COBT: a gene-based rare variant burden test for case-only study designs using aggregated genotypes from public reference cohorts

Rare disease gene discovery is limited by small cohorts and the frequent absence of matched controls. We present the Case-Only Burden Test (COBT), a gene-based burden test for case-only designs accounting for multiple variants per individual and additive effects. COBT uses a Poisson model to test for excess variants in a gene relative to expectations from population mutation rates. Simulations show high power and competitive performance versus case-control burden tests. Validation on 1000 Genomes data demonstrated good model fit and low false-positive rates. Applied to 478 ciliopathy patients, COBT re-identified known causal genes and highlighted candidate variants in unsolved cases.

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Publication Details

Journal
Genome Medicine
Published
2026-03-24
DOI
https://doi.org/10.1186/s13073-026-01619-9
Primary Topic
Cystic Fibrosis Research Advances
Type
article
Field-Weighted Citation Impact
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article

COBT: a gene-based rare variant burden test for case-only study designs using aggregated genotypes from public reference cohorts

Genome Medicine
Cystic Fibrosis Research Advances
article

COBT: a gene-based rare variant burden test for case-only study designs using aggregated genotypes from public reference cohorts

article en

Abstract

Rare disease gene discovery is limited by small cohorts and the frequent absence of matched controls. We present the Case-Only Burden Test (COBT), a gene-based burden test for case-only designs accounting for multiple variants per individual and additive effects. COBT uses a Poisson model to test for excess variants in a gene relative to expectations from population mutation rates. Simulations show high power and competitive performance versus case-control burden tests. Validation on 1000 Genomes data demonstrated good model fit and low false-positive rates. Applied to 478 ciliopathy patients, COBT re-identified known causal genes and highlighted candidate variants in unsolved cases.

Genome Medicine
Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Inserm (FR), Universidad de La Rioja (ES), Université Paris Cité (FR), Sorbonne Université (FR), Centre de Recherche des Cordeliers (FR), Assistance Publique – Hôpitaux de Paris (FR), Bicêtre Hospital (FR), Institut des Maladies Génétiques Imagine (FR)
Openalex Percentile: Top 11%
Cystic Fibrosis Research Advances
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