Multi-trait analysis of rare-variant association summary statistics using MTAR

Lan Luo, Judong Shen, Hong Zhang, Aparna Chhibber, Devan V. Mehrotra and Zheng-Zheng Tang ()
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Lan Luo: University of Wisconsin-Madison
Judong Shen: Merck & Co., Inc.
Hong Zhang: Merck & Co., Inc.
Aparna Chhibber: Merck & Co., Inc.
Devan V. Mehrotra: Merck & Co., Inc.
Zheng-Zheng Tang: University of Wisconsin-Madison

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Integrating association evidence across multiple traits can improve the power of gene discovery and reveal pleiotropy. Most multi-trait analysis methods focus on individual common variants in genome-wide association studies. Here, we introduce multi-trait analysis of rare-variant associations (MTAR), a framework for joint analysis of association summary statistics between multiple rare variants and different traits. MTAR achieves substantial power gain by leveraging the genome-wide genetic correlation measure to inform the degree of gene-level effect heterogeneity across traits. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium. 99 genome-wide significant genes were identified in the single-trait-based tests, and MTAR increases this to 139. Among the 11 novel lipid-associated genes discovered by MTAR, 7 are replicated in an independent UK Biobank GWAS analysis. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.

Date: 2020
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DOI: 10.1038/s41467-020-16591-0

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