Abstract 12295: Utilization of Whole-Exome Sequencing to Identify Causative Mutations in Familial Congenital Heart Disease

Abstract

Congenital heart disease (CHD) is the most common type of birth defect, affecting ~8 out of every 1,000 live births, but there remains a limited understanding of the etiology for the majority of cases of CHD. Familial and population based studies have identified a strong genetic component to CHD, and familial cases of almost every type of CHD have been reported. The goal of this study was to identify pathogenic segregating variants in multiplex CHD families by whole-exome sequencing (WES). Target capture was performed using the Agilent SureSelectXT Target Enrichment kit, followed by WES on an Illumina HiSeq2500, on members from 9 families with Mendelian inherited CHD. Sequence alignment, post alignment processing, variant calling, and genotyping was performed with the Churchill pipeline. Tertiary analysis and annotation consisted of identifying rare (<1% mean allele frequency in the population) segregating variants, prioritizing by matching to a CHD gene list, and predicting pathogenicity via 12 different utilities. This strategy utilized a list of 69 CHD genes selected under strict criteria and allowed for prioritization of the variants. Predicted pathogenic mutations were identified in 3 of the 9 families. A splice donor site mutation was identified in MYH11 (c.4599+1delC) in a family with autosomal dominant patent ductus arteriosus that is predicted to cause a 71 amino acid deletion, affecting functionality of the coiled-coil tail domain. A GATA4 mutation, p.G115W , was identified in a family with autosomal dominant atrial septal defects (ASD) and is predicted to affect the transactivation ability of GATA4 . A p.I263V mutation in TLL1 was identified in a family with autosomal dominant ASD; as this mutation was located in the astacin-like metalloprotease catalytic domain it is predicted to affect the enzymatic efficacy of TLL1 . In summary, our findings demonstrate the clinical utility of WES to successfully identify causative mutations in familial CHD and support the use of a CHD candidate gene list to allow for a more streamlined approach that enables rapid prioritization and identification of pathogenic variants from large WES data sets.