Article| Volume 25, ISSUE 3, 100355, March 2023

Continuous Bayesian variant interpretation accounts for incomplete penetrance among Mendelian cardiac channelopathies

Published:December 06, 2022DOI:



      The congenital Long QT Syndrome (LQTS) and Brugada Syndrome (BrS) are Mendelian autosomal dominant diseases that 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.


      We generated Bayesian penetrance 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 4 genotype-phenotype relationships and compared continuous estimates with ClinVar annotations. Posterior estimates were mapped onto protein structure.


      Bayesian penetrance estimates of KCNQ1-LQT1 and SCN5A-LQT3 are empirically equivalent to 10 and 5 clinically phenotype heterozygotes, 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. There was a wide distribution of variant penetrance estimates among identical ClinVar categories. Structural mapping revealed heterogeneity among “hot spot” regions and featured high penetrance estimates for KCNQ1 variants in contact with calmodulin and the S6 domain.


      Bayesian penetrance estimates provide a continuous framework for variant interpretation.


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        • Wright C.F.
        • West B.
        • Tuke M.
        • et al.
        Assessing the pathogenicity, penetrance, and expressivity of putative disease-causing variants in a population setting.
        Am J Hum Genet. 2019; 104: 275-286
        • McClellan J.
        • King M.C.
        Genetic heterogeneity in human disease.
        Cell. 2010; 141: 210-217
        • Richards S.
        • Aziz N.
        • Bale S.
        • et al.
        Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.
        Genet Med. 2015; 17: 405-424
        • Landrum M.J.
        • Lee J.M.
        • Riley G.R.
        • et al.
        ClinVar: public archive of relationships among sequence variation and human phenotype.
        Nucleic Acids Res. 2014; 42: D980-D985
        • Schwartz P.J.
        • Ackerman M.J.
        • Antzelevitch C.
        • et al.
        Inherited cardiac arrhythmias.
        Nat Rev Dis Primers. 2020; 6: 58
        • Hosseini S.M.
        • Kim R.
        • Udupa S.
        • et al.
        Reappraisal of reported genes for sudden arrhythmic death: evidence-based evaluation of gene validity for Brugada syndrome.
        Circulation. 2018; 138: 1195-1205
        • Adler A.
        • Novelli V.
        • Amin A.S.
        • et al.
        An international, multicentered, evidence-based reappraisal of genes reported to cause congenital long QT syndrome.
        Circulation. 2020; 141: 418-428
        • Priori S.G.
        • Napolitano C.
        • Schwartz P.J.
        Low penetrance in the long-QT syndrome: clinical impact.
        Circulation. 1999; 99: 529-533
        • Lahrouchi N.
        • Tadros R.
        • Crotti L.
        • et al.
        Transethnic genome-wide association study provides insights in the genetic architecture and heritability of long QT syndrome.
        Circulation. 2020; 142: 324-338
        • Nauffal V.
        • Morrill V.N.
        • Jurgens S.J.
        • et al.
        Monogenic and polygenic contributions to QTc prolongation in the population. medRxiv, 2021.2006.2018.21258578 (2021)..
        Circulation. 2022; 145: 1524-1533
        • van Rees J.B.
        • Borleffs C.J.
        • de Bie M.K.
        • et al.
        Inappropriate implantable cardioverter-defibrillator shocks: incidence, predictors, and impact on mortality.
        J Am Coll Cardiol. 2011; 57: 556-562
        • Schwartz P.J.
        • Spazzolini C.
        • Priori S.G.
        • et al.
        Who are the long-QT syndrome patients who receive an implantable cardioverter-defibrillator and what happens to them?: data from the European long-QT Syndrome implantable cardioverter-defibrillator (LQTS ICD) Registry.
        Circulation. 2010; 122: 1272-1282
        • Larrea-Sebal A.
        • Benito-Vicente A.
        • Fernandez-Higuero J.A.
        • et al.
        MLb-LDLr: A machine learning model for predicting the pathogenicity of LDLr missense variants.
        JACC Basic Transl Sci. 2021; 6: 815-827
        • Draelos R.L.
        • Ezekian J.E.
        • Zhuang F.
        • et al.
        GENESIS: gene-specific machine learning models for variants of uncertain significance found in catecholaminergic polymorphic ventricular tachycardia and long QT syndrome-associated genes.
        Circ Arrhythm Electrophysiol. 2022; 15e010326
        • Kozek K.
        • Wada Y.
        • Sala L.
        • et al.
        Estimating the post-test probability of long QT syndrome diagnosis for rare KCNH2 variants.
        Circ Genom Precis Med. 2021; 14
        • Kroncke B.M.
        • Smith D.K.
        • Zuo Y.
        • Glazer A.M.
        • Roden D.M.
        • Blume J.D.A.
        Bayesian method to estimate variant-induced disease penetrance.
        PLOS Genet. 2020; 16e1008862
        • Karczewski K.J.
        • Francioli L.C.
        • Tiao G.
        • et al.
        The mutational constraint spectrum quantified from variation in 141,456 humans.
        Nature. 2020; 581: 434-443
        • Kroncke B.M.
        • Mendenhall J.
        • Smith D.K.
        • et al.
        Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1.
        Comput Struct Biotechnol J. 2019; 17: 206-214
        • Zhang X.
        • Walsh R.
        • Whiffin N.
        • et al.
        Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.
        Genet Med. 2021; 23: 69-79
        • Ioannidis N.M.
        • Rothstein J.H.
        • Pejaver V.
        • et al.
        REVEL: an ensemble method for predicting the pathogenicity of rare missense variants.
        Am J Hum Genet. 2016; 99: 877-885
        • Choi Y.
        • Chan A.P.
        PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels.
        Bioinformatics. 2015; 31: 2745-2747
        • Brier G.W.
        Verification of forecasts expressed in terms of probability.
        Mon Weather Rev. 1950; 78: 1-3<0001:vofeit>;2
        • Wilde A.A.M.
        • Amin A.S.
        Clinical spectrum of SCN5A mutations: long QT syndrome, Brugada syndrome, and cardiomyopathy.
        JACC Clin Electrophysiol. 2018; 4: 569-579
        • Kapa S.
        • Tester D.J.
        • Salisbury B.A.
        • et al.
        Genetic testing for long-QT syndrome: distinguishing pathogenic mutations from benign variants.
        Circulation. 2009; 120: 1752-1760
        • Sun J.
        • MacKinnon R.
        • Cryo E.M.
        Structure of a KCNQ1/CaM complex reveals insights into congenital long QT syndrome.
        Cell. 2017; 169 (e1049. 1042-1050
        • Schwartz P.J.
        • Moreno C.
        • Kotta M.C.
        • et al.
        Mutation location and IKs regulation in the arrhythmic risk of long QT syndrome type 1: the importance of the KCNQ1 S6 region.
        Eur Heart J. 2021; 42: 4743-4755
        • Sun J.
        • MacKinnon R.
        Structural basis of human KCNQ1 modulation and gating.
        Cell. 2020; 180: 340-347.e9
        • Li Z.
        • Jin X.
        • Wu T.
        • et al.
        Structure of human Na(v)1.5 reveals the fast inactivation-related segments as a mutational hotspot for the long QT syndrome.
        Proc Natl Acad Sci U S A. 2021; 118
        • Napolitano C.
        • Priori S.G.
        • Schwartz P.J.
        • et al.
        Genetic testing in the long QT syndrome: development and validation of an efficient approach to genotyping in clinical practice.
        JAMA. 2005; 294: 2975-2980
        • Barc J.
        • Tadros R.
        • Glinge C.
        • et al.
        Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility.
        Nat Genet. 2022; 54: 232-239
        • Yagi N.
        • Itoh H.
        • Hisamatsu T.
        • et al.
        A challenge for mutation specific risk stratification in long QT syndrome type 1.
        J Cardiol. 2018; 72: 56-65
        • Lane C.M.
        • Giudicessi J.R.
        • Ye D.
        • et al.
        Long QT syndrome type 5-Lite: defining the clinical phenotype associated with the potentially proarrhythmic p.Asp85Asn-KCNE1 common genetic variant.
        Heart Rhythm. 2018; 15: 1223-1230
        • Giudicessi J.R.
        • Roden D.M.
        • Wilde A.A.M.
        • Ackerman M.J.
        Classification and reporting of potentially proarrhythmic common genetic variation in long QT syndrome genetic testing.
        Circulation. 2018; 137: 619-630
        • Schwartz P.J.
        • Crotti L.
        • George Jr., A.L.
        Modifier genes for sudden cardiac death.
        Eur Heart J. 2018; 39: 3925-3931
        • Kapplinger J.D.
        • Tester D.J.
        • Salisbury B.A.
        • et al.
        Spectrum and prevalence of mutations from the first 2,500 consecutive unrelated patients referred for the FAMILION long QT syndrome genetic test.
        Heart Rhythm. 2009; 6: 1297-1303
        • Ng C.A.
        • Ullah R.
        • Farr J.
        • et al.
        A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2.
        Am J Hum Genet. 2022; 109: 1208-1216
        • Kang P.W.
        • Westerlund A.M.
        • Shi J.
        • et al.
        Calmodulin acts as a state-dependent switch to control a cardiac potassium channel opening.
        Sci Adv. 2020; 6
        • Clerx M.
        • Heijman J.
        • Collins P.
        • Volders P.G.A.
        Predicting changes to I(Na) from missense mutations in human SCN5A.
        Sci Rep. 2018; 8: 12797
        • Heyne H.O.
        • Baez-Nieto D.
        • Iqbal S.
        • et al.
        Predicting functional effects of missense variants in voltage-gated sodium and calcium channels.
        Sci Transl Med. 2020; 12
        • Parikh V.N.
        Promise and peril of population genomics for the development of genome-first approaches in Mendelian cardiovascular disease.
        Circ Genom Precis Med. 2021; 14e002964