| Title: | Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks |
| Journal: | Proceedings of the National Academy of Sciences of the United States of America |
| Published: | 27 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41758668/ |
| DOI: | https://doi.org/10.1073/pnas.2525288123 |
Publication 17593
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Whole genome sequencing (WGS) studies in large biobanks provide an unprecedented opportunity to study the rare-variant (RV) effects on the natural history of human diseases by analyzing censored time-to-event (TTE) phenotypes, such as age at disease diagnosis, disease progression, and lifespan. Unlike existing methods developed for continuous and categorical phenotypes, rare-variant association tests (RVATs) for TTE phenotypes in large biobanks face several major challenges, including heavy censoring, cryptic relatedness, and population structure. We introduce GATE-STAAR (Genetic Analysis of Time-to-Event phenotypes via the variant-Set Test for Association using Annotation infoRmation), a powerful and computationally efficient frailty model framework for RVATs of TTE phenotypes in large biobanks. GATE-STAAR accounts for high censoring rates, cryptic relatedness, and population structure in large biobanks, while incorporating multifaceted variant functional annotations to improve power and result interpretability. We propose a rare-variant saddlepoint approximation method to effectively address heavy censoring in WGS TTE analysis. We demonstrate through extensive simulations that GATE-STAAR is powerful while maintaining proper control of type I error rates. We apply GATE-STAAR to analyze the WGS data of approximately 400,000 UK Biobank participants of white British ancestry across a variety of TTE phenotypes, and validate the findings using participants of European ancestry from the All of Us Research Program. These analyses uncover RV associations with age at diagnosis of a range of diseases.</p>
7 Keywords
- Biological Specimen Banks
- Genetic Variation
- Genome, Human
- Genome-Wide Association Study
- Humans
- Phenotype
- Whole Genome Sequencing
5 Authors
- Shuang Song
- Xihao Li
- Hufeng Zhou
- Zilin Li
- Xihong Lin
1 Application
| Application ID | Title |
|---|---|
| 52008 | Development of statistical methods to discover novel genetic associations, explain underlying biological mechanisms, and develop risk prediction models across varied complex diseases. |
Enabling scientific discoveries that improve human health