Furthermore, some parameterizations of our simulation with a normally distributed outcome variable did not demonstrate the expected increase in PRS change R-squared in models that include the PC correction. This unanticipated result likely reflects a ceiling effect in the estimation of R-squared; in these simulations, Environment and PC accounted for large amounts of variance on their own (Total model R-squared: Supplemental Figure 3; PRS change R-squared: Supplemental Figure 4). Conclusions from our series of simulations should be limited to the estimation of PRS effect sizes, rather than parameters with an explicit boundary such as R-squared. Finally, our approach to PCA uses single imputation via the K-nearest neighbors algorithm, rather than multiple imputation. We chose single imputation to improve the accessibility and flexibility of the method, but recognize that performance may be improved by the use of multiple imputation with pooled results.
