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GWAS_analysis.R
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###########################################################
### ###
### Analyse results from GWAS of 41 yeast quantitative ###
### growth traits via yeast/GWAS/GWAS_yeast.sh ###
### ###
### * dataset from Bloom et al 2013 ###
### * phenotypes processed via ###
### yeast/phenotypes/phenotypes_yeast.R ###
### * genotypes processed via ###
### yeast/genotypes/genotypes_yeast.R ###
### * relationship estimated via ###
### yeast/genotypes/relationship_yeast.R ###
### * GWAS via yeast/GWAS/GWAS_yeast.sh ###
### * univariate LMM ###
### * multivariate LMMs with LiMMBo ###
### ###
### Generates Figure 5 (publication) ###
### Figure 5.5, 5.6, B.2 (thesis) ###
### ###
###########################################################
###############################
### Libraries and Functions ###
###############################
library("data.table")
library("GenomicRanges")
library("plyr")
library("pvclust")
library("splitstackshape")
library("ggplot2")
library("cowplot")
library('ggdendro')
library('dendextend')
library("wesanderson")
source("~/projects/utils/pvclustHM.R")
source("~/projects/utils/manhattanplot.R")
source("~/projects/utils/qqplot.R")
filterLD <- function(fac, snp, ld, sigSNP, p_original, multitrait=FALSE) {
if (multitrait) {
ldtrait <- ld[[fac]]
ptrait <- p_original[[fac]]
} else {
ldtrait <- ld
ptrait <- p_original
}
snp_vec <- c(unlist(strsplit(ldtrait[snp,"TAGS"], split="|", fixed=TRUE)),
ldtrait[snp,"SNP"])
if (any(snp_vec %in% "NONE")) {
min_ld_snp <- ptrait[which(ptrait$SNP %in% ldtrait[snp,"SNP"]),]
}
if (! any(snp_vec %in% "NONE")) {
ld_snps <- ptrait[which(ptrait$SNP %in% snp_vec),]
### if nrow(ld_snps) > 1): snps which are in LD with SNPS in question
### are significantly associated with phenotype as well
### find minimun of all ld SNPs and only keep SNP with minimum p-value
if (nrow(ld_snps) > 1) {
min_ld_snp_tmp <- ld_snps[which.min(ld_snps$P),]
### make sure that this SNP has pmin out of all SNPs it's in LD with
snp_vec2 <- c(unlist(strsplit(
ldtrait[which(ldtrait[,"SNP"] == min_ld_snp_tmp$SNP),"TAGS"],
split="|", fixed=TRUE)),
ldtrait[which(ldtrait[,"SNP"] == min_ld_snp_tmp$SNP), "SNP"])
ld_snps2 <- ptrait[which(ptrait$SNP %in% snp_vec2),]
min_ld_snp <- ld_snps2[which.min(ld_snps2$P),]
} else {
min_ld_snp <- ld_snps
}
}
return( min_ld_snp)
}
chrArab2chrRom <- function(x, direction="arab2rom") {
if (direction == "arab2rom") {
switch(EXPR=x, "1"="I", "2"="II", "3"="III", "4"="IV", "5"="V",
"6"="VI", "7"="VII", "8"="VIII", "9"="IX", "10"="X",
"11"="XI", "12"="XII", "13"="XIII", "14"="XIV", "15"="XV",
"16"="XVI", "Mito"="Mito")
} else {
switch(EXPR=x, "I"=1, "II"=2, "III"=3, "IV"=4, "V"=5, "VI"=6,
"VII"=7, "VIII"=8, "IX"=9, "X"=10, "XI"=11, "XII"=12,
"XIII"=13, "XIV"=14, "XV"=15, "XVI"=16, "Mito"=17, "M"=17)
}
}
snp2genes <- function(snps, genes) {
snps <- data.frame(chrArab=snps$CHR, start=snps$BP, end=snps$BP+1,
SNP=snps$SNP, ID=snps$ID)
snps$chr <- sapply(snps$chrArab, chrArab2chrRom)
grSnps = with(snps, GRanges(chr, IRanges(start=start, end=end,
names=ID), SNPID=ID))
snpsGenes <- subsetByOverlaps(genes, grSnps)
overlaps <- findOverlaps(genes,grSnps)
rsid <- CharacterList(split(names(grSnps)[subjectHits(overlaps)],
queryHits(overlaps)))
mcols(snpsGenes) <- DataFrame(mcols(snpsGenes), rsid)
snpsGenes.df <- data.frame(snpsGenes, stringsAsFactors=FALSE)
snpsGenes.df$rsid <- sapply(snpsGenes.df$rsid, paste, collapse=",")
snpsGenes.df$seqnames <- as.character(snpsGenes.df$seqnames)
snpsGenes.df$strand <- as.character(snpsGenes.df$strand)
return(snpsGenes.df)
}
mergeSNPs <- function(pbeta) {
duplicateSNPs <- pbeta[which(pbeta$SNP %in%
pbeta$SNP[duplicated(pbeta$SNP)]),]
tmp <- lapply(unique(duplicateSNPs$SNP), function(s) {
geneIDs <- paste(as.character(duplicateSNPs$geneID[
duplicateSNPs$SNP == s]), collapse=",")
geneNames <- paste(
as.character(duplicateSNPs$geneName[duplicateSNPs$SNP == s]),
collapse=",")
data.frame(SNP=s, seqnames=duplicateSNPs[duplicateSNPs$SNP == s, ][1,2],
geneID=geneIDs, geneName=geneNames,
duplicateSNPs[duplicateSNPs$SNP == s,][1, 5:ncol(
duplicateSNPs)])
})
tmp <- do.call(rbind, tmp)
}
clusterEffectsizes <- function(effects, type, direction, nboot, iseed=10,
plot=FALSE, method.dist="correlation",
effects_pv=NULL) {
if (is.null(effects_pv)) {
effects_pv <- pvclust(effects, iseed=iseed, nboot=nboot,
method.dist=method.dist)
saveRDS(effects_pv, paste(directory, "/GWAS/pbeta_", type,
"_map_", direction, "_pv_nboot", nboot, "_",
method.dist, ".rds",
sep=""))
}
if (plot) {
if (direction == "snps") {
pvclustPlot(effects_pv, print.bp=FALSE, print.edge=TRUE,
labels=FALSE,
col.pv=wes_palette(4, name="Moonrise2",
type='continuous')[2])
} else {
pvclustPlot(effects_pv, print.bp=FALSE, print.edge=TRUE,
col.pv=wes_palette(4, name="Moonrise2",
type='continuous')[2])
}
pvclustRect(effects_pv, type = "geq", max.only=FALSE)
}
pickClusters <- function(effects_pv, max.only){
ec <- pvclustPick(effects_pv, max.only=max.only)$clusters
ec <- lapply(seq_along(ec),
function(x) {
ec[[x]] <- cbind(ec[[x]], paste("cluster", x))
}
)
ec <- data.frame(do.call(rbind, ec), stringsAsFactors=FALSE)
colnames(ec) <- c("ID", "cluster")
uniqueClusters <- unique(ec$cluster )
return(list(ec=ec, unique=uniqueClusters))
}
effects_cluster_all = pickClusters(effects_pv, max.only=FALSE)
effects_cluster = pickClusters(effects_pv, max.only=TRUE)
return(list(effects_pv=effects_pv,
effects_cluster_all = effects_cluster_all$ec,
effects_cluster = effects_cluster$ec,
uniqueClusters_all = effects_cluster_all$unique,
uniqueClusters = effects_cluster$unique))
}
############
### data ###
############
directory='~/data/LiMMBo/yeast'
# phenotypes
pheno <- fread(paste(directory, "/inputdata/BYxRM_pheno_format.txt", sep=""),
header=TRUE, data.table=FALSE, stringsAsFactors=FALSE)
# fdr thresholds for significcance in multi-variate uni-variate LMM via
# permutation obtained from GWAS_yeast.sh calling gwas.py
fdr_multi=1.150566e-05
fdr_single=8.636346e-06
# tag SNPs within 3kb window with r2 > 0.8 according to Liti et al (2009) Nature
ld <- fread(paste(directory, "/inputdata/BYxRM.3kb.tags.list", sep=""),sep=" ",
data.table=FALSE)
ld$ID <- gsub("\\d*_chr0?(\\d{1,2})_(\\d*)_.*", "chr\\1:\\2",ld$SNP)
# p values of multivariate, any effect LMM
pany <- fread(paste(directory, "/GWAS/pvalues_lmm_any.csv", sep=""), sep=",",
data.table=FALSE)
colnames(pany) <- c("ID", "P")
pany$SNP <- ld$SNP
pany$CHR <- as.numeric(gsub("chr(\\d{1,2}):(\\d*)", "\\1", pany$ID))
pany$BP <- as.numeric(gsub("chr(\\d{1,2}):(\\d*)", "\\2", pany$ID))
pany$TYPE <- 'multitrait'
# p values of univariate LMM
psingleall <- fread(paste(directory, "/GWAS/lmm_st_pvalue_genome.csv", sep=""),
header=TRUE, data.table=FALSE, stringsAsFactors=FALSE)
colnames(yeast)[4:ncol(yeast)] <- colnames(pheno)[-1]
# min p values per SNP across univariate LMM
psingle <- fread(paste(directory, "/GWAS/pvalues_lmm_single_min_adjust.csv",
sep=""), sep=",", data.table=FALSE, header=TRUE)
colnames(psingle) <- c("ID", "P")
psingle$SNP <- ld$SNP
psingle$CHR <- as.numeric(gsub("chr(\\d{1,2}):(\\d*)", "\\1", psingle$ID))
psingle$BP <- as.numeric(gsub("chr(\\d{1,2}):(\\d*)", "\\2", psingle$ID))
psingle$TYPE <- 'singletrait'
# all p values of single-variate LMM
padjust <- fread(paste(directory, "/GWAS/lmm_st_padjust_genome.csv", sep=""),
sep=",",data.table=FALSE, header=TRUE)
# effect sizes
bsingle <- fread(paste(directory, "/GWAS/betas_lmm_single.csv", sep=""), sep=",",
data.table=FALSE)
colnames(bsingle)[1] <- "SNP"
bsingle$ID <- ld$SNP
bany <- fread(paste(directory, "/GWAS/betas_lmm_any.csv", sep=""), sep=",",
data.table=FALSE)
colnames(bany)[1] <- "SNP"
bany$ID <- ld$SNP
badjust <- fread(paste(directory, "/GWAS/lmm_st_betavalue_genome.csv", sep=""),
sep=",",data.table=FALSE, header=TRUE)
colnames(badjust)[-c(1:3)] <- colnames(bany)[2:42]
colnames(padjust)[-c(1:3)] <- colnames(bany)[2:42]
# significant SNPs
# single trait analyses: at least one significant SNP
padjust_sigAll_index <- t(apply(padjust, 1, function(x) {
which(as.numeric(x[-c(1:3)]) < fdr_single) }))
names(padjust_sigAll_index) <- padjust$SNP
nonZeros <- sapply(padjust_sigAll_index, length) != 0
padjust_sig_index <- padjust_sigAll_index[which(nonZeros)]
nrSig <- sapply(padjust_sig_index, length)
pdf(paste(directory, "/GWAS/sig_trait_assoc_SNP.pdf", sep=""))
hist(nrSig, xlim=c(1,max(nrSig)),
xlab="Number of significant trait associations per SNP",
main="")
dev.off()
padjust_sig <- padjust[which(nonZeros),]
badjust_sig <- badjust[which(nonZeros),]
# single trait analyses: more than one significant SNP
more <- sapply(padjust_sig_index, length) > 1
padjust_manySig_index <- padjust_sig_index[which(more)]
# single trait analyses: exactly one significant SNP
exactlyOne <- sapply(padjust_sig_index, length) == 1
padjust_oneSig_index <- padjust_sig_index[which(exactlyOne)]
# single trait analyses: minimum significant SNP
psingle_sig <- psingle[psingle$P < fdr_single, ]
bsingle_sig <- bsingle[psingle$P < fdr_single, ]
# multi-trait analyses: singnificant SNPs
pany_sig <- pany[pany$P < fdr_multi, ]
bany_sig <- bany[bany$P < fdr_multi, ]
## Intersecting significant SNPs
common_sig <- intersect(names(padjust_sig_index), pany_sig$ID)
one_sig <- intersect(names(padjust_oneSig_index), pany_sig$ID)
many_sig <- intersect(names(padjust_manySig_index), pany_sig$ID)
padjust_common_index <- padjust_sig_index[names(padjust_sig_index) %in% common_sig]
badjust_common <- badjust[badjust$SNP %in% common_sig,]
bany <- bany[bany$SNP %in% common_sig, ]
padjust_oneSig_index <- padjust_sig_index[names(padjust_sig_index) %in% one_sig]
badjust_oneSig <- badjust[badjust$SNP %in% one_sig,]
bany_oneSig <- bany[bany$SNP %in% one_sig, ]
padjust_manySig_index <- padjust_sig_index[names(padjust_sig_index) %in% many_sig]
badjust_manySig <- badjust[badjust$SNP %in% many_sig,]
bany_manySig <- bany[bany$SNP %in% many_sig, ]
bany_manySig_single <- unlist(sapply(seq_along(padjust_manySig_index),
function(x) {
bany_manySig[x, (padjust_manySig_index[[x]] + 1)]
}))
badjust_manySig_single <- unlist(sapply(seq_along(padjust_manySig_index), function(x) {
badjust_manySig[x, (padjust_manySig_index[[x]]+3)]
}))
df <- data.frame(multi_trait=bany_manySig_single,
single_trait=badjust_manySig_single)
p <- ggplot(df, aes(x=single_trait, y=multi_trait))
p + geom_point() +
geom_abline(intercept=0, slope=1) +
ylab(expression(beta[multi-trait])) +
xlab(expression(beta[single-trait])) +
xlim(c(-6,6))
padjust_min_index <- t(apply(padjust, 1, function(x) {
which.min(x[-c(1:3)]) + 3}))
padjust_min <- cbind(padjust[,1:3], pmin =apply(padjust, 1, function(x) {
return(min(as.numeric(x[-c(1:3)])))}))
psingle_sig <- psingle[psingle$P < fdr_single, ]
pany_sig <- pany[pany$P < fdr_multi, ]
common_sig <- intersect(psingle_sig$SNP, pany_sig$SNP)
bany_sig <- bany[bany$ID %in% common_sig, ]
bany_sig.m <- melt(bany_sig[,-1], id.vars = "ID", variable.name = "Trait",
value.name = "Multi_trait")
#bany_sig.m$type <- "Multi-trait"
bsingle_sig <- bsingle[bsingle$ID %in% common_sig, ]
badjust_sig <- badjust[badjust$ID %in% common_sig, ]
bsingle_sig.m <- melt(bsingle_sig[,-1], id.vars = "ID", variable.name = "Trait",
value.name = "Single_trait")
#bsingle_sig.m$type <- "Single-trait"
b_sig <- cbind(bsingle_sig.m, Multi_trait=bany_sig.m$Multi_trait)
p <- ggplot(b_sig, aes(x=Single_trait, y=Multi_trait))
p + geom_point() +
ylab(expression(beta[multi-trait])) +
xlab(expression(beta[single-trait])) +
theme(axis.title = element_text(size=18))
ggsave(paste(directory, "/GWAS/effect_sizes_common_sig.pdf", sep=""))
# yeast chromosome sizes generated via
# fetchChromSizes sacCer3 sacCer3.chrom.sizes > sacCer3.chrom.sizes.txt
chrom_size <- read.table(paste(directory, "/inputdata/sacCer3.chrom.sizes.txt",
sep=""), stringsAsFactors=FALSE)
chrom_size$chr <- unlist(sapply(gsub("chr", "", chrom_size$V1),
chrArab2chrRom, direction="rom2arab"))
chrom_size <- chrom_size[order(chrom_size$chr),]
colnames(chrom_size)[1:2] <- c("CHR", "length")
################
### analysis ###
################
psingle <- padjust
####################
### SNP analysis ###
####################
# Filter pvalues based on LD (retain SNP with lowest pvalue from ld block)
pany_ldfiltered <- do.call(rbind, lapply(seq_along(1:nrow(pany)), function(snp)
filterLD(snp=snp, ld=ld, p_original=pany)))
pany_ldfiltered <- pany_ldfiltered[!duplicated(pany_ldfiltered$SNP),]
psingle_ldfiltered <- do.call(rbind,
lapply(seq_along(1:nrow(psingle)), function(snp)
filterLD(snp=snp, ld=ld, p_original=psingle)))
psingle_ldfiltered <- psingle_ldfiltered[!duplicated(psingle_ldfiltered$SNP),]
padjust_sig_ldfiltered <- do.call(rbind,
lapply(seq_along(1:nrow(padjust_sig)),
function(snp)
filterLD(snp=snp, ld=ld,
p_original=padjust_sig)))
padjust_sig_ldfiltered <- padjust_sig_ldfiltered[!duplicated(
padjust_sig_ldfiltered$SNP),]
# Filter SNPs based on pruned dataset
kb <- c(3, 5, 8, 10, 12, 15, 20, 30, 50, 100, 0)
sig_tags <- sapply(kb, function(k) {
if (k != 0) {
pruned <- fread(paste(directory, "/inputdata/BYxRM.", k,"kb.pruned.bim",
sep=""), data.table=FALSE)
psingle_pruned <- psingle[which(psingle$SNP %in% pruned[,2]),]
pany_pruned <- pany[which(pany$SNP %in% pruned[,2]),]
psingle_pruned_sig <- psingle_pruned[psingle_pruned$P < fdr_single,]
pany_pruned_sig <- pany_pruned[pany_pruned$P < fdr_single,]
return(c(NrSNPs=nrow(pruned),
multitrait=nrow(pany_pruned_sig),
singletrait=nrow(psingle_pruned_sig)))
} else {
return(c(NrSNPs=nrow(pany),
multitrait=nrow(pany[pany$P< fdr_single, ]),
singletrait=nrow(psingle[psingle$P< fdr_single, ])))
}
})
sig_tags <- rbind(sig_tags, sig_tags[2,]/sig_tags[3,])
colnames(sig_tags) <- paste("ld_pruned", kb, "kb", sep="")
colnames(sig_tags)[which(kb == 0)] <- "All SNPs"
rownames(sig_tags)[4] <- "multitrait:singletrait"
write.table(sig_tags, file=
paste(directory, "/GWAS/significant_tagSNPs.txt",sep=""),
col.names=NA, row.names=TRUE, quote=FALSE, sep="\t")
# chose 15kb for displaying marker SNPs
pruned <- fread(paste(directory, "/inputdata/BYxRM.", 15, "kb.pruned.bim",
sep=""), data.table=FALSE)
psingle_pruned <- psingle[which(psingle$SNP %in% pruned[,2]),]
pany_pruned <- pany[which(pany$SNP %in% pruned[,2]),]
psingle_pruned_sig <- psingle_pruned[psingle_pruned$P < fdr_single,]
pany_pruned_sig <- pany_pruned[pany_pruned$P < fdr_single,]
p_pruned_sig <- rbind(psingle_pruned_sig, pany_pruned_sig)
p_pruned_sig$MARKER <- "tag"
# get unique SNPs from LD filtered, most significant
ldfilteredSNPs <- unique(c(pany_ldfiltered$SNP, psingle_ldfiltered$SNP))
# Combine pvalues for manhattan plot
pany_commonfiltered <- pany[pany$SNP %in% ldfilteredSNPs,]
pany_commonfiltered$MARKER <- ""
psingle_commonfiltered <- psingle[psingle$SNP %in% ldfilteredSNPs,]
psingle_commonfiltered$MARKER <- ""
p_ldfiltered <- rbind(psingle_commonfiltered, pany_commonfiltered, p_pruned_sig)
p_ldfiltered$TYPE <- as.factor(p_ldfiltered$TYPE)
p_ldfiltered$MARKER <- as.factor(p_ldfiltered$MARKER)
# get yeast annotation and store as GRanges and gtf
yeastgenes <- fread(paste(directory,
'/inputdata/20170207_ScerevisaeR64-1-1.txt', sep=""),
data.table=FALSE, stringsAsFactors = FALSE)
colnames(yeastgenes) <- c("geneID", "transcriptID", "chr","start", "end",
"strand", "geneName" )
yeastgenes$CHRROM <- unlist(sapply(yeastgenes$chr, chrArab2chrRom,
direction="rom2arab"))
yeastgenes <- yeastgenes[order(yeastgenes$CHRROM, yeastgenes$start),]
grYeast = with(yeastgenes, GRanges(chr,
IRanges(start=start, end=end, names=geneID),
strand=strand, geneID=geneID,
transcriptID=transcriptID,
geneName=geneName))
# map snps to genes
snpsGWASGenes <- snp2genes(snps=pany, genes=grYeast)
write.table(snpsGWASGenes, file= paste(directory,
"/GWAS/ReferenceGWASGenes.txt",sep=""),
col.names=FALSE, row.names=FALSE, quote=FALSE, sep="\t")
# convert from genes with multiple SNPs, to single SNPs mapping to genes
ID2Genes <- apply(as.matrix(snpsGWASGenes), 1, function(x) {
ids <- unlist(strsplit( x[9], ","))
df <- expandRows(as.data.frame(t(x)), length(ids), count.is.col = FALSE)
df$SNP <- ids
return(df)
})
ID2Genes <- do.call(rbind, ID2Genes)
beta_any_map <- merge(ID2Genes, bany, by.x=10, by.y=1)
pbeta_any_map <- merge(beta_map[,c(1:2, 7, 9, 11:52)], pany[,1:2], by=1)
beta_single_map <- merge(ID2Genes, bsingle, by.x=10, by.y=1)
pbeta_single_map <- merge(beta_single_map[,c(1:2, 7, 9, 11:52)], pany[,1:2], by=1)
# some SNPs are mapped to more than one gene; get these SNPs + paste gene column
pbeta_any_map <- pbeta_any_map[ -which(pbeta_any_map$SNP %in%
pbeta_any_map$SNP[duplicated(pbeta_any_map$SNP)]),]
pbeta_any_map <- rbind(pbeta_any_map, mergeSNPs(pbeta_any_map))
pbeta_single_map <- pbeta_single_map[ -which(pbeta_single_map$SNP %in%
pbeta_single_map$SNP[duplicated(pbeta_single_map$SNP)]),]
pbeta_single_map <- rbind(pbeta_single_map, mergeSNPs(pbeta_single_map))
# Remove all non-significant SNPs
pbeta_any_map_ld <- pbeta_any_map[pbeta_any_map$SNP %in% pany_ldfiltered$ID,]
pbeta_any_map_sig <- pbeta_any_map_ld[pbeta_any_map_ld$P < fdr_multi,]
rownames(pbeta_any_map_sig) <- pbeta_any_map_sig$SNP
saveRDS(pbeta_any_map_sig, paste(directory, "/GWAS/pbeta_any_map_ld_sig.rds", sep=""))
pbeta_single_map_ld <- pbeta_single_map[pbeta_single_map$SNP %in% psingle_ldfiltered$ID,]
pbeta_single_map_sig <- pbeta_single_map_ld[pbeta_single_map_ld$P < fdr_single,]
rownames(pbeta_single_map_sig) <- pbeta_single_map_sig$SNP
saveRDS(pbeta_single_map_sig, paste(directory, "/GWAS/pbeta_single_map_ld_sig.rds", sep=""))
############################
### effect size analysis ###
############################
# cluster and determine stability of clusters with pvclust
pbeta_any_map_traits_pv <- readRDS( paste(directory, "/GWAS/20170626pbeta_map_traits_pv_50k_fdr5e-5correlation.rds", sep=""))
effects_pv_snps_any <- readRDS("~/data/LiMMBo/yeast/GWAS/20170526pbeta_map_snps_pv_fdr5e-5correlation.rds")
effects_pv_snps_single <- readRDS("~/data/LiMMBo/yeast/GWAS/pbeta_single_map_snps_pv_correlation.rds")
effects_pv_traits_any <- readRDS("~/data/LiMMBo/yeast/GWAS/pbeta_any_map_traits_pv_50k_fdr5e-5correlation.rds")
effects_pv_traits_single <- readRDS("~/data/LiMMBo/yeast/GWAS/pbeta_single_map_traits_pv_50k_fdr5e-5correlation.rds")
any_clusterSNP <- clusterEffectsizes(effects_pv=effects_pv_snps_any,
type="any", direction="snps", nboot=10000,
plot=TRUE)
single_clusterSNP <- clusterEffectsizes(effects_pv=effects_pv_snps_single,
type="single", direction="snps",
nboot=10000, plot=TRUE)
any_clusterTraits <- clusterEffectsizes(effects_pv=effects_pv_traits_any,
type="any", direction="traits",
nboot=50000, plot=TRUE)
single_clusterTraits <- clusterEffectsizes(effects_pv = effects_pv_traits_single,
type="single", direction="traits",
nboot=50000, plot=TRUE)
## for traits
any_clusterTraits <- clusterEffectsizes(effects=pbeta_any_map_sig[,5:45],
type="any", direction="traits",
nboot=50000)
single_clusterTraits <- clusterEffectsizes(effects=pbeta_single_map_sig[,5:45],
type="single", direction="traits",
nboot=50000)
## for SNPs
#pbeta_any_map_snps_pv <- readRDS("~/data/LiMMBo/feasabilityBootstrap/yeast/GWAS/20170526pbeta_map_snps_pv_fdr5e-5correlation.rds")
any_clusterSNP <- clusterEffectsizes(effects=t(pbeta_any_map_sig[,5:44]),
type="any", direction="snps", nboot=10000)
single_clusterSNP <- clusterEffectsizes(effects=t(pbeta_single_map_sig[,5:44]),
type="single", direction="snps",
nboot=10000)
## for traits
any_clusterTraits <- clusterEffectsizes(effects=pbeta_any_map_sig[,5:45],
type="any", direction="traits",
nboot=50000)
single_clusterTraits <- clusterEffectsizes(effects=pbeta_single_map_sig[,5:45],
type="single", direction="traits",
nboot=50000)
#single
#highest_edge <- 154
#any
#highest_edge <- 180
## order according to cluster order
pbeta_map_sig <- pbeta_any_map_sig
rownames(pbeta_map_sig) <- pbeta_map_sig$SNP
clusterTraits <- any_clusterTraits
clusterSNP <- any_clusterSNP
snp <- pbeta_map_sig$SNP
snp <- factor(snp[clusterSNP$effects_pv$hclust$order],
levels=unique(snp[clusterSNP$effects_pv$hclust$order]))
pbeta_map_sig_order <- pbeta_map_sig[,1:45][clusterSNP$effects_pv$hclust$order,
c(1:4, (clusterTraits$effects_pv$hclust$order)+4) ]
pbeta_map_sig_order.m <- melt(data.frame(SNP=snp, pbeta_map_sig_order[, 5:45]),
value.name = "beta",
variable.name="trait")
chr <- gsub("chr(\\d{1,2}):.*", "\\1", rownames(pbeta_map_sig_order))
chr <- factor(chr, levels=1:16)
#############
### plots ###
#############
## Manhattanplots for all single-trait analyses (Figure B.2 thesis)
colManhattan <- c("#F98400", "gray70")
plotp <- sapply(4:ncol(psingleall), function(x) {
genome <- psingleall[, c(1:3,x)]
colnames(genome)[4] <- "P"
p_manhattan <- manhattan(genome, bp="POS", color=colManhattan,
genomewideline= -log10(fdr_single),
colorGenomewide = "gray50", size.x.labels=12,
size.y.labels=12) +
theme(axis.title =element_text(size=12))
ggsave(plot=p_manhattan, height=4, width=10,
file=paste(directory,"/GWAS/", colnames(psingleall)[x],
"_manhattanplot.png", sep=""))
})
## rectangles for significant SNP clusters across different chromosomes
cluster_cols <- lapply(1:nrow(pbeta_map_sig_order), function(x) {
id <- rownames(pbeta_map_sig_order)[x]
geneID <- pbeta_map_sig_order$geneID[x]
geneName <- pbeta_map_sig_order$geneName[x]
if(any(clusterSNP$effects_cluster$ID == id)) {
tmp <- clusterSNP$effects_cluster$cluster[clusterSNP$effects_cluster$ID == id]
tmp <- data.frame(ID=rep(id, length(tmp)),
geneID = rep(geneID, length(tmp)),
geneName = rep(geneName, length(tmp)), cluster=tmp)
} else {
tmp <- data.frame(ID=id, geneID = geneID, geneName=geneName,
cluster="no_cluster")
}})
cluster_cols <- do.call(rbind, cluster_cols)
cluster_cols$chr <- gsub("chr(\\d{1,2}).*", "\\1", cluster_cols$ID)
#cluster_cols <- cluster_cols[order(cluster_cols$cluster),]
#cluster_cols$geneID <- pbeta_map_sig$geneID[clusterSNP$effects_pv$hclust$order]
#cluster_cols$geneName <- pbeta_map_sig$geneName[clusterSNP$effects_pv$hclust$order]
cluster2chromosomes <- sapply(unique(cluster_cols$cluster) , function(x) {
tmp <- cluster_cols[which(cluster_cols$cluster == x),]
if (length(unique(tmp$chr)) == 1) {
return("single chromosome")
} else {
return(paste(length(unique(tmp$chr)), "chromosomes"))
}
})
names(cluster2chromosomes) <- unique(cluster_cols$cluster)
multipleChr <- cluster2chromosomes[! grepl("single", cluster2chromosomes)]
if (any(grepl("no", multipleChr))) {
multipleChr <- multipleChr[!grepl("no", multipleChr)]
}
cluster_cols$clustertype <- sapply(cluster_cols$cluster, function(x) {
cluster2chromosomes[which(names(cluster2chromosomes) == x)]
})
#write.table(cluster_cols, paste(directory, "/GWAS/",
# "SNP_clusters.csv", sep=""), sep=",", quote=FALSE,
# col.names=TRUE)
cluster_cols_start <- which(!duplicated(cluster_cols$cluster))
cluster_cols_start <- cluster_cols_start[-which(
grepl("no",unique(cluster_cols$cluster)))]
cluster_cols_end <- sapply(cluster_cols_start, function(x)
max(which(cluster_cols$cluster==cluster_cols$cluster[x])))
cluster_cols_rect <- data.frame(xmin=cluster_cols_start,
xmax=cluster_cols_end,
ymin=rep(0, length(cluster_cols_start)),
ymax=rep(42, length(cluster_cols_start)),
colour=unique(cluster_cols$cluster)[-which(
grepl("no",unique(cluster_cols$cluster)))])
cluster_cols_rect_multiple <- cluster_cols_rect[which(cluster_cols_rect$colour
%in% names(multipleChr)),]
cluster_cols_rect_multiple$colour <- factor(cluster_cols_rect_multiple$colour,
levels= as.character(
cluster_cols_rect_multiple$colour))
## rectangles for significant trait clusters
cluster_rows <- lapply(colnames(pbeta_map_sig_order), function(x) {
if(any(clusterTraits$effects_cluster$ID == x)) {
tmp <- clusterTraits$effects_cluster$cluster[clusterTraits$effects_cluster$ID == x]
tmp <- data.frame(ID=rep(x, length(tmp)), cluster=tmp)
} else {
tmp <- data.frame(ID=x, cluster="no_cluster")
}})
cluster_rows <- do.call(rbind, cluster_rows)
cluster_rows_start <- which(!duplicated(cluster_rows$cluster))
cluster_rows_start <- cluster_rows_start[-which(
grepl("no",unique(cluster_rows$cluster)))]
cluster_rows_end <- sapply(cluster_rows_start, function(x)
max(which(cluster_rows$cluster == cluster_rows$cluster[x])))
cluster_rows_rect <- data.frame(ymin=cluster_rows_start,
ymax=cluster_rows_end,
xmin=rep(0, length(cluster_rows_start)),
xmax=rep(dim(pbeta_map_sig_order)[1],
length(cluster_rows_start)),
colour=unique(cluster_rows$cluster)[-which(
grepl("no",unique(cluster_rows$cluster)))])
### ggplots for Figure 5.5, 5.6 (thesis) and Figure 5 (publication)
## plotting parameters
colManhattan <- wes_palette(5, name="Darjeeling", type='continuous')[c(5,4)]
colStrength <- c(wes_palette(5, name="Darjeeling", type='continuous')[c(2)],
"white",
wes_palette(5, name="Darjeeling", type='continuous')[4])
Colcol <- wes_palette(18, name="Darjeeling", type='continuous')[-c(1:2)]
linesize <- 0.5
textsize <- 10
legendsize <- 10
# manhattan plot
pman <- manhattan(p_ldfiltered, genomewideline=-log10(fdr_single),
size.y.labels=textsize, size.x.labels=textsize, xscale=TRUE,
mtvsst=TRUE, cols=colManhattan, a=0.8,
colGenomewideline=colManhattan[2]) +
theme(panel.border = element_blank(),
legend.text=element_text(size=textsize),
legend.title=element_text(size=textsize),
legend.key.size = unit(0.5,"line")) +
labs(colour="GWAS set-up")
# chromosome location of clustered SNPs
chr_snp <- ggplot(data.frame(SNP=snp, variable=1, chr=chr,
stringsAsFactors=FALSE)) +
geom_tile(aes(x=snp, y=variable, fill=chr)) +
labs(fill="Chromosome" ) +
scale_fill_manual( values = Colcol, guide=guide_legend(nrow=2)) +
scale_x_discrete(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
theme(legend.position ='bottom',
axis.title=element_blank(),
axis.line = element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
legend.text=element_text(size=legendsize, angle = 0, hjust = 1),
legend.title=element_text(size=legendsize),
legend.key.size = unit(0.5,"line"),
plot.margin = unit(c(0, 0, 0, 0), "cm"))
legendChr <- get_legend(chr_snp)
legendChr$layout$t[1] <- 1
# dendrogram of clustered SNPs
relabeled_dendro_snps <- clusterSNP$effects_pv$hclust %>%
as.dendrogram %>% pvclust_show_signif(clusterSNP$effects_pv, signif_type='au')
snp_dendro <- as.ggdend(relabeled_dendro_snps )
dendrogram_snp <- ggplot() + geom_blank() +
geom_segment(data = segment(snp_dendro),
aes(x = x, y = y, xend = xend, yend = yend,
colour= as.factor(lwd))) +
scale_color_manual(values=c("black",'#03569b'),
guide=FALSE) +
labs(title="SNPs") +
scale_x_continuous(expand=c(0,0)) +
theme(axis.title = element_blank(),
axis.line = element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
plot.title = element_text(size = textsize),
plot.margin = unit(c(0, 0, 0, 0), "cm"))
# dendrogram of trait clustering
relabeled_dendro_traits <- clusterTraits$effects_pv$hclust %>%
as.dendrogram %>% pvclust_show_signif(clusterTraits$effects_pv, signif_type='au')
traits_dendro <- as.ggdend(relabeled_dendro_traits )
dendrogram_traits <- ggplot() + geom_blank() +
geom_segment(data = segment(traits_dendro),
aes(x = x, y = y, xend = xend, yend = yend,
colour= as.factor(lwd))) +
scale_x_continuous(breaks = seq_along(traits_dendro$labels$label),
labels = traits_dendro$labels$label,
expand=c(0,0),
limits=c(0,42)) +
scale_y_continuous(expand=c(0,0)) +
scale_color_manual(values=c("black",
'#03569b'),
guide=FALSE) +
coord_flip() +
theme(axis.title=element_blank(),
axis.line = element_blank(),
axis.ticks=element_blank(),
axis.text.x=element_blank(),
axis.text.y = element_text(size = textsize, angle = 0, hjust = 1))
labelTraitsDendro <- ggplot() +
annotate("text", label = "Traits", x = 1, y = 1, size = 4,
fontface="bold") +
theme_nothing()
# label cluster 28 and 31
specificCluster <- cluster_cols_rect_multiple[
cluster_cols_rect_multiple$colour %in%
c("cluster 15", "cluster 12"),]
specificCluster$xmean <- (specificCluster$xmax + specificCluster$xmin)/2
specificCluster$label <- c("a", "b")
labelClusters <- ggplot(data=specificCluster,aes(x=xmean, y=ymin,
label=label)) +
geom_text(size=4) +
scale_x_continuous(expand=c(0,0), limits=c(0,210)) +
scale_y_continuous(expand=c(0,0)) +
theme(axis.title=element_blank(),
axis.line = element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
plot.margin = unit(c(-8, 0, -8, 0), "cm"))
# effect sizes clustered by SNPs and traits and
colStrength <- c('#ca0020','#f4a582','#ffffff','#bababa','#404040')
beta_snp_hm <- ggplot(pbeta_map_sig_order.m) +
geom_tile(aes(x=as.numeric(SNP), y=as.numeric(trait),
fill=beta), height=1) +
scale_fill_gradientn( colours = colorRampPalette(colStrength)(100),
name = "Effect size") +
geom_rect(data=cluster_cols_rect_multiple, aes(xmin=xmin + 0.5,
xmax=xmax + 0.5,
ymin=ymin,
ymax=ymax), color= 'grey',
size=linesize, fill = NA) +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0), limits=c(0,42),
breaks = seq_along(traits_dendro$labels$label)) +
theme(legend.position ='left',
axis.title=element_blank(),
axis.line = element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
legend.text=element_text(size=legendsize, angle = 0, vjust = 0),
legend.title=element_text(size=legendsize, hjust = 1),
# legend.key.size = unit(0.5,"line"),
plot.margin = unit(c(0, 0, 0, 0), "cm"))
legendAbs <- get_legend(beta_snp_hm )
# combined effect size plot
effect_sizes <- plot_grid(NULL, dendrogram_snp, NULL,
NULL , chr_snp + theme(legend.position ='none'),
labelTraitsDendro,
legendAbs, beta_snp_hm + theme(legend.position =
'none'),
dendrogram_traits,
NULL , labelClusters, NULL,
NULL , legendChr, NULL,
ncol=3, nrow=5, rel_heights=c(2,0.5, 7, 0.5, 2),
#ncol=3, nrow=4, rel_heights=c(2,0.5, 7, 2),
rel_widths=c(0.5, 4,2),
align='vh')
## combine all plots
png(paste(directory, "/GWAS/manhattan_effectsizes.png", sep=""), height=12,
width=10, res=450, unit="in" )
plot_grid(pman, effect_sizes, nrow=2, rel_heights=c(3,5), labels=c("", ""))
dev.off()
pman_sep <- manhattan(p_ldfiltered, genomewideline=-log10(fdr_single),
size.y.labels=12, size.x.labels=12,
mtvsst=TRUE, color=colManhattan, a=0.8,
colorGenomewide = "gray50", chromboundaries = TRUE) +
theme(legend.position = "bottom",
legend.text = element_text(size=12),
axis.title =element_text(size=12)) +
labs(colour="GWAS set-up")
ggsave(plot=pman_sep, height=4, width=10,
file=paste(directory,"/GWAS/manhattanplot.png", sep=""))
png(paste(directory, "/GWAS/effectsizes.png", sep=""), height=10,
width=10, res=450, unit="in" )
effect_sizes
dev.off()