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
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
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
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).
Carapetis, J. R., McDonald, M. & Wilson, N. J. Acute rheumatic fever. Lancet 366, 155–168 (2005).
Steer, A. C., Danchin, M. H. & Carapetis, J. R. Group A streptococcal infections in children. J. Paediatr. Child Health 43, 203–213 (2007).
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).
Longo-Mbenza, B. et al. Survey of rheumatic heart disease in school children of Kinshasa town. Int. J. Cardiol. 63, 287–294 (1998).
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).
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).
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).
Carapetis, J. R. et al. Acute rheumatic fever and rheumatic heart disease. Nat. Rev. Dis. Prim. 2, 15084 (2016).
Yusuf, S., Narula, J. & Gamra, H. Can we eliminate rheumatic fever and premature deaths from RHD? Glob. Heart 12, 3–4 (2017).
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).
Watkins, D. A. et al. Global, regional, and national burden of rheumatic heart disease, 1990-2015. N. Engl. J. Med. 377, 713–722 (2017).
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).
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).
Paar, J. A. et al. Prevalence of rheumatic heart disease in children and young adults in Nicaragua. Am. J. Cardiol. 105, 1809–1814 (2010).
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).
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).
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).
Okello, E. et al. Socioeconomic and environmental risk factors among rheumatic heart disease patients in Uganda. PLOS ONE 7, e43917 (2012).
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).
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).
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).
Parks, T., Smeesters, P. R. & Steer, A. C. Streptococcal skin infection and rheumatic heart disease. Curr. Opin. Infect. Dis. 25, 145–153 (2012).
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).
Susser, E. & Susser, M. Familial aggregation studies. A note on their epidemiologic properties. Am. J. Epidemiol. 129, 23–30 (1989).
Austin, M. A. Genetic Epidemiology: Methods and Applications 10–12 (CABI Publishing, 2013).
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).
Washburn, A. H. Rheumatic heart disease–factors in its prognosis. Cal. West. Med. 27, 781–786 (1927).
Ferguson, J. Valvular disease of the heart, accompanied by rheumatic subcutaneous nodules. BMJ 1, 1150 (1885).
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).
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).
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).
Trowsdale, J. & Knight, J. C. Major histocompatibility complex genomics and human disease. Annu. Rev. Genomics Hum. Genet. 14, 301–323 (2013).
Martin, W. J. et al. Post-infectious group A streptococcal autoimmune syndromes and the heart. Autoimmun. Rev. 14, 710–725 (2015).
Malaria Genomic Epidemiology Network. Reappraisal of known malaria resistance loci in a large multicenter study. Nat. Genet. 46, 1197–1204 (2014).
Ioannidis, J. P. et al. A road map for efficient and reliable human genome epidemiology. Nat. Genet. 38, 3–5 (2006).
Khoury, M. J. & Dorman, J. S. The human genome epidemiology network. Am. J. Epidemiol. 148, 1–3 (1998).
Sagoo, G. S., Little, J. & Higgins, J. P. Systematic reviews of genetic association studies. Human Genome Epidemiology Network. PLOS Med. 6, e28 (2009).
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).
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).
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Knight, J. C. Approaches for establishing the function of regulatory genetic variants involved in disease. Genome Med. 6, 92 (2014).
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The Ensembl regulatory build. Genome Biol. 16, 56 (2015).
Lettre, G. & Rioux, J. D. Autoimmune diseases: insights from genome-wide association studies. Hum. Mol. Genet. 17, R116–R121 (2008).
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).
Chapman, S. J. & Hill, A. V. S. Human genetic susceptibility to infectious disease. Nat. Rev. Genet. 13, 175–188 (2012).
McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).
Steer, A. C., Lamagni, T., Curtis, N. & Carapetis, J. R. Invasive group A streptococcal disease epidemiology, pathogenesis and management. Drugs 72, 1213–1227 (2012).
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).
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).
Parks, T. et al. Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania. Nat. Commun. 8, 14946 (2017).
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).
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).
Colhoun, H. M., McKeigue, P. M. & Davey Smith, G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865–872 (2003).
Anderson, C. A. et al. Data quality control in genetic case-control association studies. Nat. Protoc. 5, 1564–1573 (2010).
Turner, S. et al. Quality control procedures for genome-wide association studies. Curr. Protoc. Hum. Genet. 68, 1.19.1–1.19.18 (2011).
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).
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).
Allen, N. E., Sudlow, C., Peakman, T. & Collins, R., UK Biobank. UK biobank data: come and get it. Sci. Transl Med. 6, 224ed4 (2014).
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).
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).
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).
Jia, X. et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLOS ONE 8, e64683 (2013).
Bustamante, C. D., Burchard, E. G. & De la Vega, F. M. Genomics for the world. Nature 475, 163–165 (2011).
US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/ct2/show/NCT02118818 (2018).
Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).
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).
Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
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).
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).
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
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
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/
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
About this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41569-019-0258-2
This article is cited by
-
The gut microbiome in systemic lupus erythematosus: lessons from rheumatic fever
Nature Reviews Rheumatology (2024)
-
Health-related quality of life and healthcare consultations among adult patients before and after diagnosis with rheumatic heart disease in Namibia
BMC Cardiovascular Disorders (2023)
-
Rheumatic heart disease prevalence in Namibia: a retrospective review of surveillance registers
BMC Cardiovascular Disorders (2022)
-
Rheumatic fever in a developed country – is it still relevant? A retrospective, 25 years follow-up
Pediatric Rheumatology (2022)
-
Ventricular strain patterns in multivalvular heart disease: a cross-sectional study
The International Journal of Cardiovascular Imaging (2022)