Traditional meta-analytic approaches for GWAS rely on fixed-effects models that assume a given variant has the same true marginal effect size across all studies. This assumption is likely to be violated in meta-analyses across diverse cohorts. Even when the causal genetic effect of a variant is constant across populations, as seems common in cross-ancestry GWAS to date (Huang et al., 2017; Lam et al., 2018), marginal effect sizes may show heterogeneity when LD structures are different. Further heterogeneity across cohorts from different populations may arise due to differences in genetic background (e.g., gene × gene interactions) and/or environmental context (e.g., gene × environment interactions), as well as differences in study design (e.g., imputation artifacts, phenotyping). As a result, it is generally appropriate to model this cross-cohort heterogeneity in meta-analysis by using a random effects or trans-ancestral meta-analysis model (Supplementary Methods Section 5, Supplementary Table S4).
