1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Data Science
  4. Volume 6, 2023
  5. Article

Annual Review of Biomedical Data Science

Volume 6, 2023

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Abstract

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype–phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype–phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype–phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

Keyword(s): cohorts, consortia, diversity, generalizability, genome-wide association studies, polygenic risk scores, precision medicine
Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-122220-113250
2023-08-10
2024-04-29
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/6/1/annurev-biodatasci-122220-113250.html?itemId=/content/journals/10.1146/annurev-biodatasci-122220-113250&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M et al. 2023. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51:D1D977–85
     [Google Scholar]
  2. 2.
    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:6822860–921
     [Google Scholar]
  3. 3.
    Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV et al. 2022. The complete sequence of a human genome. Science 376:658844–53
     [Google Scholar]
  4. 4.
    Int. HapMap Consort 2003. The International HapMap Project. Nature 426:6968789–96
     [Google Scholar]
  5. 5.
    Altshuler D, Donnelly P, Int. HapMap Consort 2005. A haplotype map of the human genome. Nature 437:70631299–320
     [Google Scholar]
  6. 6.
    Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL et al. 2007. A second generation human haplotype map of over 3.1 million SNPs. Nature 449:7164851–61
     [Google Scholar]
  7. 7.
    Int. HapMap 3 Consort., Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM et al. 2010. Integrating common and rare genetic variation in diverse human populations. Nature 467:731152–58
     [Google Scholar]
  8. 8.
    1000 Genomes Proj. Consort., Abecasis GR, Altshuler D, Auton A, Brooks LD et al. 2010. A map of human genome variation from population-scale sequencing. Nature 467:73191061–73
     [Google Scholar]
  9. 9.
    1000 Genomes Proj. Consort., Abecasis GR, Auton A, Brooks LD, DePristo MA et al. 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491:742256–65
     [Google Scholar]
  10. 10.
    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]
  11. 11.
    Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A et al. 2015. An integrated map of structural variation in 2,504 human genomes. Nature 526:757175–81
     [Google Scholar]
  12. 12.
    Wang T, Antonacci-Fulton L, Howe K, Lawson HA, Lucas JK et al. 2022. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604:7906437–46
     [Google Scholar]
  13. 13.
    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]
  14. 14.
    Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E et al. 2016. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:7616285–91
     [Google Scholar]
  15. 15.
    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]
  16. 16.
    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]
  17. 17.
    Graham BE, Plotkin B, Muglia L, Moore JH, Williams SM. 2021. Estimating prevalence of human traits among populations from polygenic risk scores. Hum. Genom. 15:70
     [Google Scholar]
  18. 18.
    Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. 2022. Challenges and opportunities for developing more generalizable polygenic risk scores. Annu. Rev. Biomed. Data Sci. 5:293–320
     [Google Scholar]
  19. 19.
    Hindorff LA, Bonham VL, Brody LC, Ginoza MEC, Hutter CM et al. 2018. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19:3175–85
     [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.
    Scott WK, Ritchie MD. 2022. Genetic Analysis of Complex Disease Hoboken, NJ: Wiley-Blackwell. , 3rd ed..
  22. 22.
    Borecki IB, Province MA. 2008. Linkage and association: basic concepts. Adv. Genet. 60:51–74
     [Google Scholar]
  23. 23.
    Collins FS, Manolio TA. 2007. Merging and emerging cohorts: necessary but not sufficient. Nature 445:7125259
     [Google Scholar]
  24. 24.
    Wellcome Trust Case Control Consort 2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:7145661–78
     [Google Scholar]
  25. 25.
    Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P et al. 2007. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat. Genet. 39:111329–37
     [Google Scholar]
  26. 26.
    Jallow M, Teo YY, Small KS, Rockett KA, Deloukas P et al. 2009. Genome-wide and fine-resolution association analysis of malaria in West Africa. Nat. Genet. 41:6657–65
     [Google Scholar]
  27. 27.
    Thye T, Vannberg FO, Wong SH, Owusu-Dabo E, Osei I et al. 2010. Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11.2. Nat. Genet. 42:9739–41
     [Google Scholar]
  28. 28.
    Loos RJF, Lindgren CM, Li S, Wheeler E, Zhao JH et al. 2008. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40:6768–75
     [Google Scholar]
  29. 29.
    Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G et al. 2010. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42:11937–48
     [Google Scholar]
  30. 30.
    Sanna S, Jackson AU, Nagaraja R, Willer CJ, Chen W-M et al. 2008. Common variants in the GDF5-UQCC region are associated with variation in human height. Nat. Genet. 40:2198–203
     [Google Scholar]
  31. 31.
    Lettre G, Jackson AU, Gieger C, Schumacher FR, Berndt SI et al. 2008. Identification of ten loci associated with height highlights new biological pathways in human growth. Nat. Genet. 40:5584–91
     [Google Scholar]
  32. 32.
    Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN et al. 2010. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467:7317832–38
     [Google Scholar]
  33. 33.
    Wood AR, Esko T, Yang J, Vedantam S, Pers TH et al. 2014. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46:111173–86
     [Google Scholar]
  34. 34.
    Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL et al. 2008. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40:2161–69
     [Google Scholar]
  35. 35.
    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM et al. 2010. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:7307707–13
     [Google Scholar]
  36. 36.
    Newton-Cheh C, Johnson T, Gateva V, Tobin MD, Bochud M et al. 2009. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41:6666–76
     [Google Scholar]
  37. 37.
    Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ et al. 2009. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41:6677–87
     [Google Scholar]
  38. 38.
    Int. Consort. Blood Pressure Genome-Wide Assoc. Studies, Ehret GB, Munroe PB, Rice KM, Bochud M et al. 2011. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478:7367103–9
     [Google Scholar]
  39. 39.
    Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N et al. 2010. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42:2105–16
     [Google Scholar]
  40. 40.
    Scott RA, Scott LJ, Mägi R, Marullo L, Gaulton KJ et al. 2017. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66:112888–902
     [Google Scholar]
  41. 41.
    Andersson C, Johnson AD, Benjamin EJ, Levy D, Vasan RS. 2019. 70-year legacy of the Framingham Heart Study. Nat. Rev. Cardiol. 16:11687–98
     [Google Scholar]
  42. 42.
    Barker DJP, Osmond C, Forsén TJ, Kajantie E, Eriksson JG. 2005. Trajectories of growth among children who have coronary events as adults. N. Engl. J. Med. 353:171802–9
     [Google Scholar]
  43. 43.
    Bao Y, Bertoia ML, Lenart EB, Stampfer MJ, Willett WC et al. 2016. Origin, methods, and evolution of the three nurses’ health studies. Am. J. Public Health 106:91573–81
     [Google Scholar]
  44. 44.
    Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK et al. 2020. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur. J. Epidemiol. 35:5483–517
     [Google Scholar]
  45. 45.
    Power C, Elliott J. 2006. Cohort profile: 1958 British birth cohort (National Child Development Study). Int. J. Epidemiol. 35:134–41
     [Google Scholar]
  46. 46.
    Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E et al. 2022. A saturated map of common genetic variants associated with human height. Nature 610:7933704–12
     [Google Scholar]
  47. 47.
    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]
  48. 48.
    DIAbetes Genet. Replication Meta-anal. (DIAGRAM) Consort., Asian Genet. Epidemiol. Netw. Type 2 Diabetes (AGEN-T2D) Consort., South Asian Type 2 Diabetes (SAT2D) Consort., Mex. Am. Type 2 Diabetes (MAT2D) Consort., Type 2 Diabetes Genet. Explor. Next-gener. seq. multi-Ethnic Samples (T2D-GENES) Consort., et al. 2014. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46:3234–44
     [Google Scholar]
  49. 49.
    Ollier W, Sprosen T, Peakman T. 2005. UK Biobank: from concept to reality. Pharmacogenomics 6:6639–46
     [Google Scholar]
  50. 50.
    Sudlow C, Gallacher J, Allen N, Beral V, Burton P et al. 2015. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 12:3e1001779
     [Google Scholar]
  51. 51.
    Constantinescu A-E, Mitchell RE, Zheng J, Bull CJ, Timpson NJ et al. 2022. A framework for research into continental ancestry groups of the UK Biobank. Hum. Genom. 16:3
     [Google Scholar]
  52. 52.
    Conroy M, Sellors J, Effingham M, Littlejohns TJ, Boultwood C et al. 2019. The advantages of UK Biobank's open-access strategy for health research. J. Intern. Med. 286:4389–97
     [Google Scholar]
  53. 53.
    Kolonel LN, Henderson BE, Hankin JH, Nomura AMY, Wilkens LR et al. 2000. A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics. Am. J. Epidemiol. 151:4346–57
     [Google Scholar]
  54. 54.
    Women's Health Initiat. Study Group 1998. Design of the Women's Health Initiative clinical trial and observational study. Control. Clin. Trials. 19:161–109
     [Google Scholar]
  55. 55.
    Crawford DC, Goodloe R, Farber-Eger E, Boston J, Pendergrass SA et al. 2015. Leveraging epidemiologic and clinical collections for genomic studies of complex traits. Hum. Hered. 79:3–4137–46
     [Google Scholar]
  56. 56.
    LaVange LM, Kalsbeek WD, Sorlie PD, Avilés-Santa LM, Kaplan RC et al. 2010. Sample design and cohort selection in the Hispanic Community Health Study/Study of Latinos. Ann. Epidemiol. 20:8642–49
     [Google Scholar]
  57. 57.
    Manolio TA, Bailey-Wilson JE, Collins FS. 2006. Genes, environment and the value of prospective cohort studies. Nat. Rev. Genet. 7:10812–20
     [Google Scholar]
  58. 58.
    Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R et al. 2013. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15:10761–71
     [Google Scholar]
  59. 59.
    Roden D, Pulley J, Basford M, Bernard G, Clayton E et al. 2008. Development of a Large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther. 84:3362–69
     [Google Scholar]
  60. 60.
    McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP et al. 2011. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genom. 4:13
     [Google Scholar]
  61. 61.
    Kvale MN, Hesselson S, Hoffmann TJ, Cao Y, Chan D et al. 2015. Genotyping informatics and quality control for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200:41051–60
     [Google Scholar]
  62. 62.
    Verma A, Damrauer SM, Naseer N, Weaver J, Kripke CM et al. 2022. The Penn Medicine BioBank: towards a genomics-enabled learning healthcare system to accelerate precision medicine in a diverse population. J. Pers. Med. 12:121974
     [Google Scholar]
  63. 63.
    Pendergrass SA, Crawford DC. 2019. Using electronic health records to generate phenotypes for research. Curr. Protoc. Hum. Genet. 100:1e80
     [Google Scholar]
  64. 64.
    Matise TC, Ambite JL, Buyske S, Carlson CS, Cole SA et al. 2011. The next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) study. Am. J. Epidemiol. 174:7849–59
     [Google Scholar]
  65. 65.
    Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA et al. 1990. The Strong Heart Study. A study of cardiovascular disease in American Indians: design and methods. Am. J. Epidemiol. 132:61141–55
     [Google Scholar]
  66. 66.
    North KE, Howard BV, Welty TK, Best LG, Lee ET et al. 2003. Genetic and environmental contributions to cardiovascular disease risk in American Indians: the Strong Heart Family Study. Am. J. Epidemiol. 157:4303–14
     [Google Scholar]
  67. 67.
    Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM et al. 1991. The cardiovascular health study: design and rationale. Ann. Epidemiol. 1:3263–76
     [Google Scholar]
  68. 68.
    ARIC (Atheroscler. Risk Commun.) Investig 1989. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am. J. Epidemiol. 129:4687–702
     [Google Scholar]
  69. 69.
    Hughes GH, Cutter G, Donahue R, Friedman GD, Hulley S et al. 1987. Recruitment in the Coronary Artery Disease Risk Development in Young Adults (CARDIA) study. Control. Clin. Trials 8:4 Suppl.68S–73S
     [Google Scholar]
  70. 70.
    Haiman CA, Fesinmeyer MD, Spencer KL, Bůžková P, Voruganti VS et al. 2012. Consistent directions of effect for established type 2 diabetes risk variants across populations. Diabetes 61:61642–47
     [Google Scholar]
  71. 71.
    Dumitrescu L, Carty CL, Taylor K, Schumacher FR, Hindorff LA et al. 2011. Genetic determinants of lipid traits in diverse populations from the Population Architecture using Genomics and Epidemiology (PAGE) study. PLOS Genet. 7:6e1002138
     [Google Scholar]
  72. 72.
    Restrepo NA, Spencer KL, Goodloe R, Garrett TA, Heiss G et al. 2014. Genetic determinants of age-related macular degeneration in diverse populations from the PAGE study. Investig. Ophthalmol. Vis. Sci. 55:106839–50
     [Google Scholar]
  73. 73.
    Bailey JNC, Wilson S, Brown-Gentry K, Goodloe R, Crawford DC. 2015. Kidney disease genetics and the importance of diversity in precision medicine. Pac. Symp. Biocomput. 21:285–96
     [Google Scholar]
  74. 74.
    Zhang L, Spencer KL, Voruganti VS, Jorgensen NW, Fornage M et al. 2013. Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: the PAGE Study. Am. J. Epidemiol. 177:9923–32
     [Google Scholar]
  75. 75.
    Klein RJ, Zeiss C, Chew EY, Tsai J-Y, Sackler RS et al. 2005. Complement factor H polymorphism in age-related macular degeneration. Science 308:5720385–89
     [Google Scholar]
  76. 76.
    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]
  77. 77.
    Genovese G, Tonna SJ, Knob AU, Appel GB, Katz A et al. 2010. A risk allele for focal segmental glomerulosclerosis in African Americans is located within a region containing APOL1 and MYH9. Kidney Int. 78:7698–704
     [Google Scholar]
  78. 78.
    Yusuf AA, Govender MA, Brandenburg J-T, Winkler CA. 2021. Kidney disease and APOL1. Hum. Mol. Genet. 30:R1R129–37
     [Google Scholar]
  79. 79.
    Carlson CS, Matise TC, North KE, Haiman CA, Fesinmeyer MD et al. 2013. Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study. PLOS Biol. 11:9e1001661
     [Google Scholar]
  80. 80.
    Buyske S, Wu Y, Carty CL, Cheng I, Assimes TL et al. 2012. Evaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study. PLOS ONE 7:4e35651
     [Google Scholar]
  81. 81.
    Hu Y, Graff M, Haessler J, Buyske S, Bien SA et al. 2020. Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study. PLOS Genet. 16:3e1008684
     [Google Scholar]
  82. 82.
    Crawford DC, Crosslin DR, Tromp G, Kullo IJ, Kuivaniemi H et al. 2014. eMERGEing progress in genomics—the first seven years. Front. Genet. 5:184
     [Google Scholar]
  83. 83.
    Jeff JM, Ritchie MD, Denny JC, Kho AN, Ramirez AH et al. 2013. Generalization of variants identified by genome-wide association studies for electrocardiographic traits in African Americans. Ann. Hum. Genet. 77:4321–32
     [Google Scholar]
  84. 84.
    Ding K, de Andrade M, Manolio TA, Crawford DC, Rasmussen-Torvik LJ et al. 2013. Genetic variants that confer resistance to malaria are associated with red blood cell traits in African-Americans: an electronic medical record-based genome-wide association study. G3 3:71061–68
     [Google Scholar]
  85. 85.
    Turner SD, Berg RL, Linneman JG, Peissig PL, Crawford DC et al. 2011. Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLOS ONE 6:5e19586
     [Google Scholar]
  86. 86.
    Rasmussen-Torvik LJ, Pacheco JA, Wilke RA, Thompson WK, Ritchie MD et al. 2012. High density GWAS for LDL cholesterol in African Americans using electronic medical records reveals a strong protective variant in APOE. Clin. Transl. Sci. 5:5394–99
     [Google Scholar]
  87. 87.
    Denny JC, Ritchie MD, Crawford DC, Schildcrout JS, Ramirez AH et al. 2010. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation 122:202016–21
     [Google Scholar]
  88. 88.
    Dumitrescu L, Ritchie MD, Denny JC, El Rouby NM, McDonough CW et al. 2017. Genome-wide study of resistant hypertension identified from electronic health records. PLOS ONE 12:2e0171745
     [Google Scholar]
  89. 89.
    Ng MCY, Shriner D, Chen BH, Li J, Chen W-M et al. 2014. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLOS Genet. 10:8e1004517
     [Google Scholar]
  90. 90.
    Ben-Eghan C, Sun R, Hleap JS, Diaz-Papkovich A, Munter HM et al. 2020. Don't ignore genetic data from minority populations. Nature 585:7824184–86
     [Google Scholar]
  91. 91.
    Psaty BM, O'Donnell CJ, Gudnason V, Lunetta KL, Folsom AR et al. 2009. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Circ. Cardiovasc. Genet. 2:173–80
     [Google Scholar]
  92. 92.
    Harris TB, Launer LJ, Eiriksdottir G, Kjartansson O, Jonsson PV et al. 2007. Age, Gene/Environment Susceptibility–Reykjavik study: multidisciplinary applied phenomics. Am. J. Epidemiol. 165:91076–87
     [Google Scholar]
  93. 93.
    Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV et al. 2002. Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156:9871–81
     [Google Scholar]
  94. 94.
    Evans DS, Avery CL, Nalls MA, Li G, Barnard J et al. 2016. Fine-mapping, novel loci identification, and SNP association transferability in a genome-wide association study of QRS duration in African Americans. Hum. Mol. Genet. 25:194350–68
     [Google Scholar]
  95. 95.
    Teo Y-Y, Small KS, Kwiatkowski DP. 2010. Methodological challenges of genome-wide association analysis in Africa. Nat. Rev. Genet. 11:2149–60
     [Google Scholar]
  96. 96.
    Pereira L, Mutesa L, Tindana P, Ramsay M. 2021. African genetic diversity and adaptation inform a precision medicine agenda. Nat. Rev. Genet. 22:5284–306
     [Google Scholar]
  97. 97.
    Homburger JR, Moreno-Estrada A, Gignoux CR, Nelson D, Sanchez E et al. 2015. Genomic insights into the ancestry and demographic history of South America. PLOS Genet. 11:12e1005602
     [Google Scholar]
  98. 98.
    Bryc K, Durand EY, Macpherson JM, Reich D, Mountain JL. 2015. The genetic ancestry of African Americans, Latinos, and European Americans across the United States. Am. J. Hum. Genet. 96:137–53
     [Google Scholar]
  99. 99.
    Winkler CA, Nelson GW, Smith MW. 2010. Admixture mapping comes of age. Annu. Rev. Genom. Hum. Genet. 11:65–89
     [Google Scholar]
  100. 100.
    Seldin MF, Pasaniuc B, Price AL. 2011. New approaches to disease mapping in admixed populations. Nat. Rev. Genet. 12:8523–28
     [Google Scholar]
  101. 101.
    Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB et al. 2019. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179:3589–603
     [Google Scholar]
  102. 102.
    Evangelou E, Ioannidis JPA. 2013. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14:6379–89
     [Google Scholar]
  103. 103.
    Lin DY, Zeng D. 2010. On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika 97:2321–32
     [Google Scholar]
  104. 104.
    Nagai A, Hirata M, Kamatani Y, Muto K, Matsuda K et al. 2017. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27:3SS2–8
     [Google Scholar]
  105. 105.
    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]
  106. 106.
    Moreno-Estrada A, Gignoux CR, Fernández-López JC, Zakharia F, Sikora M et al. 2014. The genetics of Mexico recapitulates Native American substructure and affects biomedical traits. Science 344:61891280–85
     [Google Scholar]
  107. 107.
    Wojcik GL. 2022. By their powers combined, global initiative joins forces for genomic research. Cell 185:234256–58
     [Google Scholar]
  108. 108.
    Zhou W, Kanai M, Wu K-HH, Rasheed H, Tsuo K et al. 2022. Global Biobank Meta-analysis Initiative: powering genetic discovery across human disease. Cell Genom. 2:10100192
     [Google Scholar]
  109. 109.
    COVID-19 Host Genet. Initiat 2020. The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur. J. Hum. Genet. 28:6715–18
     [Google Scholar]
  110. 110.
    Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S et al. 2016. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70:214–23
     [Google Scholar]
  111. 111.
    Majumder MA, Guerrini CJ, McGuire AL. 2021. Direct-to-consumer genetic testing: value and risk. Annu. Rev. Med. 72:151–66
     [Google Scholar]
  112. 112.
    Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X et al. 2021. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat. Genet. 53:65–75
     [Google Scholar]
  113. 113.
    Kunkle BW, Schmidt M, Klein H-U, Naj AC, Hamilton-Nelson KL et al. 2021. Novel Alzheimer Disease risk loci and pathways in African American individuals using the African genome resources panel: a meta-analysis. JAMA Neurol. 78:1102–13
     [Google Scholar]
  114. 114.
    All of Us Res. Prog. Investig., Denny JC, Rutter JL, Goldstein DB, Philippakis A et al. 2019. The “All of Us” Research Program. N. Engl. J. Med. 381:7668–76
     [Google Scholar]
  115. 115.
    Mayo KR, Basford MA, Carroll RJ, Dillion M, Fullen H et al. 2023. The All of Us Data and Research Center: creating a secure, scalable, and sustainable ecosystem for biomedical research. Annu. Rev. Biomed. Data Sci. 6: In press
    [Google Scholar]
  116. 116.
    Collins FS, Morgan M, Patrinos A. 2003. The Human Genome Project: lessons from large-scale biology. Science 300:5617286–90
     [Google Scholar]
  117. 117.
    Collins FS. 2004. The case for a US prospective cohort study of genes and environment. Nature 429:6990475–77
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-122220-113250
Loading
Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants
Annual Review of Biomedical Data Science 6, 339 (2023); https://doi.org/10.1146/annurev-biodatasci-122220-113250
/content/journals/10.1146/annurev-biodatasci-122220-113250
/content/journals/10.1146/annurev-biodatasci-122220-113250
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error