In the analysis of multi-ancestry datasets, a significant concern is false positive genetic signals due to inflated test statistics from population stratification, which occurs when disease prevalence and allelic frequency differences are correlated within or between study cohorts (Marchini et al., 2004). Two typical strategies exist for addressing this challenge while analyzing samples from multiple major/admixed populations: (1) Empirically assign samples to major continental and/or admixed populations using genome-wide data, analyze each population separately, and conduct cross-ancestry meta-analysis (stratified meta-analysis approach), and (2) analyze samples from multiple populations together, most commonly with a mixed model (joint mixed model approach). The choice between these approaches is perhaps the most broadly impactful decision currently facing analysts of genome-wide data from multiple populations since it impacts methodological considerations in all analysis steps from quality control, to reference alignment in imputation, to association model, to the suitability of results for secondary analyses. We highlight elements of GWAS where the choice between the stratified meta-analysis and joint mixed model approaches is particularly salient. Figure 2 shows a general workflow for each approach.
