Associated of Novel Locus With Rheumatic Heart Disease Susceptibility in Black African Individuals | Cardiology | JN Learning | AMA Ed Hub [Skip to Content]
[Skip to Content Landing]

Association of Novel Locus With Rheumatic Heart Disease in Black African IndividualsFindings From the RHDGen Study

Educational Objective
To describe key epidemiological and genetic concepts of rheumatic heart disease.
1 Credit CME
Key Points

Question  Is susceptibility to rheumatic heart disease (RHD) heritable in African individuals, and if so, what are the common genetic variants associated with RHD risk?

Findings  In this genome-wide association study of 4809 African individuals, 1 genetic risk locus at 11q24.1 (rs1219406) was associated with RHD at genome-wide significance in Black African individuals but not in other groups, although 1 previously described association was replicated at nominal significance. Polygenic heritability of RHD is estimated at 0.49 in African individuals.

Meaning  This study suggests that there is an important polygenic component to RHD risk in African individuals, highlighting genetic features exclusive to African individuals as well as genetic similarities with non-African individuals.

Abstract

Importance  Rheumatic heart disease (RHD), a sequela of rheumatic fever characterized by permanent heart valve damage, is the leading cause of cardiac surgery in Africa. However, its pathophysiologic characteristics and genetics are poorly understood. Understanding genetic susceptibility may aid in prevention, control, and interventions to eliminate RHD.

Objective  To identify common genetic loci associated with RHD susceptibility in Black African individuals.

Design, Setting, and Participants  This multicenter case-control genome-wide association study (GWAS), the Genetics of Rheumatic Heart Disease, examined more than 7 million genotyped and imputed single-nucleotide variations. The 4809 GWAS participants and 116 independent trio families were enrolled from 8 African countries between December 31, 2012, and March 31, 2018. All GWAS participants and trio probands were screened by use of echocardiography. Data analyses took place from May 15, 2017, until March 14, 2021.

Main Outcomes and Measures  Genetic associations with RHD.

Results  This study included 4809 African participants (2548 RHD cases and 2261 controls; 3301 women [69%]; mean [SD] age, 36.5 [16.3] years). The GWAS identified a single RHD risk locus, 11q24.1 (rs1219406 [odds ratio, 1.65; 95% CI, 1.48-1.82; P = 4.36 × 10−8]), which reached genome-wide significance in Black African individuals. Our meta-analysis of Black (n = 3179) and admixed (n = 1055) African individuals revealed several suggestive loci. The study also replicated a previously reported association in Pacific Islander individuals (rs11846409) at the immunoglobulin heavy chain locus, in the meta-analysis of Black and admixed African individuals (odds ratio, 1.16; 95% CI, 1.06-1.27; P = 1.19 × 10−3). The HLA (rs9272622) associations reported in Aboriginal Australian individuals could not be replicated. In support of the known polygenic architecture for RHD, overtransmission of a polygenic risk score from unaffected parents to affected probands was observed (polygenic transmission disequilibrium testing mean [SE], 0.27 [0.16] SDs; P = .04996), and the chip-based heritability was estimated to be high at 0.49 (SE = 0.12; P = 3.28 × 10−5) in Black African individuals.

Conclusions and Relevance  This study revealed a novel candidate susceptibility locus exclusive to Black African individuals and an important heritable component to RHD susceptibility in African individuals.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 CME Credit™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: March 25, 2021.

Published Online: June 9, 2021. doi:10.1001/jamacardio.2021.1627

Corresponding Author: Guillaume Paré, MD, MSc, Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton St E, Hamilton, ON L8L 2X2, Canada (pareg@mcmaster.ca).

Author Contributions: Ms Machipisa and Dr Paré had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Engel and Paré contributed equally to this work.

Concept and design: Machipisa, Shaboodien, de Vries, Lwabi, Okello, Musuku, ElSayed, Elhassan, Zühlke, Mulder, Ramesar, Lesosky, Parks, Cordell, Engel, Paré.

Acquisition, analysis, or interpretation of data: Machipisa, Chong, Muhamed, Chishala, Pandie, Laing, Joachim, Daniels, Ntsekhe, Hugo-Hamman, Gitura, Ogendo, Okello, Damasceno, Novela, Mocumbi, Madeira, Musuku, Mtaja, ElSayed, Elhassan, Bode-Thomas, Okeahialam, Zühlke, Mulder, Lesosky, Parks, Cordell, Keavney, Engel, Paré.

Drafting of the manuscript: Machipisa, Chong, Shaboodien, Pandie, Ogendo, Okello, Madeira, Zühlke, Engel, Paré.

Critical revision of the manuscript for important intellectual content: Machipisa, Chong, Muhamed, Chishala, de Vries, Laing, Joachim, Daniels, Ntsekhe, Hugo-Hamman, Gitura, Lwabi, Okello, Damasceno, Novela, Mocumbi, Musuku, Mtaja, ElSayed, Elhassan, Bode-Thomas, Okeahialam, Zühlke, Mulder, Ramesar, Lesosky, Parks, Cordell, Keavney, Engel, Paré.

Statistical analysis: Machipisa, Chong, Pandie, Daniels, Lesosky, Parks, Paré.

Obtained funding: Machipisa, Hugo-Hamman, Okello, Zühlke, Cordell, Engel, Paré.

Administrative, technical, or material support: Machipisa, Muhamed, Chishala, Shaboodien, Laing, Daniels, Hugo-Hamman, Lwabi, Okello, Damasceno, Madeira, Musuku, ElSayed, Elhassan, Zühlke, Ramesar, Engel, Paré.

Supervision: Chong, Hugo-Hamman, Gitura, Lwabi, Damasceno, Musuku, ElSayed, Elhassan, Bode-Thomas, Zühlke, Mulder, Cordell, Keavney, Engel, Paré.

Conflict of Interest Disclosures: Ms Machipisa reported receiving grants from Wellcome Trust during the conduct of the study; nonfinancial support from H3Africa; attending a sponsored training course at Wellcome Genome Campus; was funded by the University of Cape Town (under the Mayosi Research Group RHDGen Fellowship funded by the Wellcome Trust) the Crasnow Travel Scholarship, and via Population Health Research Institute (PHRI) and McMaster University through the inaugural Bongani Mayosi UCT (University of Cape Town)-PHRI Scholarship 2019/2020 to complete the submitted work. Mr Chong reported receiving a Canadian Institute of Health Research doctoral award and consulting fees from Bayer. Ms Pandie reported receiving grants from Wellcome Trust during the conduct of the study. Dr Damasceno reported receiving grants from University of Cape Town during the conduct of the study. Dr Musuku reported receiving grants from Wellcome Trust during the conduct of the study. Dr Mtaja reported receiving nonfinancial support from Wellcome Trust during the conduct of the study. Dr Mulder reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Parks reported receiving grants from National Institute for Health Research during the conduct of the study and the British Medical Association. Dr Cordell reported receiving support from Newcastle University and the Wellcome Trust. Dr Keavney reported receiving support from the British Heart Foundation, the Wellcome Trust, and Manchester University. Dr Engel reported receiving support from the RHDGen Wellcome Trust grant, the South African National Research Foundation (NRF) No. 116287, grant NW17SFRN33630027 from the American Heart Association, and UCT. Dr Paré reported receiving grants from Bayer and Esperion; personal fees from Bayer, Bristol Myers Squibb, Lexicomp, Amgen, Illumina, and Sanofi; and the Canada Research Chair in Genetic and Molecular Epidemiology, and CISCO Professorship in Integrated Health Systems outside the submitted work. No other disclosures were reported.

Funding/Support: RHDGen (https://h3africa.org/index.php/consortium/the-rhdgen-network-genetics-of-rheumatic-heart-disease-and-molecular-epidemiology-of-streptococcus-pyogenes-pharyngitis) was supported by grants awarded from the Wellcome Trust under the H3Africa; grant099313/B/12/A (https://app.dimensions.ai/details/grant/grant.3640606 and https://europepmc.org/grantfinder/grantdetails?query=pi%3A%22Mayosi%2BBM%22%2Bgid%3A%22099313%22%2Bga%3A%22Wellcome%20Trust%22).

Role of the Funder/Sponsor: Wellcome Trust under the H3Africa arm, provided the grant and received project progress reports at H3Africa meetings and other internal reviews. These meetings provided the funder with all details and the opportunity to provide input to the principal investigator’s plans regarding the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We would like to thank all participants for being a part of this study, as well as the members of the Mayosi Research Group Coordinating Office team and collaborating sites’ staff for the study coordination, recruitment, data entry, and cleaning. We also thank the Genetics of Rheumatic Heart Disease (RHDGen) Network Consortium members listed in eAppendix 1 in the Supplement. We acknowledge the Hatter Institute of Cardiovascular Research in Africa (HICRA), directed by Karen Sliwa, MD PhD, and its cardiovascular genetics laboratory for assisting with all of the preliminary wet laboratory work (primarily, students and staff funded by the Mayosi Research Group or RHDGen, namely: Maryam Fish, PhD, Gaurang Deshpande, PhD, Stephen Kamuli, MSc, Timothy Spracklen, PhD, Lameez Pearce, BSc, Janine Saaiman, and Zukiswa Jiki, BSc). We would also like to thank staff like Reina Ditta, MSc, of the Genetic and Molecular Epidemiology Laboratory (GMEL) for managing the laboratory and notably, Amanda Hodge, Gianluca Situm, BSc, Gillian Lampkin, BSc and Taylor MacIsaac, BSc, for assisting with the GMEL wet laboratory work. Also, we acknowledge the GMEL dry laboratory and PHRI students, affiliates, and members for providing technical consultations (especially Jenny Sjaarda, PhD, Pedrum Mohammadi-Shemirani, MSc, Marie Pigeyre, MD, PhD, Ricky Lali, MSc, Shihong Mao, PhD, Loubna Akhbir, PhD, Amel Lamri, PhD, Ann Le, MSc, Godsent Isiguzo, MD, PhD, Tinashe Chikowore, PhD, Rob Morton, PhD, Viwe Mtwise, MD, MMed, and Alexander P. Benz, MD MSc). We thank the Pacific Islands Rheumatic Heart Disease Genetics Network, South Asia Rheumatic Heart Disease Genetics Network and the UK Biobank Resource (application 11537) on which pooled analyses were based. Finally, we would like to thank everyone who took the time to provide valuable input throughout this study.

Additional Information: This article is dedicated to the memory of our mentor, friend, and colleague Bongani M Mayosi, MD, DPhil (1967-2018), who inspired and established the RHDGen Network Consortium through his great vision, leadership, guidance, and mentorship. This article is also dedicated to the memory of Lungile Pepeta, MD, recruitment site cardiologist, and Veronica Francis, RN, study coordinator, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa. Genotype and phenotype data used in this article will be deposited in the Pan African Bioinformatics Network for the Human Heredity and Health in Africa (H3ABioNet) Data Archive, as per the H3Africa guidelines.79 Some additional restrictions on access and use apply (eg, focus on RHD research). Access to certain components of the dataset requires regulatory approval from the country where the samples were obtained. The H3A BioNet Data Archive repository will provide further information about access to the dataset (link: will be provided by H3AbioNet & European Genome-phenome Archive). Please cite this article whenever any data or method provided in the resources mentioned above is used. A complete list of individuals who worked in The Genetics of Rheumatic Heart Disease (RHDGen) Network Consortium is provided in eAppendix 1 in the Supplement.

References
1.
Watkins  DA , Johnson  CO , Colquhoun  SM ,  et al.  Global, regional, and national burden of rheumatic heart disease, 1990-2015.   N Engl J Med. 2017;377(8):713-722. doi:10.1056/NEJMoa1603693 PubMedGoogle ScholarCrossref
2.
GBD 2013 Mortality and Causes of Death Collaborators.  Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.   Lancet. 2015;385(9963):117-171. doi:10.1016/S0140-6736(14)61682-2 PubMedGoogle ScholarCrossref
3.
White  A .  WHO resolution on rheumatic heart disease.   Eur Heart J. 2018;39(48):4233-4233. doi:10.1093/eurheartj/ehy764 PubMedGoogle ScholarCrossref
4.
Beaton  A , Kamalembo FB, Dale J,  et al. The American Heart Association’s call to action for reducing the global burden of rheumatic heart disease: a policy statement from the American Heart Association. Circulation. 2020;142(20):e358-e368.
5.
Yuyun  MF , Sliwa  K , Kengne  AP , Mocumbi  AO , Bukhman  G .  Cardiovascular diseases in sub-Saharan Africa compared to high-income countries: an epidemiological perspective.   Glob Heart. 2020;15(1):15. doi:10.5334/gh.403 PubMedGoogle ScholarCrossref
6.
GBD 2017 DALYs and HALE Collaborators.  Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.   Lancet. 2018;392(10159):1859-1922. doi:10.1016/S0140-6736(18)32335-3 PubMedGoogle ScholarCrossref
7.
Zühlke  L , Engel  ME , Karthikeyan  G ,  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. 2015;36(18):1115-22a. doi:10.1093/eurheartj/ehu449 PubMedGoogle ScholarCrossref
8.
Zühlke  L , Mayosi  BM .  Echocardiographic screening for subclinical rheumatic heart disease remains a research tool pending studies of impact on prognosis.   Curr Cardiol Rep. 2013;15(3):343. doi:10.1007/s11886-012-0343-1 PubMedGoogle ScholarCrossref
9.
Hajar  R .  Rheumatic Fever and Rheumatic Heart Disease a Historical Perspective.   Heart Views. 2016;17(3):120-126. doi:10.4103/1995-705X.192572PubMedGoogle ScholarCrossref
10.
Sanduja  P , Gupta  M , Somani  VK ,  et al.  Cross-serotype protection against group A Streptococcal infections induced by immunization with SPy_2191.   Nat Commun. 2020;11(1):3545. doi:10.1038/s41467-020-17299-x PubMedGoogle ScholarCrossref
12.
El-Aal  AA . Mitral stenosis in Africa: magnitude of the problem.  e-Journal Cardiol Practice. Published online June 27, 2018. Accessed May 3, 2021. https://www.escardio.org/Journals/E-Journal-of-Cardiology-Practice/Volume-16/Mitral-stenosis-in-Africa-magnitude-of-the-problem
13.
Zühlke  L , Karthikeyan  G , Engel  ME ,  et al.  Clinical outcomes in 3343 children and adults with rheumatic heart disease from 14 low- and middle-income countries: two-year follow-up of the Global Rheumatic Heart Disease Registry (the REMEDY Study).   Circulation. 2016;134(19):1456-1466. doi:10.1161/CIRCULATIONAHA.116.024769 PubMedGoogle ScholarCrossref
14.
Remenyi  B , Carapetis  J , Wyber  R , Taubert  K , Mayosi  BM ; World Heart Federation.  Position statement of the World Heart Federation on the prevention and control of rheumatic heart disease.   Nat Rev Cardiol. 2013;10(5):284-292. doi:10.1038/nrcardio.2013.34 PubMedGoogle ScholarCrossref
15.
Huntley  GD , Thaden  JJ , Nkomo  VT . Epidemiology of heart valve disease. In: Kheradvar A, ed.  Principles of Heart Valve Engineering. Elsevier; 2019:41-62. doi:10.1016/B978-0-12-814661-3.00003-4
16.
Yangni-Angate  KH , Meneas  C , Diby  F , Diomande  M , Adoubi  A , Tanauh  Y .  Cardiac surgery in Africa: a thirty-five year experience on open heart surgery in Cote d’Ivoire.   Cardiovasc Diagn Ther. 2016;6(suppl 1):S44-S63. doi:10.21037/cdt.2016.10.06 PubMedGoogle ScholarCrossref
17.
Karthikeyan  G , Connolly  SJ , Ntsekhe  M ,  et al; INVICTUS Investigators.  The INVICTUS rheumatic heart disease research program: rationale, design and baseline characteristics of a randomized trial of rivaroxaban compared to vitamin K antagonists in rheumatic valvular disease and atrial fibrillation.   Am Heart J. 2020;225:69-77. doi:10.1016/j.ahj.2020.03.018 PubMedGoogle ScholarCrossref
18.
Carapetis  JR , Beaton  A , Cunningham  MW ,  et al.  Acute rheumatic fever and rheumatic heart disease.   Nat Rev Dis Primers. 2016;2:15084-15084. doi:10.1038/nrdp.2015.84 PubMedGoogle ScholarCrossref
19.
Guilherme  L , Ramasawmy  R , Kalil  J .  Rheumatic fever and rheumatic heart disease: genetics and pathogenesis.   Scand J Immunol. 2007;66(2-3):199-207. doi:10.1111/j.1365-3083.2007.01974.x PubMedGoogle ScholarCrossref
20.
Bland  EF , Duckett Jones  T .  Rheumatic fever and rheumatic heart disease; a twenty year report on 1000 patients followed since childhood.   Circulation. 1951;4(6):836-843. doi:10.1161/01.CIR.4.6.836 PubMedGoogle ScholarCrossref
21.
Muhamed  B , Parks  T , Sliwa  K .  Genetics of rheumatic fever and rheumatic heart disease.   Nat Rev Cardiol. 2020;17(3):145-154. doi:10.1038/s41569-019-0258-2PubMedGoogle ScholarCrossref
22.
Bryant  PA , Robins-Browne  R , Carapetis  JR , Curtis  N .  Some of the people, some of the time: susceptibility to acute rheumatic fever.   Circulation. 2009;119(5):742-753. doi:10.1161/CIRCULATIONAHA.108.792135 PubMedGoogle ScholarCrossref
23.
Engel  ME , Stander  R , Vogel  J , Adeyemo  AA , Mayosi  BM .  Genetic susceptibility to acute rheumatic fever: a systematic review and meta-analysis of twin studies.   PLoS One. 2011;6(9):e25326. doi:10.1371/journal.pone.0025326 PubMedGoogle Scholar
24.
Zühlke  LJ , Beaton  A , Engel  ME ,  et al.  Group A Streptococcus, acute rheumatic fever and rheumatic heart disease: epidemiology and clinical considerations.   Curr Treat Options Cardiovasc Med. 2017;19(2):15. doi:10.1007/s11936-017-0513-y PubMedGoogle ScholarCrossref
25.
Poomarimuthu  M , Elango  S , Soundrapandian  S , Mariakuttikan  J .  “HLA-G 3'UTR gene polymorphisms and rheumatic heart disease: a familial study among South Indian population”.   Pediatr Rheumatol Online J. 2017;15(1):10. doi:10.1186/s12969-017-0140-x PubMedGoogle ScholarCrossref
26.
Aliku  T , Sable  C , Scheel  A ,  et al.  Targeted echocardiographic screening for latent rheumatic heart disease in northern Uganda: evaluating familial risk following identification of an index case.   PLoS Negl Trop Dis. 2016;10(6):e0004727. doi:10.1371/journal.pntd.0004727 PubMedGoogle Scholar
27.
Muhamed  B , Shaboodien  G , Engel  ME .  Genetic variants in rheumatic fever and rheumatic heart disease.   Am J Med Genet C Semin Med Genet. 2020;184(1):159-177. doi:10.1002/ajmg.c.31773PubMedGoogle ScholarCrossref
28.
Parks  T , Mirabel  MM , Kado  J ,  et al; Pacific Islands Rheumatic Heart Disease Genetics Network.  Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania.   Nat Commun. 2017;8:14946. doi:10.1038/ncomms14946 PubMedGoogle ScholarCrossref
29.
Gray  L-A , D’Antoine  HA , Tong  SYC ,  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. 2017;216(11):1460-1470. doi:10.1093/infdis/jix497 PubMedGoogle ScholarCrossref
30.
Auckland  K , Mittal  B , Cairns  BJ ,  et al.  The human leukocyte antigen locus and rheumatic heart disease susceptibility in south Asians and Europeans.   Sci Rep. 2020;10(1):9004. doi:10.1038/s41598-020-65855-8 PubMedGoogle ScholarCrossref
31.
H3 Africa. The RHDGen network: genetics of rheumatic heart disease and molecular epidemiology of Streptococcus pyogenes pharyngitis. Accessed April 22, 2021. https://h3africa.org/index.php/consortium/the-rhdgen-network-genetics-of-rheumatic-heart-disease-and-molecular-epidemiology-of-streptococcus-pyogenes-pharyngitis/
32.
Little  J , Higgins  JP , Ioannidis  JP ,  et al.  STrengthening the REporting of Genetic Association Studies (STREGA)—an extension of the STROBE statement.   Genet Epidemiol. 2009;33(7):581-598. doi:10.1002/gepi.20410 PubMedGoogle ScholarCrossref
33.
Choudhury  A , Ramsay  M , Hazelhurst  S ,  et al.  Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans.   Nat Commun. 2017;8(1):2062. doi:10.1038/s41467-017-00663-9 PubMedGoogle ScholarCrossref
34.
Reményi  B , Wilson  N , Steer  A ,  et al.  World Heart Federation criteria for echocardiographic diagnosis of rheumatic heart disease—an evidence-based guideline.   Nat Rev Cardiol. 2012;9(5):297-309. doi:10.1038/nrcardio.2012.7 PubMedGoogle ScholarCrossref
35.
Nunes  MCP , Sable  C , Nascimento  BR ,  et al.  Simplified echocardiography screening criteria for diagnosing and predicting progression of latent rheumatic heart disease.   Circ Cardiovasc Imaging. 2019;12(2):e007928. doi:10.1161/CIRCIMAGING.118.007928 PubMedGoogle Scholar
36.
Turner  S , Armstrong  LL , Bradford  Y ,  et al.  Quality control procedures for genome-wide association studies.   Curr Protoc Hum Genet. 2011;Chapter 1:19. doi:10.1002/0471142905.hg0119s68PubMedGoogle Scholar
37.
Sanger Imputation Service. Accessed April 22, 2021. https://imputation.sanger.ac.uk/
38.
Loh PR, Danecek P, Palamara PF, et al. Reference-based phasing using the Haplotype Reference Consortium panel.  Nat Genet. 2016;48(11):1443-1448.
39.
Schurz  H , Müller  SJ , van Helden  PD ,  et al.  Evaluating the accuracy of imputation methods in a five-way admixed population.   Front Genet. 2019;10:34-34. doi:10.3389/fgene.2019.00034 PubMedGoogle ScholarCrossref
40.
McCarthy  S , Das  S , Kretzschmar  W ,  et al; Haplotype Reference Consortium.  A reference panel of 64,976 haplotypes for genotype imputation.   Nat Genet. 2016;48(10):1279-1283. doi:10.1038/ng.3643 PubMedGoogle Scholar
41.
Yang J, Zaitlen NA, Goddard ME, Visscher MP, Price AL. Advantages and pitfalls in the application of mixed-model association methods.  Nat Genet. 2014;46(2):100-106. doi:10.1038/ng.2876
42.
Yang  J , Lee  SH , Goddard  ME , Visscher  PM .  GCTA: a tool for genome-wide complex trait analysis.   Am J Hum Genet. 2011;88(1):76-82. doi:10.1016/j.ajhg.2010.11.011 PubMedGoogle ScholarCrossref
43.
Pirinen  M., , Donnelly  P. , and Spencer  C.C. ,  Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies.   Ann Appl Statistics. 2013;7(1):369-390. doi:10.1214/12-AOAS586 Google ScholarCrossref
44.
Haidich  AB .  Meta-analysis in medical research.   Hippokratia. 2010;14(suppl 1):29-37.PubMedGoogle Scholar
46.
Pruim  RJ , Welch  RP , Sanna  S ,  et al.  LocusZoom: regional visualization of genome-wide association scan results.   Bioinformatics. 2010;26(18):2336-2337. doi:10.1093/bioinformatics/btq419 PubMedGoogle ScholarCrossref
47.
MacArthur  J , Bowler  E , Cerezo  M ,  et al.  The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog).   Nucleic Acids Res. 2017;45(D1):D896-D901. doi:10.1093/nar/gkw1133 PubMedGoogle ScholarCrossref
48.
GWAS Catalog. Accessed April 22, 2021. https://www.ebi.ac.uk/gwas/
49.
Canela-Xandri  O , Rawlik  K , Tenesa  A .  An atlas of genetic associations in UK Biobank.   Nat Genet. 2018;50(11):1593-1599. doi:10.1038/s41588-018-0248-z PubMedGoogle ScholarCrossref
50.
Gene ATLAS. Accessed April 22, 2021. http://geneatlas.roslin.ed.ac.uk/
51.
Watanabe  K , Stringer  S , Frei  O ,  et al.  A global overview of pleiotropy and genetic architecture in complex traits.   Nat Genet. 2019;51(9):1339-1348. doi:10.1038/s41588-019-0481-0 PubMedGoogle ScholarCrossref
52.
GWASATLAS. Accessed April 22, 2021. https://atlas.ctglab.nl/PheWAS
53.
Ghoussaini  M , Mountjoy  E , Carmona  M ,  et al.  Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics.   Nucleic Acids Res. 2021;49(D1):D1311-D1320. doi:10.1093/nar/gkaa840 PubMedGoogle ScholarCrossref
54.
Open Targets Genetics. Accessed April 22, 2021. https://genetics.opentargets.org/
55.
GTEx Consortium.  The Genotype-Tissue Expression (GTEx) project.   Nat Genet. 2013;45(6):580-585. doi:10.1038/ng.2653 PubMedGoogle ScholarCrossref
56.
GTEx Portal. Accessed April 26, 2021. https://www.gtexportal.org/home/
57.
Pers  TH , Karjalainen  JM , Chan  Y ,  et al; Genetic Investigation of ANthropometric Traits (GIANT) Consortium.  Biological interpretation of genome-wide association studies using predicted gene functions.   Nat Commun. 2015;6(1):5890. doi:10.1038/ncomms6890 PubMedGoogle ScholarCrossref
58.
de Leeuw  CA , Mooij  JM , Heskes  T , Posthuma  D .  MAGMA: generalized gene-set analysis of GWAS data.   PLoS Comput Biol. 2015;11(4):e1004219. doi:10.1371/journal.pcbi.1004219 PubMedGoogle Scholar
59.
Jin  X , Wang  Y , Zhang  X , Zhang  W , Wang  H , Chen  C .  Gene mapping and functional annotation of GWAS of oral ulcers using FUMA software.   Sci Rep. 2020;10(1):12205. doi:10.1038/s41598-020-68976-2 PubMedGoogle ScholarCrossref
60.
Damena  D , Chimusa  ER .  Genome-wide heritability analysis of severe malaria resistance reveals evidence of polygenic inheritance.   Hum Mol Genet. 2020;29(1):168-176. doi:10.1093/hmg/ddz258 PubMedGoogle ScholarCrossref
61.
Andreson  R , Puurand  T , Remm  M .  SNPmasker: automatic masking of SNPs and repeats across eukaryotic genomes.   Nucleic Acids Res. 2006;34(Web Server issue):W651-5. doi:10.1093/nar/gkl125PubMedGoogle Scholar
62.
Choi  SW , O’Reilly  PF .  PRSice-2: polygenic risk score software for biobank-scale data.   Gigascience. 2019;8(7):giz082. doi:10.1093/gigascience/giz082 PubMedGoogle Scholar
63.
Maher  BS .  Polygenic scores in epidemiology: risk prediction, etiology, and clinical utility.   Curr Epidemiol Rep. 2015;2(4):239-244. doi:10.1007/s40471-015-0055-3 PubMedGoogle ScholarCrossref
64.
PRS-ice2. Accessed April 26, 2021. https://www.prsice.info/
65.
Weiner  DJ , Wigdor  EM , Ripke  S ,  et al; iPSYCH-Broad Autism Group; Psychiatric Genomics Consortium Autism Group.  Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders.   Nat Genet. 2017;49(7):978-985. doi:10.1038/ng.3863 PubMedGoogle ScholarCrossref
66.
Maller  JB , McVean  G , Byrnes  J ,  et al; Wellcome Trust Case Control Consortium.  Bayesian refinement of association signals for 14 loci in 3 common diseases.   Nat Genet. 2012;44(12):1294-1301. doi:10.1038/ng.2435 PubMedGoogle ScholarCrossref
67.
Auckland  K , Mittal B, Cairns BJ,  et al.  The human leukocyte antigen locus and susceptibility to rheumatic heart disease in South Asians and Europeans.  Preprint Posted July 26, 2019. medRxiv 19003160. doi:10.1101/19003160
68.
Ottensmann  L . Comparing the performance of the gene prioritization methods DEPICT and MAGMA on genome-wide association studies of schizophrenia using the Benchmarker framework. Appril 17, 2020. Accessed February 28, 2021. https://helda.helsinki.fi/bitstream/handle/10138/314736/MT_ottensma.pdf?sequence=3&isAllowed=y
69.
Shang  L , Smith  JA , Zhao  W ,  et al.  Genetic architecture of gene expression in European and African Americans: an eQTL mapping study in GENOA.   Am J Hum Genet. 2020;106(4):496-512. doi:10.1016/j.ajhg.2020.03.002 PubMedGoogle ScholarCrossref
70.
Sallah  N , Carstensen  T , Wakeham  K ,  et al.  Whole-genome association study of antibody response to Epstein-Barr virus in an African population: a pilot.   Glob Health Epidemiol Genom. 2017;2:e18-e18. doi:10.1017/gheg.2017.16 PubMedGoogle ScholarCrossref
71.
Chikowore  T , Kamiza  AB , Oduaran  OH , Machipisa  T , Fatumo  S .  Non-communicable diseases pandemic and precision medicine: is Africa ready?   EBioMedicine. 2021;65:103260. doi:10.1016/j.ebiom.2021.103260 PubMedGoogle Scholar
72.
Yengo  L , Sidorenko  J , Kemper  KE ,  et al; GIANT Consortium.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry.   Hum Mol Genet. 2018;27(20):3641-3649. doi:10.1093/hmg/ddy271 PubMedGoogle ScholarCrossref
73.
Choudhury  A , Aron  S , Botigué  LR ,  et al; TrypanoGEN Research Group; H3Africa Consortium.  High-depth African genomes inform human migration and health.   Nature. 2020;586(7831):741-748. doi:10.1038/s41586-020-2859-7 PubMedGoogle ScholarCrossref
74.
Allen  HD ,  et al.  Moss & Adams' Heart Disease in Infants, Children, and Adolescents: Including the Fetus and Young Adult. Lippincott Williams & Wilkins; 2013.
75.
Teo  Y-Y , Small  KS , Kwiatkowski  DP .  Methodological challenges of genome-wide association analysis in Africa.   Nat Rev Genet. 2010;11(2):149-160. doi:10.1038/nrg2731 PubMedGoogle ScholarCrossref
76.
Tucci  S , Akey  JM .  The long walk to African genomics.   Genome Biol. 2019;20(1):130. doi:10.1186/s13059-019-1740-1 PubMedGoogle ScholarCrossref
77.
Tran  BX , Phan  HT , Latkin  CA ,  et al.  Understanding global HIV stigma and discrimination: are contextual factors sufficiently studied? (GAPRESEARCH).   Int J Environ Res Public Health. 2019;16(11):1899. doi:10.3390/ijerph16111899 PubMedGoogle ScholarCrossref
78.
Huck  DM , Okello  E , Mirembe  G ,  et al.  Role of natural autoantibodies in Ugandans with rheumatic heart disease and HIV.   EBioMedicine. 2016;5:161-166. doi:10.1016/j.ebiom.2016.02.006 PubMedGoogle ScholarCrossref
79.
Parker  Z , Maslamoney S, Meintjes A,  et al.  Building infrastructure for African human genomic data management.   Data Sc J. 2019;18(1):47. doi:10.5334/dsj-2019-047 Google Scholar
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_Multimedia_LoginSubscribe_Purchase
Close
If you are not a JN Learning subscriber, you can either:
Subscribe to JN Learning for one year
Buy this activity
jn-learning_Modal_Multimedia_LoginSubscribe_Purchase
Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close

Name Your Search

Save Search
Close
With a personal account, you can:
  • Track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
jn-learning_Modal_SaveSearch_NoAccess_Purchase
Close

Lookup An Activity

or

Close

My Saved Searches

You currently have no searches saved.

Close
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right
Close