GWAS have identified hundreds of common variants associated to disease risk or related traits1 (see Web Resources). These studies have overcome the dangers of population stratification, which can produce spurious associations if not properly corrected2–3. However, accounting for population structure is more challenging when family structure or cryptic relatedness is also present, motivating the development of new methods. Because the spurious associations that have been reported primarily occur at markers with unusual allele frequency differences between subpopulations2, 4, it is critical for new methods aiming to correct for stratification to be evaluated by considering unusually differentiated markers.
