Genome-wide association studies (GWAS) are an effective approach for identifying genetic variants associated to disease risk. GWAS can be confounded by population stratification—systematic ancestry differences between cases and controls—which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present, but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.
