Association of Novel Locus With Rheumatic Heart Disease in Black African Individuals: Findings From the RHDGen Study

Tafadzwa Machipisa; Michael Chong; Babu Muhamed; Chishala Chishala; Gasnat Shaboodien; Shahiemah Pandie; Jantina de Vries; Nakita Laing; Alexia Joachim; Rezeen Daniels; Mpiko Ntsekhe; Christopher T Hugo-Hamman; Bernard Gitura; Stephen Ogendo; Peter Lwabi; Emmy Okello; Albertino Damasceno; Celia Novela; Ana O Mocumbi; Goeffrey Madeira; John Musuku; Agnes Mtaja; Ahmed ElSayed; Huda H M Elhassan; Fidelia Bode-Thomas; Basil N Okeahialam; Liesl J Zühlke; Nicola Mulder; Raj Ramesar; Maia Lesosky; Tom Parks; Heather J Cordell; Bernard Keavney; Mark E Engel; Guillaume Paré

doi:10.1001/jamacardio.2021.1627

. 2021 Jun 9;6(9):1000–1011. doi: 10.1001/jamacardio.2021.1627

Association of Novel Locus With Rheumatic Heart Disease in Black African Individuals

Findings From the RHDGen Study

Tafadzwa Machipisa ^1,^2,^3,^4,⁵, Michael Chong ^3,^4,⁵, Babu Muhamed ^1,^2,^3,^4,⁵, Chishala Chishala ^1,², Gasnat Shaboodien ^1,², Shahiemah Pandie ¹, Jantina de Vries ¹, Nakita Laing ¹, Alexia Joachim ¹, Rezeen Daniels ¹, Mpiko Ntsekhe ¹, Christopher T Hugo-Hamman ⁶, Bernard Gitura ⁷, Stephen Ogendo ⁷, Peter Lwabi ⁸, Emmy Okello ⁸, Albertino Damasceno ⁹, Celia Novela ⁹, Ana O Mocumbi ¹⁰, Goeffrey Madeira ¹¹, John Musuku ¹², Agnes Mtaja ¹², Ahmed ElSayed ¹³, Huda H M Elhassan ¹³, Fidelia Bode-Thomas ¹⁴, Basil N Okeahialam ¹⁴, Liesl J Zühlke ^1,¹⁵, Nicola Mulder ¹⁶, Raj Ramesar ¹⁷, Maia Lesosky ¹, Tom Parks ¹⁸, Heather J Cordell ¹⁹, Bernard Keavney ^20,²¹, Mark E Engel ¹, Guillaume Paré ^3,^4,^5,^22,^✉

¹Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa

²Hatter Institute for Cardiovascular Diseases Research in Africa and Cape Heart Institute, Department of Medicine, University of Cape Town, Cape Town, South Africa

³Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada

⁴Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Ontario, Canada

⁵Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Ontario, Canada

⁶Rheumatic Heart Disease Clinic, Windhoek Central Hospital, Ministry of Health and Social Services, Windhoek, Republic of Namibia

⁷Cardiology Department of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya

⁸Uganda Heart Institute, Kampala, Uganda

⁹Faculty of Medicine, Eduardo Mondlane University/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique

¹⁰Instituto Nacional de Saúde Ministério da Saúde, Maputo, Moçambique

¹¹Emergency Department, World Health Organization Mozambique, Maputo, Mozambique

¹²Department of Paediatrics and Child Health, University Teaching Hospital–Children’s Hospital, University of Zambia, Lusaka, Zambia

¹³Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Center, Alzaiem Alazhari University, Khartoum, Sudan

¹⁴Department of Paediatrics, Jos University Teaching Hospital and University of Jos, Jos, Plateau State Nigeria

¹⁵Division of Paediatric Cardiology, Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital and University of Cape Town, South Africa

¹⁶Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

¹⁷Department of Pathology, University of Cape Town, Cape Town, South Africa

¹⁸Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom

¹⁹Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Newcastle upon Tyne, United Kingdom

²⁰Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, United Kingdom

²¹Manchester University National Health Service Foundation Trust, Manchester Academic Health Science CentreManchester, United Kingdom

²²Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton Ontario, Canada

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.

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Corresponding author.

PMCID: PMC8190704 PMID: 34106200

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.

This genome-wide association study identifies common genetic loci associated with rheumatic heart disease susceptibility in Black African individuals.

Introduction

In 2018, the World Health Organization made rheumatic heart disease (RHD) a global health priority, given that 40 million individuals are affected worldwide.^1,2,3,4 Annually, RHD is associated with an estimated 10.5 million disability-adjusted life-years and 300 000 premature deaths.^5,6,7,8 The prevalence of RHD has decreased significantly in high-income countries in the last 75 years owing to the advent of penicillin and major improvements in indices of social and economic development.⁹ During the last 2 decades, there has been progress in documenting RHD’s burden in low- and middle-income countries. However, RHD remains a significant cause of morbidity and mortality in poorer communities of low- and middle-income countries.^9,10 Despite Africa being home to 17% of the world’s total population,¹¹ in 2017, sub-Saharan Africa comprised 23% of the global RHD caseload,⁵ with a high prevalence of 864 per 100 000, compared with only 9.8 per 100 000 in North America and 7.7 per 100 000 in Western Europe.¹² Currently, in sub-Saharan Africa, RHD remains both the most common form of acquired cardiovascular disease in women, children, and young adults and the leading cause of cardiac surgery,^{13,14,15,16,17} with prevalence peaking between 25 and 45 years of age.¹⁸

Rheumatic heart disease is a consequence of an autoimmune response to untreated, or inadequately treated, Streptococcus pyogenes (Lancefield Group A β-hemolytic Streptococcus [GAS]) infection, often pharyngitis, in a susceptible host.¹⁹ Although not all patients with RHD have a history of rheumatic fever, the precursor of RHD, close to 60% of rheumatic fever cases progress to RHD.²⁰ Important gaps remain in our understanding of individual host susceptibility to RHD after GAS infection.^21,22 Although RHD development can be prevented by treating GAS infections with penicillin, this strategy has not been successful in poverty-stricken, overcrowded regions in Africa. In addition to prevailing social and economic factors (poverty and overcrowding), differences in genetic susceptibility and GAS strain virulence have also been hypothesized to be associated with interindividual differences in RHD susceptibility.^10,23 Understanding genetic susceptibility will help devise better prevention, control, and interventions.

The precursor of RHD, rheumatic fever, is widely considered to have a strong genetic component,²³ with heritability estimated to be 60% from twin studies.^23,24 Although there are no heritability estimates for RHD, reports of familial cases provide support for genetic susceptibility.^25,26 Several candidate gene studies tested for RHD susceptibility; however, these studies had small sample sizes, and the results remain equivocal.²⁷ Three previous genome-wide association studies (GWASs) for RHD susceptibility in 4 different populations reported (1) a genome-wide significant (GWS) locus (rs11846409; P = 4.1 × 10⁻⁹) in the immunoglobulin heavy chain (IGH) locus in Pacific Islander individuals²⁸; (2) a suggestive locus (rs9272622; P = 1.86 × 10⁻⁷) in the HLA class II gene’s HLA-DQA1 (OMIM 146880) region in Australian Aboriginal individuals, which failed to reach GWS²⁹; and (3) a GWS locus (rs201026476; P = 7.45 × 10⁻⁹) in the HLA class III gene’s locus situated in the 3′ untranslated region of the PBX2 (pre–B-cell leukemia transcription factor 2; OMIM 176311) gene in Indian and European individuals.³⁰ However, these aforementioned RHD GWASs did not include African individuals. In the present study, to better understand the RHD pathophysiologic characteristics and genetic determinants associated with RHD susceptibility, we performed a GWAS for RHD susceptibility in African individuals.

Methods

Study Design

The Genetics of Rheumatic Heart Disease (RHDGen) Network is a project within Human Hereditary and Health Africa (H3Africa).³¹ The University of Cape Town Human Research Ethics Committee approved the present study as a substudy of RHDGen. This study followed the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guideline.³² Written informed consent was obtained from all adult study participants. In the case-control study group, 4809 participants (2548 cases and 2261 controls) passed GWAS quality control, and in the family-based study group, 116 independent trio families (348 participants) passed GWAS quality control.

Participants were from 8 African countries (ie, Kenya, Mozambique, Namibia, Nigeria, South Africa, Sudan, Uganda, and Zambia; eFigure 1 in the Supplement). Any Black African ethnic group from any of the 8 African countries (mostly of Bantu descent) was included in the Black African cohort. The South African ethnic group composed primarily of persons of mixed race, comprising any admixture combination of individuals of European, Southeast Asian, South Asian, Bantu-speaking African, and/or indigenous Southern African hunter-gatherer ancestries (Khoikhoi, San, or Bushmen),³³ was renamed admixed African individuals. The race/ethnicity of an individual was self-reported and verified through visualization of top genetic principal components.

RHD Adjudication

Cases were existing, and new patients who consented to participate in the study were recruited from cardiology units with echocardiography facilities. All study participants for the GWAS group and the probands for the trio study were assessed according to the evidence-based guideline from the 2012 World Heart Federation criteria for echocardiographic diagnosis of RHD.^14,34,35 Rheumatic heart disease was diagnosed in adults on the basis of features of definite RHD, with central verification of echocardiographic images by an experienced cardiologist (C.C.) at the Project Coordinating Office through a web-based portal.

Genotyping and Imputation

We extracted DNA at the cardiovascular genetics laboratory at the Hatter Institute for Cardiovascular Research in Africa and the Cape Heart Institute, University of Cape Town, South Africa. We genotyped DNA samples using the Infinium Human Omni 2.5-8 (Illumina Inc) bead chips, version 1.1 and version 1.3 according to the manufacturer’s protocol at the Genetic and Molecular Epidemiology Laboratory located in the Population Health Research Institute, Hamilton, Ontario, Canada. We adopted the 2011 recommendations of Turner et al³⁶ for per-sample and per-marker quality control. After quality control, genotyped variants were prephased on the Sanger Imputation Server³⁷ using EAGLE2, version 2.0.5.³⁸ We then imputed genetic variants with the African Genome Resources reference panel (from the African Genome Variation Project [AGVP]) using the Positional Burrows-Wheeler Transform imputation method.^39,40

GWAS Testing and Meta-analysis

We used Genome-wide Complex Trait Analysis, version 1.90.1 beta⁴¹ to conduct a mixed linear model association⁴² to retain the highest number of individuals by including 336 related individuals (7%; non–trio study participants). We assumed an additive model and adjusted for covariates, including sex, 10 genetic principal components, and Infinium Human Omni 2.5-8 bead chip version (1.1 or 1.3). We performed stratified analyses for Black African individuals and admixed African individuals separately. Effect sizes were transformed from linear to logistic odds ratios (ORs).⁴³ A meta-analysis of the GWAS results from Black African individual and admixed African individual was subsequently performed using an inverse-variance fixed-effects model in META, version 1.7.^44,45 The GWS P value threshold was set at less than 5 × 10⁻⁸.

Secondary Analyses

Regional association plots were generated using LocusZoom.⁴⁶ We investigated lead associations for novelty using the GWAS Catalog,^47,48 GeneAtlas Phenome-Wide Association Studies (PheWAS),^49,50 GWAS Atlas PheWAS,^51,52 and the Open Targets Genetics portal.^53,54 We adjusted the GeneAtlas PheWAS P values for false-discovery rates using the Benjamini-Hochberg procedure and fine-mapped GWS single-nucleotide variations (SNVs) (eTable 1 in the Supplement). We also interrogated the Genotype-Tissue Expression Project (GTEx) portal,^55,56 version 8 database for expression quantitative trait loci.

Pathway analyses were conducted using 2 different methods. First, data-driven expression-prioritized integration for complex traits (DEPICT), an integrative tool based on estimated gene functions, systematically prioritizes the most likely causal genes at associated loci, highlights enriched pathways, and identifies tissue or cell types where genes from associated loci are highly expressed. DEPICT was run using SNVs with P ≤ 1 × 10⁻⁵,⁵⁷ as recommended. Multi-marker Analysis of GenoMic Annotation (MAGMA) was also used for gene and gene-set analyses while correcting for type 1 error rate and linkage disequilibrium (LD), using all the GWAS SNVs.^58,59

We estimated narrow-sense heritability (h²) on the liability scale using Genome-wide Complex Trait Analysis–REML (restricted maximum likelihood estimation).^42,60 To calculate the h², we adjusted for sex, chip type, and 10 principal components, with the prevalence of disease set to 0.0047 in each ethnic group.¹²

TaqMan Validation of rs1219406

We directly genotyped the lead imputed SNV, rs1219406, in a random subset of 1218 RHDGen study participants from the Black African GWAS using a Custom TaqMan SNP Assay,⁶¹ which uses real-time quantitative polymerase chain reaction technology.

Family-Based Validation With Polygenic Transmission Disequilibrium Testing

The Black African GWAS (training and validation sample) was used to develop RHD polygenic risk scores (PRS) for polygenic transmission disequilibrium testing (pTDT) within the Black African trio families (testing sample). The optimal P value threshold (P₀) for inclusion of genetic variants into the PRS for pTDT was established through the clumping and thresholding procedure of PRSice-2,^62,63,64 within training and validation data sets. Subsequently, PRS (P₀) were derived in affected probands and unaffected parents, and pTDT was performed to test whether the proband PRS deviated from the mean parental PRS^62,65 (detailed methods in eAppendix 2 in Supplement).

Results

GWAS of RHD Susceptibility

We performed a GWAS of 7 605 010 autosomal variants in Black African individuals (1687 cases and 1492 controls) and admixed African individuals (601 cases and 454 controls) separately and a meta-analysis of both groups. Study participants’ characteristics are provided in Table 1. In Black African individuals, we found a GWS association at 11q24.1 (rs1219406; minor allelic frequency (MAF) = 0.092; OR, 1.65; 95% CI, 1.48-1.82; P = 4.36 × 10⁻⁸) (Figure 1 and Table 2⁴³). No other association was observed at GWS. Bayesian fine-mapping⁶⁶ ±1 Mb of rs1219406 (eTable 1 in the Supplement) produced 27 candidate causal variants in the 99% credible set. The lead SNV (rs1219406) accounted for 43% of the posterior probability out of the total 27 SNVs in the 99% credible set; rs1219406 tags a 100-Kb LD block within which there are no genes identified (Figure 2). The nearest gene (152 276 base pairs [bp] to the canonical transcription start site) is AP001924.1 (a novel transcript; GenBank AP001924.5), and the nearest coding gene (242 554 bp to the canonical transcription start site) is the BH3-Like Motif Containing, Cell Death Inducer (BLID [OMIM 608853]).⁵³

Table 1. Clinical Characteristics of GWAS Participants^a.

Profile and medical history	Participants, No. (%)			P value
Profile and medical history	All (N = 4809 [100%])	Cases (n = 2548 [53%])	Controls (n = 2261 [47%])	P value
RHD case-control GWAS study only
Female	3301 (69)	1912 (75)	1389 (61)	<.001
Age, mean (SD), y	36.5 (16.3)	37.0 (18.8)	36.1 (13.5)	<.001
Race/ethnicity
Black African	3179 (66)	1687 (66)	1492 (66)	.25
Admixed African	1055 (22)	601 (24)	454 (20)	.07
Other African^b	575 (12)	260 (10)	315 (14)	.10
BMI, mean (SD)	26 (6.3)	26 (6.1)	27 (6.4)	<.001
Blood pressure, mean (SD), mm Hg
Diastolic	75 (15.1)	73 (18.5)	77 (11.3)	<.001
Systolic	121 (18.7)	118 (20.1)	125 (17.1)	<.001
Atrial fibrillation	581 (12)	578 (23)	3 (0.1)	<.001
Pulmonary hypertension	81 (2)	76 (3)	5 (0.2)	<.001
Left ventricular hypertrophy	148 (3)	137 (5)	11 (0.5)	<.001
Valve surgery (received prosthetic valve[s])	NA	653 (26)	NA	NA
Asymptomatic cases	NA	858 (34)	NA	NA
Valvular lesions in cases: incidence (case %)
Valvular regurgitation (all types): 4240
Aortic	NA	961 (38)	NA	NA
Mitral	NA	1535 (60)	NA	NA
Pulmonary	NA	373 (15)	NA	NA
Tricuspid	NA	1371 (54)	NA	NA
Valvular stenosis (all types): 1096
Aortic	NA	195 (8)	NA	NA
Mitral	NA	869 (34)	NA	NA
Pulmonary	NA	6 (0.2)	NA	NA
Tricuspid	NA	26 (1)	NA	NA

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Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); GWAS, genome-wide association study; NA, not applicable; RHD, rheumatic heart disease.

^{^a}

Age was deemed unreliable in 786 (16%) participants and were either set to missing value or imputed, as appropriate. Hence, these ages were excluded from calculations. The GWAS summary statistics (eTable 1 in the Supplement) that include age as a covariate have all been imputed by age at visit date March 31, 2018, because this was the last date enrollment was open. The weight values used to calculate BMI excluded weights greater than 100 kg. Without excluding outliers, R’s MICE package for missing data was used for the complete data set, including outliers with weight greater than 100 kg and abnormal height values. The unadjusted average BMI of “healthy” controls was up to 38; this suggests that “healthy” women from our 8 African sites often had obesity or often overestimated their self-reported weight. A total of 26% of the cases (653 of 2548) exhibited severe RHD (proposed by severe valvular damage), which warranted the surgical receipt of prosthetic valves. More than 1 type of valve and 1 type of valvular damage reported may be present per case. These are overall mean values for the genetics of RHD. Furthermore, asymptomatic cases were recruited after they had effective surgical and other treatment interventions. Hence, they are not currently presenting with study symptom options: chest pain, dyspnea, fatigue, fever, palpitations, and syncope.

^{^b}

Other African includes individuals ethnically classified as those of North African descent, and citizens of Africa with Arab, European, East Asian, and South Asian ancestry.

Figure 1. — Matching quantile-quantile plots are available in eFigure 2 in the Supplement. Green dots represent variants with nominally and genome-wide significant associations.

Table 2. Summary Statistics of the Top Candidate SNVs in Independent Loci From the RHDGen GWAS.

Chr	SNV	Position	Ref	Alt	P value	OR (95% CI)	Function	Nearest gene(s)	GWAS MAF	1000G Afr MAF	1000G Eur MAF
Black African individuals ^a
11	rs1219406	121744369	C	G	4.36 × 10⁻⁸	1.65 (1.48-1.82)	Intergenic variant	SORL1; MIR125B1; MIR100HG/LOC107984402; BLID; RNU6-256P; AP001977.1	0.092	0.063	0.634
1	rs1795030	214297643	A	G	1.02 × 10⁻⁶	0.78 (0.68-0.88)	Intergenic variant	PROX1; SMYD2	0.385	0.67	0.45
8	rs73295430	83380736	G	A	1.03 × 10⁻⁶	0.65 (0.48-0.82)	Intergenic variant	LOC105375931; SNX16; LOC101927141	0.095	0.092	0
12	rs10774343	698879	C	A	1.37 × 10⁻⁶	1.28 (1.18-1.38)	Intronic variant	NINJ2	0.366	0.362	0.217
10	rs11597859	68331378	G	A	1.38 × 10⁻⁶	0.58 (0.36-0.80)	Intronic variant	CTNNA3	0.052	0.067	0.075
8	rs10755888	32248017	G	C	1.50 × 10⁻⁶	0.69 (0.53-0.84)	Intronic variant	NRG1	0.115	0.89	0.94
Admixed African individuals ^a
2	rs11125426	52644166	T	C	2.10 × 10⁻⁷	0.41 (0.09-0.72)	Intergenic variant	LOC730100; MIR4431 (AC007682.1; ASB3)	0.077	0.126	0.029
11	rs11600751	6302203	A	G	3.28 × 10⁻⁶	1.55 (1.37-1.73)	Intergenic variant	CCKBR; CAVIN3	0.326	0.167	0.459
11	rs35209562	12647771	C	T	3.28 × 10⁻⁶	1.73 (1.51-1.94)	Regulatory region variant	PARVA; TEAD1	0.180	0.84	0.81
21	rs174742	19332324	G	A	4.50 × 10⁻⁶	1.49 (1.32-1.65)	Intronic variant	CHODL	0.463	0.411	0.263
1	rs7535263	196682346	A	G	5.67 × 10⁻⁶	0.68 (0.51-0.84)	Intronic variant	CFH	0.490	0.56	0.42
19	rs636565	5553551	T	C	5.69 × 10⁻⁶	1.70 (1.48-1.91)	Intergenic variant	PLAC2 (TINCR)	0.194	0.840	0.649
Meta-analysis
2	rs2386325	89956461	T	C	3.17 × 10⁻⁷	0.80 (0.71-0.88)	Intergenic variant	IGKV1D-33; MIR4436A	NA	0.395	0.073
8	rs73295430	83380736	G	A	4.39 × 10⁻⁷	0.67 (0.52-0.83)	Intergenic variant (Downstream gene variant)	LOC105375931; SNX16; LOC101927141	NA	0.092	0
7	rs10243436	67041513	G	T	4.43 × 10⁻⁷	1.49 (1.34-1.64)	Intergenic variant	LINC01372; LOC102723427	NA	0.099	0.063
2	rs13393120	150786066	A	G	6.56 × 10⁻⁷	1.32 (1.21-1.42)	Intergenic variant	LINC01931; LINC01817	NA	0.19	0.077
8	rs10755888	32248017	G	C	8.50 × 10⁻⁷	0.71 (0.57-0.84)	Intronic variant	NRG1	NA	0.89	0.94
1	rs3753395	196686652	A	T	9.20 × 10⁻⁷	0.81 (0.72-0.89)	Intronic variant	CFH	NA	0.56	0.42

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Abbreviations: AC007682.1, antisense to neurexin 1 (NRXN1) gene; Afr, African or African American; Alt, alternate allele; AP001977.1, novel transcript of long non-coding RNA, near or in a regulatory region on 11q24.1; ASB3, ankyrin repeat and SOCS (suppressor of cytokine signaling) box protein 3; BLID, BH3-Like Motif Containing, Cell Death Inducer; CAVIN3, caveolae-associated protein 3; CCKBR, cholecystokinin B receptor; CFH, complement factor H; CHODL, chondrolectin; Chr, chromosome; CTNNA3, catenin (cadherin-associated protein), alpha 3; Eur, European; GWAS, genome-wide association study; IGKV, immunoglobulin κ variant; LINC01372, long intergenic non-protein coding RNA 1372; LINC01817, long intergenic nonprotein coding RNA 1817; LINC01931, long intergenic non-protein coding RNA 1931; LOC, genes of uncertain function; MAF, minor allele frequency; MIR100HG, Mir-100-Let-7a-2-Mir-125b-1 cluster host gene; MIR125B1, microRNA 125b-1; MIR4431, microRNA 4431; MIR4436A, microRNA 4436a; NINJ2, nerve injury–induced protein 2; NRG1, neuregulin 1; OR, odds ratio; PARVA, parvin alpha; PLAC2, placenta-specific 2, also known as TINCR; PROX1, prospero homeobox 1; Ref, reference allele; RHDGen, Genetics of Rheumatic Heart Disease; RNU6-256P, RNA, U6 small nuclear 256, pseudogene; SNV, single-nucleotide variation; SMYD2, SET and MYND domain containing 2; SORL1, sortilin-related receptor 1; SXN16, sorting nexin 16; TEAD1, transcriptional enhancer factor domain 1; TINCR, tissue differentiation-inducing nonprotein coding RNA; 1000G, 1000 Genomes.

^{^a}

The summary statistics for Black African individuals and admixed African individuals were generated in Genome-wide Complex Trait Analysis with covariates: sex, chip type, and 10PCs (10 principal components). All SNV positions are based on GRCh37 from Ensembl and HaploReg. All ORs are transformed from linear to logistic ORs.⁴³ Sorted by top ethnic groups (N > 500).

Figure 2. — A, The lead genome-wide significant single-nucleotide variation (SNV) in African individuals (rs1219406) on chromosome 11 in Black African idividuals and their associated cluster of SNVs in linkage disequilibrium. Single-nucleotide variation rs1219406 is flanked with the surrounding 400-kilobase (kb) regions, upstream and downstream. B, The top 1000 Genomes Project imputed SNV region in African individuals (rs28746888) on chromosome 6 in Black African individuals and their associated cluster of SNVs in linkage disequilibrium. Single-nucleotide variation rs28746888 is flanked with the surrounding 500-kb regions, upstream and downstream.

Because rs1219406 was imputed in our study (with high imputation quality [ie, information score = 0.98]), we also sought to strengthen the association findings by direct genotyping (TaqMan) in a subset of 1218 randomly selected samples. We observed a high concordance (99%) between direct genotypes and imputed genotypes (eFigure 3 in the Supplement). Nonetheless, no association was observed in admixed African individuals (MAF = 0.41; OR, 1.02; 95% CI, 0.85-1.22; P = .84). We also tested rs1219406 for association with RHD in external data sets, including populations of Melanesian, Polynesian, Northern Indian, Fijian Indian, and European descent.^28,30 None of the aforementioned samples confirmed the association (P > .05), neither individually nor with a meta-analysis of external replication sets (pooled OR_, 0.96; 95% CI, 0.86-1.06; P = .45) (eTable 2 in the Supplement).

To our knowledge, no associations or expression quantitative trait loci have previously been reported with rs1219406 in GWAS Catalog⁴⁷ and GTEx.⁵⁵ We also performed a PheWAS portal⁴⁹ search for rs1219406 in the predominantly European GeneAtlas. We observed 9 associations with a false-discovery rate of 0.05 or lower (eTable 1 in the Supplement), including immune system–associated traits (eg, lymphocyte count and M05 seropositive rheumatoid arthritis), skeletal system–associated traits (eg, height), and metabolism-associated traits (eg, body mass index). We also performed an Open Targets Genetics and GWAS atlas search. The GWAS atlas PheWAS identified 14 phenotypes significantly associated (P < .05/231 tests) with rs1219406 (eTable 1 in the Supplement) height at GWS,^51,53 as well as other anthropometric and cardiovascular traits.

In admixed African individuals, the highest nominally significant association was on chromosome 2p16.3 (rs11125426; OR, 0.41; 95% CI, 0.09-0.72; P = 2.10 × 10⁻⁷) (Figure 1 and Table 2⁴³). The nearest gene is the long intergenic nonprotein coding RNA-1867 (LINC01867 [HGNC 52687]), and the nearest coding gene is the neurexin-1 (NRXN1 [OMIM 600565]).⁵³ In the meta-analysis of Black and admixed African individuals, the highest nominally significant association was at chromosome 2p11.2 (rs2386325, OR, 0.80; 95% CI, 0.71-0.88; P = 3.17 × 10⁻⁷) (Figure 1 and Table 2⁴³). The nearest gene is the immunoglobulin κ variable 1D-33 (IGKV1D-33 [HGNC 5753]), and the nearest coding gene is the ribose 5-phosphate isomerase A (RPIA [HGNC 10297]).⁵³ Neither of these loci reached GWS and were not replicated (eTable 2 in the Supplement).

Replication of Previously Reported RHD Loci

We systematically evaluated 75 candidate genes and loci of interest²¹ but found no significant enrichment for genetic associations (Figure 3^21,28,67; eTable 1 in the Supplement). A prior GWAS of 2852 Pacific Islander individuals implicated a variant at the IGH locus on chromosome 14 (rs11846409). In RHDGen, this locus replicated (P < .05/2 independent tests) in admixed African individuals (OR, 1.37; 95% CI, 1.17-1.56; P = 2.38 × 10⁻³) and in the combined GWAS meta-analysis (OR, 1.16; 95% CI, 1.06-1.27; P = 1.19 × 10⁻³),²⁸ with a consistent direction of effect to earlier analyses. Pooling these association statistics with those from the Pacific, European, and Indian populations, each risk allele was associated with a 1.2-fold increased risk of disease (OR, 1.23, 95% CI, 1.15-1.31; P = 9.1 × 10⁻¹⁰; Figure 3^21,28,67).

The HLA DQA1-DQB1 locus reported to be nominally associated with RHD in Aboriginal Australian individuals (rs9272622; OR, 0.90; P = 1.86 × 10⁻⁷)²⁹ was not present in the unimputed RHDGen GWAS for replication. Because rs9272622 was not available in the African Genome Resources (from the AGVP) imputation reference panel, we imputed from the 1000 Genomes 1000G Phase 3 African imputation panel and reran associations for comparison. Using this approach, rs9272622 did not show evidence of association with RHD in either Black (OR, 0.96; 95% CI, 0.66-1.26; P = .44) or admixed African individuals (OR, 0.85; 95% CI, 0.65-1.05; P = .16). The closest imputed HLA region SNV in RHDGen, rs28746888, was near HLA-DQB1 and had suggestive significance (OR, 0.70; 95% CI, 0.57-0.84; P = 3.61 × 10⁻⁷; Figure 2).

We also tried to replicate a second HLA locus (rs201026476) associated with RHD in North Indian and European individuals.³⁰ However, rs201026476 was not present in either of our available imputed data sets of African descent (AGVP or 1000G phase 3 African individuals), and hence replication was not possible.

In Silico Pathway Analyses

DEPICT analyses⁵⁷ were conducted using suggestively significant SNVs (P < 1 × 10⁻⁵). Gene-set enrichment (eTable 3 in the Supplement) and tissue enrichment (eTable 4 in the Supplement) analyses showed some biologically relevant tissues among the top hits, including heart valve tissue; however, these results were not significant after adjustment for multiple hypothesis testing (false-discovery rate P < .05).⁶⁸

We also performed MAGMA gene analysis and generalized gene-set analysis of GWAS data.⁵⁸ We observed nominally significant associations (P < .05/number of total genes) with genes and gene sets of potential RHD relevance, using the competitive approach. These include the loci for IGH (chr14q32) and IGK (chr2p12), gene sets encoding components of the complement system, and genes for the regulation of cell proliferation involved in heart morphogenesis and CC chemokine receptor activity. The top MAGMA results per ethnic group are shown in eTables 5, 6, and 7 in the Supplement. No gene set was significant after adjustment for 18 000 tests.

Polygenic Association With RHD Susceptibility

The heritability of RHD explained by common (MAF ≥0.05) genetic variants was estimated at h² = 0.49 (SE = 0.12; P = 3.28 × 10⁻⁵) in Black African individuals and h² = 0.35 (SE = 0.24; P = 6.56 × 10⁻²) in admixed African individuals. The presence of significant heritability for RHD suggests a highly polygenic architecture. To further investigate this polygenicity, we conducted pTDT in trio families, hypothesizing that polygenic risk for RHD would be overtransmitted from unaffected parents to affected probands.⁶⁵

A PRS comprising 195 putative risk variants (Black African GWAS P < 8.01 × 10⁻⁵) was found to be overtransmitted to probands. Probands with RHD had a PRS that was on average 0.27 SDs higher than that of their unaffected parents (pTDT mean, 0.27 SD; pTDT SE, 0.16 SD; and pTDT P value, .04996).

Discussion

Our study provides insights into genetic loci and pathways associated with RHD susceptibility. We identified a GWS (P < 5 × 10⁻⁸) association at a novel locus on chromosome 11q24.1 that appears to be specific to Black African individuals, given that we did not replicate the finding in admixed African individuals or those of other ancestries; rs1219406 is intergenic and located in a region harboring only long noncoding transcripts, making it challenging to pinpoint potential causal mechanisms. Owing to the paucity of functional data on Black African individuals, it remains difficult to assess the biological implications of the observed association.^55,69,70,71 PheWAS analyses identified associations of rs1219406 with traits relevant to RHD, such as autoimmune traits, including lymphocyte count, atrial fibrillation, and M05 seropositive rheumatoid arthritis (a type of inflammatory polyarthropathy); rs1219406 is also associated with height at GWS,⁷² increasing confidence that it is either a functional locus or it is in LD with 1 or more functional variant(s). Nonetheless, our results require further replication, ideally in Black African individuals.

To our knowledge, we provided the first chip-based heritability estimates for RHD in African samples. Our estimates of 0.35 in admixed African individuals and 0.49 in Black African individuals support an important association of host genetics with RHD susceptibility.²³ The high heritability observed, combined with a lack of strong associations, suggests a polygenic architecture for RHD. The weakly significant overtransmission of a PRS comprising 195 putative risk alleles in 116 trios supports this hypothesis. More important, the pTDT analysis is robust to population stratification.

Several loci have been previously reported to be associated with RHD, and our study provided the opportunity to test these in African individuals. The IGH locus SNV rs11846409, discovered in an RHD GWAS in Pacific Islander individuals of Oceanian and South Asian ancestry, was replicated in our data. These results establish IGH as a key risk locus in RHD development across multiple distinct populations.²⁸ By contrast, we did not replicate the Aboriginal Australian HLA SNV, rs9272622.²⁹ We could not test another HLA locus in major histocompatibility complex class III (rs201026476) reported in an RHD GWAS of North Indian individuals and European individuals because it was not genotyped and could not be imputed in RHDGen.³⁰ It is possible that the complex LD structure in the highly polymorphic HLA region, its geographic variability in populations⁷³ (eg, from 8 different African countries vs Aboriginal Australian individuals), and our limited GWAS sample size may have contributed to the lack of replication.

Pathway analysis and GWAS searches to assess evidence for other functional associations implicate putative biological candidates associated with immunologic and cardiovascular development. These align with some of the current theories regarding RHD pathophysiologic characteristics.⁷⁴ However, the results were nominally significant, and further replication is needed. Furthermore, source data for pathway analysis are derived from predominantly European populations and tissues and might not be fully reflective of biological processes in African individuals. Similarly, our competitive MAGMA analyses failed to meet stringent Bonferroni correction for multiple hypothesis testing.^57,59 Hence, more functional work is needed to elucidate the appropriate pathways and how they connect to RHD pathophysiologic characteristics in African individuals.

Limitations

Our study had several limitations. First, despite being the largest study of RHD genetics, the overall sample size was small compared with contemporary GWASs for other diseases. Considerable challenges remain for the collection and analysis of biological samples in Africa.^75,76 Second, the novel GWS rs1219406 locus could not be replicated in Black African individuals because no independent cohort was available. Third, the functional relevance of the associations identified is difficult to assess because of the lack of functional genetics data specific to African individuals.^60,75,76 Fourth, regional environmental factors, such as the prevalence of HIV, could potentially impact the results. Individuals’ HIV status remains difficult to obtain because of the considerable social stigma and discrimination attached to it.⁷⁷ A study in 2016 showed that patients with RHD have more severe outcomes in the setting of HIV.⁷⁸ These findings were published after we designed our study; hence, HIV status was not elicited in the case report forms nor accounted for via RHDGen’s informed consent process.

Conclusions

Our study establishes the polygenic nature of RHD in African individuals and provides a comprehensive comparison of key genetic associations with other populations. Our study mandates further research on the 11q24.1 locus as a novel candidate for RHD in Black African individuals. Our results also support a high heritability for RHD in African individuals, providing a basis for the familial aggregation of RHD cases beyond a shared environment. As with other polygenic traits, large sample sizes will be necessary to uncover the numerous variants with smaller effect sizes underlying RHD risk.

Supplement.

eAppendix 1. The Genetics of Rheumatic Heart Disease (RHDGEN) Network Consortium

eAppendix 2. Methods and Results

eFigure 1. A Map of the Eight Different African Countries (highlighted in blue), With Participants in the RHDGen Consortium Dataset: Kenya, Mozambique, Namibia, Nigeria, South Africa, Sudan, Uganda, and Zambia

eFigure 2. Quantile-Quantile Plot of Association With RHD in Black Africans, Admixed Africans, and Their Meta-Analysis

eFigure 3. Exemplary Custom TaqMan SNP Assay Allelic Discrimination Plot Run on a Plate of Random RHDGen Samples

eTable 1. Secondary Analyses on RHDGen

eTable 2. Replication Testing in External Datasets

eTable 3. DEPICT Gene-Set Enrichment Analyses Using Black and Admixed Africans GWAS SNPs

eTable 4. DEPICT Tissue Enrichment Analyses Using Black and Admixed Africans GWAS SNPs

eTable 5. MAGMA Analysis Using Black Africans GWAS SNPs and MSigD

eTable 6. MAGMA Analysis Using Admixed Africans GWAS SNPs and MSigD

eTable 7. MAGMA Meta-Analysis of the Black and Admixed African GWAS

eReferences.

Click here for additional data file.^{(962.6KB, pdf)}

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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.White A. WHO resolution on rheumatic heart disease. Eur Heart J. 2018;39(48):4233-4233. doi: 10.1093/eurheartj/ehy764 [DOI] [PubMed] [Google Scholar]
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. [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
9.Hajar R. Rheumatic Fever and Rheumatic Heart Disease a Historical Perspective. Heart Views. 2016;17(3):120-126. doi: 10.4103/1995-705X.192572 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Worldometer. Africa population (live). Accessed March 1, 2021. https://www.worldometers.info/world-population/africa-population/?TB_iframe=true&width=370.8&height=658.8
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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/s41569-019-0258-2 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google 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.31773 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google 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.hg0119s68 [DOI] [PMC free article] [PubMed] [Google 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [Google Scholar]
44.Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(suppl 1):29-37. [PMC free article] [PubMed] [Google Scholar]
45.Running META. Accessed April 22, 2021. https://mathgen.stats.ox.ac.uk/genetics_software/meta/meta.html
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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/gkl125 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI]
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 [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google 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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Allen HD, et al. Moss & Adams' Heart Disease in Infants, Children, and Adolescents: Including the Fetus and Young Adult. Lippincott Williams & Wilkins; 2013. [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Tucci S, Akey JM. The long walk to African genomics. Genome Biol. 2019;20(1):130. doi: 10.1186/s13059-019-1740-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Tran BX, Phan HT, Latkin CA, et al. Understanding global HIV stigma and discrimination: are contextual factors sufficiently studied? (GAP_RESEARCH). Int J Environ Res Public Health. 2019;16(11):1899. doi: 10.3390/ijerph16111899 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [PMC free article] [PubMed] [Google Scholar]
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 [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eAppendix 1. The Genetics of Rheumatic Heart Disease (RHDGEN) Network Consortium

eAppendix 2. Methods and Results

eFigure 2. Quantile-Quantile Plot of Association With RHD in Black Africans, Admixed Africans, and Their Meta-Analysis

eFigure 3. Exemplary Custom TaqMan SNP Assay Allelic Discrimination Plot Run on a Plate of Random RHDGen Samples

eTable 1. Secondary Analyses on RHDGen

eTable 2. Replication Testing in External Datasets

eTable 3. DEPICT Gene-Set Enrichment Analyses Using Black and Admixed Africans GWAS SNPs

eTable 4. DEPICT Tissue Enrichment Analyses Using Black and Admixed Africans GWAS SNPs

eTable 5. MAGMA Analysis Using Black Africans GWAS SNPs and MSigD

eTable 6. MAGMA Analysis Using Admixed Africans GWAS SNPs and MSigD

eTable 7. MAGMA Meta-Analysis of the Black and Admixed African GWAS

eReferences.

Click here for additional data file.^{(962.6KB, pdf)}

[hoi210033r1] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r2] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r3] 3.White A. WHO resolution on rheumatic heart disease. Eur Heart J. 2018;39(48):4233-4233. doi: 10.1093/eurheartj/ehy764 [DOI] [PubMed] [Google Scholar]

[hoi210033r4] 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. [DOI] [PubMed] [Google Scholar]

[hoi210033r5] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r6] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r7] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r8] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r9] 9.Hajar R. Rheumatic Fever and Rheumatic Heart Disease a Historical Perspective. Heart Views. 2016;17(3):120-126. doi: 10.4103/1995-705X.192572 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r10] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r11] 11.Worldometer. Africa population (live). Accessed March 1, 2021. https://www.worldometers.info/world-population/africa-population/?TB_iframe=true&width=370.8&height=658.8

[hoi210033r12] 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

[hoi210033r13] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r14] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r15] 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 [DOI] [Google Scholar]

[hoi210033r16] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r17] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r18] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r19] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r20] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r21] 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/s41569-019-0258-2 [DOI] [PubMed] [Google Scholar]

[hoi210033r22] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r23] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r24] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r25] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r26] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r27] 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.31773 [DOI] [PubMed] [Google Scholar]

[hoi210033r28] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r29] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r30] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r31] 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/

[hoi210033r32] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r33] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r34] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r35] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r36] 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.hg0119s68 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r37] 37.Sanger Imputation Service. Accessed April 22, 2021. https://imputation.sanger.ac.uk/

[hoi210033r38] 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. [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r39] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r40] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r41] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r42] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r43] 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 [DOI] [Google Scholar]

[hoi210033r44] 44.Haidich AB. Meta-analysis in medical research. Hippokratia. 2010;14(suppl 1):29-37. [PMC free article] [PubMed] [Google Scholar]

[hoi210033r45] 45.Running META. Accessed April 22, 2021. https://mathgen.stats.ox.ac.uk/genetics_software/meta/meta.html

[hoi210033r46] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r47] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r48] 48.GWAS Catalog. Accessed April 22, 2021. https://www.ebi.ac.uk/gwas/

[hoi210033r49] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r50] 50.Gene ATLAS. Accessed April 22, 2021. http://geneatlas.roslin.ed.ac.uk/

[hoi210033r51] 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 [DOI] [PubMed] [Google Scholar]

[hoi210033r52] 52.GWASATLAS. Accessed April 22, 2021. https://atlas.ctglab.nl/PheWAS

[hoi210033r53] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r54] 54.Open Targets Genetics. Accessed April 22, 2021. https://genetics.opentargets.org/

[hoi210033r55] 55.GTEx Consortium . The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580-585. doi: 10.1038/ng.2653 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r56] 56.GTEx Portal. Accessed April 26, 2021. https://www.gtexportal.org/home/

[hoi210033r57] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r58] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r59] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r60] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r61] 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/gkl125 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r62] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r63] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r64] 64.PRS-ice2. Accessed April 26, 2021. https://www.prsice.info/

[hoi210033r65] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r66] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r67] 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 [DOI]

[hoi210033r68] 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 [Google Scholar]

[hoi210033r69] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r70] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r71] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r72] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r73] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r74] 74.Allen HD, et al. Moss & Adams' Heart Disease in Infants, Children, and Adolescents: Including the Fetus and Young Adult. Lippincott Williams & Wilkins; 2013. [Google Scholar]

[hoi210033r75] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r76] 76.Tucci S, Akey JM. The long walk to African genomics. Genome Biol. 2019;20(1):130. doi: 10.1186/s13059-019-1740-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r77] 77.Tran BX, Phan HT, Latkin CA, et al. Understanding global HIV stigma and discrimination: are contextual factors sufficiently studied? (GAP_RESEARCH). Int J Environ Res Public Health. 2019;16(11):1899. doi: 10.3390/ijerph16111899 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r78] 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 [DOI] [PMC free article] [PubMed] [Google Scholar]

[hoi210033r79] 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 [DOI] [Google Scholar]

PERMALINK

Association of Novel Locus With Rheumatic Heart Disease in Black African Individuals

Tafadzwa Machipisa, MPhil

Michael Chong, MSc

Babu Muhamed, PhD

Chishala Chishala, MD, MMed

Gasnat Shaboodien, PhD

Shahiemah Pandie, BCom

Jantina de Vries, DPhil

Nakita Laing, MSc

Alexia Joachim, RN

Rezeen Daniels, NHCert(Manage)

Mpiko Ntsekhe, MD, PhD

Christopher T Hugo-Hamman, MD, MMed

Bernard Gitura, MD, MMed

Stephen Ogendo, MD, MMed

Peter Lwabi, MD, MMed

Emmy Okello, PhD

Albertino Damasceno, MD, PhD

Celia Novela, RN

Ana O Mocumbi, MD, PhD

Goeffrey Madeira, MD

John Musuku, MD, MMed

Agnes Mtaja, MD, MMed

Ahmed ElSayed, MD

Huda H M Elhassan, MD

Fidelia Bode-Thomas, MD

Basil N Okeahialam, MD

Liesl J Zühlke, MD, PhD

Nicola Mulder, PhD

Raj Ramesar, PhD

Maia Lesosky, PhD

Tom Parks, MD

Heather J Cordell, DPhil

Bernard Keavney, BM, BCh, DM

Mark E Engel, MPH, PhD

Guillaume Paré, MD, MSc

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design

RHD Adjudication

Genotyping and Imputation

GWAS Testing and Meta-analysis

Secondary Analyses

TaqMan Validation of rs1219406

Family-Based Validation With Polygenic Transmission Disequilibrium Testing

Results

GWAS of RHD Susceptibility

Table 1. Clinical Characteristics of GWAS Participantsa.

Figure 1. Manhattan Plots of All Nominally and Genome-Wide Significant Regions in All Genome-Wide Association Study Populations.

Table 2. Summary Statistics of the Top Candidate SNVs in Independent Loci From the RHDGen GWAS.

Figure 2. LocusZoom Plots.

Replication of Previously Reported RHD Loci

Figure 3. Plots of Previously Reported Studies on Rheumatic Heart Disease Loci in the Genetics of Rheumatic Heart Disease (RHDGen) Study.

In Silico Pathway Analyses

Polygenic Association With RHD Susceptibility

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Table 1. Clinical Characteristics of GWAS Participants^a.