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

Authors :
Li X
Chen H
Selvaraj MS
Van Buren E
Zhou H
Wang Y
Sun R
McCaw ZR
Yu Z
Jiang MZ
DiCorpo D
Gaynor SM
Dey R
Arnett DK
Benjamin EJ
Bis JC
Blangero J
Boerwinkle E
Bowden DW
Brody JA
Cade BE
Carson AP
Carlson JC
Chami N
Chen YI
Curran JE
de Vries PS
Fornage M
Franceschini N
Freedman BI
Gu C
Heard-Costa NL
He J
Hou L
Hung YJ
Irvin MR
Kaplan RC
Kardia SLR
Kelly TN
Konigsberg I
Kooperberg C
Kral BG
Li C
Li Y
Lin H
Liu CT
Loos RJF
Mahaney MC
Martin LW
Mathias RA
Mitchell BD
Montasser ME
Morrison AC
Naseri T
North KE
Palmer ND
Peyser PA
Psaty BM
Redline S
Reiner AP
Rich SS
Sitlani CM
Smith JA
Taylor KD
Tiwari HK
Vasan RS
Viali S
Wang Z
Wessel J
Yanek LR
Yu B
Dupuis J
Meigs JB
Auer PL
Raffield LM
Manning AK
Rice KM
Rotter JI
Peloso GM
Natarajan P
Li Z
Liu Z
Lin X
Source :
Nature computational science [Nat Comput Sci] 2025 Feb 07. Date of Electronic Publication: 2025 Feb 07.
Publication Year :
2025
Publisher :
Ahead of Print

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.<br />Competing Interests: Competing interests: Z.R.M. and R.D. are employees of Insitro. S.M.G. is an employee of Regeneron Genetics Center. M.E.M. receives research funding from Regeneron Pharmaceutical Inc., unrelated to this project. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.M.R. and S.S.R. are consultants for the TOPMed Administrative Coordinating Center (via Westat). P.N. reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech/Roche and Novartis, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Esperion Therapeutics, Foresite Capital, Foresite Labs, Genentech/Roche, GV, HeartFlow, Magnet Biomedicine, Merck, Novartis, TenSixteen Bio and Tourmaline Bio, equity in Bolt, Candela, Mercury, MyOme, Parameter Health, Preciseli and TenSixteen Bio, and spousal employment at Vertex Pharmaceuticals, all unrelated to the present work. X. Lin is a consultant of AbbVie Pharmaceuticals and Verily Life Sciences. The other authors declare no competing interests.<br /> (© 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Details

Language :
English
ISSN :
2662-8457
Database :
MEDLINE
Journal :
Nature computational science
Publication Type :
Academic Journal
Accession number :
39920506
Full Text :
https://doi.org/10.1038/s43588-024-00764-8