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

Author

Listed:
  • 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
    Wisconsin Institute for Discovery)

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.

Suggested Citation

  • Lan Luo & Judong Shen & Hong Zhang & Aparna Chhibber & Devan V. Mehrotra & Zheng-Zheng Tang, 2020. "Multi-trait analysis of rare-variant association summary statistics using MTAR," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16591-0
    DOI: 10.1038/s41467-020-16591-0
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    Cited by:

    1. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    2. E. P. Tissink & A. A. Shadrin & D. Meer & N. Parker & G. Hindley & D. Roelfs & O. Frei & C. C. Fan & M. Nagel & T. Nærland & M. Budisteanu & S. Djurovic & L. T. Westlye & M. P. Heuvel & D. Posthuma & , 2024. "Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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