From Genetic Analysis Workshop 18

To avoid inflated type I error and reduced power in genetic association studies, it is necessary to adjust properly for population stratification and known/unknown subject relatedness. It would be interesting to compare the performance of a principal component-based approach with a linear mixed model. Furthermore, with the availability of genome-wide sequencing data, the question of whether it is preferable to use common variants or rare variants for such an adjustment remains largely unknown. In this paper, we use the Genetic Analysis Workshop 18 data to empirically investigate these issues. We consider both a quantitative trait and a binary trait. Background In genetic association studies, population stratification and known or cryptic relatedness are always issues. If not sui-tably accounted for, these may cause inflated type I errors and reduced power. A popular approach to adjusting for population stratification is to construct principal compo-nents (PCs) from some similarity matrix for the samples and to include the PCs as covariates in a regression mode