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

PloS One
Zhaozhong ZhuPhil H Lee

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 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....Continue Reading

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Citations

Nov 23, 2018·The Plant Journal : for Cell and Molecular Biology·Kathleen BeilsmithJoy Bergelson
Oct 18, 2019·The European Respiratory Journal·Zhaozhong ZhuLiming Liang
Dec 25, 2019·The Journal of Headache and Pain·Anna P Andreou, Lars Edvinsson
Jul 28, 2020·Biomedicines·Francesco SansoneFrancesco Chiarelli
Apr 25, 2021·Genes, Brain, and Behavior·Rohan H C PalmerRobert W Williams

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Methods Mentioned

BETA
genotyping

Software Mentioned

ASSET2
MTAG
FEMA
ASSET
PLINK
BE
REMA
Fisher
ASSET1
WISC

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