| Title: | An allelic-series rare-variant association test for candidate-gene discovery |
| Journal: | American Journal of Human Genetics |
| Published: | 25 Jul 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37494930/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.07.001 |
| URL: | http://www.cell.com/article/S0002929723002410/pdf |
Publication 11644
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Abstract
Allelic series are of candidate therapeutic interest because of the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a collection of variants in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and we have developed a gene-based rare-variant association test specifically targeted to identifying genes containing allelic series. Building on the well-known burden test and sequence kernel association test (SKAT), we specify a variety of association models covering different genetic architectures and integrate these into a Coding-Variant Allelic-Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves the power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic-series genes for four circulating-lipid traits and five cell-count traits among 145,735 subjects with available whole-exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni-significant associations with circulating-lipid traits, on average, and 82% more with cell-count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, n = 400,000) or from common-variant associations in the GWAS Catalog. In addition to detecting many gene-trait associations present in Genebass by using only a fraction (36.9%) of the sample, COAST detects associations, such as that between ANGPTL4 and triglycerides, that are absent from Genebass but that have clear common-variant support.</p>
9 Authors
- Zachary R. McCaw
- Colm O'Dushlaine
- Hari Somineni
- Michael Bereket
- Christoph Klein
- Theofanis Karaletsos
- Francesco Paolo Casale
- Daphne Koller
- Thomas W. Soare
1 Application
| Application ID | Title |
|---|---|
| 51766 | Combining genetic analysis of disease related phenotypes with in vitro disease models for development of high efficacy and low toxicity therapeutics |
Enabling scientific discoveries that improve human health