DO_morphine_gemma
Last updated: 2020-11-19
Checks: 6 1
Knit directory: DO_Opioid/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 622975f | xhyuo | 2020-11-19 | gemma |
###library
library("ggplot2")
library("gridExtra")
library("parallel")
library("qtl2")
library(qtl)
#
# Attaching package: 'qtl'
# The following object is masked from 'package:qtl2':
#
# clean
library("tidyverse")
# ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
# ✔ tibble 3.0.1 ✔ dplyr 1.0.0
# ✔ tidyr 1.1.0 ✔ stringr 1.4.0
# ✔ readr 1.4.0 ✔ forcats 0.5.0
# ✔ purrr 0.3.4
# ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
# ✖ dplyr::combine() masks gridExtra::combine()
# ✖ dplyr::filter() masks stats::filter()
# ✖ dplyr::lag() masks stats::lag()
# ✖ readr::read_csv() masks qtl2::read_csv()
library("parallel")
library("survival")
#library("regress")
library("abind")
library("xlsx")
library(furrr)
# Loading required package: future
#
# Attaching package: 'future'
# The following object is masked from 'package:survival':
#
# cluster
library(cowplot)
theme_set(theme_cowplot())
source("code/gemma_plot.R")
source("code/cfw/R/gemma.R")
options(stringsAsFactors = FALSE)
#5 workers
# Set a "plan" for how the code should run.
#plan(multisession, workers = 5)
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}
Pvaltolod <- function(p){
if(p == 0) p = 1e-10
y = ifelse(p >= 0.5, 0, qchisq(1-2*p, df=1)/(2*log(10)))
y
}
pheno.name <- c("Survival.Time", "Recovery.Time", "Min.depression", "Status_bin")
#GM_SNP
load(url("ftp://ftp.jax.org/MUGA/GM_snps.Rdata"))#GM_snps
###combined
#collecting the permutation results
# Load the results of the GEMMA association analysis, and get the minimum p-value.
readperm <- function(p=p){
print(p)
perms.gemma <- matrix(NA,1000,1)
colnames(perms.gemma) <- p
for(i in 1:1000){
gwscan <- read.gemma.assoc(paste0("data/combined/combined_batch_gwas_", p, "_", i, ".assoc.txt"))
gwscan <- gwscan[gwscan$chr != 0 & gwscan$chr != 23,]
perms.gemma[i] <- max(gwscan$log10p)
}
class(perms.gemma) <- c("scanoneperm","matrix")
return(perms.gemma)
}
p = list("Survival.Time", "Recovery.Time", "Min.depression", "Status_bin")
args1 <- list(p)
#perms <- args1 %>% future_pmap(readperm)
#names(perms) <- pheno.name
#save(perms, file = "data/combined/perms.RData")
load("data/combined/perms.RData")
#cross2 object
gm <- get(load("output/combined_gm.RData"))
#The LMM is expected to reduce inflation of small *p*-values; a high
#level of inflation could indicate many false positive associations.
#The q-q plot is commonly used to assess inflation. This test is
#useful as a simple, heuristic diagnostic.
#import the results of the first GEMMA association analysis:
for(i in pheno.name){
print(i)
#load-gemma-pvalues}
gwscan <- read.table(paste0("data/combined/combined_batch_gwas_",i,".assoc.txt"),
header = TRUE, stringsAsFactors = F)
gwscan <- gwscan[gwscan$chr != 0 & gwscan$chr != 23,]
#add lod
gwscan$lod <- gwscan$p_lrt %>% map_dbl(Pvaltolod)
#left join with GM_snps to update pos
gwscan <- left_join(gwscan, GM_snps[,c(1:6,12)], by = c("rs" = "marker"))
#Plot the observed *p*-values against the expected *p*-values under the
#null distribution
p1 <- plot.inflation(gwscan$p_lrt, title = i)
print(p1)
#Plot genome-wide scan
# Add a column with the marker index.
n <- nrow(gwscan)
gwscan <- cbind(gwscan,marker = 1:n)
# Convert the p-values to the -log10 scale.
gwscan <- transform(gwscan,logpp_lrt = -log10(p_lrt))
# Add column "odd.chr" to the table, and find the positions of the
# chromosomes along the x-axis.
gwscan <- transform(gwscan,odd.chr = (chr.x %% 2) == 1)
x.chr <- tapply(gwscan$marker,gwscan$chr.x,mean)
# Create the genome-wide scan ("Manhattan plot").
p2 <- ggplot(gwscan,aes(x = marker,y = logpp_lrt,color = odd.chr)) +
geom_point(size = 1,shape = 20) +
geom_hline(yintercept=summary(perms[[i]], c(0.1))[,1], color = "red") +
geom_hline(yintercept=summary(perms[[i]], c(0.05))[,1], linetype="dashed",color = "red") +
scale_x_continuous(breaks = x.chr,labels = 1:19) +
scale_color_manual(values = c("skyblue","darkblue"),guide = "none") +
labs(title= i, x = "",y = "-log10 p-value") +
theme_cowplot(font_size = 10)
print(summary(perms[[i]], c(0.05))[,1])
print(p2)
#qtl2 format
qtl.out <- matrix(gwscan$lod, ncol = 1)
rownames(qtl.out) <- gwscan$rs
names(qtl.out) <- "lod"
sub.gm <- pull_markers(gm, gwscan$rs)
#for get peak for each chr
peak <- gwscan %>%
group_by(chr.x) %>%
slice_min(p_lrt, n = 1) %>%
ungroup() %>%
arrange(p_lrt) %>%
slice(1) # top 1 peak
for(chr in peak$chr.x){
print(chr)
#Identify genomic region with lowest P-value
gwscan.lowest <- peak %>% filter(chr.x == chr)
print.data.frame(gwscan.lowest)
#1.5 drop region
drop.int <- lod_int(qtl.out, sub.gm$pmap, threshold = c(gwscan.lowest$lod-0.0001), drop = 1.5, chr = chr)
print(drop.int)
dat.region <- gwscan %>% filter(chr.x == chr, between(pos, drop.int[,1], drop.int[,3]))
#ld matrix
system(paste0("/projects/csna/csna_workflow/data/GCTA/plink -bfile ",
"/projects/heh/DO_Opioid/data/combined/combined_batch ",
" --r2 --ld-window-kb 10000000 --ld-window 99999 --ld-window-r2 0 --ld-snp ",
gwscan.lowest$rs,
" --out /projects/heh/DO_Opioid/data/combined/topsnp"))
Sys.sleep(5)
#ld
ld <- read.table("data/combined/topsnp.ld", header = T, sep = "")
ld <- ld[ld$SNP_B %in% dat.region$rs,]
#left join with dat.region
dat.region <- left_join(dat.region, ld, by = c("rs" = "SNP_B")) %>%
mutate(color = case_when(
R2 >= 0 & R2 <= 0.2 ~ "darkblue",
R2 > 0.2 & R2 <= 0.4 ~ "deepskyblue",
R2 > 0.4 & R2 <= 0.6 ~ "green",
R2 > 0.6 & R2 <= 0.8 ~ "orange",
R2 > 0.8 & R2 <= 1 ~ "red"
)) %>%
mutate(shape = case_when(
lod == max(lod) ~ 17,
TRUE ~ 20
))
#annotation
query_genes <- create_gene_query_func("data/mouse_genes_mgi.sqlite")
genes <- query_genes(chr,
min(dat.region$pos),
max(dat.region$pos))
#plot parameters
top_panel_prop = 0.5
old_mfrow <- par("mfrow")
old_mar <- par("mar")
on.exit(par(mfrow=old_mfrow, mar=old_mar))
layout(rbind(1,2), heights=c(top_panel_prop, 1-top_panel_prop))
top_mar <- bottom_mar <- old_mar
top_mar[1] <- 0.1
bottom_mar[3] <- 0.1
#top
par(mfrow = c(2,1))
par(mar=top_mar)
plot(dat.region$pos, dat.region$lod, frame.plot=TRUE, xaxt='n', col = dat.region[,"color"], pch = dat.region[,"shape"], xlab = "", ylab = "LOD score", ylim = c(0,7))
legend("topright", as.character(c(0.2, 0.4, 0.6, 0.8, 1)), col = c("darkblue","deepskyblue","green","orange","red"), pch = 20, title = "R2", ncol = 5, cex = 0.75)
#bottom
par(mar=bottom_mar)
plot_genes(genes, cex=1.5)
pdf(file = paste0("output/zoompeak_", i, "_", chr,".pdf"), width = 8, height = 12)
#top
par(mfrow = c(2,1))
par(mar=top_mar)
plot(dat.region$pos, dat.region$lod, frame.plot=TRUE, xaxt='n', col = dat.region[,"color"], pch = dat.region[,"shape"], xlab = "", ylab = "LOD score", ylim = c(0,7))
legend("topright", as.character(c(0.2, 0.4, 0.6, 0.8, 1)), col = c("darkblue","deepskyblue","green","orange","red"), pch = 20, title = "R2", ncol = 5, cex = 0.75)
#bottom
par(mar=bottom_mar)
plot_genes(genes, cex=1.5)
dev.off()
}
}
# [1] "Survival.Time"
# [1] 7.276283
# [1] 1
# chr.x rs ps n_miss allele1 allele0 af logl_H1 l_mle
# 1 1 UNC503061 40069810 0 A G 0.041 -346.7855 0.3785897
# p_lrt lod chr.y pos cM A1 A2 rsID marker
# 1 2.487026e-08 6.455108 1 40.01297 15.55671 G A rs47122477 1308
# logpp_lrt odd.chr
# 1 7.60432 TRUE
# ci_lo pos ci_hi
# 1 40.00467 40.04955 40.09829
# [1] "Recovery.Time"
# [1] 6.279772
# [1] 16
# chr.x rs ps n_miss allele1 allele0 af logl_H1 l_mle
# 1 16 JAX00416833 29421922 0 A G 0.364 -981.0558 0.7356818
# p_lrt lod chr.y pos cM A1 A2 rsID marker
# 1 2.298313e-05 3.606296 16 29.42184 14.56337 A G rs31420717 91333
# logpp_lrt odd.chr
# 1 4.638591 FALSE
# ci_lo pos ci_hi
# 1 29.37 29.42184 30.25786
# [1] "Min.depression"
# [1] 5.851045
# [1] 9
# chr.x rs ps n_miss allele1 allele0 af logl_H1 l_mle
# 1 9 UNCHS025742 58286671 1 A C 0.226 539.765 0.5719939
# p_lrt lod chr.y pos cM A1 A2 rsID marker
# 1 1.670997e-05 3.737685 9 58.43886 28.1673 A C rs30310680 56007
# logpp_lrt odd.chr
# 1 4.777024 TRUE
# ci_lo pos ci_hi
# 1 55.97323 58.43886 98.00211
# [1] "Status_bin"
# [1] 5.921437
# [1] 11
# chr.x rs ps n_miss allele1 allele0 af logl_H1 l_mle
# 1 11 UNC19431523 37321071 2 A G 0.35 -403.3376 0.4443877
# p_lrt lod chr.y pos cM A1 A2 rsID marker
# 1 2.645205e-06 4.501281 11 37.50757 18.81608 A G rs26883853 66076
# logpp_lrt odd.chr
# 1 5.577541 TRUE
# ci_lo pos ci_hi
# 1 37.50252 37.50757 37.8321
###second
for(i in pheno.name){
#load-gemma-pvalues}
gwscan1 <- read.table(paste0("data/second/second_batch_gwas_",i,".assoc.txt"),
header = TRUE, stringsAsFactors = F)
gwscan1 <- gwscan1[gwscan1$chr != 0 & gwscan1$chr != 23,]
#Plot the observed *p*-values against the expected *p*-values under the
#null distribution
p1 <- plot.inflation(gwscan1$p_lrt, title = i)
print(p1)
#Plot genome-wide scan
p2 <- plot.gwscan(gwscan1, title = i)
print(p2)
}
###first
for(i in pheno.name[-3]){
#load-gemma-pvalues}
gwscan1 <- read.table(paste0("data/first/first_batch_gwas_",i,".assoc.txt"),
header = TRUE, stringsAsFactors = F)
gwscan1 <- gwscan1[gwscan1$chr != 0 & gwscan1$chr != 23,]
#Plot the observed *p*-values against the expected *p*-values under the
#null distribution
p1 <- plot.inflation(gwscan1$p_lrt, title = i)
print(p1)
#Plot genome-wide scan
p2 <- plot.gwscan(gwscan1, title = i)
print(p2)
}
sessionInfo()
# R version 4.0.0 (2020-04-24)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Ubuntu 18.04.4 LTS
#
# Matrix products: default
# BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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=C
# [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
# [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
# [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
#
# attached base packages:
# [1] parallel stats graphics grDevices utils datasets methods
# [8] base
#
# other attached packages:
# [1] cowplot_1.1.0 furrr_0.2.1 future_1.20.1 xlsx_0.6.4.2
# [5] abind_1.4-5 survival_3.1-12 forcats_0.5.0 stringr_1.4.0
# [9] dplyr_1.0.0 purrr_0.3.4 readr_1.4.0 tidyr_1.1.0
# [13] tibble_3.0.1 tidyverse_1.3.0 qtl_1.46-2 qtl2_0.22-8
# [17] gridExtra_2.3 ggplot2_3.3.2 workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] fs_1.4.1 lubridate_1.7.9 bit64_0.9-7 httr_1.4.1
# [5] rprojroot_1.3-2 tools_4.0.0 backports_1.1.6 R6_2.4.1
# [9] DBI_1.1.0 colorspace_1.4-1 withr_2.2.0 tidyselect_1.1.0
# [13] bit_1.1-15.2 compiler_4.0.0 git2r_0.27.1 cli_2.0.2
# [17] rvest_0.3.6 xml2_1.3.2 labeling_0.4.2 scales_1.1.1
# [21] digest_0.6.25 rmarkdown_2.5 pkgconfig_2.0.3 htmltools_0.4.0
# [25] parallelly_1.21.0 dbplyr_2.0.0 highr_0.8 rlang_0.4.6
# [29] readxl_1.3.1 rstudioapi_0.11 RSQLite_2.2.0 generics_0.0.2
# [33] farver_2.0.3 jsonlite_1.6.1 magrittr_1.5 Matrix_1.2-18
# [37] Rcpp_1.0.4.6 munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
# [41] stringi_1.4.6 whisker_0.4 yaml_2.2.1 grid_4.0.0
# [45] blob_1.2.1 listenv_0.8.0 promises_1.1.0 crayon_1.3.4
# [49] lattice_0.20-41 haven_2.3.1 splines_4.0.0 xlsxjars_0.6.1
# [53] hms_0.5.3 knitr_1.28 pillar_1.4.4 codetools_0.2-16
# [57] reprex_0.3.0 glue_1.4.0 evaluate_0.14 data.table_1.12.8
# [61] modelr_0.1.8 vctrs_0.3.1 httpuv_1.5.4 cellranger_1.1.0
# [65] gtable_0.3.0 assertthat_0.2.1 xfun_0.13 broom_0.7.2
# [69] later_1.0.0 rJava_0.9-13 memoise_1.1.0 globals_0.13.1
# [73] ellipsis_0.3.0This R Markdown site was created with workflowr