Integrating eQTL and GWAS: TWAS
Siming Zhao
2/3/2024
Last updated: 2024-02-13
Checks: 6 1
Knit directory: QBS-statsgen/
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Before the class
Install R packages.
Read in genotype data
We use the genotype data provided by the SuSiE package. In this demo we assume these SNPs are close to a gene, i.e. would be included in a cis-EQTL mapping analysis.
library(susieR)
data(N2finemapping)
attach(N2finemapping)
G <- scale(X)
N <- nrow(G)
M <- ncol(G)
set.seed(123)Simulate phenotype and expression
library(MASS)
library(glmnet)Loading required package: MatrixLoaded glmnet 4.1-2# Simulation function
# G: genotype matrix. h2.eQTL: h2 of eQTL. n.eQTL: number of eQTL SNPs. gamma: effect size of gene. theta: effect size of SNP.
simulate <- function(G, h2.eQTL, n.eQTL, gamma, theta) {
N <- dim(G)[1]
M <- dim(G)[2]
idx.eQTL <- sample(1:M, n.eQTL)
alpha <- rep(sqrt(h2.eQTL/n.eQTL), n.eQTL)
# simulate gene expression data
X <- G[, idx.eQTL] %*% alpha + rnorm(N, sd=1)
# simulate phenotype data
idx.SNP <- idx.eQTL[1] # choose a SNP that is one eQTL
Y <- X * gamma + as.matrix(G[, idx.SNP]) %*% theta + rnorm(N)
# return results
return (list(X=X, Y=Y, idx.eQTL=idx.eQTL, idx.SNP))
}Run a gene-level association analysis
- Simulate X and Y
h2.eQTL <- 0.3
n.eQTL <- 2
gamma <- 0.18
theta <- 0
data1 <- simulate(G, h2.eQTL, n.eQTL, gamma, theta)- Predict gene expression
cvfit <- cv.glmnet(G, data1$X)
X.tilde <- predict(cvfit, G, s = "lambda.min")
plot(data1$X, X.tilde )
- Run association analysis between predicted expression and phenotype
fit <- lm(data1$Y ~ X.tilde)
pvalue <- summary(fit)$coefficients["X.tilde",4]
print(pvalue)[1] 0.001428478TWAS is confounded by variant pleiotropic effect.
- Simulate X and Y
h2.eQTL <- 0.3
n.eQTL <- 2
gamma <- 0
theta <- 0.18
data2 <- simulate(G, h2.eQTL, n.eQTL, gamma, theta)- Predict gene expression
cvfit <- cv.glmnet(G, data2$X)
X.tilde <- predict(cvfit, G, s = "lambda.min")
plot(data2$X, X.tilde) 
- Run association analysis between predicted expression and phenotype
fit <- lm(data2$Y ~ X.tilde)
pvalue <- summary(fit)$coefficients["X.tilde",4]
print(pvalue)[1] 0.000167917
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRblas.so
LAPACK: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] glmnet_4.1-2 Matrix_1.3-3 MASS_7.3-54 susieR_0.12.35
loaded via a namespace (and not attached):
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