Many post-GWAS statistical methods have limited portability to association results from diverse and admixed populations, due to complexities with LD patterns. Caution should be taken in the downstream analysis of cross-population GWAS meta-analyses, as many common approaches such as gene-based testing (e.g., MAGMA (de Leeuw et al., 2015)), heritability and genetic correlation estimation (e.g., LD Score regression (Bulik-Sullivan et al., 2015)), and predicted gene expression (e.g., S-PrediXcan (Barbeira et al., 2018)) rely on external reference panels that may not be compatible with the ‘combining’ approach. Even methodologies such as Popcorn (Brown et al., 2016) that are specifically designed for cross-population analyses typically assume single-population summary statistics as input. Furthermore, it is unclear whether annotations of GWAS results based on observed associations in external studies (e.g., gene expression, Hi-C contacts, methylation) may also need to evaluate population specificity or include diverse samples to improve generalizability across populations. For example, 85% of GTEx eQTL annotations are from individuals of European ancestry (GTEx Consortium, 2013) and other functional genomics resources may be similarly limited.
