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 via your institution
Open Access articles citing this article.
Hypertrophic cardiomyopathy in purpose-bred cats with the A31P mutation in cardiac myosin binding protein-C
Scientific Reports Open Access 26 June 2023
Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease
Nature Communications Open Access 14 March 2023
Phenotypic and Genetic Factors Associated with Absence of Cardiomyopathy Symptoms in PLN:c.40_42delAGA Carriers
Journal of Cardiovascular Translational Research Open Access 09 January 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
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/).
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).
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).
Watkins, H., Ashrafian, H. & Redwood, C. Inherited cardiomyopathies. N. Engl. J. Med. 364, 1643–1656 (2011).
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).
Ingles, J. et al. Nonfamilial hypertrophic cardiomyopathy: prevalence, natural history and clinical implication. Circ. Cardiovasc. Genet. 10, e001620 (2017).
Watanabe, K., Taskesen, E., Van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Neubauer, S. et al. Distinct subgroups in hypertrophic cardiomyopathy in the NHLBI HCM Registry. J. Am. Coll. Cardiol. 74, 2333–2345 (2019).
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).
Dickson, S. P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D. B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).
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).
Wooten, E. C. et al. Formin homology 2 domain containing 3 variants associated with hypertrophic cardiomyopathy. Circ. Cardiovasc. Genet. 6, 10–18 (2013).
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).
Esslinger, U. et al. Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy. PLoS ONE 12, e0172995 (2017).
Selcen, D. et al. Mutation in BAG3 causes severe dominant childhood muscular dystrophy. Ann. Neurol. 65, 83–89 (2009).
Odgerel, Z. et al. Inheritance patterns and phenotypic features of myofibrillar myopathy associated with a BAG3 mutation. Neuromuscul. Disord. 20, 438–442 (2010).
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).
Fumagalli, C. et al. Association of obesity with adverse long-term outcomes in hypertrophic cardiomyopathy. JAMA Cardiol. 5, 65–72 (2019).
Ho, C. Y. et al. Genotype and lifetime burden of disease in hypertrophic cardiomyopathy. Circulation 138, 1387–1398 (2018).
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).
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).
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).
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).
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).
Roselli, C. et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat. Genet. 50, 1225–1233 (2018).
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).
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).
Shah, S. et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun. 11, 163 (2020).
Franklin, S. S. et al. Predictors of new-onset diastolic and systolic hypertension: the Framingham Heart Study. Circulation 111, 1121–1127 (2005).
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).
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).
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).
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).
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).
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).
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).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
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).
Turro, E. et al. Whole-genome sequencing of patients with rare diseases in a national health system. Nature 583, 96–102 (2020).
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).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
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).
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).
Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
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).
Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).
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).
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).
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).
Carvalho-Silva, D. et al. Open targets platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2019).
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35, 4851–4853 (2019).
Pickrell, J. K. et al. Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet. 48, 709–717 (2016).
Barrett, J. C., Fry, B., Maller, J. & Daly, M. J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).
Toepfer, C. N. et al. Hypertrophic cardiomyopathy mutations in MYBPC3 dysregulate myosin. Sci. Transl. Med. 11, eaat1199 (2019).
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).
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).
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).
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).
Petersen, S. E. et al. UK Biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18, 8 (2016).
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).
Wasserstrum, Y. et al. The impact of diabetes mellitus on the clinical phenotype of hypertrophic cardiomyopathy. Eur. Heart J. 40, 1671–1677 (2019).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).
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).
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).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 3, 215–216 (2012).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
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.
As of April 2020, A.R.H. is an employee of AstraZeneca.
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.
About this article
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
This article is cited by
European Journal of Human Genetics (2023)
Hypertrophic cardiomyopathy in purpose-bred cats with the A31P mutation in cardiac myosin binding protein-C
Scientific Reports (2023)
Current Cardiology Reports (2023)
Frontiers of Medicine (2023)
Die Kardiologie (2023)