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Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine

  • Genetics (AP Morris, Section Editor)
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Abstract

Purpose of Review

Recent advances in genomics provide opportunities for novel understanding of the biology of human traits with the goal of improving human health. Here, we review recent obesity and type 2 diabetes (T2D)–related genomic studies in African populations and discuss the implications of limited genomics studies on health disparity and precision medicine.

Recent Findings

Genome-wide association studies in Africans have yielded genetic discovery that would otherwise not be possible; these include identification of novel loci associated with obesity (SEMA-4D, PRKCA, WARS2), metabolic syndrome (CA-10, CTNNA3), and T2D (AGMO, ZRANB3). ZRANB3 was recently demonstrated to influence beta cell mass and insulin response. Despite these promising results, genomic studies in African populations are still limited and thus genomics tools and approaches such as polygenic risk scores and precision medicine are likely to have limited utility in Africans with the unacceptable possibility of exacerbating prevailing health disparities.

Summary

African populations provide unique opportunities for increasing our understanding of the genetic basis of cardiometabolic disorders. We highlight the need for more coordinated and sustained efforts to increase the representation of Africans in genomic studies both as participants and scientists.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Mbanya JC, Motala AA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet. 2010;375(9733):2254–66.

    PubMed  Google Scholar 

  2. Federation ID. IDF Diabetes Atlas. 2017 (8th edition, Brussels, Belgium).

  3. Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies. Int J Epidemiol. 2017;46(5):1421–32.

  4. Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. 2018;6(3):223–36.

    CAS  PubMed  Google Scholar 

  5. Owen JB. Genetic aspects of body composition. Nutrition. 1999;15(7–8):609–13.

    CAS  PubMed  Google Scholar 

  6. Tekola-Ayele F, Adeyemo AA, Rotimi CN. Genetic epidemiology of type 2 diabetes and cardiovascular diseases in Africa. Prog Cardiovasc Dis. 2013;56(3):251–60.

    PubMed  Google Scholar 

  7. Chen G, Adeyemo A, Zhou J, Chen Y, Huang H, Doumatey A, et al. Genome-wide search for susceptibility genes to type 2 diabetes in West Africans: potential role of C-peptide. Diabetes Res Clin Pract. 2007;78(3):e1–6.

    PubMed  Google Scholar 

  8. Chen G, Adeyemo AA, Johnson T, Zhou J, Amoah A, Owusu S, et al. A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans. Int J Obes. 2005;29(3):255–9.

    CAS  Google Scholar 

  9. Rotimi CN, Chen G, Adeyemo AA, Furbert-Harris P, Parish-Gause D, Zhou J, et al. A genome-wide search for type 2 diabetes susceptibility genes in West Africans: the Africa America Diabetes Mellitus (AADM) study. Diabetes. 2004;53(3):838–41.

    CAS  PubMed  Google Scholar 

  10. Rotimi CN, Dunston GM, Berg K, Akinsete O, Amoah A, Owusu S, et al. In search of susceptibility genes for type 2 diabetes in West Africa: the design and results of the first phase of the AADM study. Ann Epidemiol. 2001;11(1):51–8.

    CAS  PubMed  Google Scholar 

  11. Rotimi C, Abayomi A, Abimiku A, Adabayeri VM, Adebamowo C, Adebiyi E, et al. Research capacity. Enabling the genomic revolution in Africa. Science. 2014;344(6190):1346–8.

    PubMed  Google Scholar 

  12. Rotimi CN, Bentley AR, Doumatey AP, Chen G, Shriner D, Adeyemo A. The genomic landscape of African populations in health and disease. Hum Mol Genet. 2017;26(R2):R225–r36.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Helgason A, Palsson S, Thorleifsson G, Grant SF, Emilsson V, Gunnarsdottir S, et al. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat Genet. 2007;39(2):218–25.

    CAS  PubMed  Google Scholar 

  14. Adeyemo AA, Tekola-Ayele F, Doumatey AP, Bentley AR, Chen G, Huang H, et al. Evaluation of genome wide association study associated type 2 diabetes susceptibility loci in sub Saharan Africans. Front Genet. 2015;6:335.

    PubMed  PubMed Central  Google Scholar 

  15. Danquah I, Othmer T, Frank LK, Bedu-Addo G, Schulze MB, Mockenhaupt FP. The TCF7L2 rs7903146 (T) allele is associated with type 2 diabetes in urban Ghana: a hospital-based case-control study. BMC Med Genet. 2013;14:96.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Guewo-Fokeng M, Sobngwi E, Atogho-Tiedeu B, Donfack OS, Noubiap JJ, Ngwa EN, et al. Contribution of the TCF7L2 rs7903146 (C/T) gene polymorphism to the susceptibility to type 2 diabetes mellitus in Cameroon. J Diabetes Metab Disord. 2015;14:26.

    PubMed  PubMed Central  Google Scholar 

  17. Ng MC. Genetics of type 2 diabetes in African Americans. Curr Diab Rep. 2015;15(10):74.

    PubMed  PubMed Central  Google Scholar 

  18. •• Chen J, Sun M, Adeyemo A, Pirie F, Carstensen T, Pomilla C, et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia. 2019. This paper is a relatively large discovery GWAS of obesity, T2D, and related-traits conducted specifically in African populations within the past couple of years. It identified novel African-specific variants that are associated with these traits and not only expanded our understanding of the pathophysiology of metabolic disorders but also generated new hypotheses.

  19. Chen R, Corona E, Sikora M, Dudley JT, Morgan AA, Moreno-Estrada A, et al. Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases. PLoS Genet. 2012;8(4):e1002621.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Gibbons A. 12th International Congress of Human Genetics. Diabetes genes decline out of Africa. Science. 2011;334(6056):583.

    PubMed  Google Scholar 

  21. Corona E, Chen R, Sikora M, Morgan AA, Patel CJ, Ramesh A, et al. Analysis of the genetic basis of disease in the context of worldwide human relationships and migration. PLoS Genet. 2013;9(5):e1003447.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Adeyemo A, Chen G, Zhou J, Shriner D, Doumatey A, Huang H, et al. FTO genetic variation and association with obesity in West Africans and African Americans. Diabetes. 2010;59(6):1549–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ramsay M, Crowther N, Tambo E, Agongo G, Baloyi V, Dikotope S, et al. H3Africa AWI-Gen Collaborative Centre: a resource to study the interplay between genomic and environmental risk factors for cardiometabolic diseases in four sub-Saharan African countries. Glob Health Epidemiol Genom. 2016;1:e20.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Cooke Bailey JN, Igo RP Jr. Genetic risk scores. Curr Protoc Hum Genet. 2016;91:1.29.1–1..9.

    Google Scholar 

  25. Folsom AR, Tang W, Weng LC, Roetker NS, Cushman M, Basu S, et al. Replication of a genetic risk score for venous thromboembolism in whites but not in African Americans. J Thromb Haemost. 2016;14(1):83–8.

    CAS  PubMed  Google Scholar 

  26. Smith CJ, Saftlas AF, Spracklen CN, Triche EW, Bjonnes A, Keating B, et al. Genetic risk score for essential hypertension and risk of preeclampsia. Am J Hypertens. 2016;29(1):17–24.

    PubMed  Google Scholar 

  27. Charmet R, van Hylckama VA, Germain M, Roussel R, Marre M, Debette S, et al. Association of impaired renal function with venous thrombosis: a genetic risk score approach. Thromb Res. 2017;158:102–7.

    CAS  PubMed  Google Scholar 

  28. Iwasaki M, Tanaka-Mizuno S, Kuchiba A, Yamaji T, Sawada N, Goto A, et al. Inclusion of a genetic risk score into a validated risk prediction model for colorectal cancer in Japanese men improves performance. Cancer Prev Res (Phila). 2017;10(9):535–41.

    Google Scholar 

  29. Pereira A, Mendonca MI, Sousa AC, Borges S, Freitas S, Henriques E, et al. Genetic risk score and cardiovascular mortality in a southern European population with coronary artery disease. Int J Clin Pract. 2017;71(6).

    Google Scholar 

  30. Pisanu C, Preisig M, Castelao E, Glaus J, Pistis G, Squassina A, et al. A genetic risk score is differentially associated with migraine with and without aura. Hum Genet. 2017;136(8):999–1008.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Redondo MJ, Oram RA, Steck AK. Genetic risk scores for type 1 diabetes prediction and diagnosis. Curr Diab Rep. 2017;17(12):129.

    PubMed  Google Scholar 

  32. Dudbridge F, Pashayan N, Yang J. Predictive accuracy of combined genetic and environmental risk scores. Genet Epidemiol. 2018;42(1):4–19.

    PubMed  Google Scholar 

  33. Pereira A, Mendonca MI, Borges S, Freitas S, Henriques E, Rodrigues M, et al. Genetic risk analysis of coronary artery disease in a population-based study in Portugal, using a genetic risk score of 31 variants. Arq Bras Cardiol. 2018;111(1):50–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. • Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91 This paper gives a significant perspective on the genetic risk score estimation across human populations, their bias toward European ancestry populations, and the potential to widen health disparity and impede the implementation of precision medicine if they are implemented in clinical settings serving diverse populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Yako YY, Echouffo-Tcheugui JB, Balti EV, Matsha TE, Sobngwi E, Erasmus RT, et al. Genetic association studies of obesity in Africa: a systematic review. Obes Rev. 2015;16(3):259–72.

    CAS  PubMed  Google Scholar 

  36. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52.

    CAS  PubMed  Google Scholar 

  37. Yang J, Bakshi A, Zhu Z, Hemani G, Vinkhuyzen AAE, Lee SH, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. •• Chen G, Doumatey AP, Zhou J, Lei L, Bentley AR, Tekola-Ayele F, et al. Genome-wide analysis identifies an African-specific variant in SEMA4D associated with body mass index. Obesity (Silver Spring). 2017;25(4):794–800 This paper is a relatively large discovery GWAS of obesity, T2D, and related-traits conducted specifically in African populations within the past couple of years. It identified novel African-specific variants that are associated with these traits and not only expanded our understanding of the pathophysiology of metabolic disorders but also generated new hypotheses.

    CAS  Google Scholar 

  39. •• Tekola-Ayele F, Doumatey AP, Shriner D, Bentley AR, Chen G, Zhou J, et al. Genome-wide association study identifies African-ancestry specific variants for metabolic syndrome. Mol Genet Metab. 2015;116(4):305–13 This paper is a relatively large discovery GWAS of obesity, T2D, and related-traits conducted specifically in African populations within the past couple of years. It identified novel African-specific variants that are associated with these traits and not only expanded our understanding of the pathophysiology of metabolic disorders but also generated new hypotheses.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Sahibdeen V, Crowther NJ, Soodyall H, Hendry LM, Munthali RJ, Hazelhurst S, et al. Genetic variants in SEC16B are associated with body composition in black South Africans. Nutr Diabetes. 2018;8(1):43.

    PubMed  PubMed Central  Google Scholar 

  41. •• Adeyemo AA, Zaghloul NA, Chen G, Doumatey AP, Leitch CC, Hostelley TL, et al. ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Nat Commun. 2019;10(1):3195 This paper is a relatively large discovery GWAS of obesity, T2D, and related-traits conducted specifically in African populations within the past couple of years. It identified novel African-specific variants that are associated with these traits and not only expanded our understanding of the pathophysiology of metabolic disorders but also generated new hypotheses.

    PubMed  PubMed Central  Google Scholar 

  42. Wu H, Ghosh S, Perrard XD, Feng L, Garcia GE, Perrard JL, et al. T-cell accumulation and regulated on activation, normal T cell expressed and secreted upregulation in adipose tissue in obesity. Circulation. 2007;115(8):1029–38.

    CAS  PubMed  Google Scholar 

  43. Justice AE, Karaderi T, Highland HM, Young KL, Graff M, Lu Y, et al. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet. 2019;51(3):452–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care. 2008;11(5):566–72.

    PubMed  PubMed Central  Google Scholar 

  45. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359(20):2105–20.

    CAS  PubMed  Google Scholar 

  46. Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005;81(3):555–63.

    CAS  PubMed  Google Scholar 

  47. Snijder MB, Dekker JM, Visser M, Bouter LM, Stehouwer CD, Kostense PJ, et al. Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn study. Am J Clin Nutr. 2003;77(5):1192–7.

    CAS  PubMed  Google Scholar 

  48. Schleinitz D, Böttcher Y, Blüher M, Kovacs P. The genetics of fat distribution. Diabetologia. 2014;57(7):1276–86.

    CAS  PubMed  Google Scholar 

  49. Yako YY, Madubedube JH, Kengne AP, Erasmus RT, Pillay TS, Matsha TE. Contribution of ENPP1, TCF7L2, and FTO polymorphisms to type 2 diabetes in mixed ancestry ethnic population of South Africa. Afr Health Sci. 2015;15(4):1149–60.

    PubMed  PubMed Central  Google Scholar 

  50. Adebamowo SN, Tekola-Ayele F, Adeyemo AA, Rotimi CN. Genomics of cardiometabolic disorders in sub-Saharan Africa. Public Health Genomics. 2017;20(1):9–26.

    PubMed  Google Scholar 

  51. Khella MS, Hamdy NM, Amin AI, El-Mesallamy HO. The (FTO) gene polymorphism is associated with metabolic syndrome risk in Egyptian females: a case-control study. BMC Med Genet. 2017;18(1):101.

    PubMed  PubMed Central  Google Scholar 

  52. Oyeyemi BF, Ologunde CA, Olaoye AB, Alamukii NA. FTO gene associates and interacts with obesity risk, physical activity, energy intake, and time spent sitting: pilot study in a Nigerian population. J Obes. 2017;2017:3245270.

    PubMed  PubMed Central  Google Scholar 

  53. Ben Halima M, Kallel A, Baara A, Ben Wafi S, Sanhagi H, Slimane H, et al. The rs9939609 polymorphism in the fat mass and obesity associated (FTO) gene is associated with obesity in Tunisian population. Biomarkers. 2018;23(8):787–92.

    CAS  PubMed  Google Scholar 

  54. Nesrine Z, Haithem H, Imen B, Fadoua N, Asma O, Fadhel NM, et al. Leptin and leptin receptor polymorphisms, plasma leptin levels and obesity in Tunisian volunteers. Int J Exp Pathol. 2018;99(3):121–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Zayani N, Hamdouni H, Boumaiza I, Achour O, Neffati F, Omezzine A, et al. Resistin polymorphims, plasma resistin levels and obesity in Tunisian volunteers. J Clin Lab Anal. 2018;32(2).

    PubMed Central  Google Scholar 

  56. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41(1):18–24.

    CAS  PubMed  Google Scholar 

  57. Winkler TW, Justice AE, Graff M, Barata L, Feitosa MF, Chu S, et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 2015;11(10):e1005378.

    PubMed  PubMed Central  Google Scholar 

  58. Lu Y, Day FR, Gustafsson S, Buchkovich ML, Na J, Bataille V, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun. 2016;7:10495.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Rask-Andersen M, Karlsson T, Ek WE, Johansson A. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nat Commun. 2019;10(1):339.

    PubMed  PubMed Central  Google Scholar 

  60. Riveros-McKay F, Mistry V, Bounds R, Hendricks A, Keogh JM, Thomas H, et al. Genetic architecture of human thinness compared to severe obesity. PLoS Genet. 2019;15(1):e1007603.

    PubMed  PubMed Central  Google Scholar 

  61. Yang J, Loos RJ, Powell JE, Medland SE, Speliotes EK, Chasman DI, et al. FTO genotype is associated with phenotypic variability of body mass index. Nature. 2012;490(7419):267–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet. 2013;45(5):513–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Monda KL, Chen GK, Taylor KC, Palmer C, Edwards TL, Lange LA, et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet. 2013;45(6):690–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Felix JF, Bradfield JP, Monnereau C, van der Valk RJ, Stergiakouli E, Chesi A, et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet. 2016;25(2):389–403.

    CAS  PubMed  Google Scholar 

  67. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet. 2017;49(10):1458–67.

    CAS  PubMed  Google Scholar 

  68. Cornelis MC, Flint A, Field AE, Kraft P, Han J, Rimm EB, et al. A genome-wide investigation of food addiction. Obesity (Silver Spring). 2016;24(6):1336–41.

    CAS  PubMed Central  Google Scholar 

  69. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40(Database issue):D930–4.

    CAS  PubMed  Google Scholar 

  71. Graff M, Scott RA, Justice AE, Young KL, Feitosa MF, Barata L, et al. Genome-wide physical activity interactions in adiposity - a meta-analysis of 200,452 adults. PLoS Gen. 2017;13(4):e1006528.

    Google Scholar 

  72. Pravenec M, Zidek V, Landa V, Mlejnek P, Silhavy J, Simakova M, et al. Mutant Wars2 gene in spontaneously hypertensive rats impairs brown adipose tissue function and predisposes to visceral obesity. Physiol Res. 2017;66(6):917–24.

    CAS  PubMed  Google Scholar 

  73. Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin N Am. 2014;43(1):1–23.

    Google Scholar 

  74. Henneman P, Aulchenko YS, Frants RR, van Dijk KW, Oostra BA, van Duijn CM. Prevalence and heritability of the metabolic syndrome and its individual components in a Dutch isolate: the Erasmus Rucphen family study. J Med Genet. 2008;45(9):572–7.

    CAS  PubMed  Google Scholar 

  75. Lopez-Alvarenga JC, Solis-Herrera C, Kent JW, Jaju D, Albarwani S, Al Yahyahee S, et al. Prevalence and heritability of clusters for diagnostic components of metabolic syndrome: the Oman family study. Metab Syndr Relat Disord. 2008;6(2):129–35.

    PubMed  Google Scholar 

  76. Chen Y, Kittles R, Zhou J, Chen G, Adeyemo A, Panguluri RK, et al. Calpain-10 gene polymorphisms and type 2 diabetes in West Africans: the Africa America Diabetes Mellitus (AADM) study. Ann Epidemiol. 2005;15(2):153–9.

    CAS  PubMed  Google Scholar 

  77. Chikowore T, Conradie KR, Towers GW, van Zyl T. Common variants associated with type 2 diabetes in a black south African population of Setswana descent: African populations diverge. Omics. 2015;19(10):617–26.

    CAS  PubMed  Google Scholar 

  78. Guo F, Long W, Zhou W, Zhang B, Liu J, Yu B. FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis. Arch Gynecol Obstet. 2018;298(4):705–15.

    CAS  PubMed  Google Scholar 

  79. Ismail NA, Ragab S, Abd El Dayem SM, Baky A, Hamed M, Ahmed Kamel S, et al. Implication of CDKAL1 single-nucleotide polymorphism rs 9465871 in obese and non-obese Egyptian children. Med J Malaysia 2018;73(5):286–290

  80. Nfor ON, Wu MF, Lee CT, Wang L, Liu WH, Tantoh DM, et al. Body mass index modulates the association between CDKAL1 rs10946398 variant and type 2 diabetes among Taiwanese women. Sci Rep. 2018;8(1):13235.

    PubMed  PubMed Central  Google Scholar 

  81. Park S, Liu M, Kang S. Alcohol intake interacts with CDKAL1, HHEX, and OAS3 genetic variants, associated with the risk of type 2 diabetes by lowering insulin secretion in Korean adults. Alcohol Clin Exp Res. 2018;42(12):2326–36.

    CAS  PubMed  Google Scholar 

  82. Plengvidhya N, Chanprasert C, Chongjaroen N, Yenchitsomanus PT, Homsanit M, Tangjittipokin W. Impact of KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, SLC30A8, TCF7L2, and UBE2E2 on risk of developing type 2 diabetes in Thai population. BMC Med Genet. 2018;19(1):93.

    PubMed  PubMed Central  Google Scholar 

  83. Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018;50(3):390–400.

    CAS  PubMed  Google Scholar 

  84. Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012;7(12):e51954.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Iyengar SK, Sedor JR, Freedman BI, Kao WH, Kretzler M, Keller BJ, et al. Genome-wide association and trans-ethnic meta-analysis for advanced diabetic kidney disease: Family Investigation of Nephropathy and Diabetes (FIND). PLoS Genet. 2015;11(8):e1005352.

    PubMed  PubMed Central  Google Scholar 

  86. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019 Jan 8;47(D1):D1005-d12.

    PubMed Central  Google Scholar 

  87. Smith JA, Ware EB, Middha P, Beacher L, Kardia SL. Current applications of genetic risk scores to cardiovascular outcomes and subclinical phenotypes. Curr Epidemiol Rep. 2015;2(3):180–90.

    PubMed  PubMed Central  Google Scholar 

  88. Fernández-Rhodes L, Gong J, Haessler J, Franceschini N, Graff M, Nishimura KK, et al. 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. 2017;136(6):771–800.

    PubMed  PubMed Central  Google Scholar 

  89. Chikowore T, van Zyl T, Feskens EJ, Conradie KR. Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population. Diabetes Res Clin Pract. 2016;122:1–8.

    PubMed  Google Scholar 

  90. • Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100(4):635–49 This paper gives a significant perspective on the genetic risk score estimation across human populations, their bias toward European ancestry populations, and the potential to widen health disparity and impede the implementation of precision medicine if they are implemented in clinical settings serving diverse populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Domingue BW, Belsky DW, Harris KM, Smolen A, McQueen MB, Boardman JD. Polygenic risk predicts obesity in both white and black young adults. PLoS One. 2014;9(7):e101596.

    PubMed  PubMed Central  Google Scholar 

  92. Steinsbekk S, Belsky D, Guzey IC, Wardle J, Wichstrom L. Polygenic risk, appetite traits, and weight gain in middle childhood: a longitudinal study. JAMA Pediatr. 2016;170(2):e154472.

    PubMed  PubMed Central  Google Scholar 

  93. Sardahaee FS, Holmen TL, Micali N, Kvaloy K. Effects of single genetic variants and polygenic obesity risk scores on disordered eating in adolescents - the HUNT study. Appetite. 2017;118:8–16.

    PubMed  Google Scholar 

  94. Wolf EJ, Miller DR, Logue MW, Sumner J, Stoop TB, Leritz EC, et al. Contributions of polygenic risk for obesity to PTSD-related metabolic syndrome and cortical thickness. Brain Behav Immun. 2017;65:328–36.

    PubMed  PubMed Central  Google Scholar 

  95. Fang J, Gong C, Wan Y, Xu Y, Tao F, Sun Y. Polygenic risk, adherence to a healthy lifestyle, and childhood obesity. Pediatr Obes. 2019;14(4):e12489.

    PubMed  Google Scholar 

  96. Torkamani A, Topol E. Polygenic risk scores expand to obesity. Cell. 2019;177(3):518–20.

    CAS  PubMed  Google Scholar 

  97. Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177(3):587–96.e9.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Feero WG. Introducing “Genomics and Precision Health”. JAMA. 2017;317(18):1842–3.

    PubMed  Google Scholar 

  99. Mulder N. Development to enable precision medicine in Africa. Pers Med. 2017;14(6):467–70.

    CAS  Google Scholar 

  100. Letai A. Functional precision cancer medicine-moving beyond pure genomics. Nat Med. 2017;23(9):1028–35.

    CAS  PubMed  Google Scholar 

  101. Currie G, Delles C. Precision medicine and personalized medicine in cardiovascular disease. Adv Exp Med Biol. 2018;1065:589–605.

    PubMed  Google Scholar 

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Funding

This research was supported by the Intramural Research Program of the National Human Genome Research Institute in the Center for Research in Genomics and Global Health (CRGGH, Z01HG200362). Center for Research in Genomics and Global Health is also supported by National Institute of Diabetes and Digestive and Kidney Diseases, Center for Information Technology, and the Office of the Director at the National Institutes of Health.

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CR is the corresponding author for the manuscript. All authors contributed to the drafting of the paper and reviewed and approved the manuscript content.

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Correspondence to Charles N. Rotimi.

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Ayo P. Doumatey, Kenneth Ekoru, Adebowale Adeyemo, and Charles N. Rotimi declare no conflict of interest.

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All human research was conducted according to the Declaration of Helsinki. The study protocol (AADM including WA and EA) was approved by the institutional ethics review board of each participating institution. Written informed consent was obtained from each participant prior to enrollment.

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Doumatey, A.P., Ekoru, K., Adeyemo, A. et al. Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine. Curr Diab Rep 19, 105 (2019). https://doi.org/10.1007/s11892-019-1215-5

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