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sim-individual-pl8.R
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sim-individual-pl8.R
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##### Individual-level simulations (pleiotropic IV proportion = 80%) #####
# Vary beta = 0.0, 0.1, 0.2, 0.3, 0.4, 0.5 to get
# null-pl8.RData, cau1-pl8.RData, cau2-pl8.RData,
# cau3-pl8.RData, cau4-pl8.RData, cau5-pl8.RData, respectively.
# Bottom panel of Figure 1 in Main text
rm(list = ls())
library(BWMR)
library(TwoSampleMR)
source("gsmr_md.R")
library(ggplot2)
library(MASS) # Needed when sample from multivariate normal distribution
sim_null_4GM_select_sd <- function(P, N_1, N_2, Pi_11, Pi_10, Pi_01, Pi_00, beta, rho, noise, threshold){
# genotype data
# X_1: Individual-SNP matrix for exposure
X_1 <- matrix(1, N_1, P)
X_1f <- matrix(runif(N_1*P), N_1, P)
# X_2: Individual-SNP matrix for outcome
X_2 <- matrix(1, N_2, P)
X_2f <- matrix(runif(N_2*P), N_2, P)
# X_1: Individual-SNP matrix for exposure (MR)
X_3 <- matrix(1, N_1, P)
X_3f <- matrix(runif(N_1*P), N_1, P)
# f: minor allele frequencies of P SNPs
f <- runif(P, 0.05, 0.5)
f2_1 <- matrix(rep(f^2, each = N_1), N_1, P)
f2_2 <- matrix(rep(f^2, each = N_2), N_2, P)
f0_1 <- matrix(rep((1 - f)^2, each = N_1), N_1, P)
f0_2 <- matrix(rep((1 - f)^2, each = N_2), N_2, P)
# assign 2
X_1[X_1f < f2_1] <- 2
X_2[X_2f < f2_2] <- 2
X_2[X_3f < f2_1] <- 2
# assign 0
X_1[(1 - X_1f) < f0_1] <- 0
X_2[(1 - X_2f) < f0_2] <- 0
X_3[(1 - X_3f) < f0_1] <- 0
# Sigma: covariance matrix for (gamma, Gamma), under Pi_11
s1 <- 1 / (P*(Pi_11+Pi_10))
s2 <- 1 / (P*(Pi_11+Pi_01))
cov <- rho*sqrt(s1*s2)
Sigma <- matrix(c(s1, cov, cov, s2), 2, 2)
# W: SNP-(gamma, Gamma) matrix (ground truth)
W <- matrix(0, P, 2)
W[1:(P*Pi_11), ] <- mvrnorm(P*Pi_11, rep(0, 2), Sigma)
W[(P*Pi_11+1):(P*(Pi_11+Pi_10)), 1] <- rnorm(P*Pi_10, 0, sqrt(1/(P*(Pi_11+Pi_10))))
W[(P*(Pi_11+Pi_10)+1):(P*(Pi_11+Pi_10+Pi_01)), 2] <- rnorm(P*Pi_01, 0, sqrt(1/(P*(Pi_11+Pi_01))))
W[, 2] <- beta*W[, 1] + W[, 2]
# Exposure data
y0 <- X_1%*%W[, 1]
y <- y0 + noise*sqrt(drop(var(y0)))*rnorm(N_1)
# Oucome data
z0 <- X_2%*%W[, 2]
z <- z0 + noise*sqrt(drop(var(z0)))*rnorm(N_2)
# Exposure data (MR)
y03 <- X_3%*%W[, 1]
y3 <- y03 + noise*sqrt(drop(var(y03)))*rnorm(N_1)
# b.exposure, se.exposure
b.exposure <- rep(0, P)
se.exposure <- rep(0, P)
pval.exposure <- rep(0, P)
for(j in 1:P){
fit_lm <- lm(y~., data = data.frame(y, X_1[, j]))
b.exposure[j] <- summary(fit_lm)$coefficients[2, 1]
se.exposure[j] <- summary(fit_lm)$coefficients[2, 2]
pval.exposure[j] <- summary(fit_lm)$coefficients[2, 4]
}
# b.outcome, se.outcome
b.outcome <- rep(0, P)
se.outcome <- rep(0, P)
pval.outcome <- rep(0, P)
for(j in 1:P){
fit_lm <- lm(z~., data = data.frame(z, X_2[, j]))
b.outcome[j] <- summary(fit_lm)$coefficients[2, 1]
se.outcome[j] <- summary(fit_lm)$coefficients[2, 2]
pval.outcome[j] <- summary(fit_lm)$coefficients[2, 4]
}
# b.exposure, se.exposure (MR)
b.exposure.mr <- rep(0, P)
se.exposure.mr <- rep(0, P)
pval.exposure.mr <- rep(0, P)
for(j in 1:P){
fit_lm <- lm(y3~., data = data.frame(y3, X_3[, j]))
b.exposure.mr[j] <- summary(fit_lm)$coefficients[2, 1]
se.exposure.mr[j] <- summary(fit_lm)$coefficients[2, 2]
pval.exposure.mr[j] <- summary(fit_lm)$coefficients[2, 4]
}
res_bias <- list()
res_unbias <- list()
for (j in 1:length(threshold)) {
indx <- which(pval.exposure < threshold[j])
res_bias[[j]] <- data.frame(b.exposure = b.exposure[indx], b.outcome = b.outcome[indx],
se.exposure = se.exposure[indx], se.outcome = se.outcome[indx])
res_unbias[[j]] <- data.frame(b.exposure = b.exposure.mr[indx], b.outcome = b.outcome[indx],
se.exposure = se.exposure.mr[indx], se.outcome = se.outcome[indx])
}
# res_all <- data.frame(b.exposure=b.exposure.mr, b.outcome=b.outcome,
# se.exposure=se.exposure.mr, se.outcome=se.outcome)
res <- list('unbias'=res_unbias, 'bias'=res_bias, 'index'=indx)
}
Rp <- 100
L <- 1
pval.bwmr.unbias <- matrix(nrow = L, ncol = Rp)
pval.raps.unbias <- matrix(nrow = L, ncol = Rp)
pval.egger.unbias <- matrix(nrow = L, ncol = Rp)
pval.gsmr.unbias <- matrix(nrow = L, ncol = Rp)
b.bwmr.unbias <- matrix(nrow = L, ncol = Rp)
b.raps.unbias <- matrix(nrow = L, ncol = Rp)
b.egger.unbias <- matrix(nrow = L, ncol = Rp)
b.gsmr.unbias <- matrix(nrow = L, ncol = Rp)
pval.bwmr.bias <- matrix(nrow = L, ncol = Rp)
pval.raps.bias <- matrix(nrow = L, ncol = Rp)
pval.egger.bias <- matrix(nrow = L, ncol = Rp)
pval.gsmr.bias <- matrix(nrow = L, ncol = Rp)
b.bwmr.bias <- matrix(nrow = L, ncol = Rp)
b.raps.bias <- matrix(nrow = L, ncol = Rp)
b.egger.bias <- matrix(nrow = L, ncol = Rp)
b.gsmr.bias <- matrix(nrow = L, ncol = Rp)
for (i in 1:Rp) {
message(i)
set.seed(i)
res <- sim_null_4GM_select_sd(P = 10000, N_1 = 5000, N_2 = 5000,
Pi_11 = 0.08, Pi_10 = 0.02, Pi_01 = 0.02, Pi_00 = 0.88,
beta = 0.0, rho = 0, noise = sqrt(1/1), threshold = c(1e-5))
for (k in 1:1){ # k thresholds
## unbias
dat <- res$unbias[[k]]
N <- dim(dat)[1]
message("N=", N)
# BWMR
res_bwmr <- try(BWMR(gammahat = dat$b.exposure, Gammahat = dat$b.outcome,
sigmaX = dat$se.exposure, sigmaY = dat$se.outcome))
if (inherits(res_bwmr, "try-error")) {next}
b.bwmr.unbias[k, i] <- res_bwmr$beta
pval.bwmr.unbias[k, i] <- res_bwmr$P_value
# GSMR
res_gsmr <- try(gsmr(bzx = dat$b.exposure, bzx_se = dat$se.exposure,
bzy = dat$b.outcome, bzy_se = dat$se.outcome,
ldrho = diag(N),
heidi_outlier_flag = T,
heidi_outlier_thresh=0.01,
nsnps_thresh = 1))
if (inherits(res_gsmr, "try-error")) {next}
b.gsmr.unbias[k, i] <- drop(res_gsmr$bxy)
pval.gsmr.unbias[k, i] <- drop(res_gsmr$bxy_pval)
## Two sample MR
dat$id.exposure <- rep("uVXQCX", nrow(dat))
dat$id.outcome <- rep("uVXQCX", nrow(dat))
dat$mr_keep <- rep(TRUE, nrow(dat))
dat$exposure <- rep('exposure', nrow(dat))
dat$outcome <- rep('outcome', nrow(dat))
colnames(dat) <- c("beta.exposure", "beta.outcome", "se.exposure", "se.outcome", "id.exposure", "id.outcome", "mr_keep", "exposure", "outcome")
result <- try(mr(dat, method_list=c("mr_raps", "mr_egger_regression")))
if (inherits(result, "try-error")) {next}
b.raps.unbias[k, i] <- result$b[1]
pval.raps.unbias[k, i] <- result$pval[1]
b.egger.unbias[k, i] <- result$b[2]
pval.egger.unbias[k, i] <- result$pval[2]
## bias
dat <- res$bias[[k]]
N <- dim(dat)[1]
# BWMR
res_bwmr <- try(BWMR(gammahat = dat$b.exposure, Gammahat = dat$b.outcome,
sigmaX = dat$se.exposure, sigmaY = dat$se.outcome))
if (inherits(res_bwmr, "try-error")) {next}
b.bwmr.bias[k, i] <- res_bwmr$beta
pval.bwmr.bias[k, i] <- res_bwmr$P_value
# GSMR
res_gsmr <- try(gsmr(bzx = dat$b.exposure, bzx_se = dat$se.exposure,
bzy = dat$b.outcome, bzy_se = dat$se.outcome,
ldrho = diag(N),
heidi_outlier_flag = T,
heidi_outlier_thresh=0.01,
nsnps_thresh = 1))
if (inherits(res_gsmr, "try-error")) {next}
b.gsmr.bias[k, i] <- drop(res_gsmr$bxy)
pval.gsmr.bias[k, i] <- drop(res_gsmr$bxy_pval)
## Two sample MR
dat$id.exposure <- rep("uVXQCX", nrow(dat))
dat$id.outcome <- rep("uVXQCX", nrow(dat))
dat$mr_keep <- rep(TRUE, nrow(dat))
dat$exposure <- rep('exposure', nrow(dat))
dat$outcome <- rep('outcome', nrow(dat))
colnames(dat) <- c("beta.exposure", "beta.outcome", "se.exposure", "se.outcome", "id.exposure", "id.outcome", "mr_keep", "exposure", "outcome")
result <- try(mr(dat, method_list=c("mr_raps", "mr_egger_regression")))
if (inherits(result, "try-error")) {next}
b.raps.bias[k, i] <- result$b[1]
pval.raps.bias[k, i] <- result$pval[1]
b.egger.bias[k, i] <- result$b[2]
pval.egger.bias[k, i] <- result$pval[2]
}
}
save.image("null-pl8.RData")