We conducted a simulation study in R (R Core Team, 2017) to test principal component analysis (PCA) of a series of measured confounders as a correction for collider bias in tests of polygenic association. The R script used to conduct this simulation is available on GitHub (https://github.com/thomasns0/PCA_Collider.git). We sampled data from a model which tested different values for three parameters of interest: the correlation structure of the confounding data, the effect of the PRS on the heritable environment, and the proportion of confounding variables that was available for use in the PCA correction. We tested multiple values for these parameters to provide information about the empirical requirements for this method to provide adequate correction for collider bias at different levels of gene-environment correlation (rGE). The sample size was 1000 in all simulations.
