| 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 |
Publication 7880
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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.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 |
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