Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies.

ABSTRACT: Advances in recent genome wide association studies (GWAS) suggest that pleiotropic effects on human complex traits are widespread. A number of classic and recent meta-analysis methods have been used to identify genetic loci with pleiotropic effects, but the overall performance of these methods is not well understood. In this work, we use extensive simulations and case studies of GWAS datasets to investigate the power and type-I error rates of ten meta-analysis methods. We specifically focus on three conditions commonly encountered in the studies of multiple traits: (1) extensive heterogeneity of genetic effects; (2) characterization of trait-specific association; and (3) inflated correlation of GWAS due to overlapping samples. Although the statistical power is highly variable under distinc

SUBMITTER: Zhu Z 

PROVIDER: S-EPMC5832233 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature