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An allelic-series rare-variant association test for candidate-gene discovery.
Am. J. Hum. Genet. 110, 1330-1342 (2023)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Allelic Series ; Gene-based Test ; Rare-variant Association Testing ; Target Identification ; Variant Pathogenicity ; Whole-exome Sequencing; Metaanalysis; Individuals; Framework; Angptl4; Disease; Traits; Lipids; Common; Risk
ISSN (print) / ISBN
0002-9297
e-ISSN
1537-6605
Quellenangaben
Volume: 110,
Issue: 8,
Pages: 1330-1342
Publisher
Elsevier
Publishing Place
New York, NY
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
Helmholtz Pioneer Campus (HPC)