The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE

Literature Cited

1. 1.

   1000 Genomes Proj. Consort 2015. A global reference for human genetic variation. Nature 526:68–74
   [Google Scholar]
2. 2.

   Adashi EY, Walters LB, Menikoff JA 2018. The Belmont Report at 40: reckoning with time. Am. J. Public Health 108:1345–48
   [Google Scholar]
3. 3.

   ARIC Investig 1989. The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. Am. J. Epidemiol. 129:687–702
   [Google Scholar]
4. 4.

   Avery CL, Sethupathy P, Buyske S, He Q, Lin D-Y et al. 2012. Fine-mapping and initial characterization of QT interval loci in African Americans. PLOS Genet 8:e1002870
   [Google Scholar]
5. 5.

   Avery CL, Wassel CL, Richard MA, Highland HM, Bien S et al. 2017. Fine mapping of QT interval regions in global populations refines previously identified QT interval loci and identifies signals unique to African and Hispanic descent populations. Heart Rhythm 14:572–80
   [Google Scholar]
6. 6.

   Baharian S, Barakatt M, Gignoux CR, Shringarpure S, Errington J et al. 2016. The great migration and African-American genomic diversity. PLOS Genet 12:e1006059
   [Google Scholar]
7. 7.

   Bai H, Guo X, Narisu N, Lan T, Wu Q et al. 2018. Whole-genome sequencing of 175 Mongolians uncovers population-specific genetic architecture and gene flow throughout North and East Asia. Nat. Genet. 50:1696–704
   [Google Scholar]
8. 8.

   Ball TB, Ji H, Kimani J, McLaren P, Marlin C et al. 2007. Polymorphisms in IRF-1 associated with resistance to HIV-1 infection in highly exposed uninfected Kenyan sex workers. AIDS 21:1091–101
   [Google Scholar]
9. 9.

   Baran Y, Pasaniuc B, Sankararaman S, Torgerson DG, Gignoux C et al. 2012. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics 28:1359–67
   [Google Scholar]
10. 10.

    Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM et al. 2014. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 24:14–24
    [Google Scholar]
11. 11.

    Belbin GM, Nieves-Colón MA, Kenny EE, Moreno-Estrada A, Gignoux CR 2018. Genetic diversity in populations across Latin America: implications for population and medical genetic studies. Curr. Opin. Genet. Dev. 53:98–104
    [Google Scholar]
12. 12.

    Belbin GM, Odgis J, Sorokin EP, Yee M-C, Kohli S et al. 2017. Genetic identification of a common collagen disease in Puerto Ricans via identity-by-descent mapping in a health system. eLife 6:e25060
    [Google Scholar]
13. 13.

    Bien SA, Pankow JS, Haessler J, Lu YN, Pankratz N et al. 2017. Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture Using Genomics and Epidemiology (PAGE) consortium. Diabetologia 60:2384–98
    [Google Scholar]
14. 14.

    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:e0167758
    [Google Scholar]
15. 15.

    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. 156:871–81
    [Google Scholar]
16. 16.

    Bonham VL, Green ED, Pérez-Stable EJ 2018. Examining how race, ethnicity, and ancestry data are used in biomedical research. JAMA 320:1533–34
    [Google Scholar]
17. 17.

    Boyle EA, Li YI, Pritchard JK 2017. An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–86
    [Google Scholar]
18. 18.

    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. 96:37–53
    [Google Scholar]
19. 19.

    Bustamante CD, Burchard EG, De La Vega FM 2011. Genomics for the world. Nature 475:163–65
    [Google Scholar]
20. 20.

    Campbell MC, Tishkoff SA. 2008. African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu. Rev. Genom. Hum. Genet. 9:403–33
    [Google Scholar]
21. 21.

    Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA 2004. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74:106–20
    [Google Scholar]
22. 22.

    Carnethon MR, Pu J, Howard G, Albert MA, Anderson CAM et al. 2017. Cardiovascular health in African Americans: a scientific statement from the American Heart Association. Circulation 136:e393–423
    [Google Scholar]
23. 23.

    Carty CL, Spencer KL, Setiawan VW, Fernandez-Rhodes L, Malinowski J et al. 2013. Replication of genetic loci for ages at menarche and menopause in the multi-ethnic Population Architecture Using Genomics and Epidemiology (PAGE) study. Hum. Reprod. 28:1695–706
    [Google Scholar]
24. 24.

    Caswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA et al. 2018. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet. Med. 20:234–39
    [Google Scholar]
25. 25.

    Cent. Dis. Control Prev 2017. National diabetes statistics report, 2017 Rep., Cent. Dis. Control Prev Atlanta: https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
26. 26.

    Check Hayden E. 2015. Racial bias continues to haunt NIH grants. Nature 527:286–87
    [Google Scholar]
27. 27.

    Cheng C-Y, Kao WHL, Patterson N, Tandon A, Haiman CA et al. 2009. Admixture mapping of 15,280 African Americans identifies obesity susceptibility loci on chromosomes 5 and X. PLOS Genet 5:e1000490
    [Google Scholar]
28. 28.

    Cheng I, Caberto CP, Lum-Jones A, Seifried A, Wilkens LR et al. 2011. Type 2 diabetes risk variants and colorectal cancer risk: the multiethnic cohort and PAGE studies. Gut 60:1703–11
    [Google Scholar]
29. 29.

    Čolić A, Alessandrini M, Pepper MS 2015. Pharmacogenetics of CYP2B6, CYP2A6 and UGT2B7 in HIV treatment in African populations: focus on efavirenz and nevirapine. Drug Metab. Rev. 47:111–23
    [Google Scholar]
30. 30.

    Conomos MP, Laurie CA, Stilp AM, Gogarten SM, McHugh CP et al. 2016. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 98:165–84
    [Google Scholar]
31. 31.

    Conomos MP, Miller MB, Thornton TA 2015. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 39:276–93
    [Google Scholar]
32. 32.

    Conomos MP, Reiner AP, Weir BS, Thornton TA 2016. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98:127–48
    [Google Scholar]
33. 33.

    Deelen P, Menelaou A, van Leeuwen EM, Kanterakis A, van Dijk F et al. 2014. Improved imputation quality of low-frequency and rare variants in European samples using the “Genome of the Netherlands. .” Eur. J. Hum. Genet. 22:1321–26
    [Google Scholar]
34. 34.

    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. 46:234–44
    [Google Scholar]
35. 35.

    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:e1002138
    [Google Scholar]
36. 36.

    Fernández-Rhodes L, Gong J, Haessler J, Franceschini N, Graff M et al. 2017. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture Using Genomics and Epidemiology (PAGE) study reveals evidence for multiple signals at established loci. Hum. Genet. 136:771–800
    [Google Scholar]
37. 37.

    Fesinmeyer MD, Meigs JB, North KE, Schumacher FR, Bůžková P et al. 2013. Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture Using Genomics and Epidemiology (PAGE) study. BMC Med. Genet. 14:98
    [Google Scholar]
38. 38.

    Fesinmeyer MD, North KE, Ritchie MD, Lim U, Franceschini N et al. 2013. Genetic risk factors for BMI and obesity in an ethnically diverse population: results from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Obesity 21:835–46
    [Google Scholar]
39. 39.

    Franceschini N, Carty CL, Lu Y, Tao R, Sung YJ et al. 2016. Variant discovery and fine mapping of genetic loci associated with blood pressure traits in Hispanics and African Americans. PLOS ONE 11:e0164132
    [Google Scholar]
40. 40.

    Franceschini N, Shara NM, Wang H, Voruganti VS, Laston S et al. 2012. The association of genetic variants of type 2 diabetes with kidney function. Kidney Int 82:220–25
    [Google Scholar]
41. 41.

    Freedman BI, Limou S, Ma L, Kopp JB 2018. APOL1-associated nephropathy: a key contributor to racial disparities in CKD. Am. J. Kidney Dis. 72:S8–16
    [Google Scholar]
42. 42.

    Fu W, O'Connor TD, Jun G, Kang HM, Abecasis G et al. 2013. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493:216–20
    [Google Scholar]
43. 43.

    Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K et al. 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47:1091–98
    [Google Scholar]
44. 44.

    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 329:841–45
    [Google Scholar]
45. 45.

    Gignoux C, Sorokin E, Wojcik G, Belbin G, Bien S et al. 2018. The global landscape of pharmacogenomic variation Paper presented at the Annual Meeting of the American Society of Human Genetics San Diego, CA: Oct 16–20
46. 46.

    Gignoux CR, Torgerson DG, Pino-Yanes M, Uricchio LH, Galanter J et al. 2019. An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos. J. Allergy Clin. Immunol 143:957–69
    [Google Scholar]
47. 47.

    Gong J, Schumacher F, Lim U, Hindorff LA, Haessler J et al. 2013. Fine mapping and identification of BMI loci in African Americans. Am. J. Hum. Genet. 93:661–71
    [Google Scholar]
48. 48.

    Gravel S, Henn BM, Gutenkunst RN, Indap AR, Marth GT et al. 2011. Demographic history and rare allele sharing among human populations. PNAS 108:11983–88
    [Google Scholar]
49. 49.

    GTEx Consort 2013. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45:580–85
    [Google Scholar]
50. 50.

    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I et al. 2015. The African genome variation project shapes medical genetics in Africa. Nature 517:327–32
    [Google Scholar]
51. 51.

    Gusev A, Ko A, Shi H, Bhatia G, Chung W et al. 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48:245–52
    [Google Scholar]
52. 52.

    H3Africa Consort 2014. Enabling the genomic revolution in Africa. Science 344:1346–48
    [Google Scholar]
53. 53.

    Haiman CA, Fesinmeyer MD, Spencer KL, Buzková P, Voruganti VS et al. 2012. Consistent directions of effect for established type 2 diabetes risk variants across populations: the Population Architecture Using Genomics and Epidemiology (PAGE) consortium. Diabetes 61:1642–47
    [Google Scholar]
54. 54.

    Hellenthal G, Busby GBJ, Band G, Wilson JF, Capelli C et al. 2014. A genetic atlas of human admixture history. Science 343:747–51
    [Google Scholar]
55. 55.

    Henn BM, Botigué LR, Peischl S, Dupanloup I, Lipatov M et al. 2016. Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. PNAS 113:E440–49
    [Google Scholar]
56. 56.

    Hindorff LA, Bonham VL, Brody LC, Ginoza MEC, Hutter CM et al. 2018. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19:175–85
    [Google Scholar]
57. 57.

    Hindorff LA, Bonham VL, Ohno-Machado L 2018. Enhancing diversity to reduce health information disparities and build an evidence base for genomic medicine. Pers. Med. 15:403–12
    [Google Scholar]
58. 58.

    Hodonsky CJ, Jain D, Schick UM, Morrison JV, Brown L et al. 2017. Genome-wide association study of red blood cell traits in Hispanics/Latinos: the Hispanic Community Health Study/Study of Latinos. PLOS Genet 13:e1006760
    [Google Scholar]
59. 59.

    Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E 2014. Identifying causal variants at loci with multiple signals of association. Genetics 198:497–508
    [Google Scholar]
60. 60.

    Illumina 2019. Human consortia. Illumina https://www.illumina.com/science/consortia/human-consortia.html
    [Google Scholar]
61. 61.

    Int. HapMap Consort 2003. The International HapMap Project. Nature 426:789–96
    [Google Scholar]
62. 62.

    Jennes W, Verheyden S, Demanet C, Adjé-Touré CA, Vuylsteke B et al. 2006. Cutting edge: resistance to HIV-1 infection among African female sex workers is associated with inhibitory KIR in the absence of their HLA ligands. J. Immunol. 177:6588–92
    [Google Scholar]
63. 63.

    Johnston HR, Hu Y-J, Gao J, O'Connor TD, Abecasis GR et al. 2017. Identifying tagging SNPs for African specific genetic variation from the African diaspora genome. Sci. Rep. 7:46398
    [Google Scholar]
64. 64.

    Kallwitz ER, Daviglus ML, Allison MA, Emory KT, Zhao L et al. 2015. Prevalence of suspected nonalcoholic fatty liver disease in Hispanic/Latino individuals differs by heritage. Clin. Gastroenterol. Hepatol. 13:569–76
    [Google Scholar]
65. 65.

    Kao WHL, Klag MJ, Meoni LA, Reich D, Berthier-Schaad Y et al. 2008. MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nat. Genet. 40:1185–92
    [Google Scholar]
66. 66.

    Keys KL, Mak ACY, White MJ, Eckalbar W, Dahl AJ et al. 2019. On the cross-population portability of gene expression prediction models. bioRxiv 552042. https://doi.org/10.1101/552042
    [Crossref]
67. 67.

    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. 50:1219–24
    [Google Scholar]
68. 68.

    Kichaev G, Roytman M, Johnson R, Eskin E, Lindström S et al. 2017. Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 33:248–55
    [Google Scholar]
69. 69.

    Klein K, Lang T, Saussele T, Barbosa-Sicard E, Schunck W-H et al. 2005. Genetic variability of CYP2B6 in populations of African and Asian origin: allele frequencies, novel functional variants, and possible implications for anti-HIV therapy with efavirenz. Pharmacogenet. Genom. 15:861–73
    [Google Scholar]
70. 70.

    Ko W-Y, Rajan P, Gomez F, Scheinfeldt L, An P et al. 2013. Identifying Darwinian selection acting on different human APOL1 variants among diverse African populations. Am. J. Hum. Genet. 93:54–66
    [Google Scholar]
71. 71.

    Kocarnik JM, Richard M, Graff M, Haessler J, Bien S et al. 2018. Discovery, fine-mapping, and conditional analyses of genetic variants associated with c-reactive protein in multiethnic populations using the Metabochip in the Population Architecture Using Genomics and Epidemiology (PAGE) study. Hum. Mol. Genet. 27:2940–53
    [Google Scholar]
72. 72.

    Kolonel LN, Henderson BE, Hankin JH, Nomura AM, Wilkens LR et al. 2000. A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics. Am. J. Epidemiol. 151:346–57
    [Google Scholar]
73. 73.

    Kwiatkowski DP. 2005. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77:171–92
    [Google Scholar]
74. 74.

    Lama J, Planelles V. 2007. Host factors influencing susceptibility to HIV infection and AIDS progression. Retrovirology 4:52
    [Google Scholar]
75. 75.

    Lim U, Wilkens LR, Monroe KR, Caberto C, Tiirikainen M et al. 2012. Susceptibility variants for obesity and colorectal cancer risk: the multiethnic cohort and PAGE studies. Int. J. Cancer 131:E1038–43
    [Google Scholar]
76. 76.

    Lin D-Y, Tao R, Kalsbeek WD, Zeng D, Gonzalez F et al. 2014. Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 95:675–88
    [Google Scholar]
77. 77.

    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747–53
    [Google Scholar]
78. 78.

    Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA et al. 2016. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375:655–65
    [Google Scholar]
79. 79.

    Maples BK, Gravel S, Kenny EE, Bustamante CD 2013. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet. 93:278–88
    [Google Scholar]
80. 80.

    Marcus JH, Novembre J. 2017. Visualizing the geography of genetic variants. Bioinformatics 33:594–95
    [Google Scholar]
81. 81.

    Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:635–49
    [Google Scholar]
82. 82.

    Mathias RA, Taub MA, Gignoux CR, Fu W, Musharoff S et al. 2016. A continuum of admixture in the Western Hemisphere revealed by the African diaspora genome. Nat. Commun. 7:12522
    [Google Scholar]
83. 83.

    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. 48:1279–83
    [Google Scholar]
84. 84.

    Mills MC, Rahal C. 2019. A scientometric review of genome-wide association studies. Commun. Biol. 2:9
    [Google Scholar]
85. 85.

    Miwa S, Fujii H. 1996. Molecular basis of erythroenzymopathies associated with hereditary hemolytic anemia: tabulation of mutant enzymes. Am. J. Hematol. 51:122–32
    [Google Scholar]
86. 86.

    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 344:1280–85
    [Google Scholar]
87. 87.

    Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV et al. 2012. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44:981–90
    [Google Scholar]
88. 88.

    Nadkarni GN, Gignoux CR, Sorokin EP, Daya M, Rahman R et al. 2018. Worldwide frequencies of APOL1 renal risk variants. N. Engl. J. Med. 379:2571–72
    [Google Scholar]
89. 89.

    Need AC, Goldstein DB. 2009. Next generation disparities in human genomics: concerns and remedies. Trends Genet 25:489–94
    [Google Scholar]
90. 90.

    Newman LA. 2015. Disparities in breast cancer and African ancestry: a global perspective. Breast J 21:133–39
    [Google Scholar]
91. 91.

    Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ 2010. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLOS Genet 6:e1000888
    [Google Scholar]
92. 92.

    Park SL, Fesinmeyer MD, Timofeeva M, Caberto CP, Kocarnik JM et al. 2014. Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the PAGE and TRICL consortia. J. Natl. Cancer Inst. 106:dju061
    [Google Scholar]
93. 93.

    Parsa A, Kao WHL, Xie D, Astor BC, Li M et al. 2013. APOL1 risk variants, race, and progression of chronic kidney disease. N. Engl. J. Med. 369:2183–96
    [Google Scholar]
94. 94.

    Popejoy AB, Fullerton SM. 2016. Genomics is failing on diversity. Nature 538:161–64
    [Google Scholar]
95. 95.

    Popejoy AB, Ritter DI, Crooks K, Currey E, Fullerton SM et al. 2018. The clinical imperative for inclusivity: race, ethnicity, and ancestry (REA) in genomics. Hum. Mutat. 39:1713–20
    [Google Scholar]
96. 96.

    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38:904–9
    [Google Scholar]
97. 97.

    Price AL, Zaitlen NA, Reich D, Patterson N 2010. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11:459–63
    [Google Scholar]
98. 98.

    Roadmap Epigenom. Consort. Kundaje A, Meuleman W, Ernst J, Bilenky M et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature 518:317–30
    [Google Scholar]
99. 99.

    Rotimi CN, Bentley AR, Doumatey AP, Chen G, Shriner D, Adeyemo A 2017. The genomic landscape of African populations in health and disease. Hum. Mol. Genet. 26:R225–36
    [Google Scholar]
100. 100.

     Ruwende C, Khoo SC, Snow RW, Yates SN, Kwiatkowski D et al. 1995. Natural selection of hemi- and heterozygotes for G6PD deficiency in Africa by resistance to severe malaria. Nature 376:246–49
     [Google Scholar]
101. 101.

     Saab S, Manne V, Nieto J, Schwimmer JB, Chalasani NP 2016. Nonalcoholic fatty liver disease in Latinos. Clin. Gastroenterol. Hepatol. 14:5–12
     [Google Scholar]
102. 102.

     Saran R, Robinson B, Abbott KC, Agodoa LYC, Albertus P et al. 2017. US Renal Data System 2016 Annual Data Report: epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 69:Suppl. 1A7–8
     [Google Scholar]
103. 103.

     Saunders MA, Hammer MF, Nachman MW 2002. Nucleotide variability at g6pd and the signature of malarial selection in humans. Genetics 162:1849–61
     [Google Scholar]
104. 104.

     Schneider ALC, Lazo M, Selvin E, Clark JM 2014. Racial differences in nonalcoholic fatty liver disease in the U.S. population. Obesity 22:292–99
     [Google Scholar]
105. 105.

     Sempos CT, Bild DE, Manolio TA 1999. Overview of the Jackson Heart Study: a study of cardiovascular diseases in African American men and women. Am. J. Med. Sci. 317:142–46
     [Google Scholar]
106. 106.

     Setiawan VW, Haessler J, Schumacher F, Cote ML, Deelman E et al. 2012. HNF1B and endometrial cancer risk: results from the PAGE study. PLOS ONE 7:e30390
     [Google Scholar]
107. 107.

     Shriner D. 2017. Overview of admixture mapping. Curr. Protoc. Hum. Genet. 94:1231–8
     [Google Scholar]
108. 108.

     Simons YB, Bullaughey K, Hudson RR, Sella G 2018. A population genetic interpretation of GWAS findings for human quantitative traits. PLOS Biol 16:e2002985
     [Google Scholar]
109. 109.

     Sofer T, Baier LJ, Browning SR, Thornton TA, Talavera GA et al. 2017. Admixture mapping in the Hispanic Community Health Study/Study of Latinos reveals regions of genetic associations with blood pressure traits. PLOS ONE 12:e0188400
     [Google Scholar]
110. 110.

     Sorlie PD, Avilés-Santa LM, Wassertheil-Smoller S, Kaplan RC, Daviglus ML et al. 2010. Design and implementation of the Hispanic Community Health Study/Study of Latinos. Ann. Epidemiol. 20:629–41
     [Google Scholar]
111. 111.

     Spencer KL, Malinowski J, Carty CL, Franceschini N, Fernández-Rhodes L et al. 2013. Genetic variation and reproductive timing: African American women from the Population Architecture Using Genomics and Epidemiology (PAGE) study. PLOS ONE 8:e55258
     [Google Scholar]
112. 112.

     Su Y-R, Di C, Bien SA, Huang L, Dong X et al. 2018. A mixed-effects model for powerful association tests in integrative functional genomics: an application to a large-scale genome-wide association study of colorectal cancer. Am. J. Hum. Genet. 102:904–19
     [Google Scholar]
113. 113.

     Swart M, Evans J, Skelton M, Castel S, Wiesner L et al. 2015. An expanded analysis of pharmacogenetics determinants of efavirenz response that includes 3′-UTR single nucleotide polymorphisms among black South African HIV/AIDS patients. Front. Genet. 6:356
     [Google Scholar]
114. 114.

     Tishkoff SA, Varkonyi R, Cahinhinan N, Abbes S, Argyropoulos G et al. 2001. Haplotype diversity and linkage disequilibrium at human G6PD: recent origin of alleles that confer malarial resistance. Science 293:455–62
     [Google Scholar]
115. 115.

     Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR et al. 2011. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat. Genet. 43:887–92
     [Google Scholar]
116. 116.

     Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A 2015. Global cancer statistics, 2012. CA Cancer J. Clin. 65:87–108
     [Google Scholar]
117. 117.

     UNAIDS 2018. Global HIV & AIDS statistics – 2018 fact sheet Fact Sheet, UNAIDS, Geneva. http://www.unaids.org/en/resources/fact-sheet
118. 118.

     UyBico SJ, Pavel S, Gross CP 2007. Recruiting vulnerable populations into research: a systematic review of recruitment interventions. J. Gen. Intern. Med. 22:852–63
     [Google Scholar]
119. 119.

     Vergara C, Parker MM, Franco L, Cho MH, Valencia-Duarte AV et al. 2018. Genotype imputation performance of three reference panels using African ancestry individuals. Hum. Genet. 137:281–92
     [Google Scholar]
120. 120.

     Visscher PM, Brown MA, McCarthy MI, Yang J 2012. Five years of GWAS discovery. Am. J. Hum. Genet. 90:7–24
     [Google Scholar]
121. 121.

     Voight BF, Kang HM, Ding J, Palmer CD, Sidore C et al. 2012. The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLOS Genet 8:e1002793
     [Google Scholar]
122. 122.

     Wojcik GL, Fuchsberger C, Taliun D, Welch R, Martin AR et al. 2018. Imputation-aware tag SNP selection to improve power for large-scale, multi-ethnic association studies. G3 8:3255–67
     [Google Scholar]
123. 123.

     Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J et al. 2019. Genetic analyses of diverse populations improves discovery for complex traits. Nature570:514–18
     [Google Scholar]
124. 124.

     Women's Health Init. Study Group 1998. Design of the Women's Health Initiative clinical trial and observational study. Control. Clin. Trials 19:61–109
     [Google Scholar]
125. 125.

     Zhang L, Buzkova P, Wassel CL, Roman MJ, North KE et al. 2013. Lack of associations of ten candidate coronary heart disease risk genetic variants and subclinical atherosclerosis in four US populations: the Population Architecture Using Genomics and Epidemiology (PAGE) study. Atherosclerosis 228:390–99
     [Google Scholar]
126. 126.

     Zhang Y, Qi G, Park J-H, Chatterjee N 2018. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50:1318–26
     [Google Scholar]
127. 127.

     Zubair N, Graff M, Luis Ambite J, Bush WS, Kichaev G et al. 2016. Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci. Hum. Mol. Genet. 25:5500–12
     [Google Scholar]