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2. AssociationPlots.Rmd
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---
title: "Regional association, Manhattan, and QQ plotting for age of ischemic stroke onset."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | [email protected]"
date: "`r Sys.Date()`"
output:
html_notebook:
cache: yes
code_folding: hide
collapse: yes
df_print: paged
fig.align: center
fig_caption: yes
fig_height: 6
fig_retina: 2
fig_width: 7
highlight: tango
theme: lumen
toc: yes
toc_float:
collapsed: no
smooth_scroll: yes
mainfont: Arial
subtitle: "A 'druggable-MI-targets' project"
editor_options:
chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
wwarning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE, # show R code
eval = TRUE)
```
# Setup
We will clean the environment, setup the locations, define colors, and create a datestamp.
*Clean the environment.*
```{r echo = FALSE}
rm(list = ls())
```
*Set locations and working directories...*
```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan/OneDrive - UMC Utrecht"
# STORAGE_loc = "/Volumes/LaCie/"
STORAGE_loc = "/Users/swvanderlaan/"
### MacBook Air
# ROOT_loc = "/Users/slaan3/OneDrive - UMC Utrecht"
# STORAGE_loc = "/Volumes/LaCie/"
# STORAGE_loc = "/Users/slaan3/"
GENOMIC_loc = paste0(ROOT_loc, "/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
PLINK_loc=paste0(STORAGE_loc,"/PLINK")
AEGSQC_loc = paste0(PLINK_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")
MICHIMP_loc=paste0(PLINK_loc,"/_AE_ORIGINALS/AEGS_COMBINED_EAGLE2_1000Gp3v5HRCr11")
GWAS_loc=paste0(PLINK_loc,"/_GWAS_Datasets/_SiGN")
PROJECT_loc = paste0(PLINK_loc, "/analyses/stroke/AAO_IschemicStroke.git")
# use this if there is relevant information here.
TARGET_loc = paste0(PROJECT_loc, "/targets")
### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "AAO" # Phenotype
PROJECTNAME = "AAO"
cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)),
dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)),
FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")),
dir.create(file.path(ANALYSIS_loc, "/PLOTS")),
FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")),
dir.create(file.path(PLOT_loc, "/QC")),
FALSE)
QC_loc = paste0(PLOT_loc,"/QC")
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")),
dir.create(file.path(ANALYSIS_loc, "/OUTPUT")),
FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")),
dir.create(file.path(ANALYSIS_loc, "/BASELINE")),
FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```
*... a package-installation function ...*
```{r}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```
*... and load those packages.*
```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")
install.packages.auto("pander")
install.packages.auto("data.table")
install.packages.auto("R.utils")
install.packages.auto("openxlsx")
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")
install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("BlandAltmanLeh")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")
install.packages.auto("ggpubr")
install.packages.auto("ggrepel")
install.packages.auto("UpSetR")
library(credentials)
set_github_pat()
devtools::install_github("thomasp85/patchwork")
# https://github.com/YinLiLin/CMplot
install.packages.auto("CMplot")
library("CMplot")
# if you want to use the latest version on GitHub:
# source("https://raw.githubusercontent.com/YinLiLin/CMplot/master/R/CMplot.r")
# For regional association plots
install_github("oliviasabik/RACER")
# Install ggrepel package if needed
library(ggrepel)
# install ggsci
install.packages.auto("ggsci")
# plotly
# install.packages.auto("plotly")
# for coloc
# https://github.com/chr1swallace/coloc
if(!require("remotes"))
install.packages.auto("remotes") # if necessary
library(remotes)
install_github("chr1swallace/coloc@main", build_vignettes = FALSE)
```
```{r}
ggplot2::theme_set(ggplot2::theme_minimal())
pander::panderOptions("table.split.table", Inf)
```
_We will create a datestamp and define the Utrecht Science Park Colour Scheme_.
```{r Setting: Colors}
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color HEX (RGB) CHR MAF/INFO
###---------------------------------------------------------------------------------------
### 1 yellow #FBB820 (251,184,32) => 1 or 1.0>INFO
### 2 gold #F59D10 (245,157,16) => 2
### 3 salmon #E55738 (229,87,56) => 3 or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4 darkpink #DB003F ((219,0,63) => 4
### 5 lightpink #E35493 (227,84,147) => 5 or 0.8<INFO<1.0
### 6 pink #D5267B (213,38,123) => 6
### 7 hardpink #CC0071 (204,0,113) => 7
### 8 lightpurple #A8448A (168,68,138) => 8
### 9 purple #9A3480 (154,52,128) => 9
### 10 lavendel #8D5B9A (141,91,154) => 10
### 11 bluepurple #705296 (112,82,150) => 11
### 12 purpleblue #686AA9 (104,106,169) => 12
### 13 lightpurpleblue #6173AD (97,115,173/101,120,180) => 13
### 14 seablue #4C81BF (76,129,191) => 14
### 15 skyblue #2F8BC9 (47,139,201) => 15
### 16 azurblue #1290D9 (18,144,217) => 16 or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17 lightazurblue #1396D8 (19,150,216) => 17
### 18 greenblue #15A6C1 (21,166,193) => 18
### 19 seaweedgreen #5EB17F (94,177,127) => 19
### 20 yellowgreen #86B833 (134,184,51) => 20
### 21 lightmossgreen #C5D220 (197,210,32) => 21
### 22 mossgreen #9FC228 (159,194,40) => 22 or MAF>0.20 or 0.6<INFO<0.8
### 23 lightgreen #78B113 (120,177,19) => 23/X
### 24 green #49A01D (73,160,29) => 24/Y
### 25 grey #595A5C (89,90,92) => 25/XY or MAF<0.01 or 0.0<INFO<0.2
### 26 lightgrey #A2A3A4 (162,163,164) => 26/MT
###
### ADDITIONAL COLORS
### 27 midgrey #D7D8D7
### 28 verylightgrey #ECECEC"
### 29 white #FFFFFF
### 30 black #000000
###----------------------------------------------------------------------------------------------
uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
"#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
"#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
"#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
"#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
"#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
"#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
"#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
"#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
"#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```
# Introduction
We will parse the data to create regional association plots for each of the 11 loci.
# Setting the NPG colors
```{r}
library("scales")
pal_npg("nrc")(10)
show_col(pal_npg("nrc")(10))
# show_col(pal_npg("nrc", alpha = 0.6)(10))
```
# show_col(pal_npg("nrc", alpha = 0.6)(10))
# Load data
We need to load the data first.
```{r}
gwas_sumstats_racer_ALL <- readRDS(file = paste0(OUT_loc, "/gwas_sumstats_racer.all.eur.groupcov.rds"))
gwas_sumstats_racer_XY <- readRDS(file = paste0(OUT_loc, "/gwas_sumstats_racer.xy.eur.groupcov.rds"))
gwas_sumstats_racer_XX <- readRDS(file = paste0(OUT_loc, "/gwas_sumstats_racer.xx.eur.groupcov.rds"))
# str(gwas_sumstats_racer_ALL)
# Classes ‘data.table’ and 'data.frame': 6750510 obs. of 4 variables:
# $ SNP : chr "rs58108140" "rs180734498" "rs140337953" "rs116400033" ...
# $ CHR : int 1 1 1 1 1 1 1 1 1 1 ...
# $ BP : int 10583 13302 30923 51479 54490 54676 55299 58814 59040 61987 ...
# $ P_BOLT_LMM_INF: num 0.12 0.78 0.43 0.6 0.93 0.51 0.059 0.053 0.085 0.86 ...
# - attr(*, ".internal.selfref")=<externalptr>
#
```
# Regional association plotting
## Top loci
We are interested in the top loci. Let's do some plotting.
```{r}
library(openxlsx)
variant_list <- read.xlsx(paste0(TARGET_loc, "/targets.xlsx"), sheet = "TopLoci")
DT::datatable(variant_list)
```
Set the range for plotting.
```{r}
RANGE=250000
```
### Sex-combined analyses
```{r}
library(RACER)
# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/RACER")),
dir.create(file.path(PLOT_loc, "/RACER")),
FALSE)
RACER_loc = paste0(PLOT_loc,"/RACER")
variants_of_interest <- c(variant_list$rsID)
for(VARIANT in variants_of_interest){
cat(paste0("Getting data for ", VARIANT,".\n"))
tempCHR <- subset(variant_list, rsID == VARIANT)[,2]
tempSTART <- subset(variant_list, rsID == VARIANT)[,3] - RANGE
tempEND <- subset(variant_list, rsID == VARIANT)[,3] + RANGE
tempVARIANTnr <- subset(variant_list, rsID == VARIANT)[,1]
tempLOCUS <- subset(variant_list, rsID == VARIANT)[,4]
cat("\nSubset required data.\n")
temp <- subset(gwas_sumstats_racer_ALL, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND))
cat("\nFormatting association data.\n")
temp_f = RACER::formatRACER(assoc_data = temp, chr_col = 2, pos_col = 3, p_col = 4)
cat("\nGetting LD data.\n")
temp_f_ld = RACER::ldRACER(assoc_data = temp_f, rs_col = 1, pops = "EUR", lead_snp = VARIANT)
cat(paste0("\nPlotting region surrounding ", VARIANT," on ",tempCHR,":",tempSTART,"-",tempEND,".\n"))
# source(paste0(PROJECT_loc, "/scripts/functions.R"))
p1 <- singlePlotRACER2(assoc_data = temp_f_ld,
chr = tempCHR, build = "hg19",
plotby = "coord", start_plot = tempSTART, end_plot = tempEND,
snp_plot = VARIANT,
label_lead = TRUE, gene_track_h = 2, gene_name_s = 1.75)
print(p1)
cat(paste0("Saving image for ", VARIANT,".\n"))
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.ALL.png"), plot = p1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.ALL.pdf"), plot = p1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.ALL.eps"), plot = p1)
rm(temp, p1,
temp_f, temp_f_ld,
tempCHR, tempSTART, tempEND,
VARIANT, tempVARIANTnr, tempLOCUS)
}
```
### Men-only analyses
```{r}
library(RACER)
# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/RACER")),
dir.create(file.path(PLOT_loc, "/RACER")),
FALSE)
RACER_loc = paste0(PLOT_loc,"/RACER")
variants_of_interest <- c(variant_list$rsID)
# variants_of_interest_test <- "rs6051656"
for(VARIANT in variants_of_interest){
cat(paste0("Getting data for ", VARIANT,".\n"))
tempCHR <- subset(variant_list, rsID == VARIANT)[,2]
tempSTART <- subset(variant_list, rsID == VARIANT)[,3] - RANGE
tempEND <- subset(variant_list, rsID == VARIANT)[,3] + RANGE
tempVARIANTnr <- subset(variant_list, rsID == VARIANT)[,1]
tempLOCUS <- subset(variant_list, rsID == VARIANT)[,4]
cat("\nSubset required data.\n")
temp <- subset(gwas_sumstats_racer_XY, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND))
cat("\nFormatting association data.\n")
temp_f = RACER::formatRACER(assoc_data = temp, chr_col = 2, pos_col = 3, p_col = 4)
cat("\nGetting LD data.\n")
temp_f_ld = RACER::ldRACER(assoc_data = temp_f, rs_col = 1, pops = "EUR", lead_snp = VARIANT)
cat(paste0("\nPlotting region surrounding ", VARIANT," on ",tempCHR,":",tempSTART,"-",tempEND,".\n"))
# source(paste0(PROJECT_loc, "/scripts/functions.R"))
p2 <- singlePlotRACER2(assoc_data = temp_f_ld,
chr = tempCHR, build = "hg19",
plotby = "coord", start_plot = tempSTART, end_plot = tempEND,
snp_plot = VARIANT,
label_lead = TRUE, gene_track_h = 2, gene_name_s = 1.75)
print(p2)
cat(paste0("Saving image for ", VARIANT,".\n"))
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XY.png"), plot = p2)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XY.pdf"), plot = p2)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XY.eps"), plot = p2)
rm(temp, p2,
temp_f, temp_f_ld,
tempCHR, tempSTART, tempEND,
VARIANT, tempVARIANTnr, tempLOCUS)
}
```
### Women-only analyses
```{r}
library(RACER)
# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/RACER")),
dir.create(file.path(PLOT_loc, "/RACER")),
FALSE)
RACER_loc = paste0(PLOT_loc,"/RACER")
variants_of_interest <- c(variant_list$rsID)
for(VARIANT in variants_of_interest){
cat(paste0("Getting data for ", VARIANT,".\n"))
tempCHR <- subset(variant_list, rsID == VARIANT)[,2]
tempSTART <- subset(variant_list, rsID == VARIANT)[,3] - RANGE
tempEND <- subset(variant_list, rsID == VARIANT)[,3] + RANGE
tempVARIANTnr <- subset(variant_list, rsID == VARIANT)[,1]
tempLOCUS <- subset(variant_list, rsID == VARIANT)[,4]
cat("\nSubset required data.\n")
temp <- subset(gwas_sumstats_racer_XX, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND))
cat("\nFormatting association data.\n")
temp_f = RACER::formatRACER(assoc_data = temp, chr_col = 2, pos_col = 3, p_col = 4)
cat("\nGetting LD data.\n")
temp_f_ld = RACER::ldRACER(assoc_data = temp_f, rs_col = 1, pops = "EUR", lead_snp = VARIANT)
cat(paste0("\nPlotting region surrounding ", VARIANT," on ",tempCHR,":",tempSTART,"-",tempEND,".\n"))
# source(paste0(PROJECT_loc, "/scripts/functions.R"))
p3 <- singlePlotRACER2(assoc_data = temp_f_ld,
chr = tempCHR, build = "hg19",
plotby = "coord", start_plot = tempSTART, end_plot = tempEND,
snp_plot = VARIANT,
label_lead = TRUE, gene_track_h = 2, gene_name_s = 1.75)
print(p3)
cat(paste0("Saving image for ", VARIANT,".\n"))
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XX.png"), plot = p3)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XX.pdf"), plot = p3)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".regional_assoc.XX.eps"), plot = p3)
rm(temp, p3,
temp_f, temp_f_ld,
tempCHR, tempSTART, tempEND,
VARIANT, tempVARIANTnr, tempLOCUS)
}
```
### Comparing the sexes
We can compare, visually, the men-only and women-only results.
```{r}
library(RACER)
# Make directory for plots
ifelse(!dir.exists(file.path(PLOT_loc, "/RACER")),
dir.create(file.path(PLOT_loc, "/RACER")),
FALSE)
RACER_loc = paste0(PLOT_loc,"/RACER")
RANGE=100000
variants_of_interest <- c(variant_list$rsID)
for(VARIANT in variants_of_interest){
cat(paste0("Getting data for ", VARIANT,".\n"))
tempCHR <- subset(variant_list, rsID == VARIANT)[,2]
tempSTART <- subset(variant_list, rsID == VARIANT)[,3] - RANGE
tempEND <- subset(variant_list, rsID == VARIANT)[,3] + RANGE
tempVARIANTnr <- subset(variant_list, rsID == VARIANT)[,1]
tempLOCUS <- subset(variant_list, rsID == VARIANT)[,4]
cat("\nSubset required data.\n")
temp_XX <- subset(gwas_sumstats_racer_XX, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND))
temp_XY <- subset(gwas_sumstats_racer_XY, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND))
cat("\nFormatting association data.\n")
temp_f_XX = RACER::formatRACER(assoc_data = temp_XX, chr_col = 2, pos_col = 3, p_col = 4)
temp_f_XY = RACER::formatRACER(assoc_data = temp_XY, chr_col = 2, pos_col = 3, p_col = 4)
cat("\nGetting LD data.\n")
temp_f_ld_XX = RACER::ldRACER(assoc_data = temp_f_XX, rs_col = 1, pops = "EUR", lead_snp = VARIANT)
temp_f_ld_XY = RACER::ldRACER(assoc_data = temp_f_XY, rs_col = 1, pops = "EUR", lead_snp = VARIANT)
cat(paste0("\nPlotting region surrounding ", VARIANT," on ",tempCHR,":",tempSTART,"-",tempEND,".\n"))
# source(paste0(PROJECT_loc, "/scripts/functions.R"))
p_mirror1 <- mirrorPlotRACER2(assoc_data1 = temp_f_ld_XX,
assoc_data2 = temp_f_ld_XY,
name1 = "Women-only",
name2 = "Men-only",
chr = tempCHR, plotby = "coord", start_plot = tempSTART, end_plot = tempEND,
snp_plot = VARIANT,
label_lead = TRUE, gene_track_h = 5)
p_scatter1 <- scatterPlotRACER2(assoc_data1 = temp_f_ld_XX,
assoc_data2 = temp_f_ld_XY,
chr = tempCHR,
name1 = "Women-only",
name2 = "Men-only",
region_start = tempSTART,
region_end = tempEND, ld_df = 1, label = TRUE)
print(p_mirror1)
print(p_scatter1)
cat(paste0("Saving images for ", VARIANT,".\n"))
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".mirror.regional_assoc.XXvsXY.png"), plot = p_mirror1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".mirror.regional_assoc.XXvsXY.pdf"), plot = p_mirror1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".mirror.regional_assoc.XXvsXY.eps"), plot = p_mirror1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".scatter.regional_assoc.XXvsXY.png"), plot = p_scatter1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".scatter.regional_assoc.XXvsXY.pdf"), plot = p_scatter1)
ggsave(filename = paste0(RACER_loc, "/", Today, ".", tempVARIANTnr, ".", tempLOCUS,".scatter.regional_assoc.XXvsXY.eps"), plot = p_scatter1)
rm(temp_XX, temp_XY,
temp_f_XX, temp_f_XY,
temp_f_ld_XX,temp_f_ld_XY)
rm(tempCHR, tempSTART, tempEND,
p_mirror1, p_scatter1,
VARIANT, tempVARIANTnr, tempLOCUS)
}
```
### Colocalisation of sex-stratified signals
We can also formally test the overlap between men and women.
```{r}
library(coloc)
gwas_sumstats_XY <- readRDS(file = paste0(OUT_loc, "/gwas_sumstats.xy.eur.groupcov.rds"))
gwas_sumstats_XX <- readRDS(file = paste0(OUT_loc, "/gwas_sumstats.xx.eur.groupcov.rds"))
```
```{r}
RANGE=100000
variants_of_interest <- c(variant_list$rsID)
for(VARIANT in variants_of_interest){
cat(paste0("Getting data for ", VARIANT,".\n"))
tempCHR <- subset(variant_list, rsID == VARIANT)[,2]
tempSTART <- subset(variant_list, rsID == VARIANT)[,3] - RANGE
tempEND <- subset(variant_list, rsID == VARIANT)[,3] + RANGE
tempVARIANTnr <- subset(variant_list, rsID == VARIANT)[,1]
tempLOCUS <- subset(variant_list, rsID == VARIANT)[,4]
cat("\nSubset required data.\n")
temp_XX <- subset(gwas_sumstats_XX, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND) & SNP != "-9")
temp_XY <- subset(gwas_sumstats_XY, CHR == tempCHR & (BP >= tempSTART & BP <= tempEND) & SNP != "-9")
# Ref: https://chr1swallace.github.io/coloc/articles/a02_data.html
# var(beta) is the standard error of beta, squared
cat("\nCalculate variance from beta.\n")
temp_XX$varBETA <- temp_XX$SE^2
temp_XY$varBETA <- temp_XY$SE^2
cat("\nCreate minimum dataset for women-only.\n")
minimum_ccdata_XX = list(
c(temp_XX$BETA),
c(temp_XX$varBETA),
c(temp_XX$SNP),
c(temp_XX$BP)
)
names(minimum_ccdata_XX) <- c("beta", "varbeta", "snp", "position")
minimum_ccdata_XX$type = "cc"
attr(minimum_ccdata_XX$beta, "names") <- temp_XX$SNP
attr(minimum_ccdata_XX$varbeta, "names") <- temp_XX$SNP
# str(minimum_ccdata_XX)
cat("\nInspect the data for women-only.\n")
coloc::plot_dataset(minimum_ccdata_XX)
coloc::check_dataset(minimum_ccdata_XX)
cat("\nCreate minimum dataset for men-only.\n")
minimum_ccdata_XY = list(
c(temp_XY$BETA),
c(temp_XY$varBETA),
c(temp_XY$SNP),
c(temp_XY$BP)
)
names(minimum_ccdata_XY) <- c("beta", "varbeta", "snp", "position")
minimum_ccdata_XY$type = "cc"
attr(minimum_ccdata_XY$beta, "names") <- temp_XY$SNP
attr(minimum_ccdata_XY$varbeta, "names") <- temp_XY$SNP
# str(minimum_ccdata_XY)
cat("\nInspect the data for women-only.\n")
coloc::plot_dataset(minimum_ccdata_XY)
coloc::check_dataset(minimum_ccdata_XY)
cat(paste0("\nFinemap: formally test the colocalisation of the signal between women- and men-only analyses for ",tempVARIANTnr," at ",tempLOCUS,".\n"))
# Ref https://chr1swallace.github.io/coloc/articles/a03_enumeration.html
res_XX <- finemap.abf(dataset = minimum_ccdata_XX)
res_XY <- finemap.abf(dataset = minimum_ccdata_XY)
tail(res_XX,3)
tail(res_XY,3)
res.XXvsXY <- coloc.abf(dataset1 = minimum_ccdata_XX,
dataset2 = minimum_ccdata_XY)
cat("\nPosterior probabilities.\n")
print(res.XXvsXY)
library(tidyverse)
res.XXvsXY %>% write_lines(paste0(OUT_loc, "/",tempVARIANTnr,".",tempLOCUS,".coloc.res.XXvsXY.txt"))
cat("\nTop results, if any.\n")
subset(res.XXvsXY$results, SNP.PP.H4>0.01)
o <- order(res.XXvsXY$results$SNP.PP.H4, decreasing = TRUE)
cs <- cumsum(res.XXvsXY$results$SNP.PP.H4[o])
w <- which(cs > 0.95)[1]
res.XXvsXY$results[o,][1:w,]$snp
rm(o, cs, w,
res.XXvsXY,
minimum_ccdata_XX,
minimum_ccdata_XY)
rm(tempCHR, tempSTART, tempEND,
VARIANT, tempVARIANTnr, tempLOCUS)
rm(temp_XX, temp_XY)
}
```
# Manhattan and QQ plotting
```{r}
library(CMplot)
```
We rename the columns for p-values.
```{r}
colnames(gwas_sumstats_racer_ALL)[which(names(gwas_sumstats_racer_ALL) == "P_BOLT_LMM_INF")] <- "Sex-combined"
colnames(gwas_sumstats_racer_XY)[which(names(gwas_sumstats_racer_XY) == "P_BOLT_LMM_INF")] <- "Men-only"
colnames(gwas_sumstats_racer_XX)[which(names(gwas_sumstats_racer_XX) == "P_BOLT_LMM_INF")] <- "Women-only"
```
We merge the data for plotting.
```{r}
temp <- gwas_sumstats_racer_ALL %>% filter(SNP != "-9")
temp2 <- merge(temp,
subset(gwas_sumstats_racer_XY, SNP != "-9" || `Sex-combined` > 0, select = c("SNP", "Men-only")),
by.x = "SNP", by.y = "SNP",
all.x = TRUE,
sort = FALSE)
gwas_sumstats_racer_combo <- merge(temp2,
subset(gwas_sumstats_racer_XX, SNP != "-9" || `Sex-combined` > 0, select = c("SNP", "Women-only")),
by.x = "SNP", by.y = "SNP",
all.x = TRUE,
sort = FALSE)
rm(temp, temp2)
# str(gwas_sumstats_racer_combo)
# Classes ‘data.table’ and 'data.frame': 6620618 obs. of 6 variables:
# $ SNP : chr "rs58108140" "rs180734498" "rs140337953" "rs116400033" ...
# $ CHR : int 1 1 1 1 1 1 1 1 1 1 ...
# $ BP : int 10583 13302 30923 51479 54490 54676 55299 58814 59040 61987 ...
# $ Sex-combined: num 0.12 0.78 0.43 0.6 0.93 0.51 0.059 0.053 0.085 0.86 ...
# $ Men-only : num 0.34 0.68 1 0.59 0.45 0.32 0.11 0.049 0.058 0.73 ...
# $ Women-only : num 0.21 0.41 0.22 0.77 0.61 1 0.32 0.43 0.67 0.49 ...
# - attr(*, ".internal.selfref")=<externalptr>
```
This results in a dataset with 6,620,618 variants with an rsID, instead of 6,750,510.
We also create a set with fewer SNPs, excluding those with p > 0.05, as this will speed up the (Manhattan) plotting.
```{r}
gwas_sumstats_racer_combo_sub <- gwas_sumstats_racer_combo %>% filter(`Sex-combined` < 0.05)
```
List the SNPs of interest.
- _TRIB3_ locus, clumped using PLINK
- _APOE_ locus, all variants in LD (r<sup>2</sup> >= 0.1) with rs429358 using SNiPA (b37)
```{r}
trib3_clump <- read.xlsx(paste0(TARGET_loc, "/targets.xlsx"), sheet = "Clump_TRIB3")
apoe_clump <- read.xlsx(paste0(TARGET_loc, "/targets.xlsx"), sheet = "APOE")
```
```{r}
SNPs <- list(
c(apoe_clump$SNP, trib3_clump$SNP),
c(apoe_clump$SNP, trib3_clump$SNP),
c(apoe_clump$SNP, trib3_clump$SNP)
)
```
```{r}
setwd(PLOT_loc)
CMplot(gwas_sumstats_racer_combo_sub,
plot.type = "m",
multracks = TRUE,
col = c(uithof_color[25], uithof_color[16]),
ylim = c(0, 9), # make sure all the y-axes are of similar length/size
chr.den.col = NULL, # no density plot
# bin.size = 1e6,
threshold = 5e-8, # GWAS threshold
threshold.lty = 2, # type of threshold line
threshold.lwd = 1.5, # thickness of threshold line
threshold.col = uithof_color[27], # color of threshold line
amplify = TRUE,
signal.col = uithof_color[3], # do not change the color of signal
signal.cex = 1.15, # make the signal slightly bigger
file = "jpg", memo = "",
# main.cex = ,
dpi = 300, file.output = TRUE,
verbose = TRUE,
highlight = SNPs,
# highlight.text = SNPs,
# highlight.text.cex = 1.4,
highlight.col = uithof_color[2] # do not change the color of signal
)
setwd(PROJECT_loc)
```
```{r}
setwd(PLOT_loc)
CMplot(gwas_sumstats_racer_combo,
plot.type = "q",
multracks = TRUE,
col = uithof_color[16],
ylim = c(0, 9),
threshold = 5e-8, # GWAS threshold
ylab.pos = 2,
signal.pch = 17, # signal sign
signal.cex = 1.15, # size of signal
signal.col = uithof_color[3],
conf.int = TRUE,
box = FALSE,
cex.axis = 2,
file = "jpg", memo = "",
# main.cex = ,
dpi = 300, file.output = TRUE,
verbose = TRUE
)
setwd(PROJECT_loc)
```
## Lambda's
```{r}
cat("\nCalculating Z-values.\n")
z_ALL <- qnorm(gwas_sumstats_racer_ALL$`Sex-combined`/2)
z_XY <- qnorm(gwas_sumstats_racer_XY$`Men-only`/2)
z_XX <- qnorm(gwas_sumstats_racer_XX$`Women-only`/2)
#--------------------------------------------------------------------------
### CALCULATES LAMBDA AND # variants
cat("\n\nCalculating lambda from data.\n")
lambda_ALL = round(median(z_ALL^2)/qchisq(0.5, df = 1),3)
lambda_XY = round(median(z_XY^2)/qchisq(0.5, df = 1),3)
lambda_XX = round(median(z_XX^2)/qchisq(0.5, df = 1),3)
cat(paste0("\n* The lambda for sex-combined analyses: ", round(lambda_ALL, digits = 2),"."))
cat(paste0("\n* The lambda for men-only analyses: ", round(lambda_XY, digits = 2),"."))
cat(paste0("\n* The lambda for women-only analyses: ", round(lambda_XX, digits = 2),"."))
```
# Session information
------
Version: v1.1.0
Last update: 2022-04-13
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to create plot regional association plots.
Minimum requirements: R version 3.4.3 (2017-06-30) -- 'Single Candle', Mac OS X El Capitan
Changes log
* v1.1.0 Added visual comparison plots. Added formal colocalization test.
* v1.0.0 Initial version.
------
```{r eval = TRUE}
sessionInfo()
```
# Saving environment
```{r Saving}
rm(gwas_sumstats_racer_ALL, gwas_sumstats_racer_XX, gwas_sumstats_racer_XY,
gwas_sumstats_XX, gwas_sumstats_XY)
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".AssociationPlots.RData"))
```
------
<sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------