Principal components analysis (PCA) has been the prevailing approach in recent years for inferring population structure. PCA has been shown to account for population structure in samples with unrelated individuals, and the EIGENSTRAT method of Price et al. [2006], where principal components corresponding to the highest eigenvalues are included as covariates in the subsequent association analysis, has been widely applied to genome-wide association studies for protection against confounding due to ancestry differences among sample individuals. Culverhouse et al. [2013] evaluated the performance of PCA when applied to the GAW18 pedigrees and investigated the structure that is reflected by the top principal components when all individuals are given equal weights in the analysis, and when weights are proportional to the inverse of the pedigree size, so that all families equally contribute to the genetic variation.
