A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.
Journal
Nature computational science
Journal Volume
5
Journal Issue
2
Start Page
125
End Page
143
ISSN
2662-8457
Date Issued
2025-02
Author(s)
Li, Xihao
Chen, Han
Selvaraj, Margaret Sunitha
Van Buren, Eric
Zhou, Hufeng
Wang, Yuxuan
Sun, Ryan
McCaw, Zachary R
Yu, Zhi
Jiang, Min-Zhi
DiCorpo, Daniel
Gaynor, Sheila M
Dey, Rounak
Arnett, Donna K
Benjamin, Emelia J
Bis, Joshua C
Blangero, John
Boerwinkle, Eric
Bowden, Donald W
Brody, Jennifer A
Cade, Brian E
Carson, April P
Carlson, Jenna C
Chami, Nathalie
Chen, Yii-Der Ida
Curran, Joanne E
de Vries, Paul S
Fornage, Myriam
Franceschini, Nora
Freedman, Barry I
Gu, Charles
Heard-Costa, Nancy L
He, Jiang
Hou, Lifang
Hung, Yi-Jen
Irvin, Marguerite R
Kaplan, Robert C
Kardia, Sharon L R
Kelly, Tanika N
Konigsberg, Iain
Kooperberg, Charles
Kral, Brian G
Li, Changwei
Li, Yun
Lin, Honghuang
Liu, Ching-Ti
Loos, Ruth J F
Mahaney, Michael C
Martin, Lisa W
Mathias, Rasika A
Mitchell, Braxton D
Montasser, May E
Morrison, Alanna C
Naseri, Take
North, Kari E
Palmer, Nicholette D
Peyser, Patricia A
Psaty, Bruce M
Redline, Susan
Reiner, Alexander P
Rich, Stephen S
Sitlani, Colleen M
Smith, Jennifer A
Taylor, Kent D
Tiwari, Hemant K
Vasan, Ramachandran S
Viali, Satupa'itea
Wang, Zhe
Wessel, Jennifer
Yanek, Lisa R
Yu, Bing
Dupuis, Josée
Meigs, James B
Auer, Paul L
Raffield, Laura M
Manning, Alisa K
Rice, Kenneth M
Rotter, Jerome I
Peloso, Gina M
Natarajan, Pradeep
Li, Zilin
Liu, Zhonghua
Lin, Xihong
et al.
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 in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.
Type
journal article