| Title: | Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank |
| Journal: | American Journal of Human Genetics |
| Published: | 4 Apr 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37019109/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.03.010 |
| URL: | http://manuscript.elsevier.com/S0002929723000940/pdf/S0002929723000940.pdf |
Publication 7880
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
The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
6 Authors
- Wenjian Bi
- Wei Zhou
- Peipei Zhang
- Yaoyao Sun
- Weihua Yue
- Seunggeun Lee
1 Application
| Application ID | Title |
|---|---|
| 78795 | Regression methods for phenome-wide association analysis on large-scale biobank data |
Enabling scientific discoveries that improve human health