Recent studies have addressed differences in population substructure and methods to control for these differences in association testing [8–13]. Population substructure can be explored and ascertained using a variety of algorithms that apply principal component analysis (PCA) or non-hierarchical cluster analysis based on allele frequencies in individuals and groups. Unlike other multi-locus adjustments (e.g. genomic control methods [14]) these newer approaches adjust for the fact that some SNPs have large frequency variations across different populations compared to other SNPs [11]. The ability of these methods to control for large differences in population substructure has been at least partially demonstrated by both real data and simulations [6,11,12]. However, the practical application of these methods and limitations requires more extensive exploration in a variety of real datasets.
