Uncovering incomplete penetrance genes in congenital heart disease by integrative analysis of Common Fund data

PROJECT SUMMARY Congenital heart disease (CHD) is the most common birth defect and a leading cause of mortality in children. Despite a well-established heritable componentto this disorder, genetic testingis often unsuccessful. Some 60% of patients fail to receive a molecular diagnosis; for patients with isolated CHD, the failure rate is closer to 90%. Without a precise diagnosis, it is difficult to provide any patient with an accurate prognosis or personalized interventions. The main reason that diagnostic testing fails is that our knowledge of the genetic underpinnings of CHD remains woefully incomplete. CHD is not only genetically heterogeneous, but also notorious for incomplete penetrance (variants that do not always cause a condition). In this proposal we aim to quantify the role of incomplete penetrance in CHD risk genes. We will source a large cohort of CHD families fromthe Gabriella Miller Kids First resource, the Undiagnosed Disease Network, Genomics England, and an internal research study and performtwo complementary analyses to rank CHD genes. First, we will apply a genetic association study of CHD probands to prioritize genes with an increased burden of rare genetic variation among CHD patients irrespective of the inheritance pattern. This will be achieved through an innovative new resource: a remote control database of 40,000 healthy, diverse controls and a method to securely select matched controls for any given case cohort. Second, we will performpedigreeanalysis of all CHD families to identify genes in which rare large-effect variants segregate with disease. Finally, we will combine the results of both strategies into a single master table of CHD genes and use the segregation patterns observed in all families to quantify the extent of incomplete penetrance. We expect our results will serve as a reference for the diagnostic initiatives in CHD and will inform clinical geneticists about the likelihood of disease causality for genes challenged by incomplete penetrance. Our results should provide a framework for integrating association and segregation analyses that could be adapted to other genetically heterogeneous conditions.