824876_IMSGC2019" "chr2_43128185_IMSGC2019"
[19] "chr1_157716547_IMSGC2019" "chr12_57713053_IMSGC2019"
[21] "chr11_118872577_IMSGC2019" "chr12_123119506_IMSGC2019"
[23] "chr14_75547955_IMSGC2019" "chr14_88057144_IMSGC2019"
[25] "chr6_135512325_IMSGC2019" "chr5_40396323_IMSGC2019"
[27] "chr16_85987899_IMSGC2019" "chr7_50288743_IMSGC2019"
[29] "chr11_61026179_IMSGC2019" "chr19_49333989_IMSGC2019"
[31] "chr3_102030178_IMSGC2019" "chr1_192571891_IMSGC2019"
[33] "chr2_61015275_IMSGC2019" "chr2_230257114_IMSGC2019"
[35] "chr14_102799507_IMSGC2019" "chr20_54173204_IMSGC2019"
[37] "chr3_28030595_IMSGC2019" "chr22_50532837_IMSGC2019"
[39] "chr6_27099878_IMSGC2019" "chr6_159044945_IMSGC2019"
[41] "chr6_137116920_IMSGC2019" "chr6_137638318_IMSGC2019"
[43] "chr5_159332892_IMSGC2019" "chr15_90344352_IMSGC2019"
[45] "chr6_27893892_IMSGC2019" "chr16_79077400_IMSGC2019"
[47] "chr8_127801845_IMSGC2019" "chr7_149592373_IMSGC2019"
[49] "chr18_58680812_IMSGC2019"
hyprcoloc.results %>%
filter(!GWAS.accession=="IMSGC2019") %>%
pull(PhenotypeClass) %>% unique()
[1] "Expression.Splicing" "H3K4ME1"
[3] "H3K4ME3" "polyA.Splicing"
[5] "chRNA.Splicing" "H3K27AC"
[7] "GWAS" "H3K36ME3"
[9] "chRNA.Expression.Splicing" "APA_Nuclear"
[11] "APA_Total" NA
PhenotypeRecodes = c("H3K36ME3"="hQTL", "H3K27AC"="hQTL", "H3K4ME3"="hQTL", "H3K4ME1"="hQTL",
"Expression.Splicing"="eQTL", "Expression.Splicing.Subset_YRI"="eQTL", "polyA.Splicing.Subset_YRI"="sQTL", "chRNA.Expression.Splicing"="chRNA eQTL",
"APA_Nuclear"="APA QTL", "APA_Total"="APA QTL", "polyA.Splicing"="sQTL", "GWAS"="GWAS")
PhenotypeRecodes.df <- data.frame(PhenotypeRecodes) %>%
rownames_to_column("PhenotypeClass")
hyprcoloc.results %>%
filter(GWAS.Loci == "chr9_79683075_GCST004627") %>%
filter(is.na(PhenotypeClass))
# A tibble: 1 × 15
GWAS.Loci GWAS.LeadSNP.Ch… GWAS.LeadSNP.Pos GWAS.accession HyprcolocIterat…
<chr> <chr> <chr> <chr> <dbl>
1 chr9_796830… chr9 79683075 GCST004627 NA
# … with 10 more variables: PosteriorColocalizationPr <dbl>,
# RegionalAssociationPr <dbl>, TopCandidateSNP <chr>,
# ProportionPosteriorPrExplainedByTopSNP <dbl>,
# IsColocalizedWithSomething <lgl>, Trait <chr>, PhenotypeClass <chr>,
# Phenotype <chr>, ColocalizedClusterContainsGWASTrait <lgl>,
# gwas.trait <chr>
hyprcoloc.results.toplot <- hyprcoloc.results %>%
filter(!GWAS.accession=="IMSGC2019") %>%
left_join(PhenotypeRecodes.df) %>%
mutate(PhenotypeRecodes = if_else(is.na(PhenotypeRecodes), PhenotypeClass, PhenotypeRecodes)) %>%
filter(!PhenotypeRecodes == "APA QTL") %>%
group_by(GWAS.Loci, HyprcolocIteration) %>%
filter(any(ColocalizedClusterContainsGWASTrait) | PhenotypeClass=="GWAS") %>%
mutate(Category = case_when(
all(ColocalizedClusterContainsGWASTrait==FALSE) | all(is.na(HyprcolocIteration)) ~ "No molQTL colocs",
all(PhenotypeRecodes %in% c("GWAS", "hQTL")) ~ "Only hQTL colocs",
all(PhenotypeRecodes %in% c("GWAS", "eQTL")) ~ "Only eQTL colocs",
all(PhenotypeRecodes %in% c("GWAS", "eQTL", "hQTL", "chRNA eQTL")) ~ "hQTL+eQTL colocs",
all(PhenotypeRecodes %in% c("GWAS", "sQTL")) ~ "sQTL colocs",
all(PhenotypeRecodes %in% c("GWAS", "sQTL", "eQTL", "eQTL")) ~ "sQTL+eQTL colocs",
# all(PhenotypeRecodes %in% c("GWAS", "sQTL", "chRNA eQTL", "eQTL", "hQTL")) ~ "sQTL+eQTL+hQTL colocs",
TRUE ~ "Other"
)) %>%
ungroup()
# filter(Category=="Other")
Num.Gwas.loci <- hyprcoloc.results.toplot %>%
distinct(GWAS.Loci, .keep_all=T) %>%
count(GWAS.accession, gwas.trait)
hyprcoloc.results.toplot %>%
distinct(GWAS.Loci, .keep_all=T) %>%
count(Category,GWAS.accession, gwas.trait) %>%
ggplot(aes(x=gwas.trait)) +
geom_col(position="fill", aes( y=n, fill=Category)) +
geom_text(data=Num.Gwas.loci, aes(x=gwas.trait, label=n), y=Inf, hjust=1.1, size=4, angle=90) +
scale_fill_brewer(palette = "Dark2") +
Rotate_x_labels +
theme(axis.text.x = element_text(size=8)) +
labs(fill="Exclusive\nCategory", caption="Categories are exclusive; sQTL means no hQTL or eQTL\nsQTL + eQTL means no hQTLs\nhQTL + sQTL + eQTL would be Other\nTotal number GWAS loci marked at top", y="Fraction of colocalizations") +
scale_y_continuous(expand=c(0,0))
PC2.filter <- c("Expression.Splicing", "Expression.Splicing.Subset_YRI")
PC2.SignificanceFilter <- c("H3K27AC", "H3K4ME3", "H3K36ME3")
PC1.filter <- c("polyA.Splicing", "H3K27AC", "H3K4ME1", "H3K4ME3")
PC1.filter.Splicing <- PC1.filter[str_detect(PC1.filter, "Splicing")]
PC1.filter.NonSplicing <- PC1.filter[!str_detect(PC1.filter, "Splicing")]
dat.foreQTLQQ <- fread("../code/pi1/PairwisePi1Traits.P.all.txt.gz") %>%
filter((PC1 %in% PC1.filter))
IntronAnnotatins <- read_tsv("../data/IntronAnnotationsFromYang.tsv.gz") %>%
mutate(chrom = str_remove_all(chrom, "chr")) %>%
mutate(Intron = paste(chrom, start, end, sep=":")) %>%
filter(!str_detect(SuperAnnotation, "NoncodingGene"))
dat.foreQTLQQ.sQTLs <- dat.foreQTLQQ %>%
filter(PC1 %in% PC1.filter.Splicing) %>%
group_by(PC1, P1) %>%
filter(!any((PC2 %in% PC2.SignificanceFilter) & (trait.x.p.in.y < 0.01))) %>%
ungroup() %>%
filter(PC2 %in% PC2.filter) %>%
separate(P1, into=c("Intron", "Cluster"), sep=":clu", remove=F) %>%
inner_join(
IntronAnnotatins %>%
dplyr::select(Intron, SuperAnnotation),
by="Intron") %>%
group_by(PC1, PC2, Cluster) %>%
mutate(SNP_group = case_when(
all(str_detect(SuperAnnotation, "Productive")) ~ "Productive sQTL cluster",
any(str_detect(SuperAnnotation, "Unproductive")) ~ "Unproductive sQTL cluster",
TRUE ~ "sQTL Other"
))
# bind_rows(
# hyprcoloc.results.toplot %>%
# filter(Category == "sQTL+eQTL colocs") %>%
# filter(PhenotypeClass=="Expression.Splicing") %>%
# dplyr::select(GWAS.Loci, TopCandidateSNP),
# )
hyprcoloc.results.toplot %>%
# filter(Category == "sQTL+eQTL colocs") %>%
group_by(Category, GWAS.Loci) %>%
count(PhenotypeClass) %>%
ungroup() %>%
ggplot(aes(x=n)) +
geom_histogram(aes(fill=PhenotypeClass)) +
facet_wrap(~Category, scales="free") +
labs(x="Num molQTLs in colocalized cluster")
CategoriesWith_sQTLs <- c("sQTL+eQTL colocs", "Other", "sQTL")
inner_join(
hyprcoloc.results.toplot %>%
filter(Category %in% CategoriesWith_sQTLs) %>%
filter(PhenotypeClass %in% c("polyA.Splicing")) %>%
dplyr::select(GWAS.Loci, sQTL=Trait),
hyprcoloc.results.toplot %>%
filter(Category %in% CategoriesWith_sQTLs) %>%
filter(PhenotypeClass %in% c("Expression.Splicing")) %>%
dplyr::select(GWAS.Loci, eQTL=Trait, everything())
) %>%
inner_join(
dat.foreQTLQQ.sQTLs %>%
mutate(sQTL=paste(PC1, P1, sep=";"))
) %>%
mutate(IntronGroup = if_else(str_detect(SuperAnnotation, "Unproductive"), "Unproductive", "Productive")) %>%
group_by(GWAS.Loci, PC2) %>%
filter(p_permutation.x == min(p_permutation.x)) %>%
ungroup() %>%
mutate(Category = recode(Category, "Other"="...+hQTL")) %>%
mutate(PC2 = recode(PC2, "Expression.Splicing"="eQTL beta from all GEU","Expression.Splicing.Subset_YRI"="eQTL beta from YRI" )) %>%
ggplot(aes(x=beta.x, y=x.beta.in.y, color=SNP_group)) +
geom_point() +
facet_grid(IntronGroup ~ Category ~ PC2) +
theme_bw() +
# geom_smooth(method='lm') +
labs(x="sQTL beta", y="eQTL beta", title="sQTL and eQTL effects for eQTL/sQTL/GWAS colocs", caption="Only top sQTL intron per GWAS colocalization plotted\nEffects relative to top sQTL SNP\nFacets are intron level category category, gwas coloc category (Other includes sQTL+eQTL+hQTL) and whether eQTL effect measured in YRI or all")
Let’s also include the sQTL/GWAS colocs, because i’m curious how many of them have direction effects also consistent with eQTL regulation…
CandidateGenesToHighlight <- inner_join(
hyprcoloc.results.toplot %>%
filter(Category == "sQTL+eQTL colocs") %>%
filter(PhenotypeClass %in% c("polyA.Splicing")) %>%
dplyr::select(GWAS.Loci, sQTL=Trait),
hyprcoloc.results.toplot %>%
filter(Category == "sQTL+eQTL colocs") %>%
filter(PhenotypeClass %in% c("Expression.Splicing")) %>%
dplyr::select(GWAS.Loci, eQTL=Trait, everything())
) %>%
inner_join(
dat.foreQTLQQ.sQTLs %>%
mutate(sQTL=paste(PC1, P1, sep=";")) %>%
filter(PC2 == "Expression.Splicing")
) %>%
mutate(IntronGroup = if_else(str_detect(SuperAnnotation, "Unproductive"), "Unproductive", "Productive"))
CandidateGenesToHighlight %>%
filter(SNP_group == "Unproductive sQTL cluster") %>%
group_by(eQTL, GWAS.Loci) %>%
filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
ungroup() %>%
distinct(GWAS.Loci)
# A tibble: 50 × 1
GWAS.Loci
<chr>
1 chr16_30091839_GCST004606
2 chr17_46676279_GCST004606
3 chr12_6181375_GCST004616
4 chr17_46534416_GCST004626
5 chr16_30049923_GCST004617
6 chr17_46676279_GCST004617
7 chr22_41459919_GCST007800
8 chr16_29855474_GCST004599
9 chr6_10535358_GCST004609
10 chr12_8698552_GCST004619
# … with 40 more rows
CandidateGenesToHighlight %>%
filter(SNP_group == "Unproductive sQTL cluster") %>%
group_by(eQTL, GWAS.Loci) %>%
filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
ungroup() %>%
distinct(P2, .keep_all=T)
# A tibble: 16 × 45
GWAS.Loci sQTL eQTL GWAS.LeadSNP.Ch… GWAS.LeadSNP.Pos GWAS.accession
<chr> <chr> <chr> <chr> <chr> <chr>
1 chr16_30091839_… poly… Expr… chr16 30091839 GCST004606
2 chr17_46676279_… poly… Expr… chr17 46676279 GCST004606
3 chr17_46676279_… poly… Expr… chr17 46676279 GCST004606
4 chr12_6181375_G… poly… Expr… chr12 6181375 GCST004616
5 chr22_41459919_… poly… Expr… chr22 41459919 GCST007800
6 chr16_29855474_… poly… Expr… chr16 29855474 GCST004599
7 chr6_10535358_G… poly… Expr… chr6 10535358 GCST004609
8 chr12_8698552_G… poly… Expr… chr12 8698552 GCST004619
9 chr15_41404734_… poly… Expr… chr15 41404734 GCST004629
10 chr17_1879658_G… poly… Expr… chr17 1879658 GCST004630
11 chr14_105178084… poly… Expr… chr14 105178084 GCST004611
12 chr19_48871431_… poly… Expr… chr19 48871431 GCST004611
13 chr16_11373920_… poly… Expr… chr16 11373920 GCST004622
14 chr13_42452525_… poly… Expr… chr13 42452525 GCST004632
15 chr19_58440326_… poly… Expr… chr19 58440326 GCST004603
16 chr1_155208991_… poly… Expr… chr1 155208991 GCST004604
# … with 39 more variables: HyprcolocIteration <dbl>,
# PosteriorColocalizationPr <dbl>, RegionalAssociationPr <dbl>,
# TopCandidateSNP <chr>, ProportionPosteriorPrExplainedByTopSNP <dbl>,
# IsColocalizedWithSomething <lgl>, PhenotypeClass <chr>, Phenotype <chr>,
# ColocalizedClusterContainsGWASTrait <lgl>, gwas.trait <chr>,
# PhenotypeRecodes <chr>, Category <chr>, PC1 <chr>, P1 <chr>, Intron <chr>,
# Cluster <chr>, GeneLocus <chr>, p_permutation.x <dbl>, beta.x <dbl>, …
CandidateGenesToHighlight.ToPlot <- CandidateGenesToHighlight %>%
separate(Intron, into=c("IntronChrom", "IntronStart", "IntronEnd"), sep=":", convert=T, remove=F ) %>%
filter(SNP_group == "Unproductive sQTL cluster") %>%
group_by(eQTL, GWAS.Loci) %>%
filter(any(abs(x.beta.in.y)>0.25 & IntronGroup=="Unproductive")) %>%
mutate(MinIntron = min(IntronStart), MaxIntron=max(IntronEnd)) %>%
ungroup()
So far we have a list of 16 genes, many of which are represented as hits in multiple gwas for a total of 50ish sQTL/eQTL/GWAS colocs with large sQTL/eQTL effects that I want to plot…
dir.create("../code/scratch/Plot_sQTLeQTLGWASColocs/")
## Make bwList
bwList <- read_tsv("../code/PlotQTLs/bwList.tsv") %>%
filter(Group_label %in% c("polyA.RNA", "chRNA", "MetabolicLabelled.30min"))
FullGeuvadisBigwigs <- data.frame(BigwigFilepath = list.files("../code/bigwigs/Expression.Splicing")) %>%
mutate(BigwigFilepath = paste0("bigwigs/Expression.Splicing/",BigwigFilepath)) %>%
mutate(SampleID = str_replace(BigwigFilepath, "bigwigs/Expression.Splicing/(.+?).1.bw", "\\1")) %>%
mutate(Group_label = "polyA.RNA", Strand=".")
bwList %>% count(Group_label)
full_join(FullGeuvadisBigwigs, bwList) %>%
dplyr::select(SampleID, BigwigFilepath, Group_label, Strand) %>%
write_tsv("../code/PlotQTLs/bwList.AllPolyAAndchRNA.tsv")
read_tsv("../code/PlotQTLs/bwList.Groups.4col.tsv") %>%
filter(Group_label %in% c("polyA.RNA", "MetabolicLabelled.30min", "chRNA")) %>%
write_tsv("../code/PlotQTLs/bwList.Groups.4col.RNA.tsv")
#Write a bash script for plotting. Just pick one gwas row randomly for each of the distinct genes
CandidateGenesToHighlight.ToPlot %>%
mutate(score = ".", strand = str_extract(P1, "[+-]")) %>%
dplyr::select(IntronChrom, IntronStart, IntronEnd, P1, score, strand) %>%
mutate(IntronChrom=paste0("chr", IntronChrom)) %>%
arrange(IntronChrom, IntronStart, IntronEnd) %>%
distinct(.keep_all=T) %>%
write_tsv("/scratch/midway2/bjf79/Juncs.bed", col_names = F)
CandidateGenesToHighlight.ToPlot %>%
distinct(eQTL, .keep_all=T) %>%
mutate(n = row_number()) %>%
mutate(npad = str_pad(n, side="left", pad="0", width=2)) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/AggregateBigwigsForPlotting.py --BigwigList PlotQTLs/bwList.AllPolyAAndchRNA.tsv --BigwigListType KeyFile --OutputPrefix /scratch/midway2/bjf79/PlotDat --FilterJuncsByBed /scratch/midway2/bjf79/Juncs.bed --VCF Genotypes/1KG_GRCh38/{IntronChrom}.vcf.gz --SnpPos {GWAS.LeadSNP.Chrom}:{SNP_Pos} --SnpName {TopCandidateSNP} --Region {GWAS.LeadSNP.Chrom}:{MinIntron-300}-{MaxIntron+300} --GroupSettingsFile PlotQTLs/bwList.Groups.4col.RNA.tsv -vv --Bed12GenesToIni scripts/GenometracksByGenotype/PremadeTracks/gencode.v26.FromGTEx.genes.bed12.gz"),
PyGenometracksCall = str_glue("pyGenomeTracks --region {GWAS.LeadSNP.Chrom}:{MinIntron-300}-{MaxIntron+300} -out scratch/Plot_sQTLeQTLGWASColocs/{npad}_{GWAS.Loci}_{P2}.png --tracks /scratch/midway2/bjf79/PlotDattracks.ini")) %>%
dplyr::select(CustomScriptCall, PyGenometracksCall, n) %>%
gather("CommandType", "Command", -n) %>%
arrange(n) %>%
dplyr::select(Command) %>%
write_tsv("../code/scratch/Plot_sQTLeQTLGWASColocs/MakePlots.sh",col_names = F)
After plotting those, some of the nicest examples that i think are worth highlighting in the manuscript are here:
I think I will also plot genotype/phenotype boxplots for these, in polyA splicing, chRNA splicing, polyA Expression, and chRNA Expression.
# Some scratch code to quickly get data or making these specific boxplots
##NUDT14 phenotypes
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
filter(sQTL == "polyA.Splicing;14:105175987:105176534:clu_34934_-") %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/polyA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.sQTL.polyA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
filter(sQTL == "polyA.Splicing;14:105175987:105176534:clu_34934_-") %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.sQTL.chRNA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c("chr14_105178084_GCST004611")) %>%
filter(eQTL == "Expression.Splicing;ENSG00000183828.15") %>%
distinct(eQTL, .keep_all=T) %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.eQTL.chRNA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c( "chr14_105178084_GCST004611")) %>%
filter(eQTL == "Expression.Splicing;ENSG00000183828.15") %>%
distinct(eQTL, .keep_all=T) %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.NUDT14.eQTL.polyA.tsv")) %>%
pull(CustomScriptCall)
##MVP phenotypes
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
filter(sQTL == "polyA.Splicing;16:29834437:29835704:clu_38570_+") %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/polyA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.sQTL.polyA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
filter(sQTL == "polyA.Splicing;16:29834437:29835704:clu_38570_+") %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{P1}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.sQTL.chRNA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
filter(eQTL == "Expression.Splicing;ENSG00000013364.19") %>%
distinct(eQTL, .keep_all=T) %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/chRNA.Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.eQTL.chRNA.tsv")) %>%
pull(CustomScriptCall)
CandidateGenesToHighlight.ToPlot %>%
filter(GWAS.Loci %in% c("chr16_29855474_GCST004599")) %>%
filter(eQTL == "Expression.Splicing;ENSG00000013364.19") %>%
distinct(eQTL, .keep_all=T) %>%
mutate(gwas.pid = str_replace(GWAS.Loci, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")) %>%
mutate(pid = str_glue("{Phenotype}:{gwas.pid}")) %>%
separate(TopCandidateSNP, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F) %>%
mutate(CustomScriptCall = str_glue("python scripts/GenometracksByGenotype/ExtractPhenotypeBedByGenotype.py --Bed /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/QTLs/QTLTools/Expression.Splicing/OnlyFirstRepsForGWASColoc.sorted.qqnorm.bed.gz --VCF /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/Genotypes/1KG_GRCh38/Autosomes.vcf.gz -vvv --SnpName {TopCandidateSNP} --SnpPos chr{SNP_Chrom}:{SNP_Pos} --FeatureName {pid} --CisWindowAroundSnpToGetFeatures 1000000 > scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.MVP.eQTL.polyA.tsv")) %>%
pull(CustomScriptCall)
Sys.glob("../code/scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.*.tsv") %>%
setNames(str_replace(., "../code/scratch/Plot_sQTLeQTLGWASColocs/DatForBoxplots.(.+?).tsv", "\\1")) %>%
lapply(fread, col.names=c("SampleID", "NormalizedExpression", "genotype", "Ref", "Alt", "ID")) %>%
bind_rows(.id="Phenotype") %>%
filter(!genotype==3) %>%
separate(Phenotype, into=c("Locus", "xQTL", "Dataset"), sep="\\.", remove=F) %>%
mutate(genotype = factor(genotype)) %>%
ggplot(aes(x=genotype, y=NormalizedExpression)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(width=0.25, alpha=0.25) +
geom_smooth(method='lm') +
facet_grid(xQTL~Dataset~Locus) +
theme_bw()
Ok, I think the last thing I might consider plotting is the sort of coloc plot with -log10P vs -log10P scatter for pairs of colocalized traits…
library(GGally)
library(RColorBrewer)
lower_point <- function(data, mapping, ...) {
ggplot(data = data, mapping = mapping, ...) +
geom_point(..., size=0.5, alpha=0.2, color="gray") +
geom_point(data = (
data %>% filter(!is.na(TopCandidateSNP))),
shape = 21, color="black", alpha=1, size=1.5
) +
scale_fill_identity() +
# scale_fill_brewer(palette="Dark2", type="qualitative", na.value="gray") +
theme_classic()
}
my_fn <- function(data, mapping, method="p", use="pairwise", ...){
# grab data
x <- eval_data_col(data, mapping$x)
y <- eval_data_col(data, mapping$y)
# calculate correlation
corr <- cor(x, y, method=method, use=use)
# calculate colour based on correlation value
# Here I have set a correlation of minus one to blue,
# zero to white, and one to red
# Change this to suit: possibly extend to add as an argument of `my_fn`
colFn <- colorRampPalette(c("blue", "white", "red"), interpolate ='spline')
fill <- colFn(100)[findInterval(corr, seq(-1, 1, length=100))]
ggally_cor(data = data, mapping = mapping, ...) +
theme_void() +
theme(panel.background = element_rect(fill=fill))
}
Plot the coloc for NUDT14
MolPhenotypes <- c("14:105175987:105176534:clu_34934_-", "ENSG00000183828.15")
GwasLocus <- "chr14_105178084_GCST004611"
TopSNP <- "14:105172594:T:C"
GwasLocus.trait <- str_replace(GwasLocus, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")
GWAS <- str_replace(GwasLocus.trait, ".+?_N_N_(.+?)", "\\1")
TopSNP_Pos <- as.numeric(str_replace(TopSNP, "^.+?:(.+?):.+?:.+?$", "\\1"))
PlotColoc <- function(){
molQTL.SummaryStats <- fread(paste0("../code/hyprcoloc/LociWiseSummaryStatsInput/ForGWASColoc/", GWAS, ".txt.gz")) %>%
filter(gwas_locus == GwasLocus.trait) %>%
filter(phenotype %in% MolPhenotypes) %>%
mutate(xQTL = str_replace(source_file, "QTLs/QTLTools/(.+?)/.+", "\\1"))
GWAS.SummaryStats <- fread(paste0("../code/gwas_summary_stats/StatsForColoc/", GWAS, ".standardized.txt.gz")) %>%
filter(loci == GwasLocus.trait)
dat.toplot <- molQTL.SummaryStats %>%
separate(snp, into=c("SNP_Chrom", "SNP_Pos", "SNP_Ref", "SNP_Alt"), sep=":", remove=F, convert=T) %>%
mutate(SNP_Chrom = paste0("chr", SNP_Chrom)) %>%
mutate(logP = -log10(p)) %>%
dplyr::select(SNP_Chrom, SNP_Pos, logP, phenotype, xQTL ) %>%
unite(phenotype_xQTL, xQTL, phenotype) %>%
pivot_wider(names_from = phenotype_xQTL, values_from=logP) %>%
inner_join(
GWAS.SummaryStats %>%
mutate(logP.gwas=-log10(2*pnorm(abs(beta/SE), lower.tail=F))) %>%
dplyr::select(SNP_Chrom=chrom, SNP_Pos=start, logP.gwas)
) %>%
mutate(TopCandidateSNP = if_else(SNP_Pos == TopSNP_Pos, TopSNP, NA_character_)) %>%
dplyr::select(SNP_Chrom, SNP_Pos, TopCandidateSNP, everything())
P <- ggpairs(dat.toplot,
columnLabels = gsub('[;.]', ' ', colnames(dat.toplot)[-c(1:3)], perl=T),
labeller = label_wrap_gen(10),
columns = 4:ncol(dat.toplot),
diag=list(continuous = "blankDiag"),
lower=list(continuous = lower_point),
upper=list(continuous = my_fn)
)
return(list(dat=dat.toplot, P=P))
}
NUDT14.ColocPlot <- PlotColoc()
NUDT14.ColocPlot$P + labs(title="NUDT14 sQTL/eQTL/GWAS coloc")
Same for MVP
MolPhenotypes <- c("16:29834437:29835704:clu_38570_+", "ENSG00000013364.19")
GwasLocus <- "chr16_29855474_GCST004599"
TopSNP <- "16:29839384:C:T"
GwasLocus.trait <- str_replace(GwasLocus, "(^.+?_.+?_)(.+?$)", "\\1N_N_\\2")
GWAS <- str_replace(GwasLocus.trait, ".+?_N_N_(.+?)", "\\1")
TopSNP_Pos <- as.numeric(str_replace(TopSNP, "^.+?:(.+?):.+?:.+?$", "\\1"))
MVP.ColocPlot <- PlotColoc()
MVP.ColocPlot$P + labs(title="MVP sQTL/eQTL/GWAS coloc")
MVP.ColocPlot$dat %>%
dplyr::select(1:3, 4, 7, 9) %>%
ggpairs(
columnLabels = gsub('[;.]', ' ', colnames(.)[-c(1:3)], perl=T),
labeller = label_wrap_gen(10),
columns = 4:ncol(.),
diag=list(continuous = "blankDiag"),
lower=list(continuous = lower_point),
upper=list(continuous = my_fn)
) +
labs(title="MVP sQTL/eQTL/GWAS coloc")
Ok, let’s write out the data for these plots in case I want to easily plot them prettier later…
MVP.ColocPlot$dat %>%
write_tsv("../output/ColocPlotData.MVP.tsv.gz")
NUDT14.ColocPlot$dat %>%
write_tsv("../output/ColocPlotData.NUDT15.tsv.gz")
sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GGally_2.1.2 edgeR_3.38.4 limma_3.52.4 data.table_1.14.2
[5] RColorBrewer_1.1-3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[9] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
[13] ggplot2_3.3.6 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] httr_1.4.3 sass_0.4.1 bit64_4.0.5 vroom_1.5.7
[5] jsonlite_1.8.0 R.utils_2.11.0 modelr_0.1.8 bslib_0.3.1
[9] assertthat_0.2.1 highr_0.9 cellranger_1.1.0 yaml_2.3.5
[13] pillar_1.7.0 backports_1.4.1 lattice_0.20-45 glue_1.6.2
[17] digest_0.6.29 promises_1.2.0.1 rvest_1.0.2 colorspace_2.0-3
[21] plyr_1.8.7 R.oo_1.24.0 htmltools_0.5.2 httpuv_1.6.5
[25] pkgconfig_2.0.3 broom_0.8.0 haven_2.5.0 scales_1.2.0
[29] later_1.3.0 tzdb_0.3.0 git2r_0.30.1 generics_0.1.2
[33] farver_2.1.0 ellipsis_0.3.2 withr_2.5.0 cli_3.3.0
[37] magrittr_2.0.3 crayon_1.5.1 readxl_1.4.0 evaluate_0.15
[41] R.methodsS3_1.8.1 fs_1.5.2 fansi_1.0.3 xml2_1.3.3
[45] tools_4.2.0 hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0
[49] reprex_2.0.1 locfit_1.5-9.7 compiler_4.2.0 jquerylib_0.1.4
[53] rlang_1.0.2 grid_4.2.0 rstudioapi_0.13 labeling_0.4.2
[57] rmarkdown_2.14 gtable_0.3.0 reshape_0.8.9 DBI_1.1.2
[61] R6_2.5.1 lubridate_1.8.0 knitr_1.39 fastmap_1.1.0
[65] bit_4.0.4 utf8_1.2.2 workflowr_1.7.0 rprojroot_2.0.3
[69] stringi_1.7.6 parallel_4.2.0 Rcpp_1.0.8.3 vctrs_0.4.1
[73] dbplyr_2.1.1 tidyselect_1.1.2 xfun_0.30