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SISG2023_Association_Mapping/
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Before you begin:
library(data.table)
library(dplyr)
library(tidyr)
library(GWASTools)
library(ggplot2)
library(patchwork)The R template to do the exercises is here.
Note: if on the online server, set your working directory to your home directory using in R
setwd("home/<username>/")The data files are in the folder
/data/SISG2023M15/data/.
We will use a simulated dataset consisting of 3 binary traits with different amounts of case-control imbalance, as well as a genetic data set of null SNPs to examine the null distribution of the test statistics from GWAS.
We can use PLINK1.9 to simulate the genetic dataset. For \(N=10,000\) samples, Let’s simulate 10,000 variants where 5,000 are common with MAF chosen from a Uniform(0.05, 0.5) distribution and for the rare variants, we will use a Uniform(0.001, 0.01) distribution. Run the following command in R:
N <- 10e3
# Generate a configuration file specifying allele frequencies (a,b) for Uniform(a,b) distribution
write(paste0("5000 common 0.05 0.5 1 1"), "sim.config")
write(paste0("5000 rare 0.001 0.01 1 1"), "sim.config", append = TRUE)
# Run PLINK1.9
system(paste0("/data/SISG2023M15/exe/plink --make-bed --simulate sim.config --simulate-ncases ", N, " --simulate-ncontrols 0 --simulate-prevalence 0.1 --out cc_imb_geno"))You should now have files cc_imb_geno.{bed,bim,fam}.
For the phenotype data simulation, we will simulate 3 phenotypes with different levels of case-control imbalance (CCR 1:9, 1:99, and 1:199). Run the following code
# get FID/IID from FAM file
sample.ids <- fread("cc_imb_geno.fam", header = FALSE)
N <- nrow(sample.ids)
## Set prevalence = 10% (CCR 1:9)
y1 <- rbinom(N, 1, prob = 0.1 )
## Set prevalence = 1% (CCR 1:99)
y2 <- rbinom(N, 1, prob = 0.01 )
## Set prevalence = 0.5% (CCR 1:199)
y3 <- rbinom(N, 1, prob = 0.005 )
# write to file
data.frame(FID = sample.ids$V1, IID = sample.ids$V2, Y1 = y1, Y2 = y2, Y3 = y3) %>%
fwrite("cc_imb_pheno.txt", sep = "\t", na = NA, quote = FALSE)You should now have file cc_imb_pheno.txt.
We will assess the null distribution of our test statistics when performing association mapping using different models. Here are some things to try:
system("/data/SISG2023M15/exe/regenie --bed cc_imb_geno --phenoFile cc_imb_pheno.txt --step 2 --bsize 400 --bt --ignore-pred --out <output_prefix>")This will produce three files (one for each phenotype):
<output_prefix>_Y1.regenie,
<output_prefix>_Y2.regenie,
<output_prefix>_Y3.regenie
sumstats.y1 <- fread("<output_prefix>_Y1.regenie") %>% mutate(Pval = 10^(-LOG10P), Z = sign(BETA) * sqrt(CHISQ))
qqPlot( pval = sumstats.y1$Pval )plot.sumstats.hist <- function(df, title = ""){
df %>%
ggplot( aes(x = Z) ) +
geom_histogram(aes(y = ..density..), colour="black", fill="white", bins = 100) +
stat_function(
fun = dnorm,
col = "red",
args = list(mean = mean(df$Z), sd = sd(df$Z))
) +
labs(title = title)
}
# for Y1
plot.sumstats.hist(sumstats.y1, title = "Y1")What do you observe as the case-control imbalance gets more severe?
Y1:sumstats.y1.common <- sumstats.y1[ grepl("common", ID), ]
sumstats.y1.rare <- sumstats.y1[ grepl("rare", ID), ]patchwork library (e.g. p1|p2). For
exampleplot.sumstats.hist(sumstats.y1.common, title = "Y1 - Common SNPs") | plot.sumstats.hist(sumstats.y1.rare, title = "Y1 - Rare SNPs")Extra: 5. Re-run GWAS in Questions 1 but now applying Firth correction. Make a QQ plot of the -log10 p-values for Y3. The REGENIE command with Firth would be
system("/data/SISG2023M15/exe/regenie --bed cc_imb_geno --phenoFile cc_imb_pheno.txt --step 2 --bsize 400 --bt --ignore-pred --firth --out --out <output_prefix>")
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Brisbane
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.2 cli_3.6.1 knitr_1.43 rlang_1.1.1
[5] xfun_0.39 stringi_1.7.12 promises_1.2.0.1 jsonlite_1.8.5
[9] workflowr_1.7.0 glue_1.6.2 rprojroot_2.0.3 git2r_0.32.0
[13] htmltools_0.5.5 httpuv_1.6.11 sass_0.4.6 fansi_1.0.4
[17] rmarkdown_2.22 evaluate_0.21 jquerylib_0.1.4 tibble_3.2.1
[21] fastmap_1.1.1 yaml_2.3.7 lifecycle_1.0.3 whisker_0.4.1
[25] stringr_1.5.0 compiler_4.3.0 fs_1.6.2 Rcpp_1.0.10
[29] pkgconfig_2.0.3 rstudioapi_0.14 later_1.3.1 digest_0.6.31
[33] R6_2.5.1 utf8_1.2.3 pillar_1.9.0 magrittr_2.0.3
[37] bslib_0.5.0 tools_4.3.0 cachem_1.0.8