Abstract
Exome sequencing of thousands of families has revealed many individual risk genes for congenital heart defects (CHD), yet most cases cannot be explained by a single causal mutation. Further, those who carry de novo and inherited mutations in known risk genes often demonstrate variable phenotypes even within the same family, indicating the presence of genetic modifiers. To explore oligogenic causes of CHD without assessing billions of variant combinations, we developed an efficient, simulation-based method to detect gene sets that carry damaging variants in probands at a higher rate than expected given parental genotypes. We implemented this approach in software called Gene Combinations in Oligogenic Disease (GCOD) and applied it to a cohort of 3382 trios with exome sequencing. This analysis detected 353 high-confidence risk genes in 202 pairs that appear together in multiple probands but rarely or never appear in combination in their unaffected parents. Stratifying analyses by specific CHD diagnosis and considering gene combinations of higher orders yielded an additional 244 gene sets. The oligogenic genes we discovered cluster in pathways specific to heart development and suggest new molecular disease mechanisms, such as arylsulfatase activity and de novo nucleotide biosynthesis. Finally, by combining CHD families with an autism spectrum disorder cohort, we were able to detect 925 oligogenic sets transmitted in renal disease, a known co-morbidity of both conditions. 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.
Competing Interest Statement
The authors have declared no competing interest.
