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
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Mbanya JC, Motala AA, Sobngwi E, Assah FK, Enoru ST. Diabetes in sub-Saharan Africa. Lancet. 2010;375(9733):2254–66.
Federation ID. IDF Diabetes Atlas. 2017 (8th edition, Brussels, Belgium).
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.
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.
Owen JB. Genetic aspects of body composition. Nutrition. 1999;15(7–8):609–13.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ng MC. Genetics of type 2 diabetes in African Americans. Curr Diab Rep. 2015;15(10):74.
•• 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.
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.
Gibbons A. 12th International Congress of Human Genetics. Diabetes genes decline out of Africa. Science. 2011;334(6056):583.
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.
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.
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.
Cooke Bailey JN, Igo RP Jr. Genetic risk scores. Curr Protoc Hum Genet. 2016;91:1.29.1–1..9.
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.
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.
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.
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.
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).
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.
Redondo MJ, Oram RA, Steck AK. Genetic risk scores for type 1 diabetes prediction and diagnosis. Curr Diab Rep. 2017;17(12):129.
Dudbridge F, Pashayan N, Yang J. Predictive accuracy of combined genetic and environmental risk scores. Genet Epidemiol. 2018;42(1):4–19.
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.
• 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.
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.
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.
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.
•• 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.
•• 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.
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.
•• 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.
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.
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.
Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care. 2008;11(5):566–72.
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.
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.
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.
Schleinitz D, Böttcher Y, Blüher M, Kovacs P. The genetics of fat distribution. Diabetologia. 2014;57(7):1276–86.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin N Am. 2014;43(1):1–23.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
• 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.
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.
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.
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.
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.
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.
Torkamani A, Topol E. Polygenic risk scores expand to obesity. Cell. 2019;177(3):518–20.
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.
Feero WG. Introducing “Genomics and Precision Health”. JAMA. 2017;317(18):1842–3.
Mulder N. Development to enable precision medicine in Africa. Pers Med. 2017;14(6):467–70.
Letai A. Functional precision cancer medicine-moving beyond pure genomics. Nat Med. 2017;23(9):1028–35.
Currie G, Delles C. Precision medicine and personalized medicine in cardiovascular disease. Adv Exp Med Biol. 2018;1065:589–605.
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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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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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DOI: https://doi.org/10.1007/s11892-019-1215-5