A primary question currently faced in genetic analyses of diverse cohorts is whether to follow a ‘combining’ approach (analyzing all individuals together, regardless of ancestry) or a ‘stratifying’ approach (dividing the cohort into major population groups for separate analysis, followed by cross-ancestry meta-analysis; Figure 2). Concerns regarding joint analysis methods (e.g., mixed models) include inadequate control for confounding population stratification and the limited options for secondary analyses such as polygenic risk scoring and genetic correlation estimates. To the extent that stratifying individuals into major population groups remains a feature of cross-population analyses, future methods and theoretical work may continue to refine standards for how best to assign individuals to more homogenous groups. The best solution currently available combines a priori analysis plans, exploratory examination of the data, and involving collaborators with expertise in analyzing globally representative datasets. Future work will benefit from increasing diversity in reference panels, formalizing how major populations should be defined for the purposes of genetic analyses, and evaluating the performance of such methods. Continued methodological work should help resolve the tension between these approaches, clarifying if and when stratifying samples is necessary and providing improved methods for joint analysis of diverse cohorts that addresses population stratification.
