Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies

Meta-analysis of genome-wide association studies (GWAS) has achieved great success in detecting loci underlying human diseases. Incorporating GWAS results from diverse ethnic populations for meta-analysis, however, remains challenging because of the possible heterogeneity across studies. Conventional fixed-effects (FE) or random-effects (RE) methods may not be most suitable to aggregate multiethnic GWAS results because of violation of the homogeneous effect assumption across studies (FE) or low power to detect signals (RE). Three recently proposed methods, modified RE (RE-HE) model, binary-effects (BE) model and a Bayesian approach (Meta-analysis of Transethnic Association [MANTRA]), show increased power over FE and RE methods while incorporating heterogeneity of effects when meta-analyzing trans-ethnic GWAS results. We propose a two-stage approach to account for heterogeneity in trans-ethnic meta-analysis in which we clustered studies with cohort-specific ancestry information prior to meta-analysis. We compare this to a no-prior-clustering (crude) approach, evaluating type I error and power of these two strategies, in an extensive simulation study to investigate whether the two-stage approach offers any improvements over the crude approach. We find that the two-stage approach and the crude approach for all five methods (FE, RE, RE-HE, BE, MANTRA) provide well-controlled type I error. However, the two-stage approach shows increased power for BE and RE-HE, and similar power for MANTRA and FE compared to their corresponding crude approach, especially when there is heterogeneity across the multiethnic GWAS results. These results suggest that prior clustering in the two-stage approach can be an effective and efficient intermediate step in meta-analysis to account for the multiethnic heterogeneity.