Model-based assessment of replicability for genome-wide association meta-analysis

Daniel McGuire, Yu Jiang, Mengzhen Liu, J. Dylan Weissenkampen, Scott Eckert, Lina Yang, Fang Chen, Arthur Berg, Scott Vrieze, Bibo Jiang (), Qunhua Li () and Dajiang J. Liu ()
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Daniel McGuire: Penn State College of Medicine
Yu Jiang: Penn State College of Medicine
Mengzhen Liu: University of Minnesota
J. Dylan Weissenkampen: Penn State College of Medicine
Scott Eckert: Penn State College of Medicine
Lina Yang: Penn State College of Medicine
Fang Chen: Penn State College of Medicine
Arthur Berg: Penn State College of Medicine
Scott Vrieze: University of Minnesota
Bibo Jiang: Penn State College of Medicine
Qunhua Li: Penn State University
Dajiang J. Liu: Penn State College of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the “posterior-probability-of-replicability” for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.

Date: 2021
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DOI: 10.1038/s41467-021-21226-z

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