Confounding by population stratification has been extensively studied in the past [3]. Heterogeneity between studied samples can give false-positive results in association studies, as the association with the trait may by the result of the systematic ancestry difference in allele frequencies between groups [4]. Three main approaches have been proposed so far to capture population genetic differences analytically, namely a) Bayesian clustering [5], b) principal component (PC) analysis [6] and c) multidimensional scaling (MDS) analysis based upon genome-wide identity-by-state (IBS) distances [7]. With the recent availability of high density SNP data, PC and MDS methodologies have become increasingly popular because they require less computing power and have higher discriminatory power than Bayesian analysis for closely related (e.g. European) populations [8]. Therefore, PC analysis is more widely used in the literature. Examples of its recent use are provided by the analysis of high density microarray SNP data at either a global level [9], [10] or, in greater detail, for selected European populations [11]–[15] or within a single country [16]–[18].
