Individualized risk assessment, diagnosis, and treatment based on the integration of genomic data with clinical data represent the promise of precision medicine. Despite the successes of clinical exome sequencing in rare disease, more than half of all cases remain undiagnosed. The variants that are called in these clinical studies are typically protein-coding variants, where it is easier to predict what the effect of a genetic variant is on protein function. Previous studies of structural defects have identified non-coding variants have been associated with specific gene regulatory domains in preaxial polydactyly and Pierre Robins? Sequence, both isolated structural defects affecting limb and craniofacial development, respectively. Therefore, we hypothesize that there are other rare non-coding variants that can result in cardiac malformations. This proposal is focused towards the untested 99% of the genome: the non-coding genome. Non-coding variants regulates the timing and cell-specific expression of protein-coding genes. Our goal is to determine the roles of non-coding variants in congenital heart disease (CHD), focusing on both rare non-coding genetic variation (Aim1) and integration of common genetic variation using polygenic risk scores (Aim 2). The unique data sets available through the Gabriella Miller Kids First Data Resource have whole-genome sequencing data for 298 patients affected with CHD and 1 or more parents. This data set allows for novel exploration of the hypothesis that non-coding variants drive the penetrance and expressivity in CHD. Findings from our study will advance our ability to interpret how the non-coding genome can affect risk of congenital heart disease. Furthermore, we will advance precision medicine approaches with respect to integrating non-coding genomic information into clinical diagnostics.
To date, rare non-coding genomic variants have only been described in a small number of genetic conditions associated with isolated structural birth defects, despite the fact that non-coding genomic variation makes up 99% of the genome. This proposal will test that hypotheses that 1) rare noncoding genetic variants are associated with congenital heart disease (CHD), and 2) polygenic risk score that integrate many common variants can correlate with risk and/or severity of CHD. By exploring the non-coding genome, we will uncover novel insights into the genetic architecture underlying CHD.
