such as batch effect. The practice of blindly including PCs in GWAS (typically 10–20) can negatively impact results. First, if too many PCs are included association models may become overfit, which can reduce the power to detect etiologically relevant variation (inflated type-II error rates). Conversely, if too few PCs are included, which is particularly salient for admixed and diverse samples, population stratification can remain potentially leading to false positive associations (increased type-I error rates).
