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African genetic diversity and adaptation inform a precision medicine agenda


The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.

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Fig. 1: Important demographic events on the African continent.
Fig. 2: Features of African genome architecture.
Fig. 3: Adaptive genetic variants in people with African ancestry have been discovered through several approaches.
Fig. 4: Precision public health strategies could benefit African populations.
Fig. 5: Challenges in Africa, key enablers and potential benefits of African genome research.
Fig. 6: A complex implementation pathway towards precision health.


  1. 1.

    Rotimi, C. et al. Research capacity. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014).

    PubMed  Google Scholar 

  2. 2.

    Cohoon, T. J. & Bhavnani, S. P. Toward precision health: applying artificial intelligence analytics to digital health biometric datasets. Per. Med. 17, 307–316 (2020).

    CAS  PubMed  Google Scholar 

  3. 3.

    Mapesi, H. & Paris, D. H. Non-communicable diseases on the rise in sub-Saharan Africa, the underappreciated threat of a dual disease burden. Praxis 108, 997–1005 (2019).

    PubMed  Google Scholar 

  4. 4.

    Mudie, K. et al. Non-communicable diseases in sub-Saharan Africa: a scoping review of large cohort studies. J. Glob. Health 9, 020409 (2019).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Tekola-Ayele, F. & Rotimi, C. N. Translational genomics in low- and middle-income countries: opportunities and challenges. Public Health Genomics 18, 242–247 (2015).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Mulder, N. et al. H3Africa: current perspectives. Pharmgenomics Pers. Med. 11, 59–66 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Owolabi, M. O. et al. Data resource profile: Cardiovascular H3Africa Innovation Resource (CHAIR). Int. J. Epidemiol. 48, 366–367g (2019).

    PubMed  Google Scholar 

  8. 8.

    Adedokun, B. O., Olopade, C. O. & Olopade, O. I. 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 (2016).

    PubMed  Google Scholar 

  9. 9.

    Stringer, C. The origin and evolution of Homo sapiens. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150237 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Schlebusch, C. M. et al. Southern African ancient genomes estimate modern human divergence to 350,000 to 260,000 years ago. Science 358, 652–655 (2017).

    CAS  PubMed  Google Scholar 

  11. 11.

    Hublin, J. J. et al. New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens. Nature 546, 289–292 (2017).

    CAS  PubMed  Google Scholar 

  12. 12.

    Scerri, E. M. L. et al. Did our species evolve in subdivided populations across Africa, and why does it matter? Trends Ecol. Evol. 33, 582–594 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Cruciani, F. et al. A revised root for the human Y chromosomal phylogenetic tree: the origin of patrilineal diversity in Africa. Am. J. Hum. Genet. 88, 814–818 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Rito, T. et al. The first modern human dispersals across Africa. PLoS ONE 8, e80031 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Santander, C., Montinaro, F. & Capelli, C. Searching for archaic contribution in Africa. Ann. Hum. Biol. 46, 129–139 (2019).

    PubMed  Google Scholar 

  16. 16.

    Skoglund, P. & Mathieson, I. Ancient genomics of modern humans: the first decade. Annu. Rev. Genomics Hum. Genet. 19, 381–404 (2018).

    CAS  PubMed  Google Scholar 

  17. 17.

    Durvasula, A. & Sankararaman, S. Recovering signals of ghost archaic introgression in African populations. Sci. Adv. 6, eaax5097 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Chen, L., Wolf, A. B., Fu, Q., Li, L. & Akey, J. M. Identifying and interpreting apparent neanderthal ancestry in African individuals. Cell 180, 1–11 (2020).

    Google Scholar 

  19. 19.

    Veeramah, K. R. et al. An early divergence of KhoeSan ancestors from those of other modern humans is supported by an ABC-based analysis of autosomal resequencing data. Mol. Biol. Evol. 29, 617–630 (2012).

    CAS  PubMed  Google Scholar 

  20. 20.

    Verdu, P. et al. Origins and genetic diversity of pygmy hunter-gatherers from Western Central Africa. Curr. Biol. 19, 312–318 (2009).

    CAS  PubMed  Google Scholar 

  21. 21.

    Fan, S. et al. African evolutionary history inferred from whole genome sequence data of 44 indigenous African populations. Genome Biol. 20, 82 (2019).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Tishkoff, S. A. et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Hernández, C. L. et al. Human genomic diversity where the Mediterranean joins the Atlantic. Mol. Biol. Evol. 37, 1041–1055 (2020).

    PubMed  Google Scholar 

  24. 24.

    van de Loosdrecht, M. et al. Pleistocene North African genomes link Near Eastern and sub-Saharan African human populations. Science 360, 548–552 (2018).

    PubMed  Google Scholar 

  25. 25.

    Pickrell, J. K. et al. The genetic prehistory of southern Africa. Nat. Commun. 3, 1143 (2012).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Triska, P. et al. Extensive admixture and selective pressure across the Sahel Belt. Genome Biol. Evol. 7, 3484–3495 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Patin, E. et al. Dispersals and genetic adaptation of Bantu-speaking populations in Africa and North America. Science 356, 543–546 (2017). This paper examines the genetic diversity of Bantu-speakers, modelling the timing of migration and admixture during the Bantu expansion. Interestingly, the paper identifies adaptive introgression of genes from local populations along the Bantu waves, including specific immune-related genes.

    CAS  PubMed  Google Scholar 

  28. 28.

    Lopez, M. et al. The demographic history and mutational load of African hunter-gatherers and farmers. Nat. Ecol. Evol. 2, 721–730 (2018).

    PubMed  Google Scholar 

  29. 29.

    Gallego Llorente, M. et al. Ancient Ethiopian genome reveals extensive Eurasian admixture throughout the African continent. Science 350, 820–822 (2015).

    CAS  PubMed  Google Scholar 

  30. 30.

    Skoglund, P. et al. Reconstructing prehistoric African population structure. Cell 171, 59–71.e21 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Lipson, M. et al. Ancient West African foragers in the context of African population history. Nature 577, 665–670 (2020).

    CAS  PubMed  Google Scholar 

  32. 32.

    Hellenthal, G. et al. A genetic atlas of human admixture history. Science 343, 747–751 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Semo, A. et al. Along the Indian Ocean coast: genomic variation in Mozambique provides new insights into the Bantu expansion. Mol. Biol. Evol. 37, 406–416 (2019).

    PubMed Central  Google Scholar 

  34. 34.

    Choudhury, A. et al. High-depth African genomes inform human migration and health. Nature 586, 741–748 (2020). This paper presents the largest whole-genome sequence study of African populations describing >3 million novel variants and highlighting disease-associated alleles.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gurdasani, D. et al. The African genome variation project shapes medical genetics in Africa. Nature 517, 327–332 (2015). This paper introduces The African Genome Variation Project, with dense genotypes from 1481 individuals and whole-genome sequences from 320 individuals across SSA. Besides presenting novel evidence of complex population history and loci under selection, the paper demonstrates further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes.

    CAS  PubMed  Google Scholar 

  36. 36.

    Pickrell, J. K. et al. Ancient west Eurasian ancestry in southern and eastern Africa. Proc. Natl Acad. Sci. USA 111, 2632–2637 (2014).

    CAS  PubMed  Google Scholar 

  37. 37.

    Brucato, N. et al. The Comoros show the earliest Austronesian gene flow into the Swahili Corridor. Am. J. Hum. Genet. 102, 58–68 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Pierron, D. et al. Genomic landscape of human diversity across madagascar. Proc. Natl Acad. Sci. USA 114, E6498–E6506 (2017).

    CAS  PubMed  Google Scholar 

  39. 39.

    Brucato, N. et al. Evidence of austronesian genetic lineages in East Africa and South Arabia: complex dispersal from madagascar and Southeast Asia. Genome Biol. Evol. 11, 748–758 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Choudhury, A. et al. Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans. Nat. Commun. 8, 2062 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    May, A. et al. Genetic diversity in black South Africans from Soweto. BMC Genomics 14, 644 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lovejoy, P. E. The impact of the Atlantic slave trade on Africa: a review of the literature. J. Afr. Hist. 30, 365–394 (1989).

    Google Scholar 

  43. 43.

    Macaulay, V. et al. Single, rapid coastal settlement of Asia revealed by analysis of complete mitochondrial genomes. Science 308, 1034–1036 (2005).

    CAS  PubMed  Google Scholar 

  44. 44.

    Gravel, S. et al. Demographic history and rare allele sharing among human populations. Proc. Natl Acad. Sci. USA 108, 11983–11988 (2011).

    CAS  PubMed  Google Scholar 

  45. 45.

    The 1000 Genomes Project Consortium, et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  46. 46.

    Chen, J. et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204–1211 (2019).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Martin, A. R., Teferra, S., Möller, M., Hoal, E. G. & Daly, M. J. The critical needs and challenges for genetic architecture studies in Africa. Curr. Opin. Genet. Dev. 53, 113–120 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Gurdasani, D., Barroso, I., Zeggini, E. & Sandhu, M. S. Genomics of disease risk in globally diverse populations. Nat. Rev. Genet. 20, 520–535 (2019).

    CAS  PubMed  Google Scholar 

  49. 49.

    Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

    CAS  PubMed  Google Scholar 

  50. 50.

    Simons, Y. B., Turchin, M. C., Pritchard, J. K. & Sella, G. The deleterious mutation load is insensitive to recent population history. Nat. Genet. 46, 220–224 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Quintana-Murci, L. Human immunology through the lens of evolutionary genetics. Cell 177, 184–199 (2019).

    CAS  PubMed  Google Scholar 

  52. 52.

    Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703–713 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Guernier, V., Hochberg, M. E. & Guegan, J. F. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2, e141 (2004).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Cairns, M. E. et al. Seasonality in malaria transmission: implications for case-management with long-acting artemisinin combination therapy in sub-Saharan Africa. Malar. J. 14, 321 (2015).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Zhao, S., Lin, Q., He, D. & Stone, L. Meningitis epidemics shift in sub-Saharan belt. Int. J. Infect. Dis. 68, 79–82 (2018).

    PubMed  Google Scholar 

  56. 56.

    Alexander, K. A. et al. What factors might have led to the emergence of Ebola in West Africa? PLoS Negl. Trop. Dis. 9, e0003652 (2015).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Fan, S., Hansen, M. E., Lo, Y. & Tishkoff, S. A. Going global by adapting local: a review of recent human adaptation. Science 354, 54–59 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Sierra, B. et al. OSBPL10, RXRA and lipid metabolism confer African-ancestry protection against dengue haemorrhagic fever in admixed Cubans. PLoS Pathog. 13, e1006220 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Bentley, A. R. & Rotimi, C. N. Interethnic variation in lipid profiles: implications for underidentification of African-Americans at risk for metabolic disorders. Expert Rev. Endocrinol. Metab. 7, 659–667 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Coram, M. A. et al. Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am. J. Hum. Genet. 92, 904–916 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Cohen, J. et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat. Genet. 37, 161–165 (2005). This paper reports that loss-of-function mutations in the PCSK9 gene, preventing hypercholesterolaemia, were common in AAs (combined frequency, 2%) but rare in EAs (<0.1%) and were associated with a 40% reduction in plasma levels of LDL cholesterol.

    CAS  PubMed  Google Scholar 

  62. 62.

    Ng, M. C. Y. et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet. 13, e1006719 (2017).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Doumatey, A. P., Ekoru, K., Adeyemo, A. & Rotimi, C. N. Genetic basis of obesity and type 2 diabetes in Africans: impact on precision medicine. Curr. Diab Rep. 19, 105 (2019).

    PubMed  Google Scholar 

  64. 64.

    Gomez-Olive, F. X. et al. Regional and sex differences in the prevalence and awareness of hypertension: an H3Africa AWI-Gen study across 6 sites in sub-Saharan Africa. Glob. Heart 12, 81–90 (2017).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Young, J. H. et al. Differential susceptibility to hypertension is due to selection during the out-of-Africa expansion. PLoS Genet. 1, e82 (2005).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Franceschini, N. et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am. J. Hum. Genet. 93, 545–554 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Band, G., Rockett, K. A., Spencer, C. C. & Kwiatkowski, D. P. A novel locus of resistance to severe malaria in a region of ancient balancing selection. Nature 526, 253–257 (2015).

    CAS  PubMed  Google Scholar 

  68. 68.

    Hamblin, M. T., Thompson, E. E. & Di Rienzo, A. Complex signatures of natural selection at the Duffy blood group locus. Am. J. Hum. Genet. 70, 369–383 (2002).

    PubMed  Google Scholar 

  69. 69.

    McManus, K. F. et al. Population genetic analysis of the DARC locus (Duffy) reveals adaptation from standing variation associated with malaria resistance in humans. PLoS Genet. 13, e1006560 (2017).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Pierron, D. et al. Strong selection during the last millennium for African ancestry in the admixed population of Madagascar. Nat. Commun. 9, 932 (2018).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Genovese, G. et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329, 841–845 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Cooper, A. et al. APOL1 renal risk variants have contrasting resistance and susceptibility associations with African trypanosomiasis. eLife 6, e25461 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Ko, W. Y. et al. Identifying Darwinian selection acting on different human APOL1 variants among diverse African populations. Am. J. Hum. Genet. 93, 54–66 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Williams, T. N. et al. Negative epistasis between the malaria-protective effects of α+-thalassemia and the sickle cell trait. Nat. Genet. 37, 1253–1257 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Saraf, S. L. et al. APOL1, α-thalassemia, and BCL11A variants as a genetic risk profile for progression of chronic kidney disease in sickle cell anemia. Haematologica 102, e1–e6 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Zahr, R. S. et al. Children with sickle cell anemia and APOL1 genetic variants develop albuminuria early in life. Haematologica 104, e385–e387 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Engelken, J. et al. Extreme population differences in the human zinc transporter ZIP4 (SLC39A4) are explained by positive selection in sub-Saharan Africa. PLoS Genet. 10, e1004128 (2014).

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Hood, M. I. & Skaar, E. P. Nutritional immunity: transition metals at the pathogen–host interface. Nat. Rev. Microbiol. 10, 525–537 (2012).

    CAS  PubMed  Google Scholar 

  79. 79.

    Schaafsma, T. et al. Africa’s oesophageal cancer corridor: geographic variations in incidence correlate with certain micronutrient deficiencies. PLoS ONE 10, e0140107 (2015).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Vicente, M., Jakobsson, M., Ebbesen, P. & Schlebusch, C. M. Genetic affinities among southern Africa hunter-gatherers and the impact of admixing farmer and herder populations. Mol. Biol. Evol. 36, 1849–1861 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Lachance, J. et al. Evolutionary history and adaptation from high-coverage whole-genome sequences of diverse African hunter-gatherers. Cell 150, 457–469 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Xu, D. et al. Archaic hominin introgression in Africa contributes to functional salivary MUC7 genetic variation. Mol. Biol. Evol. 34, 2704–2715 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Petrovski, S. & Goldstein, D. B. Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine. Genome Biol. 17, 157 (2016).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Gurdasani, D. et al. Uganda genome resource enables insights into population history and genomic discovery in Africa. Cell 179, 984–1002.e36 (2019). By performing a multi-trait pan-African GWAS of up to 14,126 individuals, this paper identifies novel loci associated with anthropometric, haematological, lipid and glycaemic traits. Several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Rotimi, C. N. et al. The genomic landscape of African populations in health and disease. Hum. Mol. Genet. 26, R225–R236 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K. & Mardis, E. R. The next-generation sequencing revolution and its impact on genomics. Cell 155, 27–38 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Amr, S. S. et al. Using large sequencing data sets to refine intragenic disease regions and prioritize clinical variant interpretation. Genet. Med. 19, 496–504 (2017).

    CAS  PubMed  Google Scholar 

  91. 91.

    Mitropoulos, K. et al. Success stories in genomic medicine from resource-limited countries. Hum. Genomics 9, 11 (2015).

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Romdhane, L. et al. Consanguinity and inbreeding in health and disease in North African populations. Annu. Rev. Genomics Hum. Genet. 20, 155–179 (2019).

    CAS  PubMed  Google Scholar 

  93. 93.

    Krause, A., Seymour, H. & Ramsay, M. Common and founder mutations for monogenic traits in sub-Saharan African populations. Annu. Rev. Genomics Hum. Genet. 19, 149–175 (2018).

    CAS  PubMed  Google Scholar 

  94. 94.

    Kabahuma, R. I. et al. Absence of GJB2 gene mutations, the GJB6 deletion (GJB6-D13S1830) and four common mitochondrial mutations in nonsyndromic genetic hearing loss in a South African population. Int. J. Pediatr. Otorhinolaryngol. 75, 611–617 (2011).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Wonkam, A. et al. No evidence for clinical utility in investigating the connexin genes GJB2, GJB6 and GJA1 in non-syndromic hearing loss in Black Africans. S Afr. Med. J. 105, 23–26 (2015).

    CAS  PubMed  Google Scholar 

  96. 96.

    Lebeko, K. et al. Targeted genomic enrichment and massively parallel sequencing identifies novel nonsyndromic hearing impairment pathogenic variants in Cameroonian families. Clin. Genet. 90, 288–290 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Brobby, G. W., Müller-Myhsok, B. & Horstmann, R. D. Connexin 26 R143W mutation associated with recessive nonsyndromic sensorineural deafness in Africa. N. Engl. J. Med. 338, 548–550 (1998).

    CAS  PubMed  Google Scholar 

  98. 98.

    Adadey, S. M. et al. GJB2 and GJB6 mutations in non-syndromic childhood hearing impairment in Ghana. Front. Genet. 10, 841 (2019).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Hamelmann, C. et al. Pattern of connexin 26 (GJB2) mutations causing sensorineural hearing impairment in Ghana. Hum. Mutat. 18, 84–85 (2001).

    CAS  PubMed  Google Scholar 

  100. 100.

    Manga, P., Kerr, R., Ramsay, M. & Kromberg, J. G. Biology and genetics of oculocutaneous albinism and vitiligo — common pigmentation disorders in southern Africa. S Afr. Med. J. 103, 984–988 (2013).

    PubMed  Google Scholar 

  101. 101.

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Adeyemo, A. A. et al. Evaluation of genome wide association study associated type 2 diabetes susceptibility loci in sub Saharan Africans. Front. Genet. 6, 335 (2015).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Hauser, M. A. et al. Association of genetic variants with primary open-angle glaucoma among individuals with African ancestry. JAMA 322, 1682–1691 (2019).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Govind, N. et al. HLA-DRB1 amino acid positions and residues associated with antibody-positive rheumatoid arthritis in Black South Africans. J. Rheumatol. 46, 138–144 (2019).

    CAS  PubMed  Google Scholar 

  106. 106.

    Gulsuner, S. et al. Genetics of schizophrenia in the South African Xhosa. Science 367, 569–573 (2020).

    CAS  PubMed  Google Scholar 

  107. 107.

    Manolio, T. A. In retrospect: a decade of shared genomic associations. Nature 546, 360–361 (2017).

    CAS  PubMed  Google Scholar 

  108. 108.

    Drake, T. M., Knight, S. R., Harrison, E. M. & Søreide, K. Global inequities in precision medicine and molecular cancer research. Front. Oncol. 8, 346 (2018).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Burkitt, D. A sarcoma involving the jaws in African children. Br. J. Surg. 46, 218–223 (1958).

    CAS  PubMed  Google Scholar 

  110. 110.

    Spano, J. P., Atlan, D., Breau, J. L. & Farge, D. AIDS and non-AIDS-related malignancies: a new vexing challenge in HIV-positive patients. Part I: Kaposi’s sarcoma, non-Hodgkin’s lymphoma, and Hodgkin’s lymphoma. Eur. J. Intern. Med. 13, 170–179 (2002).

    PubMed  Google Scholar 

  111. 111.

    Pitt, J. J. et al. Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features. Nat. Commun. 9, 4181 (2018).

    PubMed  PubMed Central  Google Scholar 

  112. 112.

    Zheng, Y. et al. Inherited breast cancer in Nigerian women. J. Clin. Oncol. 36, 2820–2825 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Rebbeck, T. R. et al. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum. Mutat. 39, 593–620 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Fitzmaurice, C. et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the Global Burden of Disease Study. JAMA Oncol. 5, 1749–1768 (2019).

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Reay, W. R. et al. Polygenic disruption of retinoid signalling in schizophrenia and a severe cognitive deficit subtype. Mol. Psychiatry 25, 719–731 (2018).

    PubMed  PubMed Central  Google Scholar 

  117. 117.

    Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019). This paper presents a reanalysis of 10 years of polygenic scoring studies, showing that 67% involve European ancestry participants, 19% East Asian populations and 3.8% African, Hispanic or Indigenous people, revealing that the predictive performance of current polygenic scores is lower in non-European ancestry samples. There is a clear need for large-scale GWAS in diverse human populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    CAS  PubMed  Google Scholar 

  120. 120.

    Mosley, J. D. et al. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA 323, 627–635 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016).

    CAS  PubMed  Google Scholar 

  122. 122.

    Rao, A. S. & Knowles, J. W. Polygenic risk scores in coronary artery disease. Curr. Opin. Cardiol. 34, 435–440 (2019).

    PubMed  Google Scholar 

  123. 123.

    Elliott, J. et al. Predictive accuracy of a polygenic risk score-enhanced prediction model vs a clinical risk score for coronary artery disease. JAMA 323, 636–645 (2020).

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Kuchenbaecker, K. et al. The transferability of lipid loci across African, Asian and European cohorts. Nat. Commun. 10, 4330 (2019). This paper assesses whether the genetic determinants of blood lipids are shared across populations, finding evidence of reproducibility for ~75% of the major lipid loci from European discovery studies, except TG loci in the Ugandan samples (10% of loci). The authors hypothesize that the non-transferable loci might affect lipids by modifying food intake in environments rich in certain nutrients.

    PubMed  PubMed Central  Google Scholar 

  126. 126.

    Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019). The Population Architecture using Genomics and Epidemiology (PAGE) study conducts a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals, and the results show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications.

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Wei, C. Y., Lee, M. T. & Chen, Y. T. Pharmacogenomics of adverse drug reactions: implementing personalized medicine. Hum. Mol. Genet. 21, R58–R65 (2012).

    CAS  PubMed  Google Scholar 

  128. 128.

    Loscalzo, J. Precision medicine. Circ. Res. 124, 987–989 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Spear, B. B., Heath-Chiozzi, M. & Huff, J. Clinical application of pharmacogenetics. Trends Mol. Med. 7, 201–204 (2001).

    CAS  PubMed  Google Scholar 

  130. 130.

    Urban, M. F. Genomics in medicine: from promise to practice. S Afr. Med. J. 105, 545–547 (2015).

    PubMed  Google Scholar 

  131. 131.

    Hovelson, D. H. et al. Characterization of ADME gene variation in 21 populations by exome sequencing. Pharmacogenet. Genomics 27, 89–100 (2017).

    CAS  PubMed  Google Scholar 

  132. 132.

    Nordling, L. How the genomics revolution could finally help Africa. Nature 544, 20–22 (2017).

    CAS  PubMed  Google Scholar 

  133. 133.

    Masimirembwa, C., Dandara, C. & Leutscher, P. D. Rolling out efavirenz for HIV precision medicine in Africa: are we ready for pharmacovigilance and tackling neuropsychiatric adverse effects? OMICS 20, 575–580 (2016).

    CAS  PubMed  Google Scholar 

  134. 134.

    Gross, R. et al. Slow efavirenz metabolism genotype is common in Botswana. J. Acquir. Immune Defic. Syndr. 49, 336–337 (2008).

    PubMed  PubMed Central  Google Scholar 

  135. 135.

    Zembutsu, H. Pharmacogenomics toward personalized tamoxifen therapy for breast cancer. Pharmacogenomics 16, 287–296 (2015).

    CAS  PubMed  Google Scholar 

  136. 136.

    Walko, C. M. & McLeod, H. Use of CYP2D6 genotyping in practice: tamoxifen dose adjustment. Pharmacogenomics 13, 691–697 (2012).

    CAS  PubMed  Google Scholar 

  137. 137.

    Popejoy, A. B. Diversity in precision medicine and pharmacogenetics: methodological and conceptual considerations for broadening participation. Pharmgenomics Pers. Med. 12, 257–271 (2019).

    PubMed  PubMed Central  Google Scholar 

  138. 138.

    De, T. et al. Association of genetic variants with warfarin-associated bleeding among patients of African descent. JAMA 320, 1670–1677 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Hobbs, A. & Ramsay, M. Epigenetics and the burden of noncommunicable disease: a paucity of research in Africa. Epigenomics 7, 627–639 (2015).

    CAS  PubMed  Google Scholar 

  140. 140.

    Sokolowski, H. M. et al. The Drosophila foraging gene human orthologue PRKG1 predicts individual differences in the effects of early adversity on maternal sensitivity. Cogn. Dev. 42, 62–73 (2017).

    PubMed  Google Scholar 

  141. 141.

    Fraser, H. B., Lam, L. L., Neumann, S. M. & Kobor, M. S. Population-specificity of human DNA methylation. Genome Biol. 13, R8 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Huan, T. et al. Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat. Commun. 10, 4267 (2019).

    PubMed  PubMed Central  Google Scholar 

  143. 143.

    Pierce, B. L. et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat. Commun. 9, 804 (2018).

    PubMed  PubMed Central  Google Scholar 

  144. 144.

    Fagny, M. et al. The epigenomic landscape of African rainforest hunter-gatherers and farmers. Nat. Commun. 6, 10047 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Vukojevic, V. et al. Epigenetic modification of the glucocorticoid receptor gene is linked to traumatic memory and post-traumatic stress disorder risk in genocide survivors. J. Neurosci. 34, 10274–10284 (2014).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Perroud, N. et al. The Tutsi genocide and transgenerational transmission of maternal stress: epigenetics and biology of the HPA axis. World J. Biol. Psychiatry 15, 334–345 (2014).

    PubMed  Google Scholar 

  147. 147.

    Rudahindwa, S. et al. Transgenerational effects of the genocide against the Tutsi in Rwanda: a post-traumatic stress disorder symptom domain analysis. AAS Open Res. (2020).

    Article  Google Scholar 

  148. 148.

    Dominguez-Salas, P. et al. Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat. Commun. 5, 3746 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149.

    Schulze, K. V. et al. Edematous severe acute malnutrition is characterized by hypomethylation of DNA. Nat. Commun. 10, 5791 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Waterland, R. A. et al. Season of conception in rural gambia affects DNA methylation at putative human metastable epialleles. PLoS Genet. 6, e1001252 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Black, R. E. et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451 (2013).

    PubMed  PubMed Central  Google Scholar 

  152. 152.

    Bhutta, Z. A. et al. Severe childhood malnutrition. Nat. Rev. Dis. Prim. 3, 17067 (2017).

    PubMed  Google Scholar 

  153. 153.

    Steyn, A. et al. Epigenetic modification of the pentose phosphate pathway and the IGF-axis in women with gestational diabetes mellitus. Epigenomics 11, 1371–1385 (2019).

    CAS  PubMed  Google Scholar 

  154. 154.

    Costantino, S. et al. Epigenetics and precision medicine in cardiovascular patients: from basic concepts to the clinical arena. Eur. Heart J. 39, 4150–4158 (2018).

    CAS  PubMed  Google Scholar 

  155. 155.

    Musanabaganwa, C. et al. Building skills and resources for genomics, epigenetics, and bioinformatics research for Africa: report of the joint 11th Conference of the African Society of Human Genetics and 12th H3Africa Consortium, 2018. Am. J. Trop. Med. Hyg. 102, 1417–1424 (2020). Local African genetics societies are paramount in encouraging research in Africa. This paper is the meeting report for the 11th Congress of the African Society of Human Genetics (AfSHG) in conjunction with the 12th H3Africa Consortium meeting in 2018.

    PubMed  PubMed Central  Google Scholar 

  156. 156.

    El-Kamah, G. Y. et al. Developing a road map to spread genomic knowledge in Africa: 10th Conference of the African Society of Human Genetics, Cairo, Egypt. Am. J. Trop. Med. Hyg. 102, 719–723 (2020).

    PubMed  PubMed Central  Google Scholar 

  157. 157.

    Ndiaye Diallo, R. et al. Strengthening human genetics research in Africa: report of the 9th meeting of the African Society of Human Genetics in Dakar in May 2016. Glob. Health Epidemiol. Genom. 2, e10 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    de Vries, J. et al. Ethical issues in human genomics research in developing countries. BMC Med. Ethics 12, 5 (2011).

    PubMed  PubMed Central  Google Scholar 

  159. 159.

    Nyika, A. Ethical and practical challenges surrounding genetic and genomic research in developing countries. Acta Trop. 112 (Suppl. 1), S21–S31 (2009).

    PubMed  Google Scholar 

  160. 160.

    Marshall, P. A. et al. Voluntary participation and informed consent to international genetic research. Am. J. Public Health 96, 1989–1995 (2006).

    PubMed  PubMed Central  Google Scholar 

  161. 161.

    Tindana, P. et al. Seeking consent to genetic and genomic research in a rural Ghanaian setting: a qualitative study of the MalariaGEN experience. BMC Med. Ethics 13, 15 (2012).

    PubMed  PubMed Central  Google Scholar 

  162. 162.

    Ghansah, A. et al. Monitoring parasite diversity for malaria elimination in sub-Saharan Africa. Science 345, 1297–1298 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Yakubu, A. et al. Model framework for governance of genomic research and biobanking in Africa — a content description. AAS Open Res. 1, 13 (2018).

    PubMed  PubMed Central  Google Scholar 

  164. 164.

    Rotimi, C. et al. Community engagement and informed consent in the International HapMap project. Community Genet. 10, 186–198 (2007).

    PubMed  Google Scholar 

  165. 165.

    Tindana, P. et al. Community engagement strategies for genomic studies in Africa: a review of the literature. BMC Med. Ethics 16, 24 (2015).

    PubMed  PubMed Central  Google Scholar 

  166. 166.

    Sankoh, O. & Ijsselmuiden, C. Sharing research data to improve public health: a perspective from the Global South. Lancet 378, 401–402 (2011).

    PubMed  Google Scholar 

  167. 167.

    Bull, S. et al. Best practices for ethical sharing of individual-level health research data from low- and middle-income settings. J. Empir. Res. Hum. Res Ethics 10, 302–313 (2015).

    PubMed  PubMed Central  Google Scholar 

  168. 168.

    Kraft, S. A. et al. Beyond consent: building trusting relationships with diverse populations in precision medicine research. Am. J. Bioeth. 18, 3–20 (2018).

    PubMed  PubMed Central  Google Scholar 

  169. 169.

    Sabatello, M., Callier, S., Garrison, N. A. & Cohn, E. G. Trust, precision medicine research, and equitable participation of underserved populations. Am. J. Bioeth. 18, 34–36 (2018).

    PubMed  PubMed Central  Google Scholar 

  170. 170.

    Magnus, D. & Batten, J. N. Building a trustworthy precision health research enterprise. Am. J. Bioeth. 18, 1–2 (2018).

    PubMed  Google Scholar 

  171. 171.

    Tindana, P. & de Vries, J. Broad consent for genomic research and biobanking: perspectives from low- and middle-income countries. Annu. Rev. Genomics Hum. Genet. 17, 375–393 (2016).

    CAS  PubMed  Google Scholar 

  172. 172.

    Stein, D. T. & Terry, S. F. Reforming biobank consent policy: a necessary move away from broad consent toward dynamic consent. Genet. Test. Mol. Biomarkers 17, 855–856 (2013).

    PubMed  Google Scholar 

  173. 173.

    Kaye, J. et al. Dynamic consent: a patient interface for twenty-first century research networks. Eur. J. Hum. Genet. 23, 141–146 (2015).

    PubMed  Google Scholar 

  174. 174.

    Munung, N. S. et al. Obtaining informed consent for genomics research in Africa: analysis of H3Africa consent documents. J. Med. Ethics 42, 132–137 (2016).

    PubMed  Google Scholar 

  175. 175.

    Jao, I. et al. Involving research stakeholders in developing policy on sharing public health research data in Kenya: views on fair process for informed consent, access oversight, and community engagement. J. Empir. Res. Hum. Res Ethics 10, 264–277 (2015).

    PubMed  PubMed Central  Google Scholar 

  176. 176.

    van Delden, J. J. & van der Graaf, R. Revised CIOMS International Ethical Guidelines for Health-Related Research Involving Humans. JAMA 317, 135–136 (2017).

    PubMed  Google Scholar 

  177. 177.

    Nembaware, V. et al. A framework for tiered informed consent for health genomic research in Africa. Nat. Genet. 51, 1566–1571 (2019). This paper proposes a framework for the conduct of ethically sound, tiered informed-consent processes in Africa. This framework guarantees the autonomy and individual choices of African research participants but at the same time enables global health benefits gathered from sharing and meta-analysis of African genomic data.

    CAS  PubMed  PubMed Central  Google Scholar 

  178. 178.

    Knoppers, B. M., Deschenes, M., Zawati, M. H. & Tasse, A. M. Population studies: return of research results and incidental findings policy statement. Eur. J. Hum. Genet. 21, 245–247 (2013).

    PubMed  Google Scholar 

  179. 179.

    Fernandez, C. V., Kodish, E. & Weijer, C. Informing study participants of research results: an ethical imperative. IRB 25, 12–19 (2003).

    PubMed  Google Scholar 

  180. 180.

    Wonkam, A. & de Vries, J. Returning incidental findings in African genomics research. Nat. Genet. 52, 17–20 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  181. 181.

    de Vries, J. et al. Regulation of genomic and biobanking research in Africa: a content analysis of ethics guidelines, policies and procedures from 22 African countries. BMC Med. Ethics 18, 8 (2017). This comprehensive analysis of 30 ethics regulatory documents from 22 African countries concludes that there is a need for ethics guidelines in African countries to be adapted to the changing science policy landscape. Most pertinent are the principles of openness, storage, sharing and secondary use.

    PubMed  PubMed Central  Google Scholar 

  182. 182.

    Barchi, F. & Little, M. T. National ethics guidance in sub-Saharan Africa on the collection and use of human biological specimens: a systematic review. BMC Med. Ethics 17, 64 (2016).

    PubMed  PubMed Central  Google Scholar 

  183. 183.

    Ramsay, M., de Vries, J., Soodyall, H., Norris, S. A. & Sankoh, O. Ethical issues in genomic research on the African continent: experiences and challenges to ethics review committees. Hum. Genomics 8, 15 (2014).

    PubMed  PubMed Central  Google Scholar 

  184. 184.

    van Panhuis, W. G. et al. A systematic review of barriers to data sharing in public health. BMC Public Health 14, 1144 (2014).

    PubMed  PubMed Central  Google Scholar 

  185. 185.

    Qureshi, N., Modell, B. & Modell, M. Timeline: raising the profile of genetics in primary care. Nat. Rev. Genet. 5, 783–790 (2004).

    CAS  PubMed  Google Scholar 

  186. 186.

    Alwan, A. & Modell, B. Recommendations for introducing genetics services in developing countries. Nat. Rev. Genet. 4, 61–68 (2003).

    CAS  PubMed  Google Scholar 

  187. 187.

    Bentley, A. R., Callier, S. L. & Rotimi, C. N. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom. Med. 5, 5 (2020).

    PubMed  PubMed Central  Google Scholar 

  188. 188.

    Ramsay, M. Africa: continent of genome contrasts with implications for biomedical research and health. FEBS Lett. 586, 2813–2819 (2012).

    CAS  PubMed  Google Scholar 

  189. 189.

    Mulder, N. J. et al. Development of bioinformatics infrastructure for genomics research. Glob. Heart 12, 91–98 (2017).

    PubMed  PubMed Central  Google Scholar 

  190. 190.

    Gootenberg, J. S. et al. Nucleic acid detection with CRISPR–Cas13a/C2c2. Science 356, 438–442 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  191. 191.

    Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    PubMed  PubMed Central  Google Scholar 

  192. 192.

    Samuels, D. C. et al. Heterozygosity ratio, a robust global genomic measure of autozygosity and its association with height and disease risk. Genetics 204, 893–904 (2016).

    PubMed  PubMed Central  Google Scholar 

  193. 193.

    Schlebusch, C. M. et al. Genomic variation in seven Khoe-San groups reveals adaptation and complex African history. Science 338, 374–379 (2012).

    CAS  PubMed  Google Scholar 

  194. 194.

    Chatterjee, N., Shi, J. & Garcia-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  195. 195.

    Krapohl, E. et al. Multi-polygenic score approach to trait prediction. Mol. Psychiatry 23, 1368–1374 (2018).

    CAS  PubMed  Google Scholar 

  196. 196.

    Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  197. 197.

    De La Vega, F. M. & Bustamante, C. D. Polygenic risk scores: a biased prediction? Genome Med. 10, 100 (2018).

    Google Scholar 

  198. 198.

    Reisberg, S., Iljasenko, T., Lall, K., Fischer, K. & Vilo, J. Comparing distributions of polygenic risk scores of type 2 diabetes and coronary heart disease within different populations. PLoS ONE 12, e0179238 (2017).

    PubMed  PubMed Central  Google Scholar 

  199. 199.

    Retshabile, G. et al. Whole-exome sequencing reveals uncaptured variation and distinct ancestry in the southern African population of Botswana. Am. J. Hum. Genet. 102, 731–743 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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L.P. is a principal researcher at i3S, which is financed by Fundo Europeu de Desenvolvimento Regional (FEDER) funds through COMPETE 2020, Portugal 2020 and by Portuguese funds through Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Inovação (POCI-01-0145-FEDER-007274). L.M. is a Professor of Human Genetics at the University of Rwanda and principal investigator of a National Institutes of Health (NIH)-funded Human Heredity and Health in Africa (H3Africa) grant (U01MH115485) (transgenerational epigenomics of trauma and post-traumatic stress disorder in Rwanda). P.T. is a senior lecturer at the University of Ghana School of Public Health and the co-principal investigator of an NIH-funded H3Africa grant (U54HG010275). M.R. holds a South African Research Chair in Genomics and Bioinformatics of African populations hosted by the University of the Witwatersrand, funded by the Department of Science and Innovation, and administered by the National Research Foundation and is principal investigator of an NIH-funded H3Africa grant (U54HG006938). The views expressed in this Review do not necessarily reflect the views of the funding institutions.

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Africa CDC:

African Society of Human Genetics (AfSHG):


H3Africa Genotyping Chip:

H3Africa Guideline for the Return of Individual Genetic Research Findings:

United Nations Sustainable Development:

United Nations World Population Prospects:

West African Genetic Medicine Centre (WAGMC):

World Economic Forum Precision Medicine Programme:


Precision medicine

An approach to clinical practice that includes information from state-of-the-art technologies to understand the underlying causes of a disease such that a patient can receive the most appropriate therapeutic intervention for the best possible health outcome.


An estimation of the degree of variation in a phenotype that is due to genetic variation between individuals. Heritability can be estimated from general pedigrees using linear mixed models and from genomic relatedness estimated from genetic markers, but traditionally was based on twin studies.


Two populations come into contact with one another, often due to migration, and interbreeding occurs, generating a hybrid or admixed descendant population.

Coalescent models

Models that, assuming that variants sampled from a population may have originated from a common ancestor, reconstruct backwards in time how variants can be shown to originate from a single ancestor according to a random process in coalescence events. The model produces many theoretical genealogies that can be compared with the observed data to test assumptions about the demographic history of a population.

Population structure

Significant differences in allele frequencies between populations or between subpopulations in a population.

Linkage disequilibrium

(LD). A measure of the non-random association of alleles at different loci in a given population. LD is lower in African populations compared with European and Asian populations, given the more ancient ancestry of African populations.

Bantu migration

A massive migration of Bantu-speaking peoples that began 5,000 years ago in the region of Cameroon/Nigeria towards the southern and eastern parts of the African continent, with genetic, linguistic and cultural impacts. Currently, Bantu-speakers make up ~30% of the African population of ~1.3 billion people.

African diaspora

People of African origin or ancestry resident in non-African countries.

Human Heredity and Health in Africa

(H3Africa). A pan-African consortium that aims to study the genomic and environmental determinants of common and rare diseases with the goal of improving the health of African populations.

Genome-wide association study

(GWAS). A study that aims to identify candidate genetic markers associated with diseases or traits when applied to case–control cohorts or to quantitative traits within a cohort. This is done by genotyping several million common single-nucleotide polymorphisms from across the genome and applying statistical analyses to determine the probability of association between individual genetic markers and the phenotype.


A response to an environmental challenge such that an advantageous phenotype is enriched by positive or balancing selection.

Balancing selection

Multiple alleles at a locus are maintained in the population gene pool at higher frequencies than expected from genetic drift. Two possible causes are heterozygote advantage (higher fitness of heterozygotes compared with homozygotes) and frequency-dependent selection (fitness of a phenotype depends on the relative frequency of other phenotypes in the population).

Admixture mapping

A gene-mapping algorithm applied to case–control cohorts in a recently admixed population, where there are differences in the rates of the disease or trait between the two parental populations and those differences are partly due to differences in the frequencies of associated or causal variants. If the associated variant in one ancestry is protective, it will be enriched in the control group; if it is causative, it will be enriched in the case group.

Positive selection

A phenotype (and its associated alleles) confers an advantage to the individual in response to a challenge (for example, environmental), such that the variant alleles that confer the favourable phenotype rapidly increase in frequency in the population, sometimes attaining fixation.

Autochthonous African people

Indigenous or native African people.

Epistatic interactions

Interactions pertaining to epistasis, when specific combinations of multiple genetic variants at different loci have non-additive effects on a specific phenotype (for example, disease or trait).

Variants of uncertain significance

(VUS). Genetic variants in coding regions or regulatory regions of known disease-associated genes with insufficient evidence to assess their potential functional or phenotypic impact.

Incidental findings

Genetic variants of potential disease relevance that are unrelated to the condition under investigation. For example, searching for a mutation responsible for developmental delay in a child and then detecting a BRCA1 breast cancer susceptibility variant.

Polygenic risk scores

(PRSs). Predictive scores made up from multiple genetic loci associated with a trait and weighted by the relative contribution of each marker/allele to the trait that can be used to stratify a population on a spectrum of high to low risk. The higher the heritability of the trait, the more predictive the PRS will be; however, clinical use is still debatable.


One variant that exerts an effect or has an association with multiple different (but sometimes related) phenotypes.

Precision public health

Using information about genomic variation in a population to guide practices and develop policies that will benefit the majority of individuals in a given population. For example, identifying a common pharmacogenomic variant in a population that predicts an adverse drug effect, such that it leads to a policy advising against using that drug as a first-line treatment in that population.

Broad consent

Consent provided by a study participant for the use of their data and biospecimens for future research that could not be defined at the time the consent was obtained. The governance process of the resources are explained to participants and, usually, the future use will be approved by an ethics committee.

Tiered consent

A consent process that has several independent requests for specific uses of data and biospecimens. For example, research participants can consent to one or more of the following: consent for use in specific research; consent for data sharing; and consent for data and biospecimen sharing, with information on how the process will be governed.

Genomic medicine

The use of genetic information (genomic variants) to guide diagnosis and clinical interventions or to be used in weighing reproductive options.

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Pereira, L., Mutesa, L., Tindana, P. et al. African genetic diversity and adaptation inform a precision medicine agenda. Nat Rev Genet 22, 284–306 (2021).

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