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.

  • Original Article
  • Published:

Genetic association analysis of 30 genes related to obesity in a European American population

International Journal of Obesity volume 38, pages 724–729 (2014)Cite this article

Subjects

Abstract

OBJECTIVE:

Obesity, which is frequently associated with diabetes, hypertension and cardiovascular diseases, is primarily the result of a net excess of caloric intake over energy expenditure. Human obesity is highly heritable, but the specific genes mediating susceptibility in non-syndromic obesity remain unclear. We tested candidate genes in pathways related to food intake and energy expenditure for association with body mass index (BMI).

METHODS:

We reanalyzed 355 common genetic variants of 30 candidate genes in seven molecular pathways related to obesity in 1982 unrelated European Americans from the New York Cancer Project. Data were analyzed by using a Bayesian hierarchical generalized linear model. The BMIs were log-transformed and then adjusted for covariates, including age, age2, gender and diabetes status. The single-nucleotide polymorphisms (SNPs) were modeled as additive effects.

RESULTS:

With the stipulated adjustments, nine SNPs in eight genes were significantly associated with BMI: ghrelin (GHRL; rs35683), agouti-related peptide (AGRP; rs5030980), carboxypeptidase E (CPE; rs1946816 and rs4481204), glucagon-like peptide-1 receptor (GLP1R; rs2268641), serotonin receptors (HTR2A; rs912127), neuropeptide Y receptor (NPY5R;Y5R1c52), suppressor of cytokine signaling 3 (SOCS3; rs4969170) and signal transducer and activator of transcription 3 (STAT3; rs4796793). We also found a gender-by-SNP interaction (rs1745837 in HTR2A), which indicated that variants in the gene HTR2A had a stronger association with BMI in males. In addition, NPY1R was detected as having a significant gene effect even though none of the SNPs in this gene was significant.

CONCLUSION:

Variations in genes AGRP, CPE, GHRL, GLP1R, HTR2A, NPY1R, NPY5R, SOCS3 and STAT3 showed modest associations with BMI in European Americans. The pathways in which these genes participate regulate energy intake, and thus these associations are mechanistically plausible in this context.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Flegal KM, Carroll MD, Ogden CL, Curtin LR . Prevalence and trends in obesity among US Adults, 1999-2008. JAMA 2010; 303: 235–241.

    CAS  PubMed  Google Scholar 

  2. Wang YF, Beydoun MA, Liang L, Caballero B, Kumanyika SK . Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity 2008; 16: 2323–2330.

    PubMed  Google Scholar 

  3. Kelly T, Yang W, Chen CS, Reynolds K, He J . Global burden of obesity in 2005 and projections to 2030. Int J Obesity 2008; 32: 1431–1437.

    Article  CAS  Google Scholar 

  4. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003; 289: 76–79.

    Article  PubMed  Google Scholar 

  5. Bell CG, Walley AJ, Froguel P . The genetics of human obesity. Nat Rev Genet 2005; 6: 221–234.

    Article  CAS  PubMed  Google Scholar 

  6. Chung WK, Leibel RL . Considerations regarding the genetics of obesity. Obesity 2008; 16: S33–S39.

    Article  CAS  PubMed  Google Scholar 

  7. Mantzoros CS . The role of leptin in human obesity and disease: a review of current evidence. Ann Intern Med 1999; 130: 671–680.

    Article  CAS  PubMed  Google Scholar 

  8. Hofker M, Wijmenga C . A supersized list of obesity genes. Nat Genet 2009; 41: 139–140.

    Article  CAS  PubMed  Google Scholar 

  9. Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 2009; 41: 157–159.

    Article  CAS  PubMed  Google Scholar 

  10. 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: 18–24.

    Article  CAS  PubMed  Google Scholar 

  11. Willer CJ, Speliotes EK, RJF Loos, Li SX, Lindgren CM, Heid IM et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009; 41: 25–34.

    Article  CAS  PubMed  Google Scholar 

  12. Hoggart CJ, Whittaker JC, De Iorio M, Balding DJ . Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet 2008; 4: 7.

    Article  Google Scholar 

  13. Kimura M . The Neutral Theory of Molecular Evolution. Cambridge University Press: Cambridge, 1985.

    Google Scholar 

  14. Shi M, Weinberg CR . How much are we missing in SNP-by-SNP analyses of genome-wide association studies? Epidemiology 2011; 22: 845–847.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yang X, Deignan JL, Qi H, Zhu J, Qian S, Zhong J et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nat Genet 2009; 41: 415–423.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. de Los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP . Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 2013; 193: 327–345.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Tibshirani R . Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met 1996; 58: 267–288.

    Google Scholar 

  18. Wu TT, Chen YF, Hastie T, Sobel E, Lange K . Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 2009; 25: 714–721.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yi N, Liu N, Zhi D, Li J . Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects. PLOS Genet 2011; 7: e1002382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yi N, Xu S . Bayesian LASSO for quantitative trait loci mapping. Genetics 2008; 179: 1045–1055.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chung WK, Patki A, Matsuoka N, Boyer BB, Liu N, Musani SK et al. Analysis of 30 genes (355 SNPS) related to energy homeostasis for association with adiposity in European-American and Yup'ik Eskimo populations. Human Heredity 2009; 67: 193–205.

    Article  CAS  PubMed  Google Scholar 

  22. Benjamini Y, Hochberg Y . Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 1995; 57: 289–300.

    Google Scholar 

  23. Gelman A, Carlin J, Stern H, Rubin D . Bayesian data analysis. Chapman and Hall: London, UK, 2003.

    Google Scholar 

  24. Gelman AaH J . Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press: New York, NY, USA, 2007.

    Google Scholar 

  25. Ando T, Ichimaru Y, Konjiki F, Shoji M, Komaki G . Variations in the preproghrelin gene correlate with higher body mass index, fat mass, and body dissatisfaction in young Japanese women. Am J Clin Nutr 2007; 86: 25–32.

    Article  CAS  PubMed  Google Scholar 

  26. del Giudice EM, Santoro N, Cirillo G, Raimondo P, Grandone A, D'Aniello A et al. Molecular screening of the ghrelin gene in Italian obese children: the Leu72Met variant is associated with an earlier onset of obesity. Int J Obesity 2004; 28: 447–450.

    Article  Google Scholar 

  27. Hinney A, Hoch A, Geller F, Schafer H, Siegfried W, Goldschmidt H et al. Ghrelin gene: Identification of missense variants and a frameshift mutation in extremely obese children and adolescents and healthy normal weight students. J Clin Endocr Metab 2002; 87: 2716–2719.

    Article  CAS  PubMed  Google Scholar 

  28. Jo DS, Kim SL, Kim SY, Hwang PH, Lee KH, Lee DY . Preproghrelin Leu72Met polymorphism in obese Korean children. J Pediatr Endocr Met 2005; 18: 1083–1086.

    CAS  Google Scholar 

  29. Kuzuya M, Ando F, Iguchi A, Shimokata H . Preproghrelin Leu72Met variant contributes to overweight in middle-aged men of a Japanese large cohort. Int J Obesity 2006; 30: 1609–1614.

    Article  CAS  Google Scholar 

  30. Mager U, Lindi V, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H et al. Association of the Leu72Met polymorphism of the ghrelin gene with the risk of Type 2 diabetes in subjects with impaired glucose tolerance in the Finnish Diabetes Prevention Study. Diabetic Med 2006; 23: 685–689.

    Article  CAS  PubMed  Google Scholar 

  31. Steinle NI, Pollin TI, O'Connell JR, Mitchell BD, Shuldiner AR . Variants in the ghrelin gene are associated with metabolic syndrome in the old order amish. J Clin Endocr Metab 2005; 90: 6672–6677.

    Article  CAS  PubMed  Google Scholar 

  32. Ukkola O, Ravussin E, Jacobson P, Perusse L, Rankinen T, Tschop M et al. Role of ghrelin polymorphisms in obesity based on three different studies. Obes Res 2002; 10: 782–791.

    Article  CAS  PubMed  Google Scholar 

  33. Vartiainen J, Kesaniemi YA, Ukkola O . Sequencing analysis of ghrelin gene 5' flanking region: relations between the sequence variants, fasting plasma total ghrelin concentrations, and body mass index. Metabolism 2006; 55: 1420–1425.

    Article  CAS  PubMed  Google Scholar 

  34. Vivenza D, Rapa A, Castellino N, Bellone S, Petri A, Vacca G et al. Ghrelin gene polymorphisms and ghrelin, insulin, IGF-I, leptin and anthropometric data in children and adolescents. Eur J Endocrinol 2004; 151: 127–133.

    Article  CAS  PubMed  Google Scholar 

  35. Bonilla C, Panguluri RK, Taliaferro-Smith L, Argyropoulos G, Chen G, Adeyemo AA et al. Agouti-related protein promoter variant associated with leanness and decreased risk for diabetes in West Africans. Int J Obesity 2006; 30: 715–721.

    Article  CAS  Google Scholar 

  36. Kalnina I, Kapa I, Pirags V, Ignatovica V, Schioth HB, Klovins J . Association between a rare SNP in the second intron of human Agouti related protein gene and increased BMI. BMC Med Genet 2009; 10: 63.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Marks DL, Boucher N, Lanouette CM, Perusse L, Brookhart G, Comuzzie AG et al. Ala67Thr polymorphism in the agouti-related peptide gene is associated with inherited leanness in humans. Am J Med Genet A 2004; 126A: 267–271.

    Article  PubMed  Google Scholar 

  38. van Rossum CTM, Pijl H, Adan RAH, Hoebee B, Seidell JC . Polymorphisms in the NPY and AGRP genes and body fatness in Dutch adults. Int J Obesity 2006; 30: 1522–1528.

    Article  CAS  Google Scholar 

  39. Sathananthan A, Man CD, Micheletto F, Zinsmeister AR, Camilleri M, Giesler PD et al. Common genetic variation in GLP1R and insulin secretion in response to exogenous GLP-1 in nondiabetic subjects: a pilot study. Diabetes Care 2010; 33: 2074–2076.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kumar KG, Poole AC, York B, Volaufova J, Zuberi A . Richards BKS. Quantitative trait loci for carbohydrate and total energy intake on mouse chromosome 17: congenic strain confirmation and candidate gene analyses (Glo1, Glp1r). Am J Physiol-Reg I 2007; 292: R207–R216.

    CAS  Google Scholar 

  41. Vilsboll T, Christensen M, Junker AE, Knop FK, Gluud LL . Effects of glucagon-like peptide-1 receptor agonists on weight loss: systematic review and meta-analyses of randomised controlled trials. BMJ 2012; 344: d7771.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Morris DL, Rui LY . Recent advances in understanding leptin signaling and leptin resistance. Am J Physiol-Endoc M 2009; 297: E1247–E1259.

    CAS  Google Scholar 

  43. Talbert ME, Langefeld CD, Ziegler J, Mychaleckyj JC, Haffner SM, Norris JM et al. Polymorphisms near SOCS3 are associated with obesity and glucose homeostasis traits in Hispanic Americans from the insulin resistance Atherosclerosis Family Study. Human Genet 2009; 125: 153–162.

    Article  CAS  Google Scholar 

  44. Phillips CM, Goumidi L, Bertrais S, Field MR, Peloso GM, Shen J et al. Dietary saturated fat modulates the association between STAT3 polymorphisms and abdominal obesity in adults. J Nutr 2009; 139: 2011–2017.

    Article  CAS  PubMed  Google Scholar 

  45. Rittenhouse PA, Bakkum EA, Levy AD, Li Q, Carnes M, Vandekar LD . Evidence that acth-secretion is regulated by serotonin(2a/2c) (5-Ht2a/2c) receptors. J Pharmacol Exp Ther 1994; 271: 1647–1655.

    CAS  PubMed  Google Scholar 

  46. Bjorntorp P, Rosmond P . Obesity and cortisol. Nutrition 2000; 16: 924–936.

    Article  CAS  PubMed  Google Scholar 

  47. Rosmond R, Bouchard C, Bjorntorp P . 5-HT2A receptor gene promoter polymorphism in relation to abdominal obesity and cortisol. Obes Res 2002; 10: 585–589.

    Article  CAS  PubMed  Google Scholar 

  48. Rosmond R, Bouchard C, Bjorntorp P . Increased abdominal obesity in subjects with a mutation in the 5-HT2A receptor gene promoter. Ann N Y Acad Sci 2002; 967: 571–575.

    Article  CAS  PubMed  Google Scholar 

  49. Herbeth B, Aubry E, Fumeron F, Aubert R, Cailotto F, Siest G et al. Polymorphism of the 5-HT2A receptor gene and food intakes in children and adolescents: the Stanislas Family Study. Am J Clin Nutr 2005; 82: 467–470.

    Article  CAS  PubMed  Google Scholar 

  50. Mashiko S, Moriya R, Ishihara A, Gomori A, Matsushita H, Egashira S et al. Synergistic interaction between neuropeptide Y-1 and Y-5 receptor pathways in regulation of energy homeostasis. Eur J Pharmacol 2009; 615: 113–117.

    Article  CAS  PubMed  Google Scholar 

  51. Jenkinson CP, Cray K, Walder K, Herzog H, Hanson R, Ravussin E . Novel polymorphisms in the neuropeptide-Y Y5 receptor associated with obesity in Pima Indians. Int J Obesity 2000; 24: 580–584.

    Article  CAS  Google Scholar 

  52. Cool DR, Normant E, Shen FS, Chen HC, Pannell L, Zhang Y et al. Carboxypeptidase E is a regulated secretory pathway sorting receptor: Genetic obliteration leads to endocrine disorders in Cpe(fat) mice. Cell 1997; 88: 73–83.

    Article  CAS  PubMed  Google Scholar 

  53. Lin WY, Tiwari HK, Gao G, Zhang K, Arcaroli JJ, Abraham E et al. Similarity-based multimarker association tests for continuous traits. Ann Human Genet 2012; 76: 246–260.

    Article  Google Scholar 

  54. Fricker LD, Berman YL, Leiter EH, Devi LA . Carboxypeptidase E activity is deficient in mice with the fat mutation - effect on peptide processing. J Biol Chem 1996; 271: 30619–30624.

    Article  CAS  PubMed  Google Scholar 

  55. Naggert JK, Fricker LD, Varlamov O, Nishina PM, Rouille Y, Steiner DF et al. Hyperproinsulinaemia in obese fat/fat mice associated with a carboxypeptidase E mutation which reduces enzyme activity. Nat Genet 1995; 10: 135–142.

    Article  CAS  PubMed  Google Scholar 

  56. Jackson RS, Creemers JW, Ohagi S, Raffin-Sanson ML, Sanders L, Montague CT et al. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nat Genet 1997; 16: 303–306.

    Article  CAS  PubMed  Google Scholar 

  57. Jeffrey KD, Alejandro EU, Luciani DS, Kalynyak TB, Hu XK, Li H et al. Carboxypeptidase E mediates palmitate-induced beta-cell ER stress and apoptosis. Proc Natl Acad Sci USA 2008; 105: 8452–8457.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Friedman J, Hastie T, Tibshirani R . Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33: 1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kyung M, Gill J, Ghosh M, Casella G . Penalized Regression, Standard Errors, and Bayesian Lassos. Bayesian Anal 2010; 5: 369–411.

    Article  Google Scholar 

  60. Sun W, Ibrahim JG, Zou F . Genomewide multiple-loci mapping in experimental crosses by iterative adaptive penalized regression. Genetics 2010; 185: U349–U529.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers for their insightful and constructive comments. This work was supported in part by National Institute of Health (NIH) grants T32HL079888 (HKT), R01DK52431 (RLL), R01GM081488 (NL) and P30 DK26687 (RLL); the New York Cancer Project (AMDeC (Academy for Medical Development and Collaboration) Foundation); and NSC grant 102-2314-B-002-001-MY2 (WL) from the National Science Council of Taiwan.

Author information

Authors and Affiliations

  1. Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

    P Li, H K Tiwari, N Yi & N Liu

  2. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    W-Y Lin

  3. Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL, USA

    D B Allison

  4. Columbia University Medical Center, New York, NY, USA

    W K Chung & R L Leibel

  5. Naomi Berrie Diabetes Center, New York, NY, USA

    W K Chung & R L Leibel

Authors
  1. P Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H K Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. W-Y Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D B Allison
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. W K Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R L Leibel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. N Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. N Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to N Yi or N Liu.

Ethics declarations

Competing interests

Dr Allison has received payments from nonprofit and for-profit organizations with interests in obesity, including the Frontiers Foundation; Vivus, Inc; Merck; Eli Lilly & Company; Pfizer; Jason Pharmaceuticals; Kraft Foods; University of Wisconsin; University of Arizona; Paul, Weiss, Wharton & Garrison LLP; and Sage Publications. His university has also received grants and gifts to support his work from additional food, beverage, pharmaceutical and other companies. The remaining authors declare no conflict of interest.

Additional information

Author contributions

PL designed and performed the data analysis and drafted the manuscript. NY provided the analytic tools and consultation on the data analysis. NL provided the consultation on the data analysis. WKC, RLL, HKT and DBA provided the data and consultation. All the authors were involved in writing the paper.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, P., Tiwari, H., Lin, WY. et al. Genetic association analysis of 30 genes related to obesity in a European American population. Int J Obes 38, 724–729 (2014). https://doi.org/10.1038/ijo.2013.140

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2013.140

Keywords

This article is cited by

Search

Advanced search

Quick links