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.

  • Article
  • Published:

Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile

Abstract

Genome-wide association studies have identified 32 loci influencing body mass index, but this measure does not distinguish lean from fat mass. To identify adiposity loci, we meta-analyzed associations between 2.5 million SNPs and body fat percentage from 36,626 individuals and followed up the 14 most significant (P < 10−6) independent loci in 39,576 individuals. We confirmed a previously established adiposity locus in FTO (P = 3 × 10−26) and identified two new loci associated with body fat percentage, one near IRS1 (P = 4 × 10−11) and one near SPRY2 (P = 3 × 10−8). Both loci contain genes with potential links to adipocyte physiology. Notably, the body-fat–decreasing allele near IRS1 is associated with decreased IRS1 expression and with an impaired metabolic profile, including an increased visceral to subcutaneous fat ratio, insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease and decreased adiponectin levels. Our findings provide new insights into adiposity and insulin resistance.

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

Access options

Buy this article

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

Figure 1: Manhattan plot showing the significance of association with body fat percentage for SNPs in the stage 1 meta-analysis of all individuals (n = 36,626).
Figure 2: Regional plot of the loci near IRS1, near SPRY2 and in FTO that reached genome-wide significant evidence for association with body fat percentage.
Figure 3: Association of the body-fat-percentage–decreasing (T) allele of rs2943650 near IRS1 with blood lipids, insulin sensitivity traits, leptin and adiponectin.

Similar content being viewed by others

References

  1. Anonymous. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ. Tech. Rep. Ser. 894, 1–253 (2000).

  2. Maes, H.H., Neale, M.C. & Eaves, L.J. Genetic and environmental factors in relative body weight and human adiposity. Behav. Genet. 27, 325–351 (1997).

    Article  CAS  Google Scholar 

  3. Frayling, T.M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).

    Article  CAS  Google Scholar 

  4. Loos, R.J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40, 768–775 (2008).

    Article  CAS  Google Scholar 

  5. Scuteri, A. et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115 (2007).

    Article  Google Scholar 

  6. Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

    Article  CAS  Google Scholar 

  7. Willer, C.J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  Google Scholar 

  10. Rung, J. et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 41, 1110–1115 (2009).

    Article  CAS  Google Scholar 

  11. Samani, N.J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).

    Article  CAS  Google Scholar 

  12. Matsuda, M. & DeFronzo, R.A. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22, 1462–1470 (1999).

    Article  CAS  Google Scholar 

  13. Gutt, M. et al. Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. Diabetes Res. Clin. Pract. 47, 177–184 (2000).

    Article  CAS  Google Scholar 

  14. Badman, M.K. & Flier, J.S. The adipocyte as an active participant in energy balance and metabolism. Gastroenterology 132, 2103–2115 (2007).

    Article  CAS  Google Scholar 

  15. Kim, J.Y. et al. Obesity-associated improvements in metabolic profile through expansion of adipose tissue. J. Clin. Invest. 117, 2621–2637 (2007).

    Article  CAS  Google Scholar 

  16. Virtue, S. & Vidal-Puig, A. It's not how fat you are, it's what you do with it that counts. PLoS Biol. 6, e237 (2008).

    Article  Google Scholar 

  17. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    Article  CAS  Google Scholar 

  18. Zhong, H., Yang, X., Kaplan, L.M., Molony, C. & Schadt, E.E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010).

    Article  CAS  Google Scholar 

  19. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494–1499 (2007).

    Article  CAS  Google Scholar 

  20. Cabrita, M.A. & Christofori, G. Sprouty proteins, masterminds of receptor tyrosine kinase signaling. Angiogenesis 11, 53–62 (2008).

    Article  CAS  Google Scholar 

  21. Yigzaw, Y., Cartin, L., Pierre, S., Scholich, K. & Patel, T.B. The C terminus of sprouty is important for modulation of cellular migration and proliferation. J. Biol. Chem. 276, 22742–22747 (2001).

    Article  CAS  Google Scholar 

  22. Lee, C.C. et al. Overexpression of sprouty 2 inhibits HGF/SF-mediated cell growth, invasion, migration, and cytokinesis. Oncogene 23, 5193–5202 (2004).

    Article  CAS  Google Scholar 

  23. Zhang, C. et al. Regulation of vascular smooth muscle cell proliferation and migration by human sprouty 2. Arterioscler. Thromb. Vasc. Biol. 25, 533–538 (2005).

    Article  CAS  Google Scholar 

  24. Urs, S. et al. Sprouty1 is a critical regulatory switch of mesenchymal stem cell lineage allocation. FASEB J. 24, 3264–3273 (2010).

    Article  CAS  Google Scholar 

  25. Heard-Costa, N.L. et al. NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet. 5, e1000539 (2009).

    Article  Google Scholar 

  26. Lindgren, C.M. et al. Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet. 5, e1000508 (2009).

    Article  Google Scholar 

  27. Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat. Genet. 41, 157–159 (2009).

    Article  CAS  Google Scholar 

  28. Scherag, A. et al. Two new loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and German study groups. PLoS Genet. 6, e1000916 (2010).

    Article  Google Scholar 

  29. Heid, I.M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).

    Article  CAS  Google Scholar 

  30. Grarup, N., Sparso, T. & Hansen, T. Physiologic characterization of type 2 diabetes-related loci. Curr. Diab. Rep. 10, 485–497 (2010).

    Article  CAS  Google Scholar 

  31. Stumvoll, M. & Jacob, S. Multiple sites of insulin resistance: muscle, liver and adipose tissue. Exp. Clin. Endocrinol. Diabetes 107, 107–110 (1999).

    Article  CAS  Google Scholar 

  32. Arner, P. Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab. Res. Rev. 18 (Suppl 2) S5–S9 (2002).

    Article  CAS  Google Scholar 

  33. Wajchenberg, B.L. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr. Rev. 21, 697–738 (2000).

    Article  CAS  Google Scholar 

  34. Araki, E. et al. Alternative pathway of insulin signalling in mice with targeted disruption of the IRS-1 gene. Nature 372, 186–190 (1994).

    Article  CAS  Google Scholar 

  35. Tamemoto, H. et al. Insulin resistance and growth retardation in mice lacking insulin receptor substrate-1. Nature 372, 182–186 (1994).

    Article  CAS  Google Scholar 

  36. Zhou, L. et al. Insulin receptor substrate-2 (IRS-2) can mediate the action of insulin to stimulate translocation of GLUT4 to the cell surface in rat adipose cells. J. Biol. Chem. 272, 29829–29833 (1997).

    Article  CAS  Google Scholar 

  37. Kaburagi, Y. et al. Role of insulin receptor substrate-1 and pp60 in the regulation of insulin-induced glucose transport and GLUT4 translocation in primary adipocytes. J. Biol. Chem. 272, 25839–25844 (1997).

    Article  CAS  Google Scholar 

  38. Smith-Hall, J. et al. The 60 kDa insulin receptor substrate functions like an IRS protein (pp60IRS3) in adipose cells. Biochemistry 36, 8304–8310 (1997).

    Article  CAS  Google Scholar 

  39. Laustsen, P.G. et al. Lipoatrophic diabetes in Irs1(−/−)/Irs3(−/−) double knockout mice. Genes Dev. 16, 3213–3222 (2002).

    Article  CAS  Google Scholar 

  40. Miki, H. et al. Essential role of insulin receptor substrate 1 (IRS-1) and IRS-2 in adipocyte differentiation. Mol. Cell. Biol. 21, 2521–2532 (2001).

    Article  CAS  Google Scholar 

  41. Tseng, Y.H., Kriauciunas, K.M., Kokkotou, E. & Kahn, C.R. Differential roles of insulin receptor substrates in brown adipocyte differentiation. Mol. Cell. Biol. 24, 1918–1929 (2004).

    Article  CAS  Google Scholar 

  42. Reich, D., Thangaraj, K., Patterson, N., Price, A.L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    Article  CAS  Google Scholar 

  43. Barnett, A.H. et al. Type 2 diabetes and cardiovascular risk in the UK south Asian community. Diabetologia 49, 2234–2246 (2006).

    Article  CAS  Google Scholar 

  44. Pruim, R.J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).

    Article  CAS  Google Scholar 

  45. Li, Y. & Mach Abecasis, G.R. 1.0: rapid haplotype reconstruction and missing genotype inference. Am. J. Hum. Genet. S79, 2290 (2006).

    Google Scholar 

  46. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  47. Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007).

    Article  Google Scholar 

  48. Chambers, J.C. et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40, 716–718 (2008).

    Article  CAS  Google Scholar 

  49. Abecasis, G.R., Cherny, S.S., Cookson, W.O. & Cardon, L.R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).

    Article  CAS  Google Scholar 

  50. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  51. Wang, Z.V., Deng, Y., Wang, Q.A., Sun, K. & Scherer, P.E. Identification and characterization of a promoter cassette conferring adipocyte-specific gene expression. Endocrinology 151, 2933–2939 (2010).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

A full list of Acknowledgments appears in the Supplementary Note. Funding was provided by Academy of Finland (10404, 124243, 129680, 129494, 141005 and 213506); Agency for Health Care Policy Research (HS06516); Althingi (the Icelandic Parliament); American Heart Association (10SDG269004); AstraZeneca; Baltimore Geriatric Research Education and Clinical Centers; Biocentrum Helsinki Foundation; Biotechnology and Biological Sciences Research Council (G20234); British Heart Foundation (PG/07/133/24260, RG/08/008, SP/04/002, SP/07/007/23671); CamStrad; Cancer Research UK; Cedars-Sinai Board of Governors' Chair in Medical Genetics; Centre for Medical Systems Biology (The Netherlands); Centre Hospitalier Universitaire Vaudois (Lausanne); Croatian Ministry of Science, Education and Sport (196-1962766-2747, 216-1080315-0302 and 309-0061194-2023); Department of Health (UK); Department of Veterans Affairs (USA); Emil and Vera Cornell Foundation; Erasmus Medical Center (Rotterdam); Erasmus University (Rotterdam); European Commission (DG XII, FP7/2007-2013, FP7-KBBE-2010-4-266408, HEALTH-F2-2007-201681, HEALTH-F2-2008-201865-GEFOS, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2003-503041, LSHM-CT-2004-512013, QLG1-CT-2001-01252 and QLG2-CT-2002-01254); Finnish Diabetes Foundation; Finnish Diabetes Research Foundation; Finnish Foundation for Cardiovascular Research; Finnish Heart Foundation; Finnish Medical Society; Folkhälsan Research Foundation; Food Standards Agency (UK); Foundation for Life and Health in Finland; German Bündesministerium für Forschung und Technology (01AK803A-H and 01IG07015G); German Federal Ministry of Education and Research; German National Genome Research Network (NGFN-2 and NGFNPlus: 01GS0823); Giorgi-Cavaglieri Foundation; GlaxoSmithKline; Göteborg Medical Society; Gyllenberg Foundation; Health and Safety Executive (UK); Health Care Centers in Vasa, Närpes and Korsholm; Hjartavernd (the Icelandic Heart Association); John D. and Catherine T. MacArthur Foundation; Knut and Alice Wallenberg Foundation; Leenaards Foundation; Ludwig-Maximilians Universität München; Lundberg Foundation; Medical Research Council (UK); Men's Associates of Hebrew SeniorLife; Ministerio de Ciencia e Innovación (Spain) (SAF-2009 and SAF-2008-02073); Ministry for Health, Welfare and Sports (The Netherlands); Ministry of Education (Finland); Ministry of Education, Culture and Science (The Netherlands); Municipal Health Care Center and Hospital in Jakobstad; Municipality of Rotterdam; Närpes Health Care Foundation; National Institute for Health Research (UK); US National Institutes of Health (USA) (AG13196, DK063491, K23-DK080145, M01-RR00425, N01-AG12100, N01-AG62101, N01-AG62103, N01-AG62106, N01-HC15103, N01-HC25195, N01-HC35129, N01-HC45133, N01-HC55222, N01-HC75150, N01-HC85079 through N01-HC85086, P30-DK072488, R01-AG031890-01, R01-AG18728, R01-AG032098-01A1, R01-AR/AG41398, R01-AR046838, R01-DK06833603, R01-DK075787, R01-DK07568102, R01-HL036310-20A2, R01-HL087652, R01-HL08770003, R01-HL088119, U01-HL080295, U01-HL72515 and U01-HL84756); Netherlands Genomics Initiative/Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Organisation for Scientific Research (175.010.2005.011 and 911-03-012); Netherlands Organization for the Health Research and Development; Nordic Center of Excellence in Disease Genetics; Novo Nordisk Foundation; Ollqvist Foundation; Paavo Nurmi Foundation; Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Research Institute for Diseases in the Elderly (014-93-015; RIDE2); Robert Dawson Evans Endowment; Royal Society (UK); Sahlgrenska Center for Cardiovascular and Metabolic Research (A305:188); Sahlgrenska University Hospital Foundation (ALF/LUA); Science Funding programme (UK); Scottish Executive Health Department; Sigrid Juselius Foundation; State of Bavaria; Stroke Association (UK); Swedish Cultural Foundation in Finland; Swedish Foundation for Strategic Research; Swedish Research Council (K2010-54X-09894-19-3, K2010-52X-20229-05-3 and 2006-3832); Swedish Strategic Foundation; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 310000-112552 and 33CSCO-122661); TEKES (1510/31/06); Torsten and Ragnar Söderberg's Foundation; United Kingdom NIHR Cambridge Biomedical Research Centre; University of Lausanne; University of Maryland General Clinical Research Center (M01 RR 16500); Uppsala University; Västra Götaland Foundation; and Wellcome Trust (077016/Z/05/Z, 084723/Z/08/Z and 091746/Z/10/Z).

Author information

Authors and Affiliations

Authors

Contributions

A full list of author contributions appears in the Supplementary Note.

Corresponding author

Correspondence to Ruth J F Loos.

Ethics declarations

Competing interests

I.B. and spouse own stock in Incyte Ltd and GlaxoSmithKline. K.S., G.T., U.T. and U.S. are employed by deCODE Genetics. V.M. is a full-time employee of GlaxoSmithKline. G.W. and P.V. received funding from GlaxoSmithKline to build the CoLaus Study.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–9, Supplementary Figures 1–7 and Supplementary Note. (PDF 1136 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kilpeläinen, T., Zillikens, M., Stančákova, A. et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet 43, 753–760 (2011). https://doi.org/10.1038/ng.866

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.866

This article is cited by

Search

Quick links

Nature Briefing

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

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