Multi-Trait Analysis of Rare-Variant Association Summary Statistics Using MTAR

• Activity Number: 42 - Statistical Genetics I – New Approaches for Association Mapping
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Lan Luo* and Judong Shen and Zheng-Zheng Tang and Hong Zhang and Aparna Chhibber and Devan Mehrotra
• Companies: University of Wisconsin-Madison and Merck & Co., Inc. and University of Wisconsin-Madison and Merck & Co., Inc. and Genetics and Pharmacogenomics, Merck & Co., Inc. and Merck
• Keywords: genetic correlation; multi-trait analysis; random-effects model; rare variant association study; summary statistics

Abstract

There is a growing interest in integrating association evidence across multiple traits to improve the power of gene discovery and reveal pleiotropy. The majority of multi-trait analysis methods focus on individual common variants in genome-wide association studies (GWAS). 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, possibly from overlapping samples. MTAR tests accommodate a wide variety of association patterns across traits and variants and enrich their associations. We apply MTAR to rare-variant summary statistics for three lipid traits in the Global Lipids Genetics Consortium (GLGC, N?300,000). As compared to the 99 genome-wide significant genes identified in the single-trait-based tests, MTAR increases the number of associated genes to 139. Among the 11 novel lipid-associated genes exclusively discovered by MTAR, seven are replicated in an independent UK Biobank GWAS data. Our study demonstrates that MTAR is substantially more powerful than single-trait-based tests and highlights the value of MTAR for novel gene discovery.