Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genomics of disease risk in globally diverse populations

An Author Correction to this article was published on 03 July 2019

This article has been updated

Abstract

Risk of disease is multifactorial and can be shaped by socio-economic, demographic, cultural, environmental and genetic factors. Our understanding of the genetic determinants of disease risk has greatly advanced with the advent of genome-wide association studies (GWAS), which detect associations between genetic variants and complex traits or diseases by comparing populations of cases and controls. However, much of this discovery has occurred through GWAS of individuals of European ancestry, with limited representation of other populations, including from Africa, The Americas, Asia and Oceania. Population demography, genetic drift and adaptation to environments over thousands of years have led globally to the diversification of populations. This global genomic diversity can provide new opportunities for discovery and translation into therapies, as well as a better understanding of population disease risk. Large-scale multi-ethnic and representative biobanks and population health resources provide unprecedented opportunities to understand the genetic determinants of disease on a global scale.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Representation of different ethnic groups in genome-wide association studies.
Fig. 2: Genetic drift and changes in allele frequency as a function of population size.
Fig. 3: Mechanisms for observed heterogeneity of effect size between populations.

Change history

  • 03 July 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2018). This article presents an insightful review focusing on the need for increased diversity in human genetics research, and efforts by the NHGRI to increase diversity in participants as well as researchers.

    CAS  PubMed  Google Scholar 

  2. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015). A landmark study from the 1000 Genomes Project Consortium outlining the first whole-genome sequencing study of multiple diverse ethnic groups providing novel insights into differences in genomic variation across different populations.

    Google Scholar 

  3. Gurdasani, D. et al. The African Genome Variation Project shapes medical genetics in Africa. Nature 517, 327–332 (2014). This study is one of the first comprehensive evaluations of genetic diversity among different ethno-linguistic groups within Africa based on genotyping data.

    PubMed  PubMed Central  Google Scholar 

  4. Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016). A study of highly genetically diverse populations across the globe using deep whole-genome sequencing approaches.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

    CAS  PubMed  Google Scholar 

  6. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019). This article provides a summary of data within the GWAS catalogue (a collection of all GWAS study data deposited to date), including the ethnic distribution of existing studies.

    PubMed  Google Scholar 

  7. Morales, J. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 19, 21 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017). This article presents an excellent overview of the history of GWAS and their role in discovery of genetic determinants of disease.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Dries, D. L. Genetic ancestry, population admixture, and the genetic epidemiology of complex disease. Circ. Cardiovasc. Genet. 2, 540–543 (2009).

    PubMed  Google Scholar 

  14. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Ruhle, F. et al. Rare genetic variants in SMAP1, B3GAT2, and RIMS1 contribute to pediatric venous thromboembolism. Blood 129, 783–790 (2017).

    PubMed  Google Scholar 

  16. Ng, S. B. et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. 42, 790–793 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Auer, P. L. & Lettre, G. Rare variant association studies: considerations, challenges and opportunities. Genome Med. 7, 16 (2015).

    PubMed  PubMed Central  Google Scholar 

  18. Marigorta, U. M. & Navarro, A. High trans-ethnic replicability of GWAS results implies common causal variants. PLOS Genet. 9, e1003566 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Waters, K. M. et al. Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups. PLOS Genet. 6, e1001078 (2010).

    PubMed  PubMed Central  Google Scholar 

  20. Haiman, C. A. et al. Consistent directions of effect for established type 2 diabetes risk variants across populations: the Population Architecture using Genomics and Epidemiology (PAGE) Consortium. Diabetes 61, 1642–1647 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

    Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J. & Lachance, J. Genetic disease risks can be misestimated across global populations. Genome Biol. 19, 179 (2018). This article presents an important study examining the transferability of polygenic risk scores across different ethnic groups.

    PubMed  PubMed Central  Google Scholar 

  24. Ioannidis, J. P., Ntzani, E. E. & Trikalinos, T. A. ‘Racial’ differences in genetic effects for complex diseases. Nat. Genet. 36, 1312–1318 (2004).

    CAS  PubMed  Google Scholar 

  25. Ntzani, E. E., Liberopoulos, G., Manolio, T. A. & Ioannidis, J. P. Consistency of genome-wide associations across major ancestral groups. Hum. Genet. 131, 1057–1071 (2012).

    CAS  PubMed  Google Scholar 

  26. Kwiatkowski, D. P. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77, 171–192 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Lipson, M. et al. Ancient genomes document multiple waves of migration in Southeast Asian prehistory. Science 361, 92–95 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Pickrell, J. K. & Reich, D. Toward a new history and geography of human genes informed by ancient DNA. Trends Genet. 30, 377–389 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Posth, C. et al. Reconstructing the deep population history of Central and South America. Cell 175, 1185–1197 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Prufer, K. et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature 505, 43–49 (2014).

    PubMed  Google Scholar 

  32. Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Hammer, M. F., Woerner, A. E., Mendez, F. L., Watkins, J. C. & Wall, J. D. Genetic evidence for archaic admixture in Africa. Proc. Natl Acad. Sci. USA 108, 15123–15128 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Sankararaman, S., Mallick, S., Patterson, N. & Reich, D. The combined landscape of Denisovan and Neanderthal ancestry in present-day humans. Curr. Biol. 26, 1241–1247 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017). This article presents an important study examining the transferability of polygenic risk scores across different ethnic groups.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rosenberg, N. A. et al. Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11, 356–366 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Yasuda, K. et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat. Genet. 40, 1092–1097 (2008).

    CAS  PubMed  Google Scholar 

  40. Unoki, H. et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nat. Genet. 40, 1098–1102 (2008).

    CAS  PubMed  Google Scholar 

  41. Pulit, S. L., Voight, B. F. & de Bakker, P. I. Multiethnic genetic association studies improve power for locus discovery. PLOS ONE 5, e12600 (2010). This article presents an important study of how inclusion of multi-ethnic populations influences power for discovery in GWAS, in comparison with inclusion of homogeneous populations.

    PubMed  PubMed Central  Google Scholar 

  42. Mathieson, I. & McVean, G. Differential confounding of rare and common variants in spatially structured populations. Nat. Genet. 44, 243–246 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Novembre, J., Galvani, A. P. & Slatkin, M. The geographic spread of the CCR5 Delta32 HIV-resistance allele. PLOS Biol. 3, e339 (2005).

    PubMed  PubMed Central  Google Scholar 

  44. Franceschini, N., Reiner, A. P. & Heiss, G. Recent findings in the genetics of blood pressure and hypertension traits. Am. J. Hypertens. 24, 392–400 (2011).

    PubMed  Google Scholar 

  45. Yasukochi, Y. et al. Longitudinal exome-wide association study to identify genetic susceptibility loci for hypertension in a Japanese population. Exp. Mol. Med. 49, e409 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Kent, S. T. et al. PCSK9 loss-of-function variants, low-density lipoprotein cholesterol, and risk of coronary heart disease and stroke: data from 9 studies of blacks and whites. Circ. Cardiovasc. Genet. 10, e001632 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    CAS  PubMed  Google Scholar 

  48. Dhandapany, P. S. et al. A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nat. Genet. 41, 187–191 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Mefford, H. C. et al. Rare copy number variants are an important cause of epileptic encephalopathies. Ann. Neurol. 70, 974–985 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Miller, D. T. et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Li, J. et al. Whole genome distribution and ethnic differentiation of copy number variation in Caucasian and Asian populations. PLOS ONE 4, e7958 (2009).

    PubMed  PubMed Central  Google Scholar 

  52. Cook, J. P. & Morris, A. P. Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur. J. Hum. Genet. 24, 1175–1180 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Ioannidis, J. P., Patsopoulos, N. A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLOS ONE 2, e841 (2007).

    PubMed  PubMed Central  Google Scholar 

  54. Jing, L., Su, L. & Ring, B. Z. Ethnic background and genetic variation in the evaluation of cancer risk: a systematic review. PLOS ONE 9, e97522 (2014).

    PubMed  PubMed Central  Google Scholar 

  55. Magi, R. et al. 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, 3639–3650 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Holmes, M. V. et al. Effect modification by population dietary folate on the association between MTHFR genotype, homocysteine, and stroke risk: a meta-analysis of genetic studies and randomised trials. Lancet 378, 584–594 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Helgason, A. et al. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat. Genet. 39, 218–225 (2007).

    CAS  PubMed  Google Scholar 

  58. Moonesinghe, R., Khoury, M. J., Liu, T. & Ioannidis, J. P. Required sample size and nonreplicability thresholds for heterogeneous genetic associations. Proc. Natl Acad. Sci. USA 105, 617–622 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kulminski, A. M. et al. Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency. Sci. Rep. 6, 35390 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee, C. H., Eskin, E. & Han, B. Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects. Bioinformatics 33, i379–i388 (2017). This study outlines an important meta-analytic method to maximize power to detect associations in multi-ethnic GWAS with heterogeneous effects.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    PubMed Central  Google Scholar 

  62. Roadmap Epigenomics Consortium. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    PubMed Central  Google Scholar 

  63. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central  Google Scholar 

  64. Martin, A. R. et al. Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture. PLOS Genet. 10, e1004549 (2014).

    PubMed  PubMed Central  Google Scholar 

  65. Mele, M. et al. Human genomics. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Tian, L. et al. Genome-wide comparison of allele-specific gene expression between African and European populations. Hum. Mol. Genet. 27, 1067–1077 (2018).

    CAS  PubMed  Google Scholar 

  67. Kelly, D. E., Hansen, M. E. B. & Tishkoff, S. A. Global variation in gene expression and the value of diverse sampling. Curr. Opin. Syst. Biol. 1, 102–108 (2017).

    PubMed  PubMed Central  Google Scholar 

  68. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013). Thie study is one of the first investigations of the transcriptome across diverse populations from the 1000 Genomes Project, examining the key differences in gene expression and transcriptome structure among populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Mogil, L. S. et al. Genetic architecture of gene expression traits across diverse populations. PLOS Genet. 14, e1007586 (2018).

    PubMed  PubMed Central  Google Scholar 

  70. Giuliani, C. et al. Epigenetic variability across human populations: a focus on DNA methylation profiles of the KRTCAP3, MAD1L1 and BRSK2 genes. Genome Biol. Evol. 8, 2760–2773 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Moen, E. L. et al. Genome-wide variation of cytosine modifications between European and African populations and the implications for complex traits. Genetics 194, 987–996 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Husquin, L. T. et al. Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation. Genome Biol. 19, 222 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Hatzikotoulas, K., Gilly, A. & Zeggini, E. Using population isolates in genetic association studies. Brief Funct. Genomics 13, 371–377 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Kristiansson, K., Naukkarinen, J. & Peltonen, L. Isolated populations and complex disease gene identification. Genome Biol. 9, 109 (2008). This review provides an overview of how studying isolated populations has enhanced discovery through GWAS.

    PubMed  PubMed Central  Google Scholar 

  76. Dabelea, D. et al. Increasing prevalence of type II diabetes in American Indian children. Diabetologia 41, 904–910 (1998).

    CAS  PubMed  Google Scholar 

  77. Knowler, W. C., Bennett, P. H., Hamman, R. F. & Miller, M. Diabetes incidence and prevalence in Pima Indians: a 19-fold greater incidence than in Rochester, Minnesota. Am. J. Epidemiol. 108, 497–505 (1978).

    CAS  PubMed  Google Scholar 

  78. Schulz, L. O. et al. Effects of traditional and western environments on prevalence of type 2 diabetes in Pima Indians in Mexico and the US. Diabetes Care 29, 1866–1871 (2006).

    PubMed  Google Scholar 

  79. Pollin, T. I. et al. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322, 1702–1705 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Tachmazidou, I. et al. A rare functional cardioprotective APOC3 variant has risen in frequency in distinct population isolates. Nat. Commun. 4, 2872 (2013).

    PubMed  Google Scholar 

  81. Gilly, A. et al. Very low-depth sequencing in a founder population identifies a cardioprotective APOC3 signal missed by genome-wide imputation. Hum. Mol. Genet. 25, 2360–2365 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Moltke, I. et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512, 190–193 (2014).

    CAS  PubMed  Google Scholar 

  83. Laitinen, T. et al. Characterization of a common susceptibility locus for asthma-related traits. Science 304, 300–304 (2004).

    CAS  PubMed  Google Scholar 

  84. Mockenhaupt, F. P. et al. Alpha(+)-thalassemia protects African children from severe malaria. Blood 104, 2003–2006 (2004).

    CAS  PubMed  Google Scholar 

  85. Elguero, E. et al. Malaria continues to select for sickle cell trait in Central Africa. Proc. Natl Acad. Sci. USA 112, 7051–7054 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Luzzatto, L. G6PD deficiency: a polymorphism balanced by heterozygote advantage against malaria. Lancet Haematol. 2, e400–e401 (2015).

    PubMed  Google Scholar 

  87. Chen, Z. et al. Genome-wide association analysis of red blood cell traits in African Americans: the COGENT Network. Hum. Mol. Genet. 22, 2529–2538 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Hodonsky, C. J. et al. Genome-wide association study of red blood cell traits in Hispanics/Latinos: the Hispanic Community Health Study/Study of Latinos. PLOS Genet. 13, e1006760 (2017).

    PubMed  PubMed Central  Google Scholar 

  89. Malaria Genomic Epidemiology Network. Reappraisal of known malaria resistance loci in a large multicenter study. Nat. Genet. 46, 1197–1204 (2014).

    PubMed Central  Google Scholar 

  90. Soranzo, N. et al. A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat. Genet. 41, 1182–1190 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Hedrick, P. W. Resistance to malaria in humans: the impact of strong, recent selection. Malar. J. 11, 349 (2012).

    PubMed  PubMed Central  Google Scholar 

  92. Ralph, P. & Coop, G. Parallel adaptation: one or many waves of advance of an advantageous allele? Genetics 186, 647–668 (2010).

    PubMed  PubMed Central  Google Scholar 

  93. Tennessen, J. A. & Akey, J. M. Parallel adaptive divergence among geographically diverse human populations. PLOS Genet. 7, e1002127 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Meylan, E., Tschopp, J. & Karin, M. Intracellular pattern recognition receptors in the host response. Nature 442, 39–44 (2006).

    CAS  PubMed  Google Scholar 

  95. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Li, Y. et al. Carriers of rare missense variants in IFIH1 are protected from psoriasis. J. Invest. Dermatol. 130, 2768–2772 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Manolio, T. A. et al. Ethnic differences in the relationship of carotid atherosclerosis to coronary calcification: the Multi-Ethnic Study of Atherosclerosis. Atherosclerosis 197, 132–138 (2008).

    CAS  PubMed  Google Scholar 

  98. Zhu, X. et al. Admixture mapping for hypertension loci with genome-scan markers. Nat. Genet. 37, 177–181 (2005).

    CAS  PubMed  Google Scholar 

  99. Zhu, X. & Cooper, R. S. Admixture mapping provides evidence of association of the VNN1 gene with hypertension. PLOS ONE 2, e1244 (2007).

    PubMed  PubMed Central  Google Scholar 

  100. Darvasi, A. & Shifman, S. The beauty of admixture. Nat. Genet. 37, 118–119 (2005).

    CAS  PubMed  Google Scholar 

  101. Cyr, D. D. et al. Evaluating genetic susceptibility to Staphylococcus aureus bacteremia in African Americans using admixture mapping. Genes Immun. 18, 95–99 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Freedman, M. L. et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc. Natl Acad. Sci. USA 103, 14068–14073 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Reich, D. et al. A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nat. Genet. 37, 1113–1118 (2005).

    CAS  PubMed  Google Scholar 

  104. Scherer, M. L. et al. Admixture mapping of ankle–arm index: identification of a candidate locus associated with peripheral arterial disease. J. Med. Genet. 47, 1–7 (2010).

    CAS  PubMed  Google Scholar 

  105. Elbein, S. C., Das, S. K., Hallman, D. M., Hanis, C. L. & Hasstedt, S. J. Genome-wide linkage and admixture mapping of type 2 diabetes in African American families from the American Diabetes Association GENNID (Genetics of NIDDM) Study Cohort. Diabetes 58, 268–274 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Jeong, C. et al. Admixture facilitates genetic adaptations to high altitude in Tibet. Nat. Commun. 5, 3281 (2014).

    PubMed  Google Scholar 

  107. Bittles, A. H. & Black, M. L. Evolution in health and medicine Sackler colloquium: consanguinity, human evolution, and complex diseases. Proc. Natl Acad. Sci. USA 107 (Suppl. 1), 1779–1786 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Weatherall, D. J. The inherited diseases of hemoglobin are an emerging global health burden. Blood 115, 4331–4336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Lyons, E. J., Frodsham, A. J., Zhang, L., Hill, A. V. & Amos, W. Consanguinity and susceptibility to infectious diseases in humans. Biol. Lett. 5, 574–576 (2009).

    PubMed  PubMed Central  Google Scholar 

  110. Rudan, I. et al. Inbreeding and the genetic complexity of human hypertension. Genetics 163, 1011–1021 (2003).

    PubMed  PubMed Central  Google Scholar 

  111. Heckerman, D. et al. Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proc. Natl Acad. Sci. USA 113, 7377–7382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Saudi Mendeliome Group. Comprehensive gene panels provide advantages over clinical exome sequencing for Mendelian diseases. Genome Biol. 16, 134 (2015).

    PubMed Central  Google Scholar 

  113. Asimit, J. L., Hatzikotoulas, K., McCarthy, M., Morris, A. P. & Zeggini, E. Trans-ethnic study design approaches for fine-mapping. Eur. J. Hum. Genet. 24, 1330–1336 (2016). This study assesses the impact of inclusion of populations of different ancestries on resolution of causal loci and shows that fine-mapping is greatly improved by inclusion of individuals of African ancestry.

    PubMed  PubMed Central  Google Scholar 

  114. Evangelou, E. & Ioannidis, J. P. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14, 379–389 (2013).

    CAS  PubMed  Google Scholar 

  115. Hong, J., Lunetta, K. L., Cupples, L. A., Dupuis, J. & Liu, C. T. Evaluation of a two-stage approach in trans- ethnic meta-analysis in genome-wide association studies. Genet. Epidemiol. 40, 284–292 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  117. International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

    Google Scholar 

  118. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    PubMed Central  Google Scholar 

  119. Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Marquez-Luna, C., Loh, P. R., South Asian Type 2 Diabetes Consortium, The SIGMA Type 2 Diabetes Consortium & Price, A. L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).

    PubMed  PubMed Central  Google Scholar 

  121. Popejoy, A. B. et al. The clinical imperative for inclusivity: race, ethnicity, and ancestry (REA) in genomics. Hum. Mutat. 39, 1713–1720 (2018).

    PubMed  PubMed Central  Google Scholar 

  122. Schrijver, I. et al. The spectrum of CFTR variants in nonwhite cystic fibrosis patients: implications for molecular diagnostic testing. J. Mol. Diagn. 18, 39–50 (2016).

    CAS  PubMed  Google Scholar 

  123. Rohlfs, E. M. et al. Cystic fibrosis carrier testing in an ethnically diverse US population. Clin. Chem. 57, 841–848 (2011).

    CAS  PubMed  Google Scholar 

  124. Wheeler, E. et al. Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLOS Med. 14, e1002383 (2017).

    PubMed  PubMed Central  Google Scholar 

  125. Johnson, J. A. Ethnic differences in cardiovascular drug response: potential contribution of pharmacogenetics. Circulation 118, 1383–1393 (2008).

    PubMed  PubMed Central  Google Scholar 

  126. Caraco, Y., Blotnick, S. & Muszkat, M. CYP2C9 genotype-guided warfarin prescribing enhances the efficacy and safety of anticoagulation: a prospective randomized controlled study. Clin. Pharmacol. Ther. 83, 460–470 (2008).

    CAS  PubMed  Google Scholar 

  127. H3Africa Consortium. Research capacity. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014). This work is an important article outlining the H3Africa initiative joint funded through the National Institutes of Health–Wellcome to facilitate genomics research in Africa, with a focus on capacity-building.

    Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  129. Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Smith, M. W. & O’Brien, S. J. Mapping by admixture linkage disequilibrium: advances, limitations and guidelines. Nat. Rev. Genet. 6, 623–632 (2005).

    CAS  PubMed  Google Scholar 

  131. Kim, Y. & Nielsen, R. Linkage disequilibrium as a signature of selective sweeps. Genetics 167, 1513–1524 (2004).

    PubMed  PubMed Central  Google Scholar 

  132. Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLOS Biol. 4, e72 (2006).

    PubMed  PubMed Central  Google Scholar 

  133. Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

D.G. is funded by a UKRI HDR-UK Innovation Fellowship (reference number MR/S003711/1). I.B. acknowledges funding from Wellcome (WT206194). M.S. acknowledges funding from the Wellcome Sanger Institute (WT098051), the UK Medical Research Council (G0901213-92157, G0801566 and MR/K013491/1) and the National Institute for Health Research Cambridge Biomedical Research Centre.

Reviewer information

Nature Reviews Genetics thanks H. Hakonarson, T. Manolio and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Contributions

D.G. and M.S.S. researched the literature and wrote the manuscript. All authors substantially contributed to discussions of the content, and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Manjinder S. Sandhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

China Kadoorie Biobank: http://www.ckbiobank.org/

East London Genes and Health Study: http://www.genesandhealth.org/

Finnish Biobanks: https://www.biopankki.fi/en/finnish-biobanks

gnomAD: https://gnomad.broadinstitute.org/

H3Africa: https://h3africa.org/

Million Veteran Program: https://www.research.va.gov/mvp

NIH All of Us: https://allofus.nih.gov/

Qatar Genomes Project: https://qatargenome.org.qa/

TOPMed Programme: https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program

Web PopGen simulator: https://www.radford.edu/~rsheehy/Gen_flash/popgen

Glossary

Genome-wide association studies

Hypothesis-free studies of association between genetic variants and quantitative traits or diseases; typically, associations are examined across the whole genome using genotype array or sequencing approaches.

Imputation

Statistical inference of unobserved genotypes in individuals based on a collection of observed haplotypes among another set of individuals (usually referred to as the reference panel).

Minor allele frequency

The frequency of the less common allele at a site of genetic variation across a sample of individuals or a population.

Genetic variance

The contribution of genetic variation among individuals to phenotypic variation.

Linkage disequilibrium

The non-random association of alleles at loci along the genome in a given population.

Heterogeneity of effect

Statistically significant differences in effect size observed for associations between genetic variants and traits or disease among different studies or populations.

Allelic heterogeneity

The phenomenon whereby multiple causal variants within a given gene can be associated with the same trait or disease.

Genetic drift

A process by which frequencies of alleles in a given population change over time due to random sampling of individuals who may reproduce at every generation.

Selection

A process in which environmental or genetic influences determine which types of organism thrive better than others. Regarded as a factor in evolution.

Population bottleneck

An event that drastically reduces the size of a population. Such events can greatly reduce the genetic diversity of a population and make the population more susceptible to the influence of genetic drift.

Non-reference alleles

An allele that is different from the allele in the human reference genome at a given position. The human reference genome is a curated human genome assembly that is based on existing knowledge about the human genome at a given time.

Adaptive selection

Evolutionary changes to the genome that occur due to selection and are adaptive to the given environment.

Fixation

The change in the genetic pool of a population from the presence of two alleles at a given locus to only one allele being present; this allele is said to be fixed.

Admixture

Interbreeding or mixing of two or more populations that were previously isolated.

Consanguinity

The state of being closely related to someone by sharing a recent ancestor; in genetics, commonly used to refer to mating with close relatives.

Endogamy

The practice of marrying only within the limits of a local community, clan or tribe.

Autozygosity

Stretches of the two homologous chromosomes within the same individual that are identical by descent; occurs when there is non-random mating.

Inbreeding coefficient

The probability that two alleles at a locus in an individual are identical by descent from a common ancestor, that is, the chance that an individual is homozygous for an ancestral allele by inheritance (not by mutation).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gurdasani, D., Barroso, I., Zeggini, E. et al. Genomics of disease risk in globally diverse populations. Nat Rev Genet 20, 520–535 (2019). https://doi.org/10.1038/s41576-019-0144-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-019-0144-0

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing