Unravelling the Role of Genome in Pediatric Long QT Syndrome variable expressivity

Long QT syndrome (LQTS) is a heart disease characterized by delayed repolarization which can result in sudden cardiac death. Family-based linkage studies have explained the genetic causes of 75-80% of LQTS cases leaving ~20% of LQTS cases elusive. It is statistically infeasible to use traditional genome-wide association studies to discover these remaining genetic causes; therefore alternative approaches are required. To discover patient-specific differential eQTL (deQTL), we will use the human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) model to compare gene expression in hiPSC-CMs from 50 patients with LQTS harboring known LQTS variants (Phenotype+, Genotype+), 50 patients with LQTS and not harboring any known LQTS variant (P+G-), and 50 controls known not to have LQTS (P-G-). For each patient's hiPSC-CMs, we will electrophysiologically phenotype the cells for prolonged action potential. To establish deQTL, we will then compare differential gene/splice variant isoforms expression and correlate these with LQTS disease status and in vitro phenotype. This will allow us to identify transcriptional patterns uniquely present in P+G- but not in neither controls nor P+G+. Having QT intervals measured clinically and in patient-derived cardiomyocytes will allow us to pinpoint altered gene expression associated with prolonged action potential in LQTS patients. We will first focus on whole-genome sequencing-identified variants associated with P+G+ specific genes when compared to P-G-, completing a genotype-phenotype association analysis to pinpoint deQTL that are over-represented in LQTS patients. We will then complete a comparison of P+G+ vs. P+G- and P+G- vs. P-G- to identify deQTL unique to patients who do not have known LQTS variants. By identifying SNPs that are causative in regulating gene expression in LQTS, we will be able to find highly powered genetic modifiers of the LQTS phenotype and then validate them by genome editing in the hiPSC-CM model. Fi