Genotype Phenotype Correlations in KCNH2 variants from 31,000 Whole Exome Sequences Identified in a Biobank Cohort

Long QT syndrome increases the risk for the life-threatening polymorphic ventricular tachycardia called torsades de pointes (torsades). When diagnosed and treated properly, the risk of sudden death dramatically decreases. Unfortunately, thousands children and young adults still die suddenly each year from LQTS because it is difficult to diagnose. Thirty percent of genotype-positive LQTS patients have concealed LQTS, which is characterized by a normal to borderline resting QTc interval. Genetic testing alone cannot be used to determine who has LQTS because ~10% of the general population harbors a rare non-synonymous (missense) variant in one of the major LQTS-susceptibility genes. In other words the large degree of phenotypic overlap between healthy and LQTS patients, coupled with a high degree of background genetic noise, prevents us from accurately diagnosing LQTS. The long-term goal of our research program is to improve the diagnostic, prognostic and therapeutic value for a genotype-positive LQTS test from Whole Exome Sequences (WES). Improving the clinical value of a genetic testing will enable patients to live healthier lives free of life-threatening arrhythmias. The purpose of this American Heart Association (AHA) Cardiovascular Genome-Phenome Study (CVGPS) Predictors of Cardiovascular Disease (CVD) Discovery Grant is to determine whether the combination of genomics and biomarkers lead to improved predictive utility for clinical endpoints. Specifically we hope to devise a rubric, using WES, functional testing, and electronic health records (EHR) to identify patients that suffer LQTS or concealed LQTS. The Geisinger MyCode Community Health Initiative has 30,000 WES directly linked to the patients’ EHR. In the next year we will i) identify likely loss of function (LOF) KCNH2 mutations from 30,000 WES; ii) validate the LOF KCNH2 molecular phenotypes using a combination of in vitro and in silico testing, and iii) examine the EHR for patients with validated LOF KCNH2 mutations for clinical indicators of LQTS. Completion of this proposal will lay the foundation for identifying LQTS patients using WES.