The meta-analysis approach, combining separate analyses of samples stratified by similar genetic background, currently has several pragmatic advantages. First, computational pipelines developed for single-ancestry analyses can be used for each cohort. Separate analysis also naturally provides ancestry-specific results, which may be valuable for secondary analyses including PRS (Bulik-Sullivan et al., 2015; Lam et al., 2018). Reduced environmental variability within a subset may also improve power. On the other hand, splitting each cohort may be challenging due to continuous gradients of admixture or small sample sizes within an ancestry group. This loss of information from excluding individuals from diverse genomic backgrounds is a missed opportunity for discovery and validation of GWAS findings, and thus additional approaches need to be developed and leveraged.
