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Annual Review of Biomedical Data Science

Volume 5, 2022

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine

Abstract

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

Keyword(s): Africa, diversity, GWAS, human genomics, polygenic risk score, precision medicine
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/content/journals/10.1146/annurev-biodatasci-122220-112550
2022-08-10
2024-04-28
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Literature Cited

  1. 1.
    Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI et al. 2017. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101:15–22
     [Google Scholar]
  2. 2.
    Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. 2008. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27:81133–63
     [Google Scholar]
  3. 3.
    100,000 Genomes Proj. Pilot Investig 2021. 100: 000 Genomes pilot on rare-disease diagnosis in health care—preliminary report. N. Engl. J. Med. 385:201868–80
     [Google Scholar]
  4. 4.
    Sabatine MS. 2019. PCSK9 inhibitors: clinical evidence and implementation. Nat. Rev. Cardiol. 16:3155–65
     [Google Scholar]
  5. 5.
    Frangoul H, Altshuler D, Cappellini MD, Chen Y-S, Domm J et al. 2021. CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. N. Engl. J. Med. 384:3252–60
     [Google Scholar]
  6. 6.
    Mills MC, Rahal C. 2019. A scientometric review of genome-wide association studies. Commun. Biol. 2:9
     [Google Scholar]
  7. 7.
    Ge T, Chen C-Y, Neale BM, Sabuncu MR, Smoller JW. 2017. Phenome-wide heritability analysis of the UK Biobank. PLOS Genet 13:4e1006711
     [Google Scholar]
  8. 8.
    Golan D, Lander ES, Rosset S. 2014. Measuring missing heritability: inferring the contribution of common variants. PNAS 111:49E5272–81
     [Google Scholar]
  9. 9.
    Ma Y, Zhou X. 2021. Genetic prediction of complex traits with polygenic scores: a statistical review. Trends Genet 37:11995–1011
     [Google Scholar]
  10. 10.
    Vujkovic M, Keaton JM, Lynch JA, Miller DR, Zhou J et al. 2020. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52:7680–91
     [Google Scholar]
  11. 11.
    Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR et al. 2018. Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry. Hum. Mol. Genet. 27:203641–49
     [Google Scholar]
  12. 12.
    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:91219–24
     [Google Scholar]
  13. 13.
    Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J et al. 2019. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177:3587–96.e9
     [Google Scholar]
  14. 14.
    Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
     [Google Scholar]
  15. 15.
    Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K et al. 2019. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10:3328
     [Google Scholar]
  16. 16.
    Bien SA, Wojcik GL, Hodonsky CJ, Gignoux CR, Cheng I et al. 2019. The future of genomic studies must be globally representative: perspectives from PAGE. Annu. Rev. Genom. Hum. Genet. 20:181–200
     [Google Scholar]
  17. 17.
    Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA et al. 2021. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590:7845290–99
     [Google Scholar]
  18. 18.
    All Us Res. Prog. Investig 2019. The “All of Us” Research Program. N. Engl. J. Med. 381:7668–76
     [Google Scholar]
  19. 19.
    H3Africa Consort. Rotimi C, Abayomi A, Abimiku A, Adabayeri VM et al. 2014. Enabling the genomic revolution in Africa. Science 344:61901346–48
     [Google Scholar]
  20. 20.
    Sirugo G, Williams SM, Tishkoff SA. 2019. The missing diversity in human genetic studies. Cell 177:126–31
     [Google Scholar]
  21. 21.
    Popejoy AB, Fullerton SM. 2016. Genomics is failing on diversity. Nature 538:7624161–64
     [Google Scholar]
  22. 22.
    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]
  23. 23.
    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]
  24. 24.
    Mills MC, Rahal C. 2020. The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet. 52:242–43
     [Google Scholar]
  25. 25.
    Curtis D. 2018. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr. Genet. 28:585–89
     [Google Scholar]
  26. 26.
    Wang Y, Guo J, Ni G, Yang J, Visscher PM, Yengo L. 2020. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11:3865
     [Google Scholar]
  27. 27.
    Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes (SAT2D) Consort., SIGMA Type 2 Diabetes Consort., Price AL. 2017. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41:8811–23
     [Google Scholar]
  28. 28.
    Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ et al. 2021. Leveraging fine-mapping and non-European training data to improve cross-population polygenic risk scores. medRxiv 10.1101/2021.01.19.21249483. https://doi.org/10.1101/2021.01.19.21249483
    [Crossref]
  29. 29.
    Ruan Y, Lin Y-F, Feng Y-CA, Chen C-Y, Lam M et al. 2021. Improving polygenic prediction in ancestrally diverse populations. medRxiv 10.1101/2020.12.27.20248738. https://doi.org/10.1101/2020.12.27.20248738
    [Crossref]
  30. 30.
    Brown BC, Ye CJ, Price AL, Zaitlen N. 2016. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99:176–88
     [Google Scholar]
  31. 31.
    Veturi Y, de los Campos G, Yi N, Huang W, Vazquez AI, Kühnel B 2019. Modeling heterogeneity in the genetic architecture of ethnically diverse groups using random effect interaction models. Genetics 211:41395–407
     [Google Scholar]
  32. 32.
    Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J et al. 2002. The structure of haplotype blocks in the human genome. Science 296:55762225–29
     [Google Scholar]
  33. 33.
    Galinsky KJ, Reshef YA, Finucane HK, Loh P-R, Zaitlen N et al. 2019. Estimating cross-population genetic correlations of causal effect sizes. Genet. Epidemiol. 43:2180–88
     [Google Scholar]
  34. 34.
    Shi H, Gazal S, Kanai M, Koch EM, Schoech AP et al. 2021. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12:1098
     [Google Scholar]
  35. 35.
    Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P et al. 2017. Penetrance of polygenic obesity susceptibility loci across the body mass index distribution. Am. J. Hum. Genet. 101:6925–38
     [Google Scholar]
  36. 36.
    Rask-Andersen M, Karlsson T, Ek WE, Johansson Å. 2017. Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status. PLOS Genet 13:9e1006977
     [Google Scholar]
  37. 37.
    Robinson MR, English G, Moser G, Lloyd-Jones LR, Triplett MA et al. 2017. Genotype-covariate interaction effects and the heritability of adult body mass index. Nat. Genet. 49:81174–81
     [Google Scholar]
  38. 38.
    Wang H, Zhang F, Zeng J, Wu Y, Kemper KE et al. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Sci. Adv. 5:8eaaw3538
     [Google Scholar]
  39. 39.
    Sung YJ, Winkler TW, de las Fuentes L, Bentley AR, Brown MR et al. 2018. A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am. J. Hum. Genet. 102:3375–400
     [Google Scholar]
  40. 40.
    Waken RJ, de las Fuentes L, Rao DC. 2017. A review of the genetics of hypertension with a focus on gene-environment interactions. Curr. Hypertens. Rep. 19:323
     [Google Scholar]
  41. 41.
    Arnau-Soler A, Macdonald-Dunlop E, Adams MJ, Clarke T-K, MacIntyre DJ et al. 2019. Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland. Transl. Psychiatry. 9:14
     [Google Scholar]
  42. 42.
    Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. 2020. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9:e48376
     [Google Scholar]
  43. 43.
    Young AI, Benonisdottir S, Przeworski M, Kong A. 2019. Deconstructing the sources of genotype-phenotype associations in humans. Science 365:64601396–400
     [Google Scholar]
  44. 44.
    Wei W-H, Hemani G, Haley CS. 2014. Detecting epistasis in human complex traits. Nat. Rev. Genet. 15:11722–33
     [Google Scholar]
  45. 45.
    Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA et al. 1997. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease: a meta-analysis. JAMA 278:161349–56
     [Google Scholar]
  46. 46.
    Griswold AJ, Celis K, Bussies PL, Rajabli F, Whitehead PL et al. 2021. Increased APOE ε4 expression is associated with the difference in Alzheimer's disease risk from diverse ancestral backgrounds. Alzheimers Dement. 17:71179–88
     [Google Scholar]
  47. 47.
    Marca-Ysabel MV, Rajabli F, Cornejo-Olivas M, Whitehead PG, Hofmann NK et al. 2021. Dissecting the role of Amerindian genetic ancestry and the ApoE ε4 allele on Alzheimer disease in an admixed Peruvian population. Neurobiol. Aging. 101:298.e11–15
     [Google Scholar]
  48. 48.
    Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ et al. 2021. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53:2195–204
     [Google Scholar]
  49. 49.
    Kerminen S, Martin AR, Koskela J, Ruotsalainen SE, Havulinna AS et al. 2019. Geographic variation and bias in the polygenic scores of complex diseases and traits in Finland. Am. J. Hum. Genet. 104:61169–81
     [Google Scholar]
  50. 50.
    Zaidi AA, Mathieson I. 2020. Demographic history mediates the effect of stratification on polygenic scores. eLife 9:e61548
     [Google Scholar]
  51. 51.
    Rajabli F, Feliciano BE, Celis K, Hamilton-Nelson KL, Whitehead PL et al. 2018. Ancestral origin of ApoE ε4 Alzheimer disease risk in Puerto Rican and African American populations. PLOS Genet 14:12e1007791
     [Google Scholar]
  52. 52.
    Patterson N, Price AL, Reich D. 2006. Population structure and eigenanalysis. PLOS Genet 2:12e190
     [Google Scholar]
  53. 53.
    Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H et al. 2019. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8:e39725
     [Google Scholar]
  54. 54.
    Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR et al. 2019. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8:e39702
     [Google Scholar]
  55. 55.
    Koyama S, Ito K, Terao C, Akiyama M, Horikoshi M et al. 2020. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52:111169–77
     [Google Scholar]
  56. 56.
    Asgari S, Luo Y, Akbari A, Belbin GM, Li X et al. 2020. A positively selected FBN1 missense variant reduces height in Peruvian individuals. Nature 582:7811234–39
     [Google Scholar]
  57. 57.
    Takeuchi F, Akiyama M, Matoba N, Katsuya T, Nakatochi M et al. 2018. Interethnic analyses of blood pressure loci in populations of East Asian and European descent. Nat. Commun. 9:5052
     [Google Scholar]
  58. 58.
    1000 Genomes Proj. Consort 2015. A global reference for human genetic variation. Nature 526:757168–74
     [Google Scholar]
  59. 59.
    Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J et al. 2019. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570:7762514–18
     [Google Scholar]
  60. 60.
    Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM et al. 2018. Genetics of blood lipids among ∼300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50:111514–23
     [Google Scholar]
  61. 61.
    Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E. 2016. Trans-ethnic study design approaches for fine-mapping. Eur. J. Hum. Genet. 24:91330–36
     [Google Scholar]
  62. 62.
    Graham SE, Clarke SL, Wu K-HH, Kanoni S, Zajac GJM et al. 2021. The power of genetic diversity in genome-wide association studies of lipids. Nature 600:7890675–79
     [Google Scholar]
  63. 63.
    Canela-Xandri O, Rawlik K, Tenesa A. 2018. An atlas of genetic associations in UK Biobank. Nat. Genet. 50:111593–99
     [Google Scholar]
  64. 64.
    Crawford NG, Kelly DE, Hansen MEB, Beltrame MH, Fan S et al. 2017. Loci associated with skin pigmentation identified in African populations. Science 358:6365eaan8433
     [Google Scholar]
  65. 65.
    Wei C-Y, Zhu M-X, Lu N-H, Peng R, Yang X et al. 2019. Bioinformatics-based analysis reveals elevated MFSD12 as a key promoter of cell proliferation and a potential therapeutic target in melanoma. Oncogene 38:111876–91
     [Google Scholar]
  66. 66.
    Wainschtein P, Jain D, Zheng Z, TOPMed Anthropom. Work. Group, TOPMed Consort., et al. 2021. Recovery of trait heritability from whole genome sequence data. bioRxiv 10.1101/588020. https://doi.org/10.1101/588020
    [Crossref]
  67. 67.
    Marouli E, Graff M, Medina-Gomez C, Lo KS, Wood AR et al. 2017. Rare and low-frequency coding variants alter human adult height. Nature 542:7640186–90
     [Google Scholar]
  68. 68.
    Li X, Kim Y, Tsang EK, Davis JR, Damani FN et al. 2017. The impact of rare variation on gene expression across tissues. Nature 550:7675239–43
     [Google Scholar]
  69. 69.
    Hernandez RD, Uricchio LH, Hartman K, Ye C, Dahl A, Zaitlen N. 2019. Ultrarare variants drive substantial cis heritability of human gene expression. Nat. Genet. 51:91349–55
     [Google Scholar]
  70. 70.
    Mathieson I, McVean G. 2012. Differential confounding of rare and common variants in spatially structured populations. Nat. Genet. 44:3243–46
     [Google Scholar]
  71. 71.
    Persyn E, Redon R, Bellanger L, Dina C 2018. The impact of a fine-scale population stratification on rare variant association test results. PLOS ONE 13:12e0207677
     [Google Scholar]
  72. 72.
    Ma S, Shi G. 2020. On rare variants in principal component analysis of population stratification. BMC Genet 21:34
     [Google Scholar]
  73. 73.
    Momozawa Y, Mizukami K. 2021. Unique roles of rare variants in the genetics of complex diseases in humans. J. Hum. Genet. 66:111–23
     [Google Scholar]
  74. 74.
    Nicolae DL. 2016. Association tests for rare variants. Annu. Rev. Genom. Hum. Genet. 17:117–30
     [Google Scholar]
  75. 75.
    Lee S, Abecasis GR, Boehnke M, Lin X. 2014. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95:15–23
     [Google Scholar]
  76. 76.
    Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E. 2016. Trans-ethnic study design approaches for fine-mapping. Eur. J. Hum. Genet. 24:91330–36
     [Google Scholar]
  77. 77.
    Spain SL, Barrett JC. 2015. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24:R1R111–19
     [Google Scholar]
  78. 78.
    Marigorta UM, Navarro A. 2013. High trans-ethnic replicability of GWAS results implies common causal variants. PLOS Genet 9:6e1003566
     [Google Scholar]
  79. 79.
    Evans DM, Cardon LR. 2005. A comparison of linkage disequilibrium patterns and estimated population recombination rates across multiple populations. Am. J. Hum. Genet. 76:4681–87
     [Google Scholar]
  80. 80.
    Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M et al. 2020. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52:121346–54
     [Google Scholar]
  81. 81.
    Kichaev G, Pasaniuc B. 2015. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97:2260–71
     [Google Scholar]
  82. 82.
    Hill WG, Goddard ME, Visscher PM. 2008. Data and theory point to mainly additive genetic variance for complex traits. PLOS Genet 4:2e1000008
     [Google Scholar]
  83. 83.
    Barton AR, Sherman MA, Mukamel RE, Loh P-R. 2021. Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. Nat. Genet. 53:81260–69
     [Google Scholar]
  84. 84.
    McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR et al. 2016. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48:101279–83
     [Google Scholar]
  85. 85.
    Røttingen J-A, Chamas C, Goyal LC, Harb H, Lagrada L, Mayosi BM. 2012. Securing the public good of health research and development for developing countries. Bull. World Health Organ. 90:5398–400
     [Google Scholar]
  86. 86.
    WHO (World Health Organ.) 2002. Genomics and world health: report of the Advisory Committee on Health Research Tech. Rep. WHO Geneva:
  87. 87.
    Wonkam A, Tekendo CN, Sama DJ, Zambo H, Dahoun S et al. 2011. Initiation of a medical genetics service in sub-Saharan Africa: experience of prenatal diagnosis in Cameroon. Eur. J. Med. Genet. 54:4e399–404
     [Google Scholar]
  88. 88.
    Kromberg JGR, Sizer EB, Christianson AL. 2013. Genetic services and testing in South Africa. J. Commun. Genet. 4:3413–23
     [Google Scholar]
  89. 89.
    Kengne Kamga K, De Vries J, Nguefack S, Munung NS, Wonkam A 2021. Explanatory models for the cause of Fragile X Syndrome in rural Cameroon. J. Genet. Couns. 30:61727–36
     [Google Scholar]
  90. 90.
    Kengne-Ouafo JA, Millard JD, Nji TM, Tantoh WF, Nyoh DN et al. 2016. Understanding of research, genetics and genetic research in a rapid ethical assessment in north west Cameroon. Int. Health 8:3197–203
     [Google Scholar]
  91. 91.
    Baynam GS, Groft S, van der Westhuizen FH, Gassman SD, du Plessis K et al. 2020. A call for global action for rare diseases in Africa. Nat. Genet. 52:21–26
     [Google Scholar]
  92. 92.
    Moodley K, Kleinsmidt A. 2021. Allegations of misuse of African DNA in the UK: Will data protection legislation in South Africa be sufficient to prevent a recurrence?. Dev. World Bioeth. 21:3125–30
     [Google Scholar]
  93. 93.
    Adepoju P. 2019. Africa's first biobank start-up receives seed funding. Lancet 394:10193108
     [Google Scholar]
  94. 94.
    Callaway E. 2017. South Africa's San people issue ethics code to scientists. Nature 543:7646475–76
     [Google Scholar]
  95. 95.
    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]
  96. 96.
    Wonkam A, Kenfack MA, Muna WFT, Ouwe-Missi-Oukem-Boyer O 2011. Ethics of human genetic studies in sub-Saharan Africa: the case of Cameroon through a bibliometric analysis. Dev. World Bioeth. 11:3120–27
     [Google Scholar]
  97. 97.
    Heitmueller A, Henderson S, Warburton W, Elmagarmid A, Pentland AS, Darzi A. 2014. Developing public policy to advance the use of big data in health care. Health Aff. 33:91523–30
     [Google Scholar]
  98. 98.
    Horton RH, Lucassen AM. 2019. Recent developments in genetic/genomic medicine. Clin. Sci. 133:5697–708
     [Google Scholar]
  99. 99.
    Ienca M, Ferretti A, Hurst S, Puhan M, Lovis C, Vayena E. 2018. Considerations for ethics review of big data health research: a scoping review. PLOS ONE 13:10e0204937
     [Google Scholar]
  100. 100.
    Green ED, Gunter C, Biesecker LG, Di Francesco V, Easter CL et al. 2020. Strategic vision for improving human health at The Forefront of Genomics. Nature 586:7831683–92
     [Google Scholar]
  101. 101.
    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]
  102. 102.
    Choudhury A, Aron S, Botigué LR, Sengupta D, Botha G et al. 2020. High-depth African genomes inform human migration and health. Nature 586:7831741–48
     [Google Scholar]
  103. 103.
    Shriner D, Rotimi CN. 2018. Whole-genome-sequence-based haplotypes reveal single origin of the sickle allele during the Holocene Wet Phase. Am. J. Hum. Genet. 102:4547–56
     [Google Scholar]
  104. 104.
    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]
  105. 105.
    Sierra B, Triska P, Soares P, Garcia G, Perez AB et al. 2017. OSBPL10, RXRA and lipid metabolism confer African-ancestry protection against dengue haemorrhagic fever in admixed Cubans. PLOS Pathog 13:2e1006220
     [Google Scholar]
  106. 106.
    Cohen J, Pertsemlidis A, Kotowski IK, Graham R, Garcia CK, Hobbs HH. 2005. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nat. Genet. 37:2161–65
     [Google Scholar]
  107. 107.
    Gulsuner S, Stein DJ, Susser ES, Sibeko G, Pretorius A et al. 2020. Genetics of schizophrenia in the South African Xhosa. Science 367:6477569–73
     [Google Scholar]
  108. 108.
    Genovese G, Fromer M, Stahl EA, Ruderfer DM, Chambert K et al. 2016. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19:111433–41
     [Google Scholar]
  109. 109.
    OMIM (Online Mendel. Inherit. Man) 2022. . OMIM gene map statistics Web Resour., OMIM Baltimore, MD: accessed Dec. 15, 2021. https://www.omim.org/statistics/geneMap
  110. 110.
    Lebeko K, Sloan-Heggen CM, Noubiap JJN, Dandara C, Kolbe DL et al. 2016. Targeted genomic enrichment and massively parallel sequencing identifies novel nonsyndromic hearing impairment pathogenic variants in Cameroonian families. Clin. Genet. 90:3288–90
     [Google Scholar]
  111. 111.
    Yan D, Tekin D, Bademci G, Foster J, Cengiz FB et al. 2016. Spectrum of DNA variants for non-syndromic deafness in a large cohort from multiple continents. Hum. Genet. 135:8953–61
     [Google Scholar]
  112. 112.
    Wonkam A, Manyisa N, Bope CD, Dandara C, Chimusa ER. 2021. Whole exome sequencing reveals pathogenic variants in MYO3A, MYO15A and COL9A3 and differential frequencies in ancestral alleles in hearing impairment genes among individuals from Cameroon. Hum. Mol. Genet. 29:233729–43
     [Google Scholar]
  113. 113.
    Sloan-Heggen CM, Bierer AO, Shearer AE, Kolbe DL, Nishimura CJ et al. 2016. Comprehensive genetic testing in the clinical evaluation of 1119 patients with hearing loss. Hum. Genet. 135:4441–50
     [Google Scholar]
  114. 114.
    Bien SA, Wojcik GL, Zubair N, Gignoux CR, Martin AR et al. 2016. Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping array. PLOS ONE 11:12e0167758
     [Google Scholar]
  115. 115.
    Das S, Forer L, Schönherr S, Sidore C, Locke AE et al. 2016. Next-generation genotype imputation service and methods. Nat. Genet. 48:101284–87
     [Google Scholar]
  116. 116.
    Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37:8907–15
     [Google Scholar]
  117. 117.
    Rakocevic G, Semenyuk V, Lee W-P, Spencer J, Browning J et al. 2019. Fast and accurate genomic analyses using genome graphs. Nat. Genet. 51:2354–62
     [Google Scholar]
  118. 118.
    Wonkam A. 2021. Sequence three million genomes across Africa. Nature 590:7845209–11
     [Google Scholar]
  119. 119.
    Wonkam A, de Vries J. 2020. Returning incidental findings in African genomics research. Nat. Genet. 52:17–20
     [Google Scholar]
  120. 120.
    Sulc J, Mounier N, Günther F, Winkler T, Wood AR et al. 2020. Quantification of the overall contribution of gene-environment interaction for obesity-related traits. Nat. Commun. 11:1385
     [Google Scholar]
  121. 121.
    Ye J, Wen Y, Sun X, Chu X, Li P et al. 2021. Socioeconomic deprivation index is associated with psychiatric disorders: an observational and genome-wide gene-by-environment interaction analysis in the UK Biobank cohort. Biol. Psychiatry 89:9888–95
     [Google Scholar]
  122. 122.
    Shrider EA, Kollar M, Chen F, Semega J 2021. Income and poverty in the United States: 2020 Gov. Rep. U.S. Census Bur. Washington, DC:
  123. 123.
    Fort D, Wilcox AB, Weng C. 2014. Could patient self-reported health data complement EHR for phenotyping?. AMIA Annu. Symp. Proc. 2014:1738–47
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-122220-112550
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Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine
Annual Review of Biomedical Data Science 5, 321 (2022); https://doi.org/10.1146/annurev-biodatasci-122220-112550
/content/journals/10.1146/annurev-biodatasci-122220-112550
/content/journals/10.1146/annurev-biodatasci-122220-112550
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