Methods that explicitly infer genetic ancestry generally provide an effective correction for population stratification in data sets where population structure is the only type of sample structure. In the Structured Association approach, samples are assigned to subpopulation clusters (possibly allowing fractional cluster membership) using a model-based clustering program such as STRUCTURE13–14, and association statistics are computed by stratifying by cluster (STRAT; see Web Resources)15. The applicability of this approach to large genome-wide data sets has historically been limited by its high computational cost when allowing fractional cluster membership, but faster model-based approaches for inferring population structure have recently been developed16 (ADMIXTURE; see Web Resources). Thus, applying Structured Association to both infer population structure and compute association statistics in genome-wide data sets is likely to become a practical approach.
