1932

Abstract

African populations are diverse in their ethnicity, language, culture, and genetics. Although plagued by high disease burdens, until recently the continent has largely been excluded from biomedical studies. Along with limitations in research and clinical infrastructure, human capacity, and funding, this omission has resulted in an underrepresentation of African data and disadvantaged African scientists. This review interrogates the relative abundance of biomedical data from Africa, primarily in genomics and other omics. The visibility of African science through publications is also discussed. A challenge encountered in this review is the relative lack of annotation of data on their geographical or population origin, with African countries represented as a single group. In addition to the abovementioned limitations,the global representation of African data may also be attributed to the hesitation to deposit data in public repositories. Whatever the reason, the disparity should be addressed, as African data have enormous value for scientists in Africa and globally.

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2021-07-20
2024-03-29
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Literature Cited

  1. 1. 
    Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A et al. 2009. The genetic structure and history of Africans and African Americans. Science 324:59301035–44
    [Google Scholar]
  2. 2. 
    WHO (World Health Organ.) 2020. World health statistics 2020: monitoring health for the SDGs. Tech. Rep. World Health Organ. Geneva: http://www.who.int/gho/publications/world_health_statistics/2020/en/
    [Google Scholar]
  3. 3. 
    WHO (World Health Organ.) 2020. Gross domestic R&D expenditure on health (health GERD) as a % of gross domestic product (GDP). Tech. Rep. World Health Organ. Geneva: accessed Oct. 27. http://www.who.int/research-observatory/indicators/gerd_gdp/en/
    [Google Scholar]
  4. 4. 
    Shriner D, Tekola-Ayele F, Adeyemo A, Rotimi CN. 2014. Genome-wide genotype and sequence-based reconstruction of the 140,000 year history of modern human ancestry. Sci. Rep. 4:6055
    [Google Scholar]
  5. 5. 
    Campbell MC, Tishkoff SA. 2008. African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu. Rev. Genom. Hum. Genet. 9:403–33
    [Google Scholar]
  6. 6. 
    Pennisi E. 2007. Human genetic variation. Science 318:58581842–43
    [Google Scholar]
  7. 7. 
    Vasseur E, Quintana-Murci L. 2013. The impact of natural selection on health and disease: uses of the population genetics approach in humans. Evol. Appl. 6:4596–607
    [Google Scholar]
  8. 8. 
    Elguero E, Délicat-Loembet LM, Rougeron V, Arnathau C, Roche B et al. 2015. Malaria continues to select for sickle cell trait in Central Africa. PNAS 112:227051–54
    [Google Scholar]
  9. 9. 
    Genovese G, Friedman DJ, Ross MD, Lecordier L, Uzureau P et al. 2010. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329:5993841–45
    [Google Scholar]
  10. 10. 
    Mbanefo EC, Ahmed AM, Titouna A, Elmaraezy A, Trang NTH et al. 2017. Association of glucose-6-phosphate dehydrogenase deficiency and malaria: a systematic review and meta-analysis. Sci. Rep. 7:45963
    [Google Scholar]
  11. 11. 
    Wright GEB, Carleton B, Hayden MR, Ross CJD. 2018. The global spectrum of protein-coding pharmacogenomic diversity. Pharmacogenom. J. 18:187–95
    [Google Scholar]
  12. 12. 
    Morales J, Welter D, Bowler EH, Cerezo M, Harris LW et al. 2018. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol 19:21
    [Google Scholar]
  13. 13. 
    Partn Natl. Genom. Data Cent. Memb. 2020. Database resources of the National Genomics Data Center in 2020. Nucleic Acids Res 48:D1D24–33
    [Google Scholar]
  14. 14. 
    ESFRI (Eur. Strateg. Forum Res. Infrastruct.) 2018. Roadmap 2018: strategy report on research infrastructures. Strateg. Rep. Eur. Strateg. Forum Res. Infrastruct. Brussels:
    [Google Scholar]
  15. 15. 
    BIG Data Cent. Memb 2018. Database Resources of the BIG Data Center in 2018. Nucleic Acids Res 46:D1D14–20
    [Google Scholar]
  16. 16. 
    Durinx C, McEntyre J, Appel R, Apweiler R, Barlow M et al. 2017. Identifying ELIXIR core data resources. F1000Research 5:2422
    [Google Scholar]
  17. 17. 
    Editorial 2020. Promoting best practice in nucleotide sequence data sharing. Sci. Data 7:152
    [Google Scholar]
  18. 18. 
    Cook CE, Bergman MT, Cochrane G, Apweiler R, Birney E. 2018. The European Bioinformatics Institute in 2017: data coordination and integration. Nucleic Acids Res 46:D1D21–29
    [Google Scholar]
  19. 19. 
    Bilofsky HS, Burks C. 1988. The GenBank genetic sequence data bank. Nucleic Acids Res 16:5 Part A1861–63
    [Google Scholar]
  20. 20. 
    Kodama Y, Mashima J, Kosuge T, Kaminuma E, Ogasawara O et al. 2018. DNA Data Bank of Japan: 30th anniversary. Nucleic Acids Res 46:D1D30–35
    [Google Scholar]
  21. 21. 
    Stoesser G, Tuli MA, Lopez R, Sterk P. 1999. The EMBL Nucleotide Sequence Database. Nucleic Acids Res 27:118–24
    [Google Scholar]
  22. 22. 
    Choudhury A, Ramsay M, Hazelhurst S, Aron S, Bardien S et al. 2017. Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans. Nat. Commun. 8:2062
    [Google Scholar]
  23. 23. 
    Wohlers I, Künstner A, Munz M, Olbrich M, Fähnrich A et al. 2020. An integrated personal and population-based Egyptian genome reference. Nat. Commun. 11:4719
    [Google Scholar]
  24. 24. 
    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I et al. 2015. The African Genome Variation Project shapes medical genetics in Africa. Nature 517:7534327–32
    [Google Scholar]
  25. 25. 
    BMIC (BioMed. Inform. Coord. Comm.) 2020. Open domain-specific data sharing repositories. Internet Resour. Natl. Inst. Health Bethesda, MD: accessed Oct. 27. https://www.nlm.nih.gov/NIHbmic/domain_specific_repositories.html
    [Google Scholar]
  26. 26. 
    Krause A, Wainstein T, Essop FB, Goodyear Q. 2013. Testing for haemoglobinopathies in Johannesburg, South Africa: a 30-year review. S. Afr. Med. J. 103:12 Suppl. 1989–93
    [Google Scholar]
  27. 27. 
    Adekile AD, Haider MZ. 2010. Haptoglobin gene polymorphisms in sickle cell disease patients with different βS-globin gene haplotypes. Med. Princ. Pract. 19:6447–50
    [Google Scholar]
  28. 28. 
    1000 Genomes Proj. Consort., Auton A, Brooks LD, Durbin RM, Garrison EP et al. 2015. A global reference for human genetic variation. Nature 526:757168–74
    [Google Scholar]
  29. 29. 
    Fan S, Kelly DE, Beltrame MH, Hansen MEB, Mallick S et al. 2019. African evolutionary history inferred from whole genome sequence data of 44 indigenous African populations. Genome Biol 20:82
    [Google Scholar]
  30. 30. 
    Choudhury A, Aron S, Botigué LR, Sengupta D, Botha G et al. 2020. High-depth African genomes inform human migration and health. Nature 586:741–48
    [Google Scholar]
  31. 31. 
    Beltrame MH, Rubel MA, Tishkoff SA. 2016. Inferences of African evolutionary history from genomic data. Curr. Opin. Genet. Dev. 41:159–66
    [Google Scholar]
  32. 32. 
    Vergara-Lope A, Jabalameli MR, Horscroft C, Ennis S, Collins A, Pengelly RJ 2019. Linkage disequilibrium maps for European and African populations constructed from whole genome sequence data. Sci. Data 6:208
    [Google Scholar]
  33. 33. 
    Schlebusch CM, Sjödin P, Breton G, Günther T, Naidoo T et al. 2020. Khoe-San genomes reveal unique variation and confirm the deepest population divergence in Homo sapiens. Mol. Biol. Evol. 37:102944–54
    [Google Scholar]
  34. 34. 
    Popejoy AB, Fullerton SM. 2016. Genomics is failing on diversity. Nat. News 538:7624161
    [Google Scholar]
  35. 35. 
    Wong KM, Langlais K, Tobias GS, Fletcher-Hoppe C, Krasnewich D et al. 2017. The dbGaP data browser: a new tool for browsing dbGaP controlled-access genomic data. Nucleic Acids Res 45:D819–26
    [Google Scholar]
  36. 36. 
    Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J et al. 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581:7809434–43
    [Google Scholar]
  37. 37. 
    Bycroft C, Freeman C, Petkova D, Band G, Elliott LT et al. 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562:7726203–9
    [Google Scholar]
  38. 38. 
    Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L et al. 2019. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104:121–34
    [Google Scholar]
  39. 39. 
    Liyanarachchi S, Gudmundsson J, Ferkingstad E, He H, Jonasson JG et al. 2020. Assessing thyroid cancer risk using polygenic risk scores. italicPNAS 117115997–6002
    [Google Scholar]
  40. 40. 
    Telenti A, Pierce LCT, Biggs WH, di Iulio J, Wong EHM et al. 2016. Deep sequencing of 10,000 human genomes italicPNAS 1134211901–6
    [Google Scholar]
  41. 41. 
    Gurdasani D, Carstensen T, Fatumo S, Chen G, Franklin CS et al. 2019. Uganda Genome Resource enables insights into population history and genomic discovery in Africa. Cell 179:4984–1002.e36
    [Google Scholar]
  42. 42. 
    Mallick S, Li H, Lipson M, Mathieson I, Gymrek M et al. 2016. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538:7624201–6
    [Google Scholar]
  43. 43. 
    Bergström A, McCarthy SA, Hui R, Almarri MA, Ayub Q et al. 2020. Insights into human genetic variation and population history from 929 diverse genomes. Science 367:6484eaay5012
    [Google Scholar]
  44. 44. 
    McVean GA, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR et al. 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491:742256–65
    [Google Scholar]
  45. 45. 
    Sherman RM, Forman J, Antonescu V, Puiu D, Daya M et al. 2019. Assembly of a pan-genome from deep sequencing of 910 humans of African descent. Nat. Genet. 51:30–35
    [Google Scholar]
  46. 46. 
    Pagani L, Schiffels S, Gurdasani D, Danecek P, Scally A et al. 2015. Tracing the route of modern humans out of Africa by using 225 human genome sequences from Ethiopians and Egyptians. Am. J. Hum. Genet. 96:6986–91
    [Google Scholar]
  47. 47. 
    Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA et al. 2019. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. bioRxiv 563866. https://doi.org/10.1101/563866
    [Crossref]
  48. 48. 
    Owolabi MO, Akpa OM, Made F, Adebamowo SN, Ojo A et al. 2019. Data resource profile: Cardiovascular H3Africa Innovation Resource (CHAIR). Int. J. Epidemiol. 48:2366–67g
    [Google Scholar]
  49. 49. 
    Stevenson A, Akena D, Stroud RE, Atwoli L, Campbell MM et al. 2019. Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis): a case-control study protocol and GWAS in Ethiopia, Kenya, South Africa and Uganda. BMJ Open 9:2e025469
    [Google Scholar]
  50. 50. 
    Ghattaoraya GS, Middleton D, Santos EJM, Dickson R, Jones AR, Alfirevic A. 2017. Human leucocyte antigen–adverse drug reaction associations: from a perspective of ethnicity. Int. J. Immunogenet. 44:17–26
    [Google Scholar]
  51. 51. 
    Chou YC, Chen CH, Chen MJ, Chang CW, Chen PH et al. 2020. Killer cell immunoglobulin-like receptors (KIR) and human leukocyte antigen-C (HLA-C) allorecognition patterns in women with endometriosis. Sci. Rep. 10:4897
    [Google Scholar]
  52. 52. 
    Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J et al. 2019. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1D1005–12
    [Google Scholar]
  53. 53. 
    Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y et al. 2020. The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation. medRxiv. 2020.05.20.20108217. https://doi.org/10.1101/2020.05.20.20108217
    [Crossref]
  54. 54. 
    Al Olama AA, Kote-Jarai Z, Berndt SI, Conti DV, Schumacher F et al. 2014. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46:101103–9
    [Google Scholar]
  55. 55. 
    Landrum MJ, Lee JM, Benson M, Brown GR, Chao C et al. 2018. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res 46:D1062–67
    [Google Scholar]
  56. 56. 
    Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS et al. 2014. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42:D980–85
    [Google Scholar]
  57. 57. 
    Wilson H. 2014. Pharmacogenomics failing to reach developing countries. Pharmacogenomics 15:731–32
    [Google Scholar]
  58. 58. 
    Radouani F, Zass L, Hamdi Y, da Rocha J, Sallam R et al. 2020. A review of clinical pharmacogenetics studies in African populations. Pers. Med. 17:2155–70
    [Google Scholar]
  59. 59. 
    Barbarino JM, Whirl-Carrillo M, Altman RB, Klein TE. 2018. PharmGKB: a worldwide resource for pharmacogenomic information. Wiley Interdiscip. Rev. Syst. Biol. Med. 10:4e1417
    [Google Scholar]
  60. 60. 
    Gong L, Owen RP, Gor W, Altman RB, Klein TE. 2008. PharmGKB: an integrated resource of pharmacogenomic data and knowledge. Curr. Protoc. Bioinform. 23:114.7.1–14.7.17
    [Google Scholar]
  61. 61. 
    Huddart R, Fohner AE, Whirl-Carrillo M, Wojcik GL, Gignoux CR et al. 2019. Standardized biogeographic grouping system for annotating populations in pharmacogenetic research. Clin. Pharmacol. Ther. 105:51256–62
    [Google Scholar]
  62. 62. 
    da Rocha J, Othman H, Botha G, Cottino L, Twesigomwe D et al. 2020. The extent and impact of variation in ADME genes in sub-Saharan African populations. bioRxiv. 2020.06.14.108217 . https://doi.org/10.1101/2020.06.14.108217
    [Crossref]
  63. 63. 
    Tshabalala S, Choudhury A, Beeton-Kempen N, Martinson N, Ramsay M, Mancama D 2019. Targeted ultra-deep sequencing of a South African Bantu-speaking cohort to comprehensively map and characterize common and novel variants in 65 pharmacologically-related genes. Pharmacogenet. Genom. 29:7167–78
    [Google Scholar]
  64. 64. 
    Zhang F, Finkelstein J. 2019. Inconsistency in race and ethnic classification in pharmacogenetics studies and its potential clinical implications. Pharmacogenom. Pers. Med. 12:107–23
    [Google Scholar]
  65. 65. 
    FDA (US Food Drug Admin.) 2020. Table of pharmacogenomic biomarkers in drug labeling. Web Resour. US Food Drug Admin. White Oak, MD: https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling
    [Google Scholar]
  66. 66. 
    PharmGKB 2020. Clinical guideline annotations. Web Resour PharmGKB Stanford, CA.: https://www.pharmgkb.org/guidelineAnnotations
    [Google Scholar]
  67. 67. 
    WHO (World Health Organ.) 2020. Monitoring processes to R&D. Tech. Rep., World Health Organ Geneva: accessed Oct. 27. http://www.who.int/research-observatory/monitoring/processes/clinical_trials_2/en/
    [Google Scholar]
  68. 68. 
    Alemayehu C, Mitchell G, Nikles J. 2018. Barriers for conducting clinical trials in developing countries—a systematic review. Int. J. Equity Health 17:37
    [Google Scholar]
  69. 69. 
    Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM et al. 2017. Discovering and linking public ‘omics’ datasets using the Omics Discovery Index. Nat. Biotechnol. 35:5406–9
    [Google Scholar]
  70. 70. 
    Athar A, Füllgrabe A, George N, Iqbal H, Huerta L et al. 2019. ArrayExpress update—from bulk to single-cell expression data. Nucleic Acids Res 47:D1D711–15
    [Google Scholar]
  71. 71. 
    Clough E, Barrett T. 2016. The Gene Expression Omnibus database. Methods Mol. Biol. 1418:93–110
    [Google Scholar]
  72. 72. 
    Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E et al. 2013. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45:6580–85
    [Google Scholar]
  73. 73. 
    Ndimba BK, Thomas LA. 2008. Proteomics in South Africa: current status, challenges and prospects. Biotechnol. J. 3:111368–74
    [Google Scholar]
  74. 74. 
    Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S et al. 2019. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res 47:D1D442–50
    [Google Scholar]
  75. 75. 
    Gupta VK, Paul S, Dutta C. 2017. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front. Microbiol. 8:1162
    [Google Scholar]
  76. 76. 
    Porras AM, Brito IL. 2019. The internationalization of human microbiome research. Curr. Opin. Microbiol. 50:50–55
    [Google Scholar]
  77. 77. 
    Berg G, Rybakova D, Fischer D, Cernava T, Vergès M-CC et al. 2020. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8:103
    [Google Scholar]
  78. 78. 
    Wattam AR, Brettin T, Davis JJ, Gerdes S, Kenyon R et al. 2018. Assembly, annotation, and comparative genomics in PATRIC, the all bacterial bioinformatics resource center. Methods Mol. Biol. 1704:79–101
    [Google Scholar]
  79. 79. 
    Shu Y, McCauley J. 2017. GISAID: global initiative on sharing all influenza data—from vision to reality. Eurosurveillance 22:1330494
    [Google Scholar]
  80. 80. 
    Tijssen RJW. 2007. Africa's contribution to the worldwide research literature: new analytical perspectives, trends, and performance indicators. Scientometrics 71:2303–27
    [Google Scholar]
  81. 81. 
    Rahman M, Fukui T. 2003. Biomedical publication—global profile and trend. Public Health 117:4274–80
    [Google Scholar]
  82. 82. 
    Rosmarakis ES, Vergidis PI, Soteriades ES, Paraschakis K, Papastamataki PA, Falagas ME. 2005. Estimates of global production in cardiovascular diseases research. Int. J. Cardiol. 100:3443–49
    [Google Scholar]
  83. 83. 
    Uthman OA, Uthman MB. 2007. Geography of Africa biomedical publications: an analysis of 1996–2005 PubMed papers. Int. J. Health Geogr. 6:46
    [Google Scholar]
  84. 84. 
    Hofman KJ, Kanyengo CW, Rapp BA, Kotzin S. 2009. Mapping the health research landscape in Sub-Saharan Africa: a study of trends in biomedical publications. J. Med. Libr. Assoc. 97:141–44
    [Google Scholar]
  85. 85. 
    Mbaye R, Gebeyehu R, Hossmann S, Mbarga N, Bih-Neh E et al. 2019. Who is telling the story? A systematic review of authorship for infectious disease research conducted in Africa, 1980–2016. BMJ Glob. Health 4:5e001855
    [Google Scholar]
  86. 86. 
    Adedokun BO, Olopade CO, Olopade OI. 2016. Building local capacity for genomics research in Africa: recommendations from analysis of publications in Sub-Saharan Africa from 2004 to 2013. Glob. Health Action 9:31026
    [Google Scholar]
  87. 87. 
    Rees CA, Lukolyo H, Keating EM, Dearden KA, Luboga SA et al. 2017. Authorship in paediatric research conducted in low- and middle-income countries: parity or parasitism?. Trop. Med. Int. Health 22:111362–70
    [Google Scholar]
  88. 88. 
    Pastrana T, Vallath N, Mastrojohn J, Namukwaya E, Kumar S et al. 2010. Disparities in the contribution of low- and middle-income countries to palliative care research. J. Pain Symptom. Manag. 39:154–68
    [Google Scholar]
  89. 89. 
    Aluede EE, Phillips J, Bleyer J, Jergesen HE, Coughlin R. 2012. Representation of developing countries in orthopaedic journals: a survey of four influential orthopaedic journals. Clin. Orthop. 470:82313–18
    [Google Scholar]
  90. 90. 
    Chersich MF, Blaauw D, Dumbaugh M, Penn-Kekana L, Dhana A et al. 2016. Local and foreign authorship of maternal health interventional research in low- and middle-income countries: systematic mapping of publications 2000–2012. Glob. Health 12:135
    [Google Scholar]
  91. 91. 
    Adam T, Akuffo H, Carter JG, Charat Z, Cheetham MJ et al. 2020. World RePORT: a database for mapping biomedical research funding. Lancet Glob. Health 8:1e27–29
    [Google Scholar]
  92. 92. 
    Ghani M, Hurrell R, Verceles AC, McCurdy MT, Papali A. 2021. Geographic, subject, and authorship trends among LMIC-based scientific publications in high-impact global health and general medicine journals: a 30-month bibliometric analysis. J. Epidemiol. Glob. Health 11:19297
    [Google Scholar]
  93. 93. 
    Tastan Bishop Ö, Adebiyi EF, Alzohairy AM, Everett D, Ghedira K et al. 2015. Bioinformatics education—perspectives and challenges out of Africa. Brief Bioinform 16:2355–64
    [Google Scholar]
  94. 94. 
    Mulder NJ, Adebiyi E, Alami R, Benkahla A, Brandful J et al. 2016. H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa. Genome Res 26:2271–77
    [Google Scholar]
  95. 95. 
    Gurwitz KT, Aron S, Panji S, Maslamoney S, Fernandes PL et al. 2017. Designing a course model for distance-based online bioinformatics training in Africa: the H3ABioNet experience. PLOS Comput. Biol. 13:10e1005715
    [Google Scholar]
  96. 96. 
    Chasapi A, Promponas VJ, Ouzounis CA. 2020. The bioinformatics wealth of nations. Bioinformatics 36:92963–5
    [Google Scholar]
  97. 97. 
    Martin AR, Kanai M, Kamatani Y, Okada Y, BM Neale, Daly MJ. 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
    [Google Scholar]
  98. 98. 
    Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M et al. 2017. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the clinical genome resource. Am. J. Hum. Genet. 100:6895–906
    [Google Scholar]
  99. 99. 
    Bope CD, Chimusa ER, Nembaware V, Mazandu GK, de Vries J, Wonkam A. 2019. Dissecting in silico mutation prediction of variants in African genomes: challenges and perspectives. Front. Genet. 10:601
    [Google Scholar]
  100. 100. 
    Amendola LM, Dorschner MO, Robertson PD, Salama JS, Hart R et al. 2015. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res 25:3305–15
    [Google Scholar]
  101. 101. 
    Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA et al. 2016. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375:7655–65
    [Google Scholar]
  102. 102. 
    World Bank 2021. World Bank country and lending groups. Data Resour. World Bank Washington, DC.: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-group
    [Google Scholar]
  103. 103. 
    WHO (World Health Organ.). 2018. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2016 World Health Organ. Geneva: https://www.who.int/healthinfo/global_burden_disease/estimates/en/
  104. 104. 
    Francioli L, MacArthur D. 2019. gnomAD v3.0. gnomAD Blog, Oct. 16. https://gnomad.broadinstitute.org/blog/2019-10-gnomad-v3-0/
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