Strategies for the Genomic Analysis of Admixed Populations

Literature Cited

1. 1.

   Sirugo G, Williams SM, Tishkoff SA. 2019. The missing diversity in human genetic studies. Cell 177:126–31
   [Google Scholar]
2. 2.

   Popejoy AB, Fullerton SM 2016. Genomics is failing on diversity. Nature 538:161–64
   [Google Scholar]
3. 3.

   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. 100:4635–49
   [Google Scholar]
4. 4.

   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]
5. 5.

   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]
6. 6.

   Coram MA, Fang H, Candille SI, Assimes TL, Tang H. 2017. Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations. Am. J. Hum. Genet. 101:2218–26
   [Google Scholar]
7. 7.

   Huang H, Peloso GM, Howrigan D, Rakitsch B, Simon-Gabriel CJ et al. 2016. Bootstrat: population informed bootstrapping for rare variant tests. bioRxiv 068999. https://doi.org/10.1101/068999
8. 8.

   Lander ES, Schork NJ. 1994. Genetic dissection of complex traits. Science 265:51812037–48
   [Google Scholar]
9. 9.

   Martin ER, Tunc I, Liu Z, Slifer SH, Beecham AH, Beecham GW. 2018. Properties of global- and local-ancestry adjustments in genetic association tests in admixed populations. Genet. Epidemiol. 42:2214–29
   [Google Scholar]
10. 10.

    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]
11. 11.

    Sul JH, Martin LS, Eskin E. 2018. Population structure in genetic studies: confounding factors and mixed models. PLOS Genet 14:12e1007309
    [Google Scholar]
12. 12.

    Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ et al. 2018. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21:121656–69
    [Google Scholar]
13. 13.

    TOPMed (Trans-Omics Precis. Med.) 2021. TOPMed Whole Genome Sequencing Project—freeze 5b, phases 1 and 2 Tech. Rep., TOPMed, Natl. Heart Lung Blood Inst., Bethesda, MD, updated Oct. 28. https://topmed.nhlbi.nih.gov/topmed-whole-genome-sequencing-project-freeze-5b-phases-1-and-2
14. 14.

    Rotimi C, Abayomi A, Abimiku A, Adabayeri VM, Adebamowo C et al. 2014. Research capacity. Enabling the genomic revolution in Africa. Science 344:61901346–48
    [Google Scholar]
15. 15.

    Stevenson A, Akena D, Stroud RE, Atwoli L, Campbell MM et al. 2019. Neuropsychiatric Genetics of African Populations-Psychosis (NeuroGAP-Psychosis): a case-control study protocol and GWAS in Ethiopia, Kenya, South Africa and Uganda. BMJ Open 9:2e025469
    [Google Scholar]
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. Genomics Hum. Genet. 20:181–200
    [Google Scholar]
17. 17.

    Logue MW, Amstadter AB, Baker DG, Duncan L, Koenen KC et al. 2015. The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic stress disorder enters the age of large-scale genomic collaboration. Neuropsychopharmacology 40:102287–97
    [Google Scholar]
18. 18.

    Precis. Med. Initiat. (PMI) Work. Group 2015. The Precision Medicine Initiative Cohort Program—building a research foundation for 21st century medicine Tech. Rep., PMI Work. Group., Sept 17: https://acd.od.nih.gov/documents/reports/DRAFT-PMI-WG-Report-9-11-2015-508.pdf
19. 19.

    Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J et al. 2022. The All of Us Research Program: data quality, utility, and diversity. Patterns 3:8100570
    [Google Scholar]
20. 20.

    Korunes KL, Goldberg A. 2021. Human genetic admixture. PLOS Genet 17:3e1009374
    [Google Scholar]
21. 21.

    Nelis M, Esko T, Mägi R, Zimprich F, Toncheva D et al. 2009. Genetic structure of Europeans: a view from the North-East. PLOS ONE 4:5e5472
    [Google Scholar]
22. 22.

    Balding DJ, Nichols RA. 1995. A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity. Genetica 96:1/23–12
    [Google Scholar]
23. 23.

    Pfaff CL, Parra EJ, Bonilla C, Hiester K, McKeigue PM et al. 2001. Population structure in admixed populations: effect of admixture dynamics on the pattern of linkage disequilibrium. Am. J. Hum. Genet. 68:1198–207
    [Google Scholar]
24. 24.

    Padhukasahasram B. 2014. Inferring ancestry from population genomic data and its applications. Front. Genet. 5:204
    [Google Scholar]
25. 25.

    Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:2945–59
    [Google Scholar]
26. 26.

    Alexander DH, Novembre J, Lange K. 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:91655–64
    [Google Scholar]
27. 27.

    Lawson DJ, van Dorp L, Falush D. 2018. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat. Commun. 9:3258
    [Google Scholar]
28. 28.

    Alexander DH, Lange K. 2011. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinform 12:246
    [Google Scholar]
29. 29.

    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. 38:8904–9
    [Google Scholar]
30. 30.

    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]
31. 31.

    Patterson N, Price AL, Reich D. 2006. Population structure and eigenanalysis. PLOS Genet 2:122074–93
    [Google Scholar]
32. 32.

    McVean G. 2009. A genealogical interpretation of principal components analysis. PLOS Genet 5:10e1000686
    [Google Scholar]
33. 33.

    Zheng X, Weir BS. 2016. Eigenanalysis of SNP data with an identity by descent interpretation. Theor. Popul. Biol. 107:65–76
    [Google Scholar]
34. 34.

    Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR et al. 2008. Genes mirror geography within Europe. Nature 456:721898–101
    [Google Scholar]
35. 35.

    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. 39:4276–93
    [Google Scholar]
36. 36.

    Smith MW, O'Brien SJ 2005. Mapping by admixture linkage disequilibrium: advances, limitations and guidelines. Nat. Rev. Genet. 6:8623–32
    [Google Scholar]
37. 37.

    Zhang QS, Browning BL, Browning SR. 2016. ASAFE: ancestry-specific allele frequency estimation. Bioinformatics 32:142227–29
    [Google Scholar]
38. 38.

    Wegmann D, Kessner DE, Veeramah KR, Mathias RA, Nicolae DL et al. 2011. Recombination rates in admixed individuals identified by ancestry-based inference. Nat. Genet. 43:9847–53
    [Google Scholar]
39. 39.

    Gravel S, Zakharia F, Moreno-Estrada A, Byrnes JK, Muzzio M et al. 2013. Reconstructing Native American migrations from whole-genome and whole-exome data. PLOS Genet 9:12e1004023
    [Google Scholar]
40. 40.

    Moreno-Estrada A, Gravel S, Zakharia F, McCauley JL, Byrnes JK et al. 2013. Reconstructing the population genetic history of the Caribbean. PLOS Genet 9:11e1003925
    [Google Scholar]
41. 41.

    Loh PR, Lipson M, Patterson N, Moorjani P, Pickrell JK et al. 2013. Inferring admixture histories of human populations using linkage disequilibrium. Genetics 193:41233–54
    [Google Scholar]
42. 42.

    Shriner D. 2013. Overview of admixture mapping. Curr. Protoc. Hum. Genet. 76:1.23.1–1.23.8
    [Google Scholar]
43. 43.

    Wu J, Liu Y, Zhao Y. 2021. Systematic review on local ancestor inference from a mathematical and algorithmic perspective. Front. Genet. 12:639877
    [Google Scholar]
44. 44.

    Geza E, Mugo J, Mulder NJ, Wonkam A, Chimusa ER, Mazandu GK. 2019. A comprehensive survey of models for dissecting local ancestry deconvolution in human genome. Brief. Bioinform. 20:51709–24
    [Google Scholar]
45. 45.

    Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA et al. 2004. Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet. 74:5979–1000
    [Google Scholar]
46. 46.

    Smith MW, Patterson N, Lautenberger JA, Truelove AL, Mcdonald GJ et al. 2004. A high-density admixture map for disease gene discovery in African Americans. Am. J. Hum. Genet. 74:51001–13
    [Google Scholar]
47. 47.

    Price AL, Tandon A, Patterson N, Barnes KC, Rafaels N et al. 2009. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLOS Genet 5:6e1000519
    [Google Scholar]
48. 48.

    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. 93:2278–88
    [Google Scholar]
49. 49.

    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]
50. 50.

    Hilmarsson H, Kumar AS, Rastogi R, Bustamante CD, Montserrat M, Ioannidis AG. 2021. High resolution ancestry deconvolution for next generation genomic data. bioRxiv 10.1101/2021.09.19.460980. https://doi.org/10.1101/2021.09.19.460980
51. 51.

    Uren C, Hoal EG, Möller M. 2020. Putting RFMix and ADMIXTURE to the test in a complex admixed population. BMC Genet 21:40
    [Google Scholar]
52. 52.

    Peterson RE, Kuchenbaecker K, Walters RK, Chen CY, Popejoy AB et al. 2019. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179:3589–603
    [Google Scholar]
53. 53.

    Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M. 2010. Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11:5356–66
    [Google Scholar]
54. 54.

    Seldin MF, Pasaniuc B, Price AL. 2011. New approaches to disease mapping in admixed populations. Nat. Rev. Genet. 12:8523–28
    [Google Scholar]
55. 55.

    Sariya S, Lee JH, Mayeux R, Vardarajan BN, Reyes-Dumeyer D et al. 2019. Rare variants imputation in admixed populations: comparison across reference panels and bioinformatics tools. Front. Genet. 10:239
    [Google Scholar]
56. 56.

    Schurz H, Müller SJ, van Helden PD, Tromp G, Hoal EG et al. 2019. Evaluating the accuracy of imputation methods in a five-way admixed population. Front. Genet. 10:34
    [Google Scholar]
57. 57.

    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]
58. 58.

    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]
59. 59.

    Kowalski MH, Qian H, Hou Z, Rosen JD, Tapia AL et al. 2019. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLOS Genet 15:12e1008500
    [Google Scholar]
60. 60.

    O'Connell J, Yun T, Moreno M, Li H, Litterman N et al. 2021. A population-specific reference panel for improved genotype imputation in African Americans. Commun. Biol. 4:1269
    [Google Scholar]
61. 61.

    Pasaniuc B, Zaitlen N, Lettre G, Chen GK, Tandon A et al. 2011. Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a breast cancer consortium. PLOS Genet 7:4e1001371
    [Google Scholar]
62. 62.

    Liu EY, Li M, Wang W, Li Y. 2013. MaCH-Admix: genotype imputation for admixed populations. Genet. Epidemiol. 37:125–37
    [Google Scholar]
63. 63.

    Rybicki BA, Iyengar SK, Harris T, Liptak R, Elston RC et al. 2002. Prospects of admixture linkage disequilibrium mapping in the African-American genome. Cytometry 47:163–65
    [Google Scholar]
64. 64.

    Zaidi A. 2021. Why does admixture create LD between unlinked loci?. Arslan Zaidi Personal Blog Oct. 9. https://www.arslanzaidi.com/post/why-does-admixture-create-ld-between-unlinked-loci/
    [Google Scholar]
65. 65.

    Reich D, Cargill M, Bolk S, Ireland J, Sabeti P et al. 2001. Linkage disequilibrium in the human genome. Nature 411:199–204
    [Google Scholar]
66. 66.

    Chakraborty R, Weisst KM. 1988. Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci (linkage disequilibrium/genetic epidemiology). PNAS 85:239119–23
    [Google Scholar]
67. 67.

    Zhang J, Stram DO. 2014. The role of local ancestry adjustment in association studies using admixed populations. Genet. Epidemiol. 38:6502–15
    [Google Scholar]
68. 68.

    Hoggart CJ, Shriver MD, Kittles RA, Clayton DG, McKeigue PM. 2004. Design and analysis of admixture mapping studies. Am. J. Hum. Genet. 74:5965–78
    [Google Scholar]
69. 69.

    Redden DT, Divers J, Vaughan LK, Tiwari HK, Beasley TM et al. 2006. Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLOS Genet 2:81254–64
    [Google Scholar]
70. 70.

    Carty CL, Johnson NA, Hutter CM, Reiner AP, Peters U et al. 2012. Genome-wide association study of body height in African Americans: The Women's Health Initiative SNP Health Association Resource (SHARe). Hum. Mol. Genet. 21:3711–20
    [Google Scholar]
71. 71.

    Coram MA, Duan Q, Hoffmann TJ, Thornton T, Knowles JW et al. 2013. Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am. J. Hum. Genet. 92:6904–16
    [Google Scholar]
72. 72.

    Reiner AP, Beleza S, Franceschini N, Auer PL, Robinson JG et al. 2012. Genome-wide association and population genetic analysis of C-reactive protein in African American and Hispanic American women. Am. J. Hum. Genet. 91:3502–12
    [Google Scholar]
73. 73.

    Grinde KE, Brown LA, Reiner AP, Thornton TA, Browning SR. 2019. Genome-wide significance thresholds for admixture mapping studies. Am. J. Hum. Genet. 104:3454–65
    [Google Scholar]
74. 74.

    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. 143:3957–69
    [Google Scholar]
75. 75.

    Shetty PB, Tang H, Feng T, Tayo B, Morrison AC et al. 2015. Variants for HDL-C, LDL-C, and triglycerides identified from admixture mapping and fine-mapping analysis in African American families. Circ. Cardiovasc. Genet. 8:1106–13
    [Google Scholar]
76. 76.

    Spear ML, Hu D, Pino-Yanes M, Huntsman S, Eng C et al. 2019. A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma. Pharmacogenom. J. 19:3249–59
    [Google Scholar]
77. 77.

    Qin H, Morris N, Kang SJ, Li M, Tayo B et al. 2010. Interrogating local population structure for fine mapping in genome-wide association studies. Bioinformatics 26:232961–68
    [Google Scholar]
78. 78.

    Wang X, Zhu X, Qin H, Cooper RS, Ewens WJ et al. 2011. Adjustment for local ancestry in genetic association analysis of admixed populations. Bioinformatics 27:5670–77
    [Google Scholar]
79. 79.

    Liu J, Lewinger JP, Gilliland FD, Gauderman WJ, Conti DV. 2013. Confounding and heterogeneity in genetic association studies with admixed populations. Am. J. Epidemiol. 177:4351–60
    [Google Scholar]
80. 80.

    Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK et al. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:7565–69
    [Google Scholar]
81. 81.

    Zaitlen N, Pasaniuc B, Sankararaman S, Bhatia G, Zhang J et al. 2014. Leveraging population admixture to characterize the heritability of complex traits. Nat. Genet. 46:121356–62
    [Google Scholar]
82. 82.

    Yao Y, Ochoa A. 2022. Limitations of principal components in quantitative genetic association models for human studies. bioRxiv 10.1101/2022.03.25.485885. https://doi.org/10.1101/2022.03.25.485885
83. 83.

    Zhang Y, Pan W. 2015. Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements?. Genet. Epidemiol. 39:3149–55
    [Google Scholar]
84. 84.

    Wang K, Hu X, Peng Y. 2013. An analytical comparison of the principal component method and the mixed effects model for association studies in the presence of cryptic relatedness and population stratification. Hum. Hered. 76:11–9
    [Google Scholar]
85. 85.

    Shin J, Lee C. 2015. A mixed model reduces spurious genetic associations produced by population stratification in genome-wide association studies. Genomics 105:4191–96
    [Google Scholar]
86. 86.

    Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME et al. 2018. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50:91335–41
    [Google Scholar]
87. 87.

    Chen H, Wang C, Conomos MP, Stilp AM, Li Z et al. 2016. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98:4653–66
    [Google Scholar]
88. 88.

    Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38:2203–8
    [Google Scholar]
89. 89.

    Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C et al. 2007. An Arabidopsis example of association mapping in structured samples. PLOS Genet 3:171–82
    [Google Scholar]
90. 90.

    Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. 2010. Robust relationship inference in genome-wide association studies. Bioinformatics 26:222867–73
    [Google Scholar]
91. 91.

    Thornton T, Tang H, Hoffmann TJ, Ochs-Balcom HM, Caan BJ, Risch N. 2012. Estimating kinship in admixed populations. Am. J. Hum. Genet. 91:1122–38
    [Google Scholar]
92. 92.

    Conomos MP, Reiner AP, Weir BS, Thornton TA. 2016. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98:1127–48
    [Google Scholar]
93. 93.

    Gogarten SM, Sofer T, Chen H, Yu C, Brody JA et al. 2019. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35:245346–48
    [Google Scholar]
94. 94.

    Lin M, Park DS, Zaitlen NA, Henn BM, Gignoux CR. 2021. Admixed populations improve power for variant discovery and portability in genome-wide association studies. Front. Genet. 12:673167
    [Google Scholar]
95. 95.

    Shriner D, Adeyemo A, Rotimi CN. 2011. Joint ancestry and association testing in admixed individuals. PLOS Comput. Biol. 7:12e1002325
    [Google Scholar]
96. 96.

    Skotte L, Jørsboe E, Korneliussen TS, Moltke I, Albrechtsen A. 2019. Ancestry-specific association mapping in admixed populations. Genet. Epidemiol. 43:5506–21
    [Google Scholar]
97. 97.

    Bitarello BD, Mathieson I. 2020. Polygenic scores for height in admixed populations. G3 10:114027–36
    [Google Scholar]
98. 98.

    Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J et al. 2019. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570:514–18
    [Google Scholar]
99. 99.

    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]
100. 100.

     Patel RA, Musharoff SA, Spence JP, Pimentel H, Tcheandjieu C et al. 2022. Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits. Am. J. Hum. Genet. 109:71286–97
     [Google Scholar]
101. 101.

     Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A et al. 2022. Causal effects on complex traits are similar across segments of different continental ancestries within admixed individuals. medRxiv 2022.08.16.22278868 . https://doi.org/10.1101/2022.08.16.22278868
102. 102.

     Park DS, Eskin I, Kang EY, Gamazon ER, Eng C et al. 2018. An ancestry-based approach for detecting interactions. Genet. Epidemiol. 42:149–63
     [Google Scholar]
103. 103.

     Aschard H, Gusev A, Brown R, Pasaniuc B. 2015. Leveraging local ancestry to detect gene-gene interactions in genome-wide data. BMC Genet 16:124
     [Google Scholar]
104. 104.

     Mathieson I, McVean G. 2012. Differential confounding of rare and common variants in spatially structured populations. Nat. Genet. 44:3243–46
     [Google Scholar]
105. 105.

     Mathieson I, McVean G. 2014. Demography and the age of rare variants. PLOS Genet 10:8e1004528
     [Google Scholar]
106. 106.

     O'Connor TD, Kiezun A, Bamshad M, Rich SS, Smith JD et al. 2013. Fine-scale patterns of population stratification confound rare variant association tests. PLOS ONE 8:7e65834
     [Google Scholar]
107. 107.

     Zaidi AA, Mathieson I 2020. Demographic history mediates the effect of stratification on polygenic scores. eLife 9:e61548
     [Google Scholar]
108. 108.

     Cannon ME, Duan Q, Wu Y, Zeynalzadeh M, Xu Z et al. 2017. Trans-ancestry fine mapping and molecular assays identify regulatory variants at the ANGPTL8 HDL-C GWAS locus. G3 7:93217–27
     [Google Scholar]
109. 109.

     Grinde KE, Qi Q, Thornton TA, Liu S, Shadyab AH et al. 2019. Generalizing polygenic risk scores from Europeans to Hispanics/Latinos. Genet. Epidemiol. 43:150–62
     [Google Scholar]
110. 110.

     Morris AP. 2011. Transethnic meta-analysis of genome-wide association studies. Genet. Epidemiol. 35:8809–22
     [Google Scholar]
111. 111.

     Schaid DJ, Chen W, Larson NB. 2018. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19:491–504
     [Google Scholar]
112. 112.

     Mägi R, Horikoshi M, Sofer T, Mahajan A, Kitajima H et al. 2017. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26:183639–50
     [Google Scholar]
113. 113.

     Turley P, Martin AR, Goldman G, Li H, Kanai M et al. 2021. Multi-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations. bioRxiv 10.1101/2021.04.23.441003. https://doi.org/10.1101/2021.04.23.441003
     [Crossref]
114. 114.

     Lewis CM, Vassos E. 2020. Polygenic risk scores: from research tools to clinical instruments. Genom. Med. 12:44
     [Google Scholar]
115. 115.

     Cavazos TB, Witte JS. 2021. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. Hum. Genet. Genom. Adv. 2:1100017
     [Google Scholar]
116. 116.

     Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW. 2019. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10:1776
     [Google Scholar]
117. 117.

     Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S et al. 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97:4576–92
     [Google Scholar]
118. 118.

     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]
119. 119.

     Ruan Y, Lin Y-F, Feng Y-CA, Chen C-Y, Lam M et al. 2022. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54:5573–80
     [Google Scholar]
120. 120.

     Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ et al. 2022. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat. Genet. 54:4450–58
     [Google Scholar]
121. 121.

     Marnetto D, Pärna K, Läll K, Molinaro L, Montinaro F et al. 2020. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nat. Commun. 11:1628
     [Google Scholar]
122. 122.

     Zaitlen N, Paşaniuc B, Gur T, Ziv E, Halperin E. 2010. Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet. 86:123–33
     [Google Scholar]
123. 123.

     Li YR, Keating BJ. 2014. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med 6:91
     [Google Scholar]
124. 124.

     Mahajan A, Go MJ, Zhang W, Below JE, Gaulton KJ 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]
125. 125.

     Spain SL, Barrett JC. 2015. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24:R1R111–19
     [Google Scholar]
126. 126.

     Kichaev G, Pasaniuc B. 2015. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet. 97:2260–71
     [Google Scholar]
127. 127.

     LaPierre N, Taraszka K, Huang H, He R, Hormozdiari F, Eskin E. 2021. Identifying causal variants by fine mapping across multiple studies. PLOS Genet 17:9e1009733
     [Google Scholar]
128. 128.

     Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N et al. 2012. Ancient admixture in human history. Genetics 192:31065–93
     [Google Scholar]
129. 129.

     Peter BM. 2016. Admixture, population structure, and F-statistics. Genetics 202:41485–501
     [Google Scholar]
130. 130.

     Gravel S. 2012. Population genetics models of local ancestry. Genetics 191:2607–19
     [Google Scholar]
131. 131.

     Pool JE, Nielsen R. 2009. Inference of historical changes in migration rate from the lengths of migrant tracts. Genetics 181:2711–19
     [Google Scholar]
132. 132.

     Pugach I, Matveyev R, Wollstein A, Kayser M, Stoneking M. 2011. Dating the age of admixture via wavelet transform analysis of genome-wide data. Genome Biol 12:2R19
     [Google Scholar]
133. 133.

     Hamid I, Korunes K, Beleza S, Goldberg A 2021. Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde. eLife 10:e63177
     [Google Scholar]
134. 134.

     Parker K, Horowitz JM, Morin R, Lopez MH. 2015. Multiracial in America: proud, diverse and growing in numbers Rep., Pew Res. Cent Washington, DC:
135. 135.

     Lam M, Chen CY, Li Z, Martin AR, Bryois J et al. 2019. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51:121670–78
     [Google Scholar]
136. 136.

     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]
137. 137.

     Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R et al. 2015. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47:9979–86
     [Google Scholar]
138. 138.

     Marigorta UM, Navarro A. 2013. High trans-ethnic replicability of GWAS results implies common causal variants. PLOS Genet 9:6e1003566
     [Google Scholar]
139. 139.

     Kuchenbaecker K, Telkar N, Reiker T, Walters RG, Lin K et al. 2019. The transferability of lipid loci across African, Asian and European cohorts. Nat. Commun. 10:4330
     [Google Scholar]
140. 140.

     Waters KM, Stram DO, Hassanein MT, Le Marchand L, Wilkens LR et al. 2010. Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups. PLOS Genet 6:8e1001078
     [Google Scholar]