| Title: | SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests |
| Journal: | Nature Genetics |
| Published: | 22 Sep 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36138231/ |
| DOI: | https://doi.org/10.1038/s41588-022-01178-w |
| URL: | https://www.nature.com/articles/s41588-022-01178-w.pdf |
| Citations: | 45 (44 in last 2 years) as of 8 Aug 2024 |
Publication 10472
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Abstract
Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene-phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations.</p>
9 Authors
- Wei Zhou
- Wenjian Bi
- Zhangchen Zhao
- Kushal K. Dey
- Karthik A. Jagadeesh
- Konrad J. Karczewski
- Mark J. Daly
- Benjamin M. Neale
- Seunggeun Lee
1 Application
| Application ID | Title |
|---|---|
| 45227 | Scalable and Robust methods for biobank data analysis |
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