Background: The congenital Long QT Syndrome (LQTS) and Brugada Syndrome (BrS) are Mendelian autosomal dominant diseases which frequently precipitate fatal cardiac arrhythmias. Incomplete penetrance is a barrier to clinical management of heterozygotes harboring variants in the major implicated disease genes KCNQ1, KCNH2, and SCN5A. We apply and evaluate a Bayesian penetrance estimation strategy that accounts for this phenomenon and evaluate penetrance distributions and rationalize their structural underpinnings across four genotype-phenotype pairs.
Methods: We generated Bayesian penetrance estimation models for KCNQ1-LQT1 and SCN5A-LQT3 using variant-specific features and clinical data from the literature, international arrhythmia genetic centers, and population controls. We analyzed the distribution of posterior penetrance estimates across four-genotype phenotype relationships and compared continuous estimates to ClinVar annotations. Posterior estimates were mapped onto protein structure.
Results: Bayesian models of KCNQ1-LQT1 and SCN5A-LQT3 are well-calibrated to clinical observations. Variant-informed penetrance estimates of KCNQ1-LQT1 and SCN5A-LQT3 are empirically equivalent to 10 and 5 heterozygote clinical phenotypes, respectively. Posterior penetrance estimates were bimodal for KCNQ1-LQT1 and KCNH2-LQT2, with a higher fraction of missense variants with high penetrance among KCNQ1 variants. SCN5A-LQT3 and SCN5A-BrS had comparatively more variants with predicted low penetrance. There was a wide distribution of variant penetrance estimates among similar categories of ClinVar annotations. Structural mapping revealed heterogeneity among hot spot regions and featured high penetrance estimates for KCNQ1 variants in contact with calmodulin and the S6 domain.
Conclusions: Bayesian penetrance estimates provide a continuous framework for variant interpretation, provide higher resolution within hot spot domains, and facilitate prospective clinical management of variant heterozygotes.