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.

  • Review Article
  • Published:

Genetics of rheumatic fever and rheumatic heart disease

Abstract

Rheumatic heart disease (RHD) is a complication of group A streptococcal infection that results from a complex interaction between the genetic make-up of the host, the infection itself and several other environmental factors, largely reflecting poverty. RHD is estimated to affect 33.4 million people and results in 10.5 million disability-adjusted life-years lost globally. The disease has long been considered heritable but still little is known about the host genetic factors that increase or reduce the risk of developing RHD. In the 1980s and 1990s, several reports linked the disease to the human leukocyte antigen (HLA) locus on chromosome 6, followed in the 2000s by reports implicating additional candidate regions elsewhere in the genome. Subsequently, the search for susceptibility loci has been reinvigorated by the use of genome-wide association studies (GWAS) through which millions of variants can be tested for association in thousands of individuals. Early findings implicate not only HLA, particularly the HLA-DQA1 to HLA-DQB1 region, but also the immunoglobulin heavy chain locus, including the IGHV4-61 gene segment, on chromosome 14. In this Review, we assess the emerging role of GWAS in assessing RHD, outlining both the advantages and disadvantages of this approach. We also highlight the potential use of large-scale, publicly available data and the value of international collaboration to facilitate comprehensive studies that produce findings that have implications for clinical practice.

Key points

  • Rheumatic heart disease (RHD) remains a public health priority in low-income and middle-income countries, despite being nearly eliminated in high-income countries.

  • A combination of risk factors can contribute to increased susceptibility to group A streptococcal infection, rheumatic fever and, ultimately, RHD.

  • The risk of rheumatic fever in an individual with a family history of RHD is nearly fivefold higher than that in an individual with no family history of RHD.

  • Plausible susceptibility loci have been evaluated on chromosome 6 in the human leukocyte antigen (HLA) region and elsewhere in the human genome.

  • Initial findings from genome-wide association studies (GWAS), through which millions of variants can be tested for association in thousands of individuals, implicate not only the HLA region, but also the immunoglobulin heavy chain (IGH) locus on chromosome 14.

  • Large-scale collaborative efforts to combine GWAS data have the potential to advance our understanding of the genetics of RHD.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Pathogenesis of rheumatic fever and rheumatic heart disease.
Fig. 2: Reported susceptibility genes for rheumatic fever and rheumatic heart disease.
Fig. 3: Detection of underlying causal variation.
Fig. 4: Effect sizes for the IGHV4-61*02 risk allele by diagnostic certainty.
Fig. 5: Power calculations for genome-wide association studies.
Fig. 6: Statistical estimation of variants using imputation.

Similar content being viewed by others

References

  1. Erdem, G. et al. Group A streptococcal isolates temporally associated with acute rheumatic fever in Hawaii: differences from the continental United States. Clin. Infect. Dis. 45, e20–e24 (2007).

    PubMed  Google Scholar 

  2. Carapetis, J. R., McDonald, M. & Wilson, N. J. Acute rheumatic fever. Lancet 366, 155–168 (2005).

    PubMed  Google Scholar 

  3. Steer, A. C., Danchin, M. H. & Carapetis, J. R. Group A streptococcal infections in children. J. Paediatr. Child Health 43, 203–213 (2007).

    PubMed  Google Scholar 

  4. Ferretti, J. & Kohler, W. in Streptococcus pyogenes: Basic Biology to Clinical Manifestations Ch. 1 (eds Ferretti, J. J., Stevens, D. L. & Fischetti, V. A.) (University of Oklahoma Health Sciences Center, 2016).

  5. Longo-Mbenza, B. et al. Survey of rheumatic heart disease in school children of Kinshasa town. Int. J. Cardiol. 63, 287–294 (1998).

    CAS  PubMed  Google Scholar 

  6. Meira, Z. M., Goulart, E. M., Colosimo, E. A. & Mota, C. C. Long term follow up of rheumatic fever and predictors of severe rheumatic valvar disease in Brazilian children and adolescents. Heart 91, 1019–1022 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Massell, B. F., Chute, C. G., Walker, A. M. & Kurland, G. S. Penicillin and the marked decrease in morbidity and mortality from rheumatic fever in the United States. N. Engl. J. Med. 318, 280–286 (1988).

    CAS  PubMed  Google Scholar 

  8. Gordis, L. The virtual disappearance of rheumatic fever in the United States: lessons in the rise and fall of disease. T. Duckett Jones Memorial Lecture. Circulation 72, 1155–1162 (1985).

    CAS  PubMed  Google Scholar 

  9. Carapetis, J. R. et al. Acute rheumatic fever and rheumatic heart disease. Nat. Rev. Dis. Prim. 2, 15084 (2016).

    PubMed  Google Scholar 

  10. Yusuf, S., Narula, J. & Gamra, H. Can we eliminate rheumatic fever and premature deaths from RHD? Glob. Heart 12, 3–4 (2017).

    PubMed  Google Scholar 

  11. Carapetis, J. R., Steer, A. C., Mulholland, E. K. & Weber, M. The global burden of group A streptococcal diseases. Lancet Infect. Dis. 5, 685–694 (2005).

    PubMed  Google Scholar 

  12. Watkins, D. A. et al. Global, regional, and national burden of rheumatic heart disease, 1990-2015. N. Engl. J. Med. 377, 713–722 (2017).

    PubMed  Google Scholar 

  13. Rothenbuhler, M. et al. Active surveillance for rheumatic heart disease in endemic regions: a systematic review and meta-analysis of prevalence among children and adolescents. Lancet Glob. Health 2, e717–e726 (2014).

    PubMed  Google Scholar 

  14. Bhaya, M., Panwar, S., Beniwal, R. & Panwar, R. B. High prevalence of rheumatic heart disease detected by echocardiography in school children. Echocardiography 27, 448–453 (2010).

    PubMed  Google Scholar 

  15. Paar, J. A. et al. Prevalence of rheumatic heart disease in children and young adults in Nicaragua. Am. J. Cardiol. 105, 1809–1814 (2010).

    PubMed  PubMed Central  Google Scholar 

  16. Cheadle, W. B. Barbeian lectures on the various manifestation of the rheumatic state as exemplified in childhood and early life. Lancet 133, 871–877 (1889).

    Google Scholar 

  17. Engel, M. E., Stander, R., Vogel, J., Adeyemo, A. A. & Mayosi, B. M. Genetic susceptibility to acute rheumatic fever: a systematic review and meta-analysis of twin studies. PLOS ONE 6, e25326 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Bryant, P. A., Robins-Browne, R., Carapetis, J. R. & Curtis, N. Some of the people, some of the time: susceptibility to acute rheumatic fever. Circulation 119, 742–753 (2009).

    PubMed  Google Scholar 

  19. Okello, E. et al. Socioeconomic and environmental risk factors among rheumatic heart disease patients in Uganda. PLOS ONE 7, e43917 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Guilherme, L. & Kalil, J. Rheumatic heart disease: molecules involved in valve tissue inflammation leading to the autoimmune process and anti-S. pyogenes vaccine. Front. Immunol. 4, 352 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. Guilherme, L. et al. Rheumatic fever: how S. pyogenes-primed peripheral T cells trigger heart valve lesions. Ann. N. Y. Acad. Sci. 1051, 132–140 (2005).

    CAS  PubMed  Google Scholar 

  22. Madsen, T. & Kalbak, K. Investigation on rheumatic fever subsequent to some epidemics of septic sore throat (especially milk epidemics). Acta Pathol. Microbiol. Scand. 37, 305–327 (1940).

    Google Scholar 

  23. Parks, T., Smeesters, P. R. & Steer, A. C. Streptococcal skin infection and rheumatic heart disease. Curr. Opin. Infect. Dis. 25, 145–153 (2012).

    PubMed  Google Scholar 

  24. Wang, S. S., Beaty T. H. & Khoury, M. J. in Vogel and Motulsky’s Human Genetics Vol. 4 (eds Speicher, M., Antonarakis, S. E. & Motulsky, A. G.) 617–634 (Springer-Verlag, 2010).

  25. Susser, E. & Susser, M. Familial aggregation studies. A note on their epidemiologic properties. Am. J. Epidemiol. 129, 23–30 (1989).

    CAS  PubMed  Google Scholar 

  26. Austin, M. A. Genetic Epidemiology: Methods and Applications 10–12 (CABI Publishing, 2013).

  27. Wilson, M. G. & Schweitzer, M. D. Rheumatic fever as a familial disease. environment, communicability and heredity in their relation to the observed familial incidence of the disease. J. Clin. Invest. 16, 555–570 (1937).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Washburn, A. H. Rheumatic heart disease–factors in its prognosis. Cal. West. Med. 27, 781–786 (1927).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Ferguson, J. Valvular disease of the heart, accompanied by rheumatic subcutaneous nodules. BMJ 1, 1150 (1885).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Davies, A. M. & Lazarov, E. Heredity, infection and chemoprophylaxis in rheumatic carditis: an epidemiological study of a communal settlement. J. Hyg. 58, 263–276 (1960).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Denbow, C. E., Barton, E. N. & Smikle, M. F. The prophylaxis of acute rheumatic fever in a pair of monozygotic twins. The public health implications. West Indian Med. J. 48, 242–243 (1999).

    CAS  PubMed  Google Scholar 

  32. Olerup, O. & Zetterquist, H. HLA-DR typing by PCR amplification with sequence-specific primers (PCR-SSP) in 2 hours: an alternative to serological DR typing in clinical practice including donor-recipient matching in cadaveric transplantation. Tissue Antigens 39, 225–235 (1992).

    CAS  PubMed  Google Scholar 

  33. Trowsdale, J. & Knight, J. C. Major histocompatibility complex genomics and human disease. Annu. Rev. Genomics Hum. Genet. 14, 301–323 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Martin, W. J. et al. Post-infectious group A streptococcal autoimmune syndromes and the heart. Autoimmun. Rev. 14, 710–725 (2015).

    PubMed  Google Scholar 

  35. Malaria Genomic Epidemiology Network. Reappraisal of known malaria resistance loci in a large multicenter study. Nat. Genet. 46, 1197–1204 (2014).

    PubMed Central  Google Scholar 

  36. Ioannidis, J. P. et al. A road map for efficient and reliable human genome epidemiology. Nat. Genet. 38, 3–5 (2006).

    CAS  PubMed  Google Scholar 

  37. Khoury, M. J. & Dorman, J. S. The human genome epidemiology network. Am. J. Epidemiol. 148, 1–3 (1998).

    CAS  PubMed  Google Scholar 

  38. Sagoo, G. S., Little, J. & Higgins, J. P. Systematic reviews of genetic association studies. Human Genome Epidemiology Network. PLOS Med. 6, e28 (2009).

    PubMed  Google Scholar 

  39. Muhamed, B., Engel, M. E., Shaboodien, G., Pare, G. & Mayosi B. M. Genetics of rheumatic fever and rheumatic heart disease in Africans. Thesis, Univ. Cape Town (2018).

  40. Ntzani, E. E., Liberopoulos, G., Manolio, T. A. & Ioannidis, J. P. Consistency of genome-wide associations across major ancestral groups. Hum. Genet. 131, 1057–1071 (2012).

    CAS  PubMed  Google Scholar 

  41. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Knight, J. C. Approaches for establishing the function of regulatory genetic variants involved in disease. Genome Med. 6, 92 (2014).

    PubMed  PubMed Central  Google Scholar 

  44. Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The Ensembl regulatory build. Genome Biol. 16, 56 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Lettre, G. & Rioux, J. D. Autoimmune diseases: insights from genome-wide association studies. Hum. Mol. Genet. 17, R116–R121 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Hu, X. & Daly, M. What have we learned from six years of GWAS in autoimmune diseases, and what is next? Curr. Opin. Immunol. 24, 571–575 (2012).

    CAS  PubMed  Google Scholar 

  47. Chapman, S. J. & Hill, A. V. S. Human genetic susceptibility to infectious disease. Nat. Rev. Genet. 13, 175–188 (2012).

    CAS  PubMed  Google Scholar 

  48. McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).

    CAS  PubMed  Google Scholar 

  49. Steer, A. C., Lamagni, T., Curtis, N. & Carapetis, J. R. Invasive group A streptococcal disease epidemiology, pathogenesis and management. Drugs 72, 1213–1227 (2012).

    PubMed  PubMed Central  Google Scholar 

  50. Zühlke, L. et al. Characteristics, complications, and gaps in evidence-based interventions in rheumatic heart disease: the Global Rheumatic Heart Disease Registry (the REMEDY study). Eur. Heart J. 36, 1115–1122a (2015).

    PubMed  Google Scholar 

  51. Remenyi, B. et al. World Heart Federation criteria for echocardiographic diagnosis of rheumatic heart disease—an evidence-based guideline. Nat. Rev. Cardiol. 9, 297–309 (2012).

    PubMed  PubMed Central  Google Scholar 

  52. Parks, T. et al. Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania. Nat. Commun. 8, 14946 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Gray, L. A. et al. Genome-wide analysis of genetic risk factors for rheumatic heart disease in Aboriginal Australians provides support for pathogenic molecular mimicry. J. Infect. Dis. 216, 1460–1470 (2017).

    CAS  PubMed  Google Scholar 

  54. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

    Google Scholar 

  55. Colhoun, H. M., McKeigue, P. M. & Davey Smith, G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865–872 (2003).

    PubMed  Google Scholar 

  56. Anderson, C. A. et al. Data quality control in genetic case-control association studies. Nat. Protoc. 5, 1564–1573 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Turner, S. et al. Quality control procedures for genome-wide association studies. Curr. Protoc. Hum. Genet. 68, 1.19.1–1.19.18 (2011).

    Google Scholar 

  58. Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    PubMed  PubMed Central  Google Scholar 

  59. Tian, C. et al. Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections. Nat. Commun. 8, 599 (2017).

    PubMed  PubMed Central  Google Scholar 

  60. Allen, N. E., Sudlow, C., Peakman, T. & Collins, R., UK Biobank. UK biobank data: come and get it. Sci. Transl Med. 6, 224ed4 (2014).

    PubMed  Google Scholar 

  61. Katzenellenbogen, J. M. et al. Low positive predictive value of International Classification of Diseases, 10th Revision codes in relation to rheumatic heart disease: a challenge for global surveillance. Intern. Med. J. 49, 400–403 (2019).

    PubMed  Google Scholar 

  62. Watson, C. T. & Breden, F. The immunoglobulin heavy chain locus: genetic variation, missing data, and implications for human disease. Genes Immun. 13, 363–373 (2012).

    CAS  PubMed  Google Scholar 

  63. Auckland, K. et al. The human leukocyte antigen locus and susceptibility to rheumatic heart disease in South Asians and Europeans. Preprint at MedRxiv https://doi.org/10.1101/19003160 (2019).

    Article  Google Scholar 

  64. Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLOS ONE 8, e64683 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Bustamante, C. D., Burchard, E. G. & De la Vega, F. M. Genomics for the world. Nature 475, 163–165 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/ct2/show/NCT02118818 (2018).

  67. Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    PubMed  PubMed Central  Google Scholar 

  68. Spencer, C. C., Su, Z., Donnelly, P. & Marchini, J. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLOS Genet. 5, e1000477 (2009).

    PubMed  PubMed Central  Google Scholar 

  69. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).

    CAS  PubMed  Google Scholar 

  70. Gumpinger, A. C., Roqueiro, D., Grimm, D. G. & Borgwardt, K. M. Methods and tools in genome-wide association studies. Methods Mol. Biol. 1819, 93–136 (2018).

    CAS  PubMed  Google Scholar 

  71. Fike, A. J., Elcheva, I. & Rahman, Z. S. M. The post-GWAS era: how to validate the contribution of gene variants in lupus. Curr. Rheumatol. Rep. 21, 3 (2019).

    PubMed  Google Scholar 

Download references

Acknowledgements

T.P. is supported by the UK National Institute for Health Research (ACF-2016–20–001). The views expressed are those of the author(s) and not necessarily those of the National Health Service, the National Institute for Health Research or the Department of Health.

Author information

Authors and Affiliations

Authors

Contributions

B.M. and K.S. discussed the content of the article, T.P. compiled an outline version, and B.M. and T.P. wrote the first draft. All the authors reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Karen Sliwa.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Cardiology thanks L. Guilherme, E. Okello and C. Sable 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.

Related links

23andMe: https://www.23andme.com/

ExAC database: http://exac.broadinstitute.org/

RHDGen Network: https://h3africa.org/index.php/consortium/projects/the-rhdgen-network-genetics-of-rheumatic-heart-disease-and-molecular-epidemiology-of-streptococcus-pyogenes-pharyngitis/

UK Biobank: https://www.ukbiobank.ac.uk/

Glossary

Monozygotic twins

Often termed ‘identical’, monozygotic twins result from the fertilization of a single egg that splits into two and share close to 100% of their genetic material.

Dizygotic twins

Often termed ‘non-identical’, dizygotic twins result from the fertilization of two separate eggs during the same pregnancy; like most other siblings, they share approximately 50% of their genetic material.

Traits

A character or phenotype in genetic research.

Alleles

A version of a gene or other genetic sequence.

Population structure

The presence of a systematic difference in allele frequencies between subgroups of a population, possibly owing to different ancestry.

Linkage disequilibrium

Statistical association between particular alleles at separate but linked loci, normally the result of the ancestral haplotype being common in population studies.

Cryptic relatedness

When individuals in a genetic association study are more closely related to one another than assumed by the investigators, which can be a confounding factor in both case–control and genome-wide association studies.

Mendelian randomization

A method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies.

Imputation

A statistical process used in genetics research to estimate genotypes that are not directly assayed in a sample of individuals.

Linear mixed models

Regression models that take into account both variation that is explained by the independent variables of interest (fixed effects) and variation that is not explained by the independent variables of interest (random effects).

Haplotypes

A collection of genetic variants that occur in close proximity on a single chromosome and are inherited together.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muhamed, B., Parks, T. & Sliwa, K. Genetics of rheumatic fever and rheumatic heart disease. Nat Rev Cardiol 17, 145–154 (2020). https://doi.org/10.1038/s41569-019-0258-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41569-019-0258-2

This article is cited by

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