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########################################
##### combined-stage eQTL analysis #####
########################################
#### prepare the script working environment ####
remove(list = ls())
gc()
set.seed(1000)
# Set working directory ####
work.dir <- "C:/Users/harta005/Projects/seed-germination-qtl"
setwd(work.dir)
# dependencies ####
library(dplyr)
library(ggplot2)
library(tidyr)
library(doParallel)
library(VennDiagram)
library(UpSetR)
library(forcats)
library(grid)
library(heritability)
library(topGO)
library(org.At.tair.db)
# unused libraries ####
# library(Mfuzz)
# library(Biobase)
# library(gplots)
# library(RColorBrewer)
# library(limma)
# library(factoextra)
# library(stats)
# library(amap)
#
#
#
# library(gridExtra)
# library(RCy3)
# library(corrplot)
# library(reshape2)
# library(Hmisc)
# library(igraph)
# library(threejs)
# library(MASS)
#
# load required function ####
setwd('functions/')
for(i in 1:length(dir())){
source(dir()[i])
} # read function from Mark
setwd(work.dir)
overlap <- function(table, stage) {
for (i in 1:length(stage)) {
if (nrow(table) != 0) {
table <- subset(table, table[stage[i]] == T)
} else {
break
}
}
nrow(table)
} # function to determine the overlapping qtl between 2 stages
# load the data ####
seed.stage <- c('pd', 'ar', 'im', 'rp')
# presentation
presentation <- theme(axis.text.x = element_text(size=10, face="bold", color="black"),
axis.text.y = element_text(size=10, face="bold", color="black"),
axis.title.x = element_text(size=12, face="bold", color="black"),
axis.title.y = element_text(size=12, face="bold", color="black"),
strip.text.x = element_text(size=12, face="bold", color="black"),
strip.text.y = element_text(size=12, face="bold", color="black"),
strip.text = element_text(size =12, face="bold", color="black"),
#plot.title = element_text(size=15, face="bold"),
panel.background = element_rect(fill = "white",color="black"),
panel.grid.major = element_line(colour = "grey80"),
panel.grid.minor = element_blank())
#### combined-stage eQTL mapping - reduced model ####
stage <- 'rm'
qtl.data <- QTL.data.prep(trait.matrix = trait.matrix,
strain.trait = colnames(trait.matrix),
strain.map = genetic.map,
strain.marker = marker)
cluster <- makeCluster(n.cores, type = "PSOCK")
registerDoParallel(cluster)
output <- foreach(i = 1:nrow(trait.matrix), .combine = 'cbind') %dopar% {
map.all.marker(trait = trait.matrix[i, ], markers = genetic.map)
}
stopCluster(cluster)
pval.out <- t(output[, which(colnames(output) == 'LOD')])
eff.out <- t(output[, which(colnames(output) == 'Eff')])
colnames(pval.out) <- rownames(marker); rownames(pval.out) <- rownames(trait.matrix)
colnames(eff.out) <- rownames(marker); rownames(eff.out) <- rownames(trait.matrix)
qtl.profile <- NULL; qtl.profile <- as.list(qtl.profile)
qtl.profile[[1]] <- round(pval.out,digits=2)
qtl.profile[[2]] <- round(eff.out,digits=3)
qtl.profile[[3]] <- trait.matrix
qtl.profile[[4]] <- genetic.map
qtl.profile[[5]] <- marker
names(qtl.profile) <- c("LOD","Effect","Trait","Map","Marker")
write.EleQTL(map1.output = qtl.profile, filename = paste0("qtl-tables/table_single-stage-eqtl_", 'rm'))
saveRDS(object = qtl.profile,
file = paste0("qtl-profiles/profile_combined-stage-eqtl_", stage, ".rds"))
# eQTL peak finder - combined-stage rm ####
stage <- 'rm'
# peak finder
qtl.profile <- readRDS(paste0('qtl-profiles/profile_single-stage-eqtl_', stage, '.rds'))
qtl.peak <- mapping.to.list(map1.output = qtl.profile) %>%
peak.finder(threshold = 4.3)
qtl.peak <- na.omit(qtl.peak)
saveRDS(object = qtl.peak,
file = paste0("qtl-peaks/peak_combined-stage-eqtl_", stage, ".rds"))
# eQTL table- combined-stage rm ####
stage <- 'rm'
qtl.profile <- readRDS(paste0('qtl-profiles/profile_combined-stage-eqtl_', stage, '.rds'))
qtl.peak <- readRDS(paste0('qtl-peaks/peak_combined-stage-eqtl_', stage, '.rds'))
eqtl.table <- eQTL.table(peak.list.file = qtl.peak, trait.annotation = gene.info) %>%
eQTL.table.addR2(QTL.prep.file = qtl.data)
eqtl.table$qtl_chromosome <- as.factor(eqtl.table$qtl_chromosome)
eqtl.table$gene_chromosome <- as.factor(eqtl.table$gene_chromosome)
# add heritability - combined-stage rm #####
h2.result <- as.list(NULL)
n.perm <- 100
qtl.genes <- eqtl.table$trait.matrix
map <- genetic.map
map <- apply(map, 2, as.numeric) # make sure the alleles are treated as numeric
trait <- trait.matrix
map2 <- map
#map2[map2 == 0] <- 0.5
map2[map2 == -1] <- round(0, 0)
#map2[is.na(map2)] <- 0.5
kinship.matrix <- emma.kinship(map2)
colnames(kinship.matrix) <- colnames(map2); rownames(kinship.matrix) <- colnames(map2)
cluster <- makeCluster(n.cores, type = 'PSOCK')
clusterExport(cl = cluster, c('trait', 'map2', 'marker_h2', 'kinship.matrix', 'h2.REML'))
h2 <- t(parApply(cl = cluster, X = trait, MARGIN = 1, FUN = h2.REML, strain.names = colnames(map2), kinship.matrix = kinship.matrix, Vg.factor = 1))
stopCluster(cluster)
h2 <- cbind.data.frame(trait = rownames(h2), h2)
eqtl.table <- merge(x = eqtl.table, y = h2[, 1:2], by = 'trait', all.x = T)
eqtl.table <- rename(eqtl.table, h2 = h2_REML)
# trans bands identification - combined-stage rm #####
window.nu <- 2e6
maxsize <- 100e6
chr.num <- 5
transband.id <- mutate(eqtl.table, interval = findInterval(qtl_bp, seq(1, maxsize, by = window.nu))) %>%
group_by(qtl_chromosome, interval, qtl_type) %>%
summarise(n.ct = length(unique(trait))) %>%
data.frame() %>%
group_by(qtl_type) %>%
mutate(exp.ct = mean(as.numeric(unlist(n.ct)))) %>%
data.frame() %>%
mutate(transband_significance = ppois(n.ct, lambda = exp.ct, lower.tail = F)) %>%
filter(transband_significance < 0.0001, qtl_type == "trans")
transband.id$transband_id <- with(transband.id, paste0("ch", qtl_chromosome, ":",
(interval - 1) * 2, "-",
interval * 2, "Mb"))
transband.id$stage <- stage
saveRDS(object = transband.id,
file = paste0("trans-bands/trans.bands_", stage, ".rds"))
# for rm
if( stage == 'rm' ) {
eqtl.table <- mutate(eqtl.table,
trans_band = ifelse(qtl_type == "trans" &
qtl_chromosome == 5 &
qtl_bp > 6e6 &
qtl_bp <= 8e6, "ch5:6-8Mb",
ifelse(qtl_type == "trans" &
qtl_chromosome == 1 &
qtl_bp > 26e6 &
qtl_bp <= 28e6, "ch1:26-28Mb",
ifelse(qtl_type == "trans" &
qtl_chromosome == 2 &
qtl_bp > 10e6 &
qtl_bp <= 14e6, "ch2:10-14Mb",
ifelse(qtl_type == "trans" &
qtl_chromosome == 4 &
qtl_bp > 0e6 &
qtl_bp <= 2e6, "ch4:0-2Mb",
ifelse(qtl_type == "trans" &
qtl_chromosome == 5 &
qtl_bp > 14e6 &
qtl_bp <= 16e6, "ch5:14-16Mb","none"))))))
}
print("finish")
write.csv(x = eqtl.table,
file = paste0("qtl-tables/table_combined-stage-eqtl_", stage, ".csv"),
row.names = T)
saveRDS(object = eqtl.table,
file = paste0("qtl-tables/table_combined-stage-eqtl_", stage, ".rds"))
table(eqtl.table$qtl_type, eqtl.table$trans_band != "none")
# GO for trans bands - combined-stage rm ####
ontology <- 'BP'
x <- org.At.tairCHR
all.genes <- as.list(rownames(trait.matrix))
transband.go <- as.data.frame(matrix(data = NA, nrow = 0, ncol = 9))
colnames(transband.go) <- c('GO.ID', 'Term', 'Annotated', 'Significant', 'Expected', 'Fisher',
'FDR', 'stage', 'transband')
eqtl.table <- readRDS(paste0('qtl-tables/table_combined-stage-eqtl_', stage, '.rds'))
transband.id <- unique(eqtl.table$trans_band)
transband.id <- transband.id[!transband.id %in% 'none']
transband.go.perstage <- as.data.frame(matrix(data = NA, nrow = 0, ncol = 9))
colnames(transband.go.perstage) <- c('GO.ID', 'Term', 'Annotated', 'Significant', 'Expected', 'Fisher',
'FDR', 'stage', 'transband')
for (i in 1:length(transband.id)) {
gene.set <- eqtl.table[which(eqtl.table$trans_band == transband.id[i]), 'trait']
gene.set <- factor(as.integer(all.genes %in% gene.set))
names(gene.set) <- all.genes
GOdata <- new("topGOdata",
description = "GOE for genes in regulated by trans bands", ontology = ontology,
allGenes = gene.set,
annot = annFUN.org,mapping= "org.At.tair.db")
resultFisher <- runTest(GOdata, algorithm = 'weight', statistic = "fisher")
result.df <- GenTable(GOdata, Fisher = resultFisher,
orderBy = "Fisher", ranksOf = "Fisher", topNodes = length(resultFisher@score))
result.df$stage <- stage
result.df$transband <- transband.id[i]
result.df$Fisher <- as.numeric(result.df$Fisher)
result.df$FDR <- p.adjust(p = result.df$Fisher, method = 'fdr')
result.df <- result.df[order(result.df$FDR), ]
result.df <- dplyr::filter(result.df, Fisher <= 0.01)
transband.go <- rbind(transband.go, result.df)
}
write.csv(transband.go, paste0('files/trans-bands-rm-go', ontology, '.csv'))
# prepare eqtl table and plotting - combined-stage rm ####
eqtl.table <- eqtl.table %>%
mutate(qtl_type = ifelse(qtl_type == 'cis', 'local', 'distant'))
# cis-trans plot - combined-stage rm ####
eqtl.plot <- ggplot(eqtl.table, aes(x = qtl_bp, y = gene_bp)) +
geom_segment(aes(x = qtl_bp_left, y = gene_bp, xend = qtl_bp_right, yend = gene_bp),
alpha = 0.25, colour = "grey") +
geom_point(aes(colour = ordered(qtl_type, levels = c('local', 'distant'))), alpha = .75) +
facet_grid(fct_rev(gene_chromosome) ~ qtl_chromosome, space = "free", scales = "free") +
presentation +
scale_colour_manual('eQTL type', values = c('black', '#377eb8')) +
labs(x = "eQTL peak position (Mb)", y = "Gene position (Mb)") +
scale_x_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40)) +
scale_y_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40))
pdf(file = paste0("figures/cis-trans-plot-rm.pdf"), width = 10, height = 8)
eqtl.plot
dev.off()
# histogram - combined-stage rm ####
eqtl.hist <- ggplot(eqtl.table %>% filter(qtl_type != 'local'),
aes(x=qtl_bp)) + # 750 x 400
geom_histogram(binwidth = 2000000, alpha = 0.8, fill = '#377EB8') +
facet_grid(. ~ qtl_chromosome, space = "free",scales="free") +
presentation +
scale_fill_manual(values = 'blue') +
theme(legend.position = "right", legend.text=element_text(size=10)) +
labs(x="eQTL peak position (Mb)",y="eQTL counts") +
ylim(c(0, 80)) +
geom_hline(aes(yintercept = 11.72),
linetype = 'dashed',
color = 'red',
size = .1) +
scale_x_continuous(breaks=c(5, 10, 15, 20, 25, 30, 35, 40)*10^6,labels=c(5, 10, 15, 20, 25, 30, 35, 40))
pdf(file = "figures/trans-eqtl-histogram-rm.pdf", width = 10, height = 5)
eqtl.hist
dev.off()
# R2 vs heritability - combined-stage rm ####
ggplot(eqtl.table, aes(x = qtl_R2_sm,
fill = factor(qtl_type, levels = c('local', 'distant')),
color = factor(qtl_type, levels = c('local', 'distant')))) +
geom_histogram(aes(y = ..density..), binwidth = 0.01, position = 'identity', alpha = 0) +
geom_density(alpha = 0.5, size = 0) +
geom_vline(aes(xintercept = median(qtl_R2_sm),
color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
presentation +
scale_color_brewer(palette = 'Set1') +
scale_fill_brewer(palette = 'Set1') +
xlab('explained phenotypic variance (R2)') +
ylab('count of eQTL') +
labs(fill = 'eQTL type', color = 'eQTL type') +
#ylim(0, 1) +
xlim(0, 1) +
theme(strip.text.x = element_text(size = 8))
ggplot(eqtl.table, aes(x = h2, y = qtl_R2_sm,
fill = factor(qtl_type, levels = c('local', 'distant')),
color = factor(qtl_type, levels = c('local', 'distant')))) +
geom_point(size = 1, alpha = 0.5) +
geom_vline(data = group_by(eqtl.table, qtl_type) %>%
summarise(med = median(h2, na.rm = T)),
aes(xintercept = med, color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
geom_hline(data = group_by(eqtl.table, qtl_type) %>%
summarise(med = median(qtl_R2_sm, na.rm = T)),
aes(yintercept = med, color = factor(qtl_type, levels = c('local', 'distant'))),
linetype = 'dashed', size = .5) +
geom_smooth(model = lm) +
geom_abline(aes(slope = 1, intercept = 0), size = .75, linetype = 'dashed') +
presentation +
scale_color_brewer(palette = 'Set1') +
scale_fill_brewer(palette = 'Set1') +
xlab('narrow-sense heritabllity (h2)') +
ylab('explained phenotypic variance (R2)') +
labs(fill = 'eQTL type', color = 'eQTL type') +
ylim(0, 1) +
xlim(0, 1) +
theme(strip.text.x = element_text(size = 8))