Article

Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands

  • Nature Genetics volume 49, pages 15931601 (2017)
  • doi:10.1038/ng.3970
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Abstract

Congenital heart disease (CHD) is the leading cause of mortality from birth defects. Here, exome sequencing of a single cohort of 2,871 CHD probands, including 2,645 parent–offspring trios, implicated rare inherited mutations in 1.8%, including a recessive founder mutation in GDF1 accounting for 5% of severe CHD in Ashkenazim, recessive genotypes in MYH6 accounting for 11% of Shone complex, and dominant FLT4 mutations accounting for 2.3% of Tetralogy of Fallot. De novo mutations (DNMs) accounted for 8% of cases, including 3% of isolated CHD patients and 28% with both neurodevelopmental and extra-cardiac congenital anomalies. Seven genes surpassed thresholds for genome-wide significance, and 12 genes not previously implicated in CHD had >70% probability of being disease related. DNMs in 440 genes were inferred to contribute to CHD. Striking overlap between genes with damaging DNMs in probands with CHD and autism was also found.

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References

  1. 1.

    et al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J. Am. Coll. Cardiol. 58, 2241–2247 (2011).

  2. 2.

    , , , & Prevalence of congenital anomalies in newborns with congenital heart disease diagnosis. Ann. Pediatr. Cardiol. 7, 86–91 (2014).

  3. 3.

    et al. Neurodevelopmental outcomes in children with congenital heart disease: evaluation and management: a scientific statement from the American Heart Association. Circulation 126, 1143–1172 (2012).

  4. 4.

    et al. Contribution of global rare copy-number variants to the risk of sporadic congenital heart disease. Am. J. Hum. Genet. 91, 489–501 (2012).

  5. 5.

    et al. Increased frequency of de novo copy number variants in congenital heart disease by integrative analysis of single nucleotide polymorphism array and exome sequence data. Circ. Res. 115, 884–896 (2014).

  6. 6.

    & Genetics and genomics of congenital heart disease. Circ. Res. 120, 923–940 (2017).

  7. 7.

    Pediatric Cardiac Genomics Consortium. et al. The Congenital Heart Disease Genetic Network Study: rationale, design, and early results. Circ. Res. 112, 698–706 (2013).

  8. 8.

    et al. De novo mutations in histone-modifying genes in congenital heart disease. Nature 498, 220–223 (2013).

  9. 9.

    et al. De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science 350, 1262–1266 (2015).

  10. 10.

    et al. Recurrence of congenital heart defects in families. Circulation 120, 295–301 (2009).

  11. 11.

    et al. Global genetic analysis in mice unveils central role for cilia in congenital heart disease. Nature 521, 520–524 (2015).

  12. 12.

    , & Insights into the genetic structure of congenital heart disease from human and murine studies on monogenic disorders. Cold Spring Harb. Perspect. Med. 4, a013946 (2014).

  13. 13.

    et al. Distinct genetic architectures for syndromic and nonsyndromic congenital heart defects identified by exome sequencing. Nat. Genet. 48, 1060–1065 (2016).

  14. 14.

    et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582–588 (2015).

  15. 15.

    et al. VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix. Genet. Epidemiol. 37, 622–634 (2013).

  16. 16.

    et al. A probabilistic disease-gene finder for personal genomes. Genome Res. 21, 1529–1542 (2011).

  17. 17.

    et al. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am. J. Hum. Genet. 94, 599–610 (2014).

  18. 18.

    & DMLE+: Bayesian linkage disequilibrium gene mapping. Bioinformatics 18, 894–895 (2002).

  19. 19.

    et al. Recessively inherited right atrial isomerism caused by mutations in growth/differentiation factor 1 (GDF1). Hum. Mol. Genet. 19, 2747–2753 (2010).

  20. 20.

    Expression of growth/differentiation factor 1 in the nervous system: conservation of a bicistronic structure. Proc. Natl. Acad. Sci. USA 88, 4250–4254 (1991).

  21. 21.

    , , & Regulation of left–right patterning in mice by growth/differentiation factor-1. Nat. Genet. 24, 262–265 (2000).

  22. 22.

    , , , & Long-range action of Nodal requires interaction with GDF1. Genes Dev. 21, 3272–3282 (2007).

  23. 23.

    et al. Mutation in myosin heavy chain 6 causes atrial septal defect. Nat. Genet. 37, 423–428 (2005).

  24. 24.

    et al. Coding sequence rare variants identified in MYBPC3, MYH6, TPM1, TNNC1, and TNNI3 from 312 patients with familial or idiopathic dilated cardiomyopathy. Circ. Cardiovasc. Genet. 3, 155–161 (2010).

  25. 25.

    et al. Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly. Circulation 105, 446–451 (2002).

  26. 26.

    et al. Mitral valve morphology and morbidity/mortality in Shone's complex. Am. J. Cardiol. 95, 541–543 (2005).

  27. 27.

    et al. Recessive MYH6 mutations in hypoplastic left heart with reduced ejection fraction. Circ. Cardiovasc. Genet. 8, 564–571 (2015).

  28. 28.

    , & Congenital heart disease and primary ciliary dyskinesia. Paediatr. Respir. Rev. 18, 25–32 (2016).

  29. 29.

    et al. Missense mutations interfere with VEGFR-3 signalling in primary lymphoedema. Nat. Genet. 25, 153–159 (2000).

  30. 30.

    et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

  31. 31.

    et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

  32. 32.

    et al. Nonsynonymous variants in the SMAD6 gene predispose to congenital cardiovascular malformation. Hum. Mutat. 33, 720–727 (2012).

  33. 33.

    et al. Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and common BMP2 alleles. eLife 5, e20125 (2016).

  34. 34.

    , & Consanguinity and the risk of congenital heart disease. Am. J. Med. Genet. A. 158A, 1236–1241 (2012).

  35. 35.

    et al. Expression of the fms-like tyrosine kinase 4 gene becomes restricted to lymphatic endothelium during development. Proc. Natl. Acad. Sci. USA 92, 3566–3570 (1995).

  36. 36.

    et al. Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage. Cell 151, 206–220 (2012).

  37. 37.

    et al. A temporal chromatin signature in human embryonic stem cells identifies regulators of cardiac development. Cell 151, 221–232 (2012).

  38. 38.

    et al. KMT2D regulates specific programs in heart development via histone H3 lysine 4 di-methylation. Development 143, 810–821 (2016).

  39. 39.

    , & Long-term outcomes in children with congenital heart disease: National Health Interview Survey. J. Pediatr. 166, 119–124 (2015).

  40. 40.

    et al. Prepregnancy diabetes and offspring risk of congenital heart disease: a nationwide cohort study. Circulation 133, 2243–2253 (2016).

  41. 41.

    , , , & Influence of genetic and maternal diabetes in the pathogenesis of visceroatrial heterotaxy in mice. Teratology 54, 183–190 (1996).

  42. 42.

    , , , & High throughput in vivo functional validation of candidate congenital heart disease genes in Drosophila. eLife 6, e22617 (2017).

  43. 43.

    et al. Comparison of shunt types in the Norwood procedure for single-ventricle lesions. N. Engl. J. Med. 362, 1980–1992 (2010).

  44. 44.

    et al. Factors associated with neurodevelopment for children with single ventricle lesions. J. Pediatr. 165, 490–496.e8 (2014).

  45. 45.

    & The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192–195 (2010).

  46. 46.

    et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  47. 47.

    et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

  48. 48.

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  49. 49.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  50. 50.

    et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  51. 51.

    , & dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum. Mutat. 34, E2393–E2402 (2013).

  52. 52.

    et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015).

  53. 53.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  54. 54.

    et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  55. 55.

    et al. Recessive mutations in DGKE cause atypical hemolytic-uremic syndrome. Nat. Genet. 45, 531–536 (2013).

  56. 56.

    et al. Ancestry estimation and control of population stratification for sequence-based association studies. Nat. Genet. 46, 409–415 (2014).

  57. 57.

    et al. Signatures of founder effects, admixture, and selection in the Ashkenazi Jewish population. Proc. Natl. Acad. Sci. USA 107, 16222–16227 (2010).

  58. 58.

    et al. A Bayesian framework for de novo mutation calling in parents–offspring trios. Bioinformatics 31, 1375–1381 (2015).

  59. 59.

    , , & Interpreting de novo variation in human disease using denovolyzeR. Curr. Protoc. Hum. Genet. 87, 7.25.1–7.25.15 (2015).

  60. 60.

    et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

  61. 61.

    Statistical Methods for Research Workers (Oliver and Boyd, 1925).

  62. 62.

    , , , & GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).

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Acknowledgements

We are grateful to the patients and families who participated in this research. We thank the following people for outstanding contributions to patient recruitment: A. Julian, M. Mac Neal, Y. Mendez, T. Mendiz-Ramdeen and C. Mintz (Icahn School of Medicine at Mount Sinai); N. Cross (Yale School of Medicine); J. Ellashek and N. Tran (Children's Hospital of Los Angeles); B. McDonough, J. Geva and M. Borensztein (Harvard Medical School), K. Flack, L. Panesar and N. Taylor (University College London); E. Taillie (University of Rochester School of Medicine and Dentistry); S. Edman, J. Garbarini, J. Tusi and S. Woyciechowski (Children's Hospital of Philadelphia); D. Awad, C. Breton, K. Celia, C. Duarte, D. Etwaru, N. Fishman, M. Kaspakoval, J. Kline, R. Korsin, A. Lanz, E. Marquez, D. Queen, A. Rodriguez, J. Rose, J.K. Sond, D. Warburton, A. Wilpers and R. Yee (Columbia Medical School). We are grateful to J. Ekstein and D. Yeshorim for provision of anonymized DNA samples. The authors thank S. Wang for critical discussion. This work was supported by U01 HL098153 and grant UL1TR000003 from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health; grants to the Pediatric Cardiac Genomics Consortium (UM1-HL098147, UM1-HL128761, UM1-HL098123, UM1-HL128711, UM1-HL098162, UO1-HL131003, UO1-HL098188, UO1-HL098153, UO1-HL098163); the NIH Centers for Mendelian Genomics (5U54HG006504); the Howard Hughes Medical Institute (R.P.L. and C.E.S.); and the Simons Foundation (W.K.C.). S.C.J. was supported by the James Hudson Brown-Alexander Brown Coxe Postdoctoral Fellowship at the Yale University School of Medicine. J.H. was supported by the John S. LaDue Fellowship at Harvard Medical School and is a recipient of the Alan Lerner Research Award at the Brigham and Women's Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, the National Center for Research Resources or the NIH.

Author information

Author notes

    • Sheng Chih Jin
    • , Jason Homsy
    •  & Samir Zaidi

    These authors contributed equally to this work.

    • Christine E Seidman
    • , Richard P Lifton
    •  & Martina Brueckner

    These authors jointly directed this project.

Affiliations

  1. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Sheng Chih Jin
    • , Samir Zaidi
    • , Xue Zeng
    • , Michael C Sierant
    • , Wei-Chien Hung
    • , Junhui Zhang
    • , Richard P Lifton
    •  & Martina Brueckner
  2. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Jason Homsy
    • , Steven R DePalma
    • , Jonathan G Seidman
    •  & Christine E Seidman
  3. Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Jason Homsy
    •  & Christine E Seidman
  4. Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

    • Qiongshi Lu
    •  & Hongyu Zhao
  5. Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.

    • Sarah Morton
  6. Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, USA.

    • Hongjian Qi
  7. Department of Pediatrics, Columbia University Medical Center, New York, New York, USA.

    • Weni Chang
  8. Department of Computational Chemistry, University College London School of Pharmacy, London, UK.

    • Shozeb Haider
  9. Yale Center for Genome Analysis, Yale University, New Haven, Connecticut, USA.

    • James Knight
    • , Robert D Bjornson
    • , Christopher Castaldi
    • , Irina R Tikhonoa
    • , Kaya Bilguvar
    •  & Shrikant M Mane
  10. Department of Psychiatry, University of California San Francisco, San Francisco, California, USA.

    • Stephan J Sanders
  11. Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.

    • Seema Mital
  12. Division of Pediatric Cardiology, University of Michigan, Ann Arbor, Michigan, USA.

    • Mark W Russell
  13. Department of Pediatric Cardiac Surgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.

    • J William Gaynor
  14. Department of Cardiology, University College London and Great Ormond Street Hospital, London, UK.

    • John Deanfield
    •  & Alessandro Giardini
  15. Department of Pediatrics, University of Rochester Medical Center, The School of Medicine and Dentistry, Rochester, New York, USA.

    • George A Porter Jr
  16. Gladstone Institute of Cardiovascular Disease, San Francisco, California, USA.

    • Deepak Srivastava
  17. Roddenberry Stem Cell Center at Gladstone, San Francisco, California, USA.

    • Deepak Srivastava
  18. Departments of Pediatrics and Biochemistry & Biophysics, University of California, San Francisco, San Francisco, California, USA.

    • Deepak Srivastava
  19. Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

    • Cecelia W Lo
  20. Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, New York, USA.

    • Yufeng Shen
  21. Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, Utah, USA.

    • W Scott Watkins
    • , Mark Yandell
    •  & H Joseph Yost
  22. USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, Utah, USA.

    • Mark Yandell
  23. Division of Pediatric Cardiology, University of Utah, Salt Lake City, Utah, USA.

    • Martin Tristani-Firouzi
  24. Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA.

    • Jane W Newburger
    •  & Amy E Roberts
  25. Pediatric Cardiac Surgery, Children's Hospital of Los Angeles, Los Angeles, California, USA.

    • Richard Kim
  26. Heart Development and Structural Diseases Branch, Division of Cardiovascular Sciences, NHLBI/NIH, Bethesda, Maryland, USA.

    • Jonathan R Kaltman
  27. Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Elizabeth Goldmuntz
  28. Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, New York, USA.

    • Wendy K Chung
  29. Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Bruce D Gelb
  30. Howard Hughes Medical Institute, Harvard University, Boston, Massachusetts, USA.

    • Christine E Seidman
  31. Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, New York, USA.

    • Richard P Lifton
  32. Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Martina Brueckner

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Contributions

Study design: M.B., W.K.C., M.T.-F., B.D.G., E.G., J.R.K., R.P.L., J.G.S., C.E.S.; cohort ascertainment, phenotypic characterization and recruitment: M.B., W.C., W.K.C., J.D., A.G., B.D.G., E.G., J.W.G., J.H., R.K., S.M., J.W.N., G.A.P., A.E.R., M.W.R., C.E.S.; exome sequencing production and validation: K.B., C.C., R.P.L., S.M.M., I.R.T., J.Z.; exome sequencing analysis: M.B., R.D.B., S.R.D., S.C.J., J.H., W.-C.H., J.K., R.P.L., S.M., S.M.M., H.Q., C.E.S., J.G.S., M.C.S., S.J.S., Y.S., W.S.W., M.Y., S.Z., X.Z.; statistical analysis: J.H., S.C.J., R.P.L., Q.L., S.M., C.E.S., S.W., M.Y., H.Z., S.Z.; biophysical simulation for GDF1: S.H.; writing and review of manuscript: M.B., W.K.C., M.T.-F., B.D.G., E.G., J.H., S.C.J., J.R.K., C.W.L., R.P.L., Q.L., C.E.S., D.S., J.G.S., H.J.Y., S.Z. All authors read and approved the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Richard Kim or Jonathan G Seidman or Bruce D Gelb or Christine E Seidman or Martina Brueckner.

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