A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Xihao Li, Han Chen, Margaret Sunitha Selvaraj, Eric Van Buren, Hufeng Zhou, Yuxuan Wang, Ryan Sun, Zachary R McCaw, Zhi Yu, Donna K Arnett, Joshua C Bis, John Blangero, Eric Boerwinkle, Donald W Bowden, Jennifer A Brody, Brian E Cade, April P Carson, Jenna C Carlson, Nathalie Chami, Yii-Der Ida ChenJoanne E Curran, Paul S de Vries, Myriam Fornage, Nora Franceschini, Barry I Freedman, Charles Gu, Nancy L Heard-Costa, Jiang He, Lifang Hou, Yi-Jen Hung, Marguerite R Irvin, Robert C Kaplan, Sharon L R Kardia, Tanika Kelly, Iain Konigsberg, Charles Kooperberg, Brian G Kral, Changwei Li, Ruth J F Loos, Michael C Mahaney, Lisa W Martin, Rasika A Mathias, Ryan L Minster, Braxton D Mitchell, May E Montasser, Alanna C Morrison, Nicholette D Palmer, Patricia A Peyser, Bruce M Psaty, Laura M Raffield, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

Publikation: Working paperPreprint

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Abstract

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

OriginalsprogEngelsk
UdgiverbioRxiv
Antal sider71
DOI
StatusUdgivet - 2023

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