Date Published: March 1, 2018
Publisher: Public Library of Science
Author(s): Zhaozhong Zhu, Verneri Anttila, Jordan W. Smoller, Phil H. Lee, Zhongxue Chen.
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 distinct study conditions, we found the superior power of several methods under diverse heterogeneity. In particular, classic fixed-effects model showed surprisingly good performance when a variant is associated with more than a half of study traits. As the number of traits with null effects increases, ASSET performed the best along with competitive specificity and sensitivity. With opposite directional effects, CPASSOC featured the first-rate power. However, caution is advised when using CPASSOC for studying genetically correlated traits with overlapping samples. We conclude with a discussion of unresolved issues and directions for future research.
Pleiotropy refers to a biological phenomenon where a single variant or a gene affects multiple phenotypes. In recent years, a startling level of genome-wide genetic correlation has been revealed between various complex traits and disorders.[2–5] Moreover, a growing number of genetic loci have shown pleiotropic effects on multiple, sometimes seemingly distinct traits, providing an intriguing opportunity to enhance our understanding of the shared genetic mechanisms.[7–11] The identification, characterization, and potential clinical translation of pleiotropic genetic effects present immense opportunities for genomic medicine, of which the major focus includes the development of new drugs and therapeutic targets with broad efficacy, while minimizing the unexpected side effects.
Cross-phenotype GWAS have opened a wide field of genomics research in pleiotropy, but little attention has been put into the evaluation and comparison of analytic methods used in the field. In this study, we aimed to fill this gap by evaluating the performance and the properties of ten meta-analysis methods for GWAS, especially in the context of cross-phenotype studies.