Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity

Abstract

Hypertrophic cardiomyopathy (HCM) is a common, serious, genetic heart disorder. Rare pathogenic variants in sarcomere genes cause HCM, but with unexplained phenotypic heterogeneity. Moreover, most patients do not carry such variants. We report a genome-wide association study of 2,780 cases and 47,486 controls that identified 12 genome-wide-significant susceptibility loci for HCM. Single-nucleotide polymorphism heritability indicated a strong polygenic influence, especially for sarcomere-negative HCM (64% of cases; h2g = 0.34 ± 0.02). A genetic risk score showed substantial influence on the odds of HCM in a validation study, halving the odds in the lowest quintile and doubling them in the highest quintile, and also influenced phenotypic severity in sarcomere variant carriers. Mendelian randomization identified diastolic blood pressure (DBP) as a key modifiable risk factor for sarcomere-negative HCM, with a one standard deviation increase in DBP increasing the HCM risk fourfold. Common variants and modifiable risk factors have important roles in HCM that we suggest will be clinically actionable.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Study design for the HCM genome-wide association analysis.
Fig. 2: Validation of an HCM GRS.
Fig. 3: Relationship between standardized GRS and maximum left ventricular wall thickness.
Fig. 4: Two-sample inverse-variance-weighted Mendelian randomization identifies modifiable risk factors for HCM.

Data availability

All of the relevant data are included within the paper and/or its Supplementary Information files. The datasets generated during this study are available from the corresponding author upon reasonable request. The institutional domain www.well.ox.ac.uk/hcm will provide summary-level statistics.

Code availability

Publicly available software tools were used to analyze these data. These include: SAIGE (https://github.com/weizhouUMICH/SAIGE), SNPTEST (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html), GCTA (https://cnsgenomics.com/software/gcta/), PLINK (https://www.cog-genomics.org/plink/1.9/data), BGENIX (https://bitbucket.org/gavinband/bgen/wiki/bgenix), QCTOOL (https://www.well.ox.ac.uk/~gav/qctool_v2/), GWAMA (https://genomics.ut.ee/en/tools/gwama) and MR-Base (http://www.mrbase.org/).

References

  1. Yotti, R., Seidman, C. E. & Seidman, J. G. Advances in the genetic basis and pathogenesis of sarcomere cardiomyopathies. Annu. Rev. Genomics Hum. Genet. 20, 129–153 (2019).

    CAS  PubMed  Google Scholar 

  2. Harper, A. R., Parikh, V. N., Goldfeder, R. L., Caleshu, C. & Ashley, E. A. Delivering clinical grade sequencing and genetic test interpretation for cardiovascular medicine. Circ. Cardiovasc. Genet. 10, e001221 (2017).

    PubMed  Google Scholar 

  3. Watkins, H., Ashrafian, H. & Redwood, C. Inherited cardiomyopathies. N. Engl. J. Med. 364, 1643–1656 (2011).

    CAS  PubMed  Google Scholar 

  4. Thomson, K. L. et al. Analysis of 51 proposed hypertrophic cardiomyopathy genes from genome sequencing data in sarcomere negative cases has negligible diagnostic yield. Genet. Med. 21, 1576–1584 (2019).

    CAS  PubMed  Google Scholar 

  5. Ingles, J. et al. Nonfamilial hypertrophic cardiomyopathy: prevalence, natural history and clinical implication. Circ. Cardiovasc. Genet. 10, e001620 (2017).

    CAS  PubMed  Google Scholar 

  6. Watanabe, K., Taskesen, E., Van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

  8. Neubauer, S. et al. Distinct subgroups in hypertrophic cardiomyopathy in the NHLBI HCM Registry. J. Am. Coll. Cardiol. 74, 2333–2345 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Wray, N. R., Purcell, S. M. & Visscher, P. M. Synthetic associations created by rare variants do not explain most GWAS results. PLoS Biol. 9, e1000579 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).

    PubMed  PubMed Central  Google Scholar 

  11. Orozco, G., Barrett, J. C. & Zeggini, E. Synthetic associations in the context of genome-wide association scan signals. Hum. Mol. Genet. 19, R137–R144 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Wooten, E. C. et al. Formin homology 2 domain containing 3 variants associated with hypertrophic cardiomyopathy. Circ. Cardiovasc. Genet. 6, 10–18 (2013).

    CAS  PubMed  Google Scholar 

  13. Ochoa, J. P. et al. Formin homology 2 domain containing 3 (FHOD3) is a genetic basis for hypertrophic cardiomyopathy. J. Am. Coll. Cardiol. 72, 2457–2467 (2018).

    CAS  PubMed  Google Scholar 

  14. Esslinger, U. et al. Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy. PLoS ONE 12, e0172995 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. Selcen, D. et al. Mutation in BAG3 causes severe dominant childhood muscular dystrophy. Ann. Neurol. 65, 83–89 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Odgerel, Z. et al. Inheritance patterns and phenotypic features of myofibrillar myopathy associated with a BAG3 mutation. Neuromuscul. Disord. 20, 438–442 (2010).

    PubMed  PubMed Central  Google Scholar 

  17. Villard, E. et al. A genome-wide association study identifies two loci associated with heart failure due to dilated cardiomyopathy. Eur. Heart J. 32, 1065–1076 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Fumagalli, C. et al. Association of obesity with adverse long-term outcomes in hypertrophic cardiomyopathy. JAMA Cardiol. 5, 65–72 (2019).

    PubMed Central  Google Scholar 

  19. Ho, C. Y. et al. Genotype and lifetime burden of disease in hypertrophic cardiomyopathy. Circulation 138, 1387–1398 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).

    CAS  PubMed  Google Scholar 

  23. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700 000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Roselli, C. et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat. Genet. 50, 1225–1233 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51, 957–972 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Shah, S. et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun. 11, 163 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Franklin, S. S. et al. Predictors of new-onset diastolic and systolic hypertension: the Framingham Heart Study. Circulation 111, 1121–1127 (2005).

    PubMed  Google Scholar 

  30. Franklin, S. S., Jacobs, M. J., Wong, N. D., L’Italien, G. J. & Lapuerta, P. Predominance of isolated systolic hypertension among middle-aged and elderly US hypertensives: analysis based on National Health and Nutrition Examination Survey (NHANES) III. Hypertension 37, 869–874 (2001).

    CAS  PubMed  Google Scholar 

  31. Gersh, B. J. et al. 2011 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. Circulation 124, e783–e831 (2011).

    PubMed  Google Scholar 

  32. Harper, A. R. et al. A re-evaluation of the South Asian MYBPC3Δ25bp intronic deletion in hypertrophic cardiomyopathy. Circ. Genom. Precis. Med. 13, e002783 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Richards, 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. 17, 405–424 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. Walsh, R. et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. 19, 192–203 (2017).

    PubMed  Google Scholar 

  35. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Loh, P. R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Pirinen, M., Donnelly, P. & Spencer, C. C. A. Including known covariates can reduce power to detect genetic effects in case–control studies. Nat. Genet. 44, 848–851 (2012).

    CAS  PubMed  Google Scholar 

  39. Turro, E. et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature 583, 96–102 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Yang, J., Zeng, J., Goddard, M. E., Wray, N. R. & Visscher, P. M. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

    CAS  PubMed  Google Scholar 

  44. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    CAS  PubMed  Google Scholar 

  47. Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. Tadros, R. et al. Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect. Nat. Genet. https://doi.org/10.1038/s41588-020-00762-2 (2021).

  49. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Carvalho-Silva, D. et al. Open targets platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).

    CAS  PubMed  Google Scholar 

  52. Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35, 4851–4853 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    CAS  PubMed  Google Scholar 

  55. Toepfer, C. N. et al. Hypertrophic cardiomyopathy mutations in MYBPC3 dysregulate myosin. Sci. Transl. Med. 11, eaat1199 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Toepfer, C. N. et al. Myosin sequestration regulates sarcomere function, cardiomyocyte energetics, and metabolism, informing the pathogenesis of hypertrophic cardiomyopathy. Circulation 141, 828–842 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Kelly, M. A. et al. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel. Genet. Med. 20, 351–359 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Wright, C. F. et al. Assessing the pathogenicity, penetrance, and expressivity of putative disease-causing variants in a population setting. Am. J. Hum. Genet. 104, 275–286 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Van Hout, C. V. et al. Exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).

    PubMed  PubMed Central  Google Scholar 

  61. Karam, R., Lever, H. M. & Healy, B. P. Hypertensive hypertrophic cardiomyopathy or hypertrophic cardiomyopathy with hypertension? A study of 78 patients. J. Am. Coll. Cardiol. 13, 580–584 (1989).

    CAS  PubMed  Google Scholar 

  62. Wasserstrum, Y. et al. The impact of diabetes mellitus on the clinical phenotype of hypertrophic cardiomyopathy. Eur. Heart J. 40, 1671–1677 (2019).

    PubMed  Google Scholar 

  63. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 3, 215–216 (2012).

    Google Scholar 

  67. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by funding from the British Heart Foundation (BHF), the Medical Research Council (MRC), the National Heart, Lung, and Blood Institute (NIH grant U01HL117006-01A1), the Wellcome Trust (201543/B/16/Z), Wellcome Trust core awards (090532/Z/09/Z and 203141/Z/16/Z) and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. A.R.H. has received support from the MRC Doctoral Training Partnership. A.G. has received support from the BHF, European Commission (LSHM-CT-2007-037273 and HEALTH-F2-2013-601456) and Tripartite Immunometabolism Consortium (TrIC)-NovoNordisk Foundation (NNF15CC0018486). S.E.P. acknowledges support from the NIHR Barts Biomedical Research Centre. A.W. has received support from the Wellcome Trust. S.N., M.F. and H.W. are members of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). We are grateful for access to the high-performance Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute that is supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the author(s) and do not necessarily reflect those of the NHS, NIHR, Department of Health or Department of Health and Social Care. We thank the NIHR BioResource volunteers for their participation and gratefully acknowledge the NIHR BioResource centers, NHS Trusts and staff for their contribution. We thank the NIHR and NHS Blood and Transplant. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project, which is managed by Genomics England (a wholly owned company of the Department of Health and Social Care) and funded by the NIHR and NHS England with research infrastructure funding from the Wellcome Trust, Cancer Research UK and the MRC. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We acknowledge the contribution of the Oxford Medical Genetics Laboratories.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

A.R.H., M.F. and H.W. conceived of and designed the study. A.R.H., A.G., C.G., K.L.T., S.E.P., A.W., E.O., C.M.K., S.N. and C.Y.H. acquired, analyzed and interpreted the data. X.X. and R.T. provided assistance with replication. A.R.H., M.F. and H.W. wrote the manuscript. J.S.W., C.R.B. and R.T. critically revised the manuscript for important intellectual content.

Corresponding author

Correspondence to Hugh Watkins.

Ethics declarations

Competing interests

As of April 2020, A.R.H. is an employee of AstraZeneca.

Additional information

Peer review information Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Tables 1–25

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harper, A.R., Goel, A., Grace, C. et al. Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity. Nat Genet 53, 135–142 (2021). https://doi.org/10.1038/s41588-020-00764-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-020-00764-0

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing