For the case–control design, a large panel of genetic markers can be used to estimate genetic ancestry by using principal components analysis (PCA) [10, 11] or related dimension reduction techniques [12], which are referred to as eigenmaps. These low dimensional maps encode the relative genetic similarities and differences amongst individuals. Indeed, the principal eigenvectors often reflect geographical distribution as well as hidden structures in human populations [13, 14]. Given ancestry coordinates, the effects of population stratification can be removed either by regressing out their effects [10], or by matching cases and controls of similar ancestry and performing conditional logistic regression (CLR) [12, 15-18].
