Our goal in this paper is to test PCA as a method to use information from measured covariates in order to construct principal components that reduce collider bias in polygenic association studies. We present results from a simulated implementation of the method alongside complementary application in observed data. Specifically, we examine two complementary assumptions required for phenotypic PC covariates to reduce collider bias related to the proportion of confounding data that is measured and the correlation structure of the measured and unmeasured confounding data. We provide evidence that these assumptions are complementary, such that meeting one assumption relaxes the requirements for the other. We further provide two examples of applications of the method in observed data to demonstrate the utility of this method to reduce collider bias in polygenic association studies, which deflates the partial effect size of the PRS (ß) and the variance accounted for by the PRS (R2), while inflating the combined R2 of the PRS and heritable collider. Finally, we provide some suggestions about the practical utility of this method and directions for future applications.
