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Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization

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Abstract

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain 8–10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval–associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

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Figure 1: Genome-wide association results for GWAS meta-analysis, annotated with gene names.

Change history

  • 20 July 2014

    In the version of this article initially published online, the name of Fabiola del Greco M was misspelled in the author list. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. Schwartz, P.J., Crotti, L. & Insolia, R. Long-QT syndrome: from genetics to management. Circ. Arrhythm. Electrophysiol. 5, 868–877 (2012).

    Article  Google Scholar 

  2. Newton-Cheh, C. et al. QT interval is a heritable quantitative trait with evidence of linkage to chromosome 3 in a genome-wide linkage analysis: The Framingham Heart Study. Heart Rhythm 2, 277–284 (2005).

    Article  Google Scholar 

  3. Newton-Cheh, C. et al. Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat. Genet. 41, 399–406 (2009).

    CAS  Article  Google Scholar 

  4. Pfeufer, A. et al. Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat. Genet. 41, 407–414 (2009).

    CAS  Article  Google Scholar 

  5. Arking, D.E. et al. A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat. Genet. 38, 644–651 (2006).

    CAS  Article  Google Scholar 

  6. Nolte, I.M. et al. Common genetic variation near the phospholamban gene is associated with cardiac repolarisation: meta-analysis of three genome-wide association studies. PLoS ONE 4, e6138 (2009).

    Article  Google Scholar 

  7. Holm, H. et al. Several common variants modulate heart rate, PR interval and QRS duration. Nat. Genet. 42, 117–122 (2010).

    CAS  Article  Google Scholar 

  8. Noseworthy, P.A. et al. Common genetic variants, QT interval, and sudden cardiac death in a Finnish population-based study. Circ. Cardiovasc. Genet. 4, 305–311 (2011).

    Article  Google Scholar 

  9. Kim, J.W. et al. A common variant in SLC8A1 is associated with the duration of the electrocardiographic QT interval. Am. J. Hum. Genet. 91, 180–184 (2012).

    CAS  Article  Google Scholar 

  10. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    CAS  Article  Google Scholar 

  11. Voight, B.F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).

    CAS  Article  Google Scholar 

  12. Smith, J.G. et al. Impact of ancestry and common genetic variants on QT interval in African Americans. Circ. Cardiovasc. Genet. 5, 647–655 (2012).

    Article  Google Scholar 

  13. den Hoed, M. et al. Identification of heart rate–associated loci and their effects on cardiac conduction and rhythm disorders. Nat. Genet. 45, 621–631 (2013).

    CAS  Article  Google Scholar 

  14. Sotoodehnia, N. et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nat. Genet. 42, 1068–1076 (2010).

    CAS  Article  Google Scholar 

  15. Pfeufer, A. et al. Genome-wide association study of PR interval. Nat. Genet. 42, 153–159 (2010).

    CAS  Article  Google Scholar 

  16. Elks, C.E. et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat. Genet. 42, 1077–1085 (2010).

    CAS  Article  Google Scholar 

  17. Zabaneh, D. & Balding, D.J. A genome-wide association study of the metabolic syndrome in Indian Asian men. PLoS ONE 5, e11961 (2010).

    Article  Google Scholar 

  18. Lemaitre, R.N. et al. Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS Genet. 7, e1002193 (2011).

    CAS  Article  Google Scholar 

  19. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    CAS  Article  Google Scholar 

  20. Chambers, J.C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat. Genet. 43, 1131–1138 (2011).

    CAS  Article  Google Scholar 

  21. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).

    CAS  Article  Google Scholar 

  22. Bernstein, B.E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    CAS  Article  Google Scholar 

  23. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  Article  Google Scholar 

  24. Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 24, 1–13 (2014).

    CAS  Article  Google Scholar 

  25. Segrè, A.V. et al. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010).

    Article  Google Scholar 

  26. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    CAS  Article  Google Scholar 

  27. Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    CAS  Article  Google Scholar 

  28. Lundby, A. et al. Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat. Methods 10.1038/nmeth.2997 (22 June 2014).

  29. Yoshida, M. et al. Impaired Ca2+ store functions in skeletal and cardiac muscle cells from sarcalumenin-deficient mice. J. Biol. Chem. 280, 3500–3506 (2005).

    CAS  Article  Google Scholar 

  30. Shimura, M. et al. Sarcalumenin alleviates stress-induced cardiac dysfunction by improving Ca2+ handling of the sarcoplasmic reticulum. Cardiovasc. Res. 77, 362–370 (2008).

    CAS  Article  Google Scholar 

  31. Jiao, Q. et al. Sarcalumenin is essential for maintaining cardiac function during endurance exercise training. Am. J. Physiol. Heart Circ. Physiol. 297, H576–H582 (2009).

    CAS  Article  Google Scholar 

  32. Splawski, I. et al. Severe arrhythmia disorder caused by cardiac L-type calcium channel mutations. Proc. Natl. Acad. Sci. USA 102, 8089–8096, discussion 8086–8088 (2005).

    CAS  Article  Google Scholar 

  33. Milberg, P. et al. Inhibition of the Na+/Ca2+ exchanger suppresses torsades de pointes in an intact heart model of long QT syndrome-2 and long QT syndrome-3. Heart Rhythm 5, 1444–1452 (2008).

    Article  Google Scholar 

  34. Milberg, P. et al. Acute inhibition of the Na+/Ca2+ exchanger reduces proarrhythmia in an experimental model of chronic heart failure. Heart Rhythm 9, 570–578 (2012).

    Article  Google Scholar 

  35. Pott, C. et al. Proarrhythmia in a non-failing murine model of cardiac-specific Na+/Ca 2+ exchanger overexpression: whole heart and cellular mechanisms. Basic Res. Cardiol. 107, 247 (2012).

    Article  Google Scholar 

  36. Braz, J.C. et al. PKC-α regulates cardiac contractility and propensity toward heart failure. Nat. Med. 10, 248–254 (2004).

    CAS  Article  Google Scholar 

  37. Sakuntabhai, A. et al. Mutations in ATP2A2, encoding a Ca2+ pump, cause Darier disease. Nat. Genet. 21, 271–277 (1999).

    CAS  Article  Google Scholar 

  38. Ji, Y. et al. Disruption of a single copy of the SERCA2 gene results in altered Ca2+ homeostasis and cardiomyocyte function. J. Biol. Chem. 275, 38073–38080 (2000).

    CAS  Article  Google Scholar 

  39. Pani, B. et al. Up-regulation of transient receptor potential canonical 1 (TRPC1) following sarco(endo)plasmic reticulum Ca2+ ATPase 2 gene silencing promotes cell survival: a potential role for TRPC1 in Darier's disease. Mol. Biol. Cell 17, 4446–4458 (2006).

    CAS  Article  Google Scholar 

  40. Lyon, A.R. et al. SERCA2a gene transfer decreases sarcoplasmic reticulum calcium leak and reduces ventricular arrhythmias in a model of chronic heart failure. Circ. Arrhythm. Electrophysiol. 4, 362–372 (2011).

    CAS  Article  Google Scholar 

  41. Jin, J. et al. Deletion of Trpm7 disrupts embryonic development and thymopoiesis without altering Mg2+ homeostasis. Science 322, 756–760 (2008).

    CAS  Article  Google Scholar 

  42. Runnels, L.W., Yue, L. & Clapham, D.E. TRP-PLIK, a bifunctional protein with kinase and ion channel activities. Science 291, 1043–1047 (2001).

    CAS  Article  Google Scholar 

  43. Elizondo, M.R. et al. Defective skeletogenesis with kidney stone formation in dwarf zebrafish mutant for trpm7. Curr. Biol. 15, 667–671 (2005).

    CAS  Article  Google Scholar 

  44. Arduini, B.L. & Henion, P.D. Melanophore sublineage-specific requirement for zebrafish touchtone during neural crest development. Mech. Dev. 121, 1353–1364 (2004).

    CAS  Article  Google Scholar 

  45. Sah, R. et al. Ion channel–kinase TRPM7 is required for maintaining cardiac automaticity. Proc. Natl. Acad. Sci. USA 110, E3037–E3046 (2013).

    CAS  Article  Google Scholar 

  46. Wei, C. et al. Calcium flickers steer cell migration. Nature 457, 901–905 (2009).

    CAS  Article  Google Scholar 

  47. Du, J. et al. TRPM7-mediated Ca2+ signals confer fibrogenesis in human atrial fibrillation. Circ. Res. 106, 992–1003 (2010).

    CAS  Article  Google Scholar 

  48. Sah, R. et al. Timing of myocardial trpm7 deletion during cardiogenesis variably disrupts adult ventricular function, conduction, and repolarization. Circulation 128, 101–114 (2013).

    CAS  Article  Google Scholar 

  49. Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  Article  Google Scholar 

  50. Pe'er, I., Yelensky, R., Altshuler, D. & Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  Google Scholar 

  51. Ernst, J. & Kellis, M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–825 (2010).

    CAS  Article  Google Scholar 

  52. Schwartz, P.J., Moss, A.J., Vincent, G.M. & Crampton, R.S. Diagnostic criteria for the long QT syndrome. An update. Circulation 88, 782–784 (1993).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

A full listing of acknowledgments is provided in the Supplementary Note.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

Author contributions are indicated by cohort and group. All coauthors revised and approved the manuscript.

Writing group. C.N.-C. takes overall responsibility for the QT-IGC study. The study design was developed by M.J.A., D.E.A., A. Chakravarti, L.C., P.I.W.d.B., T.T.K., P.B.M., C.N.-C., A. Pfeufer, S.L.P., P.J.S. and N.S. in consultation with the respective study groups. The manuscript was written by C.N.-C. The manuscript was critically revised in detail by members of the writing team before circulation to all coauthors.

GWAS cohorts. AGES: Phenotyping: V.G. Data analysis: A.V.S. Oversight: T.B.H., L.J.L., V.G. Amish studies: Clinical data collection, genotyping and oversight: A.R.S. EKG data collection: W.S.P. Analysis: A. Parsa, J.R.O. Interpretation: A. Parsa, W.S.P. ARIC: Study design: A.A., D.E.A., A. Chakravarti, W.H.L.K. Analyses: D.E.A., J.S.B., A. Chakravarti, G.E., H. Huang. Steering: D.E.A., A. Chakravarti. Writing: D.E.A., A. Chakravarti. BLSA: Analysis: T.T. Phenotype collection: J.B.S. Overall project supervision: L. Ferrucci. BRIGHT: Phenotyping: M. Brown, M.J.C., P.W.M., P.B.M., N.J.S. Genotyping: P.B.M., S.J.N. Analysis: S.J.N. Overall study supervision: M. Brown, M.J.C., P.B.M., N.J.S. Carlantino: Sample and/or data collection: M.C., L.Z. Overall study supervision: P.G. Data collection and/or statistical analysis: S.U. CHS: Study design: J.C.B., S.R.H., B.M.P., N.S. Data collection: S.R.H., B.M.P., D.S.S. Genotyping: J.I.R. Analysis, interpretation: J.C.B., N.S. Supervision of analyses: B.M.P. Funding for GWAS: B.M.P. Croatia-Korcula and Croatia-Split: GWAS analysis: C.H. Data collection, phenotype measurement, data entry and field work supervision: I.K., O.P. Study design and funding: I.R., A.F.W. DCCT/EDIC: Analyses: D.W. Supervision of analyses: A.D.P. deCODE: Data collection: D.O.A., H. Hólm. Study design: K. Stefansson, U.T., H. Hólm, D.F.G., D.O.A. Data alignment, imputation and statistical analysis: D.F.G. Additional analysis and interpretation of results: K. Stefansson, U.T., H. Hólm, D.F.G., D.O.A. eMERGE: Data curation and GWAS analysis: R.L.Z., Y.B. Supervision of quality control and analysis of data set: M.D.R. Study conception and analysis framework: D.M.R. Algorithm for case ascertainment: J.C.D. ERF: Analysis: A. Isaacs. Data acquisition: C.M.v.D., A. Isaacs, B.A.O., J.A.K., A.G.U. Overall study principal investigators: C.M.v.D., B.A.O. FHS: Analysis plan development: C.N.-C., C.J.O., P.A.N., M.G.L. GWAS analysis: X.Y., M.G.L. Secured funding: C.N.-C., C.J.O. FVG: Data collection: M. Bobbo. Primary analysis: A. Iorio. Statistical analysis: A.P.D.A. Overall study supervision: G.S. Health2000: Data analysis, replication genotyping and quality control: A.M.L. Primary data analysis: A. Marjamaa. Phenotyping, including ECGs: A.J. Electrocardiographic measurements: K.P. GWAS and replication genotyping: M.P. Design of ECG study, analysis and interpretation: L.O. Genetic data collection and analysis: K.K.K. Principal investigator and supervision: V.S. HealthABC: Data collection and supervision: S.R.C., Y.L. Data analysis: D.S.E., M.A.N. HNR: Data collection: H.K. Data generation: H.K., T.W.M. Genetic data generation: M.M.N., P.H., T.W.M. Data analysis: L.E., P.H., T.W.M., M.M.N. Overall study design and principal investigators: R.E., K.-H.J. KORA-F3/S4: Overall QT project supervision: A. Pfeufer. Genotyping oversight: T.M. ECG collection, measurement and interpretation: M.F.S., S. Perz, B.M.B., E.M. Primary genetic analysis: C.G., M.M.-N. Interpretation of results: C.G., A. Pfeufer, H.P., S. Kääb, T.M., M.W. Overall study principal investigator: A. Peters. LifeLines: Phenotyping: R.A.d.B., P.A.v.d.V. Genotyping: L. Franke. Analyses: I.M.N. and L. Franke. MICROS: Sample recruitment and overall study principal investigator: P.P.P. Study supervision, genotyping and data coordination: A.A.H. Data analysis: F.D.G.M., C.F. ORCADES: Phenotype collection: S.H.W. Genotype generation: H.C., J.F.W. GWAS analysis: P.N. Raised funding: J.F.W. Overall study supervision: J.F.W. PopGen: Recruitment and phenotyping: N.E.E.M., N.F. Genotyping and data preparation: A.F. Data preparation and analysis: D.E. PREVEND: Phenotyping: M.P.v.d.B., D.J.v.V., G.N. Genotyping and data analysis: F.W.A., I.M.L., P.v.d.H. Obtained funding: G.N., D.J.v.V., F.W.A., P.v.d.H. Rotterdam Study I and II: Study concept and design: M.E., B.H.S. Data acquisition: M.E., O.H.F., B.P.K., J.A.K., A.H., J.C.M.W., B.H.S., A.G.U. Statistical analysis: M.E. Interpretation: M.E., B.H.S. Obtained funding: A.H., J.C.M.W., A.G.U., B.H.S. Study supervision: B.H.S. SardiNIA: Phenotyping: M.O. Genotyping and data analysis: G.R.A., E.G.L., A. Mulas, M.O., S.S., D.S., K.V.T., M.U. Overall study supervision and principal investigators: D.S., M.U. SHIP: Data acquisition: M.D., M.R.P.M., U.V., S.B.F. Statistical analysis: U.V., M.D., M.R.P.M. Interpretation: U.V., M.D., S.B.F. Obtained funding: S.B.F., U.V. TwinsUK: Study concept and design: H.S., Y.J. Data acquisition: T.D.S. Statistical analysis and interpretation: I.M.N., H.S., Y.J. Obtained funding: Y.J., T.D.S. Young Finns Study: Data collection: T.J.L., O.T.R., M. Kähönen, J.S.V. Genotyping: T.J.L., N.M. Genotyping: T.J.L. Phenotype preparation: O.T.R., M. Kähönen, J.S.V. Analysis: T.J.L., O.T.R., M. Kähönen, J.S.V., L.-P.L. Obtained funding: T.J.L., O.T.R., M. Kähönen, J.S.V.

Directly genotyped SNP replication cohorts. (Author contributions for cohorts that contributed to both GWAS and replication genotyping are shown under the GWAS entry above.) BRHS: Analysis: R.W.M. Custodian of genetic resource: R.W.M., P.H.W. Data collection for genetic resource: P.H.W. Development of genetic resource: A.D.H. Overall study supervision and principal investigators: P.H.W., R.W.M. ECG analyses: P.W.M. Bruneck: Data analysis, interpretation and writing: S. Kiechl. DNA preparation: F. Kronenberg, C.L. ECG measurement and database: M. Knoflach. Supervision, funding, administration and principal investigator: J.W. Carla: Study concept and design: K.H.G., K.W. Genotyping: H.M.z.S. Supervision: K.W. Study design and analysis: A.K. Study concept, supervision and principal investigator: J.H. Cyprus: Study concept, funding, supervision and analysis: A.N.N. Data acquisition, analysis and interpretation: M.G. Genetic, biochemical data acquisition and statistical analysis: A.G.P. Czech Post-MONICA: Data collection and submission: J.A.H., V.A. Galicia: Cohort collection: M. Brion. Study design: M. Brion. Genotyping platform management: A. Carracedo. Genotyping: M.T. Analysis: M.T. Interpretation: M. Brion, A. Carracedo. Financial support: A. Carracedo. Intergene: Genotyping, data analysis and epidemiology expertise: F.N. Genotyping and genetic expertise: Å.T.N. Study design, data collection and disease area knowledge: D.S.T. MIDSPAN Family Study: Data acquisition, statistical analysis and interpretation: S. Padmanabhan. Genotyping: W.K.L. Overall study supervision, data collection and funding: A.F.D., G.C.M.W. PIVUS: Genotyping: A.-C.S. Phenotyping: L.L., J.Ä., J.S. Data analysis: S.G. Overall supervision and principal investigator: E.I. SAPHIR: DNA preparation: F. Kronenberg, C.L. Data collection: L.K., B.P., B.S. Data analysis: L.K., F. Kronenberg, C.L., B.P., B.S. Study design and principal investigator: B.P. ULSAM: Genotyping: A.-C.S. Phenotyping: L.L., J.Ä., J.S. Data analysis: S.G. Overall supervision and principal investigator: E.I. Whitehall II: Data collection and submission: M. Kumari. Overall supervision: M. Kivimaki. Funding: A.D.H.

Meta-analysis of GWAS and replication. D.E.A. and S.L.P. independently performed quality control and meta-analysis of GWAS and replication association results. Polygenic analysis: R.D.K. P.I.W.d.B., A. Pfeufer. and C.N.-C. supervised the analyses.

Non-QT trait lookups. CARe-COGENT: Meta-analysis and lookup: J.G.S. HRGEN: Meta-analysis and lookup: M.d.H. Overall study supervision: R.J.F.L. QRS GWAS: Study supervision: N.S. Meta-analysis: D.E.A., P.I.W.d.B. Results lookup: S.L.P.

Non-cardiac eQTL analyses. Data set acquisition: A.S.P., V.E. Analysis and interpretation: A.D.J. Cell type–specific enrichment tests: S.R., K. Slowikowski.

Left ventricle eQTL analyses. Overall supervision: T.P.C. Recruitment, sample collection: K.M., C.E.M. Sample processing and expression analysis: J. Brandimarto. Statistical analysis: M.M.

Left ventricle enhancer analyses. Analysis: X.W. Overall supervision: L.A.B., M. Kellis.

Mouse knockout. Enrichment tests: S.R., K. Slowikowski. Candidate gene list: P.v.d.H.

LQTS mutation screening. Amsterdam: SLC8A1 sequencing: T.T.K. Clinical data collection: A.B., N.H., A.A.M.W. Study supervision: C.R.B., A.A.M.W. London: Recruitment, phenotyping and strategy: E.R.B. Screening for mutations in LQT1, LQT2 and LQT3 and sample management: C.D. Mayo Clinic: LQTS cohort characteristic organization: D.J.T. TRPM7 mutation analysis and interpretation: D.J.T., A.M.-D., J.R.G. Patient collection, study design, data review and overall supervision: M.J.A. Munich: Study oversight: S. Kääb, A. Pfeufer. Patient collection: B.M.B., E.M. Genotyping: H.P. Nantes: Scientific management: J.-J.S. Clinical, genetic information collection: S.C. ATP2A2 sequencing: S.C. Screening for mutations in LQT1, LQT2 and LQT3 and clinical data collection: J. Barc. LQTS gene diagnosis management: F. Kyndt. Patient enrollment: V.P. Pavia: Patient collection, patient selection and molecular screening supervision: L.C., P.J.S. SRL mutation screening: A.G., R.I. Toronto: Identification of patients with LQTS free of LQT1, LQT2 and LQT3 mutations: R.M.H. Program codevelopment: S.W.S.

DAPPLE analysis. Concept, design and analysis: E.J.R. Supervision: K.L., M.J.D.

Immunoprecipitation experiments. Proteomic experiments and analysis: A.L. Overall study supervision: J.V.O.

Corresponding author

Correspondence to Christopher Newton-Cheh.

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Competing interests

H. Hólm is a former and D.O.A., D.F.G., K. Stefansson and U.T. are current full or part-time employees of deCODE Genetics/Amgen, Inc. A.S.P. was previously an employee of Merck Research Laboratory and is a current employee of Sanofi. F.N. is an employee of AstraZeneca. B.P. serves on the data and safety monitoring board for a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. J.S. serves on the advisory board of Itrim. M.J.A. is a consultant for Boston Scientific, Gilead Sciences, Medtronic and St. Jude Medical. M.J.A. and the Mayo Clinic receive royalties from Transgenomic with respect to their FAMILION-LQTS and FAMILION-CPVT genetic tests.

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Full list of members and affiliations appear in the Supplementary Note.

Full list of members and affiliations appear in the Supplementary Note.

Full list of members and affiliations appear in the Supplementary Note.

Full list of members and affiliations appear in the Supplementary Note.

Full list of members and affiliations appear in the Supplementary Note.

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Supplementary Figures 1–5, Supplementary Tables 1–3 and 5–18, and Supplementary Note (PDF 12908 kb)

Supplementary Table 4

Metabochip QT replication + fine-mapping of SNP content. (XLSX 835 kb)

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Arking, D., Pulit, S., Crotti, L. et al. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat Genet 46, 826–836 (2014). https://doi.org/10.1038/ng.3014

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