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

Authors

Xihao Li
Han Chen
Margaret Sunitha Selvaraj
Eric Van Buren
John Blangero, The University of Texas Rio Grande ValleyFollow
Joanne E. Curran, The University of Texas Rio Grande ValleyFollow
Michael C. Mahaney, The University of Texas Rio Grande ValleyFollow
Ravi Duggirala, The University of Texas Rio Grande Valley
Juan M. Peralta, The University of Texas Rio Grande ValleyFollow
Harald HH Goring, The University of Texas Rio Grande ValleyFollow

Document Type

Article

Publication Date

2-7-2025

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.

Comments

Original published version available at https://doi.org/10.1038/s43588-024-00764-8

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Recommended Citation

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., … Lin, X. (2025). A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. Nature Computational Science, 5(2), 125–143. https://doi.org/10.1038/s43588-024-00764-8

Publication Title

Nature Computational Science

DOI

10.1038/s43588-024-00764-8

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics