A statistical framework for 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; Min-Zhi Jiang; Daniel DiCorpo; Sheila M. Gaynor; Rounak Dey; Donna K. Arnett; Emelia J. Benjamin; 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 Chen; Joanne 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 N. Kelly; Iain Konigsberg; Charles Kooperberg; Brian G. Kral; Changwei Li; Yun Li; Honghuang Lin; Ruth J. F. Loos; Wei-Min Chen; Yu Liu; NHLBI Trans Omics Precision Med TOPMed Consortium

doi:10.1038/s43588-024-00764-8

A statistical framework for 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, Min-Zhi Jiang, Daniel DiCorpo, Sheila M. Gaynor, Rounak Dey, Donna K. Arnett, Emelia J. Benjamin, Joshua C. Bis, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. BrodyBrian E. Cade, April P. Carson, Jenna C. Carlson, Nathalie Chami, Yii-Der Ida Chen, Joanne 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 N. Kelly, Iain Konigsberg, Charles Kooperberg, Brian G. Kral, Changwei Li, Yun Li, Honghuang Lin, Ruth J. F. Loos, Wei-Min Chen, Yu Liu, NHLBI Trans Omics Precision Med TOPMed Consortium

Loos Group

Research output: Contribution to journal › Journal article › Research › peer-review

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.

Original language	English
Journal	Nature Computational Science
Volume	5
Pages (from-to)	125–143
DOIs	https://doi.org/10.1038/s43588-024-00764-8
Publication status	Published - 2025

Keywords

Population-structure
Association
Prediction
Pleiotropy
Diseases
Heart

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Li, X., Chen, H., Selvaraj, M. S., Van Buren, E., Zhou, H., Wang, Y., Sun, R., McCaw, Z. R., Yu, Z., Jiang, M.-Z., DiCorpo, D., Gaynor, S. M., Dey, R., Arnett, D. K., Benjamin, E. J., Bis, J. C., Blangero, J., Boerwinkle, E., Bowden, D. W., ... NHLBI Trans Omics Precision Med TOPMed Consortium (2025). A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. Nature Computational Science, 5, 125–143. https://doi.org/10.1038/s43588-024-00764-8

Li, X, Chen, H, Selvaraj, MS, Van Buren, E, Zhou, H, Wang, Y, Sun, R, McCaw, ZR, Yu, Z, Jiang, M-Z, 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, Y-DI, Curran, JE, de Vries, PS, Fornage, M, Franceschini, N, Freedman, BI, Gu, C, Heard-Costa, NL, He, J, Hou, L, Hung, Y-J, Irvin, MR, Kaplan, RC, Kardia, SLR, Kelly, TN, Konigsberg, I, Kooperberg, C, Kral, BG, Li, C, Li, Y, Lin, H, Loos, RJF, Chen, W-M, Liu, Y & NHLBI Trans Omics Precision Med TOPMed Consortium 2025, 'A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies', Nature Computational Science, vol. 5, pp. 125–143. https://doi.org/10.1038/s43588-024-00764-8

@article{232e3963efb34ef1bd8187cae2a9864b,

title = "A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies",

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.",

keywords = "Population-structure, Association, Prediction, Pleiotropy, Diseases, Heart",

author = "Xihao Li and Han Chen and Selvaraj, {Margaret Sunitha} and {Van Buren}, Eric and Hufeng Zhou and Yuxuan Wang and Ryan Sun and McCaw, {Zachary R.} and Zhi Yu and Min-Zhi Jiang and Daniel DiCorpo and Gaynor, {Sheila M.} and Rounak Dey and Arnett, {Donna K.} and Benjamin, {Emelia J.} and Bis, {Joshua C.} and John Blangero and Eric Boerwinkle and Bowden, {Donald W.} and Brody, {Jennifer A.} and Cade, {Brian E.} and Carson, {April P.} and Carlson, {Jenna C.} and Nathalie Chami and Chen, {Yii-Der Ida} and Curran, {Joanne E.} and {de Vries}, {Paul S.} and Myriam Fornage and Nora Franceschini and Freedman, {Barry I.} and Charles Gu and Heard-Costa, {Nancy L.} and Jiang He and Lifang Hou and Yi-Jen Hung and Irvin, {Marguerite R.} and Kaplan, {Robert C.} and Kardia, {Sharon L. R.} and Kelly, {Tanika N.} and Iain Konigsberg and Charles Kooperberg and Kral, {Brian G.} and Changwei Li and Yun Li and Honghuang Lin and Loos, {Ruth J. F.} and Wei-Min Chen and Yu Liu and {NHLBI Trans Omics Precision Med TOPMed Consortium}",

year = "2025",

doi = "10.1038/s43588-024-00764-8",

language = "English",

volume = "5",

pages = "125–143",

journal = "Nature Computational Science",

issn = "2662-8457",

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TY - JOUR

T1 - A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

AU - Li, Xihao

AU - Chen, Han

AU - Selvaraj, Margaret Sunitha

AU - Van Buren, Eric

AU - Zhou, Hufeng

AU - Wang, Yuxuan

AU - Sun, Ryan

AU - McCaw, Zachary R.

AU - Yu, Zhi

AU - Jiang, Min-Zhi

AU - DiCorpo, Daniel

AU - Gaynor, Sheila M.

AU - Dey, Rounak

AU - Arnett, Donna K.

AU - Benjamin, Emelia J.

AU - Bis, Joshua C.

AU - Blangero, John

AU - Boerwinkle, Eric

AU - Bowden, Donald W.

AU - Brody, Jennifer A.

AU - Cade, Brian E.

AU - Carson, April P.

AU - Carlson, Jenna C.

AU - Chami, Nathalie

AU - Chen, Yii-Der Ida

AU - Curran, Joanne E.

AU - de Vries, Paul S.

AU - Fornage, Myriam

AU - Franceschini, Nora

AU - Freedman, Barry I.

AU - Gu, Charles

AU - Heard-Costa, Nancy L.

AU - He, Jiang

AU - Hou, Lifang

AU - Hung, Yi-Jen

AU - Irvin, Marguerite R.

AU - Kaplan, Robert C.

AU - Kardia, Sharon L. R.

AU - Kelly, Tanika N.

AU - Konigsberg, Iain

AU - Kooperberg, Charles

AU - Kral, Brian G.

AU - Li, Changwei

AU - Li, Yun

AU - Lin, Honghuang

AU - Loos, Ruth J. F.

AU - Chen, Wei-Min

AU - Liu, Yu

AU - NHLBI Trans Omics Precision Med TOPMed Consortium

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Population-structure

KW - Association

KW - Prediction

KW - Pleiotropy

KW - Diseases

KW - Heart

U2 - 10.1038/s43588-024-00764-8

DO - 10.1038/s43588-024-00764-8

M3 - Journal article

C2 - 39920506

SN - 2662-8457

VL - 5

SP - 125

EP - 143

JO - Nature Computational Science

JF - Nature Computational Science

ER -