Genetics of Obesity in Diverse Populations


Purpose of Review

The prevalence of obesity continues to rise, fueling a global public health crisis characterized by dramatic increases in type 2 diabetes, cardiovascular disease, and many cancers. In the USA, several minority populations, who bear much of the obesity burden (47% in African Americans and Hispanic/Latinos, compared to 38% in European descent groups), are particularly at risk of downstream chronic disease. Compounding these disparities, most genome-wide association studies (GWAS)—including those of obesity—have largely been conducted in populations of European or East Asian ancestry. In fact, analysis of the GWAS Catalog found that while the proportion of participants of non-European or non-Asian descent had risen from 4% in 2009 to 19% in 2016, African-ancestry participants are still just 3% of GWAS, Hispanic/Latinos are < 0.5%, and other ancestries are < 0.3% or not represented at all. This review summarizes recent developments in obesity genomics in US minority populations, with the goal of reducing obesity health disparities and improving public health programs and access to precision medicine.

Recent Findings

GWAS of populations with the highest burden of obesity are essential to narrow candidate variants for functional follow-up, to identify additional ancestry-specific variants that contribute to individual genetic susceptibility, and to advance both public health and precision medicine approaches to obesity.


Given the global public health burden posed by obesity and downstream chronic conditions which disproportionately affect non-European populations, GWAS of obesity-related traits in diverse populations is essential to (1) locate causal variants in GWAS-identified regions through fine mapping, (2) identify variants which influence obesity across ancestries through generalization, and (3) discover novel ancestry-specific variants which may be low frequency in European populations but common in other groups. Recent efforts to expand obesity genomic studies to understudied and underserved populations, including AAAGC, PAGE, and HISLA, are working to reduce obesity health disparities, improve public health, and bring the promise of precision medicine to all.

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

  1. 1.

    Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS Data Brief, no 288. Hyattsville: National Center for Health Statistics; 2017.

    Google Scholar 

  2. 2.

    Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA. 2018;319:1723–5.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Zamosky L. The obesity epidemic. While America swallows $147 billion in obesity-related healthcare costs, physicians called on to confront the crisis. Med Econ. 2013;90:14–7.

    PubMed  Google Scholar 

  4. 4.

    Global BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju S, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388:776–86.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Cao B. Future healthy life expectancy among older adults in the US: a forecast based on cohort smoking and obesity history. Popul Health Metrics. 2016;14:23.

    Article  Google Scholar 

  6. 6.

    • Preston SH, Vierboom YC, Stokes A. The role of obesity in exceptionally slow US mortality improvement. PNAS. 2018;115:957–61. Analysis of NHANES data describing the impact of increasing BMI on the rate of US mortality improvement.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults - United States, 2005-2015. MMWR Morb Mortal Wkly Rep. 2016;65:1205–11.

    Article  PubMed  Google Scholar 

  8. 8.

    McCurley JL, Penedo F, Roesch SC, et al. Psychosocial factors in the relationship between socioeconomic status and cardiometabolic risk: the HCHS/SOL sociocultural ancillary study. Ann Behav Med. 2017;51:477–88.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Stepanikova I, Baker EH, Simoni ZR, Zhu A, Rutland SB, Sims M, et al. The role of perceived discrimination in obesity among African Americans. Am J Prev Med. 2017;52:S77–85.

    Article  PubMed  Google Scholar 

  10. 10.

    The Lancet Neurology. Disparities in stroke: not just black and white. Lancet Neurol. 2013;12:623.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Sturtz LA, Melley J, Mamula K, Shriver CD, Ellsworth RE. Outcome disparities in African American women with triple negative breast cancer: a comparison of epidemiological and molecular factors between African American and Caucasian women with triple negative breast cancer. BMC Cancer. 2014;14:62.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Grubbs SS, Polite BN, Carney J, Bowser W, Rogers J, Katurakes N, et al. Eliminating racial disparities in colorectal cancer in the real world: it took a village. J Clin Oncol. 2013;31:1928–30.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Martin DN, Starks AM, Ambs S. Biological determinants of health disparities in prostate cancer. Curr Opin Oncol. 2013;25:235–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Sharma A, Colvin-Adams M, Yancy CW. Heart failure in African Americans: disparities can be overcome. Cleve Clin J Med. 2014;81:301–11.

    Article  PubMed  Google Scholar 

  15. 15.

    Flegal KM, Kruszon-Moran D, Carroll MD, Fryar CD, Ogden CL. Trends in obesity among adults in the United States, 2005 to 2014. JAMA. 2016;315:2284–91.

    CAS  Article  Google Scholar 

  16. 16.

    Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118:1752–70.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Bastien M, Poirier P, Lemieux I, Després J-P. Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis. 2014;56:369–81.

    Article  PubMed  Google Scholar 

  18. 18.

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

    Article  PubMed  Google Scholar 

  19. 19.

    •• Locke AE, Kahali B, Berndt SI et al (2015) Genetic studies of body mass index yield new insights for obesity biology. 518:197–206. Most recent GIANT BMI GWAS of >339,000 individuals, identifying 97 BMI loci.

  20. 20.

    •• Shungin D, Winkler TW, Croteau-Chonka DC, et al (2015) New genetic loci link adipose and insulin biology to body fat distribution. 518:187–196. Most recent GIANT central adiposity GWAS of >220,000 individuals, identifying 49 central adiposity loci.

  21. 21.

    •• Turcot V, Turcot V, Lu Y, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50:26–41. GIANT BMI GWAS of low frequency and rare (MAF <5%) coding variants in >700,000 individuals, identifying 14 coding variants associated with BMI, several with effect sizes ~10x larger than that of common variants.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Willer CJ, Schmidt EM, Sengupta S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    •• Ng MCY, Graff M, Lu Y, et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet. 2017;13:e1006719. GWAS and fine-mapping of obesity traits in African Americans, identifying three novel and replicating seven established loci for BMI, and three novel and replicating one established locus for WHRadjBMI, as well as reducing credible SNP sets in established loci.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    • Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538:161–4. Commentary on the continuing lack of diversity in genomic studies.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    • Bentley AR, Callier S, Rotimi CN. Diversity and inclusion in genomic research: why the uneven progress? J Community Genet. 2017;8:255–66. Commentary on the factors contributing to limited diversity in genomic studies.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    • Morales J, Welter D, Bowler EH, et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 2018;19:1–10. Framework for consistent reporting of ancestry in genomic studies.

    Article  Google Scholar 

  28. 28.

    Jih J, Mukherjea A, Vittinghoff E, Nguyen TT, Tsoh JY, Fukuoka Y, et al. Using appropriate body mass index cut points for overweight and obesity among Asian Americans. Prev Med. 2014;65:1–6.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    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:R225–36.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Moltke I, Grarup N, Jørgensen ME, Bjerregaard P, Treebak JT, Fumagalli M, et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature. 2014;512:190–3.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Minster RL, Hawley NL, Su C-T, Sun G, Kershaw EE, Cheng H, et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat Genet. 2016;48:1049–54.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    López-Cortegano E, Caballero A. Inferring the nature of missing heritability in human traits. bioRxiv. 2018.

  33. 33.

    Yang J, Bakshi A, Zhu Z, et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 2015;47:1114–20.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Fujikura K. Global carrier rates of rare inherited disorders using population exome sequences. PLoS One. 2016;11:e0155552.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Palmer ND, Ng MCY, Hicks PJ, Mudgal P, Langefeld CD, Freedman BI, et al. Evaluation of candidate nephropathy susceptibility genes in a genome-wide association study of African American diabetic kidney disease. PLoS One. 2014;9:e88273.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Parsa A, Kao WHL, Xie D, Astor BC, Li M, Hsu CY, et al. APOL1 risk variants, race, and progression of chronic kidney disease. NEJM. 2013;369:2183–96.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    • Conomos MP, Laurie CA, Stilp AM, Gogarten SM, McHugh CP, Nelson SC, et al. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic Community Health Study/Study of Latinos. AJHG. 2016;98:165–84. Describes method to account for diversity beyond PCs in highly admixed samples.

    CAS  Article  Google Scholar 

  38. 38.

    Behar DM, Rosset S, Tzur S, Selig S, Yudkovsky G, Bercovici S, et al. African ancestry allelic variation at the MYH9 gene contributes to increased susceptibility to non-diabetic end-stage kidney disease in Hispanic Americans. Hum Mol Genet. 2010;19:1816–27.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The MetaboChip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8:e1002793.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Gong J, Schumacher F, Lim U, Hindorff LA, Haessler J, Buyske S, et al. Fine mapping and identification of BMI loci in African Americans. AJHG. 2013;93:661–71.

    CAS  Article  Google Scholar 

  41. 41.

    • Fernandez-Rhodes L, Gong J, Haessler J, 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;136:771–800. Trans-ethnic fine-mapping MetaboChip study of 36 BMI loci which identifies multiple independent signals at nine loci, and novel independent signals at 7 loci.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Gong J, Nishimura KK, Fernández-Rhodes L, et al. Trans-ethnic analysis of MetaboChip data identifies two new loci associated with BMI. Int J Obes. 2017;4:579–390.

    Google Scholar 

  44. 44.

    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 Genet. 2017;13:e1006528.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Bien SA, Wojcik GL, Zubair N, Gignoux CR, Martin AR, Kocarnik JM, et al. Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping array. PLoS One. 2016;11:e0167758.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Lin D-Y, Tao R, Kalsbeek WD, Zeng D, Gonzalez F II, Fernandez-Rhodes L, et al. Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. AJHG. 2014;95:675–88.

    CAS  Article  Google Scholar 

  47. 47.

    Conomos MP, Reiner AP, Weir BS, Thornton TA. Model-free estimation of recent genetic relatedness. AJHG. 2016;98:127–48.

    CAS  Article  Google Scholar 

  48. 48.

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

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    •• Wojcik G, Graff M, Nishimura KK, et al. Genetic diversity turns a new PAGE in our understanding of complex traits. bioRxiv. 2017. First GWAS of 26 phenotypes (including BMI) using the MEGA array in diverse populations.

  50. 50.

    He J, Chen DL, Samocha-Bonet D, Gillinder KR, Barclay JL, Magor GW, et al. Fibroblast growth factor-1 (FGF-1) promotes adipogenesis by downregulation of carboxypeptidase A4 (CPA4) - a negative regulator of adipogenesis implicated in the modulation of local and systemic insulin sensitivity. Growth Factors. 2016;34:210–6.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Mozaffarian D, Kabagambe EK, Johnson CO, Lemaitre RN, Manichaikul A, Sun Q, et al. Genetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. AJCN. 2015;101:398–406.

    CAS  Article  Google Scholar 

  52. 52.

    Jonker JW, Suh JM, Atkins AR, Ahmadian M, Li P, Whyte J, et al. A PPARγ-FGF1 axis is required for adaptive adipose remodelling and metabolic homeostasis. Nature. 2012;485:391–4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Gasser E, Moutos CP, Downes M, Evans RM. FGF1 - a new weapon to control type 2 diabetes mellitus. Nat Rev Endocrinol. 2017;13:599–609.

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    The SIGMA Type 3 Diabetes Consortium. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nat Commun. 2014;506:97–101.

    Article  CAS  Google Scholar 

  55. 55.

    Ruiz-Linares A, Adhikari K, Acuña-Alonzo V, Quinto-Sanchez M, Jaramillo C, Arias W, et al. Admixture in Latin America: geographic structure, phenotypic diversity and self-perception of ancestry based on 7,342 individuals. PLoS Genet. 2014;10:e1004572.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44.

    Article  CAS  Google Scholar 

  57. 57.

    Mahajan A, Wessel J, Willems SM, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet. 2018;50:559–71.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Dr. Kari E. North was funded by N01HC65233 and 1R01DK101855.

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Correspondence to Kristin L. Young.

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Kristin L. Young, Mariaelisa Graff, Lindsay Fernandez-Rhodes, and Kari E. North declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Young, K.L., Graff, M., Fernandez-Rhodes, L. et al. Genetics of Obesity in Diverse Populations. Curr Diab Rep 18, 145 (2018).

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  • Obesity
  • GWAS
  • Health disparities
  • Precision medicine