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Before you begin:
library(data.table)
library(dplyr)
library(qqman)
library(ggplot2)We will generate a simulated dataset consisting of 3 binary traits with different amounts of case-control imbalance, and a genetic data set of null SNPs to examine the distribution of the test statistics when scanning for associations.
We first need to define the path to the PLINK 1.9 and REGENIE binaries.
plink_binary <- "/SISGM19/bin/plink1.9"
regenie_binary <- "/SISGM19/bin/regenie" If you don’t have REGENIE installed on your machine, download the R
implementation here
and change the path of the variable regenie_script to the
path of the script on your machine.
regenie_script <- "/SISGM19/data/run_regenie.r"
source(regenie_script)We 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 to get the simulated data:
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
cmd <- sprintf("%s --make-bed --simulate sim.config --simulate-ncases %d --simulate-ncontrols 0 --simulate-prevalence 0.1 --out cc_imb_geno", plink_binary, N)
system(cmd, intern = T)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 (casse-control ratios [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
fwrite(
data.frame(FID = sample.ids$V1, IID = sample.ids$V2, Y1 = y1, Y2 = y2, Y3 = y3),
"cc_imb_pheno.txt",
sep = "\t", na = NA, quote = FALSE
)You should now have a file named cc_imb_pheno.txt.
We will now assess the null distribution of our test statistics when performing association testing using different models.
cmd <- sprintf('%s --bed cc_imb_geno --phenoFile cc_imb_pheno.txt --bt --step 2 --bsize 2000 --ignore-pred --out test_regenie', regenie_binary)
system(cmd, intern = T)This will produce three sumstats files (one for each phenotype) which you can read in R:
sumstats.y1 <- fread("test_regenie_Y1.regenie") To run the R implementation instead, run the following for each trait (this computes the association tests and stores it in the R variable directly)
sumstats.y1 <- run_regenie_step2_bt(
bedfile = "cc_imb_geno",
phenofile = "cc_imb_pheno.txt",
phenocol = "Y1",
bsize = 300
) qq(10^-sumstats.y1$LOG10P)plot.sumstats.hist <- function(df, title = ""){
df$Z_STAT <- sign(df$BETA) * sqrt(df$CHISQ)
ggplot(df, aes(x = Z_STAT) ) +
geom_histogram(aes(y = after_stat(density)), colour="black", fill="white", bins = 100) +
stat_function(
fun = dnorm,
col = "red",
args = list(
mean = mean(df$Z_STAT, na.rm = TRUE),
sd = sd(df$Z_STAT, na.rm = TRUE)
)
) +
theme_bw(16) +
labs(title = title)
}Now make histogram plot for each trait.
# for Y1
plot.sumstats.hist(sumstats.y1, title = "Y1")What do you observe as the case-control imbalance gets more severe?
plot.sumstats.hist.by.group <- function(df, title = ""){
df$Z_STAT <- sign(df$BETA) * sqrt(df$CHISQ)
df$group <- ifelse(grepl("rare", df$ID), "Rare SNPs", "Common SNPs")
# Step 2: Generate normal density data for each group
moment.ests <- with(df, tapply(Z_STAT, group, function(x) c(mean=mean(x, na.rm = TRUE), sd=sd(x, na.rm = TRUE))))
z_stat_seq <- seq(min(df$Z_STAT, na.rm = TRUE), max(df$Z_STAT, na.rm = TRUE), length.out = 100)
normal_curve_data <- do.call(rbind, lapply(unique(df$group), function(grp) {
mean <- moment.ests[[grp]]['mean']; sd <- moment.ests[[grp]]['sd']
density <- dnorm(z_stat_seq, mean = mean, sd = sd)
data.frame(Z_STAT = z_stat_seq, density = density, group = grp)
}))
ggplot(df, aes(x = Z_STAT) ) +
geom_histogram(aes(y = ..density..), colour="black", fill="white", bins = 100) +
geom_line(data = normal_curve_data, aes(x = Z_STAT, y = density), col = "red", size = 1) +
facet_wrap(~group) +
theme_bw(16) +
labs(title = title)
}# for Y1
plot.sumstats.hist.by.group(sumstats.y1, "Y1")cmd <- sprintf('%s --bed cc_imb_geno --phenoFile cc_imb_pheno.txt --bt --step 2 --bsize 2000 --firth --approx --ignore-pred --out test_regenie_wFirth', regenie_binary)
system(cmd, intern = T)This will produce three files (one for each phenotype) which you can read in R:
sumstats.y1.firth <- fread("test_regenie_wFirth_Y1.regenie") To run the R implementation instead, run the following (this computes the association tests and stores it in the R variable directly)
sumstats.y1.firth <- run_regenie_step2_bt(
bedfile = "cc_imb_geno",
phenofile = "cc_imb_pheno.txt",
phenocol = "Y1",
bsize = 300,
firth = TRUE
)
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.5
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: America/New_York
tzcode source: internal
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] logistf_1.25.0 ggplot2_3.4.2 qqman_0.1.8 dplyr_1.1.2
[5] data.table_1.14.8
loaded via a namespace (and not attached):
[1] gtable_0.3.3 shape_1.4.6 xfun_0.39
[4] bslib_0.5.0 formula.tools_1.7.1 lattice_0.21-8
[7] vctrs_0.6.2 tools_4.3.0 generics_0.1.3
[10] tibble_3.2.1 fansi_1.0.4 pan_1.8
[13] jomo_2.7-6 pkgconfig_2.0.3 Matrix_1.5-4
[16] lifecycle_1.0.3 compiler_4.3.0 stringr_1.5.0
[19] git2r_0.32.0 munsell_0.5.0 codetools_0.2-19
[22] httpuv_1.6.11 htmltools_0.5.5 sass_0.4.6
[25] yaml_2.3.7 glmnet_4.1-7 mice_3.16.0
[28] nloptr_2.0.3 later_1.3.1 pillar_1.9.0
[31] jquerylib_0.1.4 whisker_0.4.1 tidyr_1.3.0
[34] MASS_7.3-58.4 cachem_1.0.8 iterators_1.0.14
[37] rpart_4.1.19 boot_1.3-28.1 mitml_0.4-5
[40] foreach_1.5.2 nlme_3.1-162 tidyselect_1.2.0
[43] digest_0.6.31 stringi_1.7.12 purrr_1.0.1
[46] splines_4.3.0 operator.tools_1.6.3 rprojroot_2.0.3
[49] fastmap_1.1.1 grid_4.3.0 colorspace_2.1-0
[52] cli_3.6.1 magrittr_2.0.3 survival_3.5-5
[55] utf8_1.2.3 broom_1.0.5 withr_2.5.0
[58] scales_1.2.1 promises_1.2.0.1 backports_1.4.1
[61] calibrate_1.7.7 rmarkdown_2.22 nnet_7.3-18
[64] lme4_1.1-33 workflowr_1.7.0 evaluate_0.21
[67] knitr_1.43 mgcv_1.8-42 rlang_1.1.1
[70] Rcpp_1.0.10 glue_1.6.2 minqa_1.2.5
[73] rstudioapi_0.14 jsonlite_1.8.5 R6_2.5.1
[76] fs_1.6.2