Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Authors

Xihao Li
Corbin Quick
Hufeng Zhou
Sheila M. Gaynor
John Blangero, The University of Texas Rio Grande ValleyFollow
Joanne E. Curran, The University of Texas Rio Grande ValleyFollow
Ravindranath Duggirala, The University of Texas Rio Grande Valley
Harald H. H. Goring, The University of Texas Rio Grande Valley
Michael Mahaney, The University of Texas Rio Grande Valley
Juan M. Peralta, The University of Texas Rio Grande Valley

Document Type

Article

Publication Date

1-2023

Abstract

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Comments

Copyright © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

https://rdcu.be/c4t5o

Recommended Citation

Li, X., Quick, C., Zhou, H., Gaynor, S. M., Liu, Y., Chen, H., Selvaraj, M. S., Sun, R., Dey, R., Arnett, D. K., Bielak, L. F., Bis, J. C., Blangero, J., Boerwinkle, E., Bowden, D. W., Brody, J. A., Cade, B. E., Correa, A., Cupples, L. A., Curran, J. E., … Lin, X. (2023). Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nature genetics, 55(1), 154–164. https://doi.org/10.1038/s41588-022-01225-6

Publication Title

Nat Genet

DOI

10.1038/s41588-022-01225-6

Academic Level

faculty

Mentor/PI Department

Office of Human Genetics