The oligogenic inheritance test GCOD detects risk genes and their interactions in congenital heart defects

Abstract

Exome sequencing of thousands of families has revealed many risk genes for congenital heart defects (CHDs), yet most cases cannot be explained by a single causal mutation. Even within the same family, individuals carrying a particular mutation in a known risk gene often demonstrate variable phenotypes, suggesting the presence of genetic modifiers. To explore oligogenic causes of CHD without assessing billions of variant combinations, we develop an efficient, simulation-based method to detect gene sets that carry co-occurring damaging variants in probands at a higher rate than expected given parental genotypes. We implement this approach in software called Gene Combinations in Oligogenic Disease (GCOD) and apply it to a cohort of 3377 CHD trios with exome sequencing. This analysis detects 160 gene pairs in which damaging variants are transmitted with higher-than-expected frequency to CHD probands but rarely or never appear in combination in their unaffected parents. Stratifying by specific phenotypes and considering gene combinations of higher orders yields an additional 6026 gene sets. Genes found in oligogenic sets are overrepresented in pathways related to heart development and often co-occur in sets of cell type marker genes from single-cell expression data. Compound heterozygosity of the newly identified digenic pair Gata6–Por leads to higher CHD incidence in mice compared with single hemizygotes, validating predicted genetic interactions. As genome sequencing is applied to more families and other disorders, GCOD will enable detection of increasingly large, novel gene combinations, shedding light on combinatorial causes of genetic diseases.