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Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution

A Corrigendum to this article was published on 27 October 2011

This article has been updated

Abstract

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10−9 to P = 1.8 × 10−40) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10−3 to P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

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Figure 1: Genome-wide association analyses for WHR in discovery studies.
Figure 2: Regional plots of 14 loci with genome-wide significant association.
Figure 3: Association of the 14 WHR loci with waist and hip circumference.

Change history

  • 12 October 2011

    In the version of this article initially published, there were errors in Table 1. Specifically, for eight SNPs, the effect allele frequencies were reversed. The correct effect allele frequencies for rs9491696, rs984222, rs4846567, rs1011731, rs718314, rs1294421, rs6795735 and rs2076529 are 0.480, 0.635, 0.717, 0.428, 0.259, 0.613, 0.594 and 0.430, respectively. These errors have been corrected in the HTML and PDF versions of the article.

References

  1. Carey, V.J. et al. Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. The Nurses' Health Study. Am. J. Epidemiol. 145, 614–619 (1997).

    Google Scholar 

  2. Wang, Y., Rimm, E.B., Stampfer, M.J., Willett, W.C. & Hu, F.B. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am. J. Clin. Nutr. 81, 555–563 (2005).

    Google Scholar 

  3. Canoy, D. Distribution of body fat and risk of coronary heart disease in men and women. Curr. Opin. Cardiol. 23, 591–598 (2008).

    Google Scholar 

  4. Pischon, T. et al. General and abdominal adiposity and risk of death in Europe. N. Engl. J. Med. 359, 2105–2120 (2008).

    Google Scholar 

  5. Snijder, M.B. 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. 77, 1192–1197 (2003).

    Google Scholar 

  6. Snijder, M.B. et al. Trunk fat and leg fat have independent and opposite associations with fasting and postload glucose levels: the Hoorn study. Diabetes Care 27, 372–377 (2004).

    Google Scholar 

  7. Mills, G.W. et al. Heritability estimates for beta cell function and features of the insulin resistance syndrome in UK families with an increased susceptibility to type 2 diabetes. Diabetologia 47, 732–738 (2004).

    Google Scholar 

  8. Souren, N.Y. et al. Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: heritabilities. Diabetologia 50, 2107–2116 (2007).

    Google Scholar 

  9. Rose, K.M., Newman, B., Mayer-Davis, E.J. & Selby, J.V. Genetic and behavioral determinants of waist-hip ratio and waist circumference in women twins. Obes. Res. 6, 383–392 (1998).

    Google Scholar 

  10. Selby, J.V. et al. Genetic and behavioral influences on body fat distribution. Int. J. Obes. 14, 593–602 (1990).

    Google Scholar 

  11. Agarwal, A.K. & Garg, A. Genetic disorders of adipose tissue development, differentiation, and death. Annu. Rev. Genomics Hum. Genet. 7, 175–199 (2006).

    Google Scholar 

  12. Garg, A. Acquired and inherited lipodystrophies. N. Engl. J. Med. 350, 1220–1234 (2004).

    Google Scholar 

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

    Google Scholar 

  14. 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).

    Google Scholar 

  15. 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).

    Google Scholar 

  16. 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).

    Google Scholar 

  17. Zillikens, M.C. et al. Sex-specific genetic effects influence variation in body composition. Diabetologia 51, 2233–2241 (2008).

    Google Scholar 

  18. 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).

    Google Scholar 

  19. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

    Google Scholar 

  20. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    Google Scholar 

  21. Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142–148 (2010).

    Google Scholar 

  22. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Google Scholar 

  23. Thomas, P.D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003).

    Google Scholar 

  24. Bochukova, E.G. et al. Large, rare chromosomal deletions associated with severe early-onset obesity. Nature 463, 666–670 (2010).

    Google Scholar 

  25. Walters, R.G. et al. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 463, 671–675 (2010).

    Google Scholar 

  26. Conrad, D.F. et al. Origins and functional impact of copy number variation in the human genome. Nature 464, 704–712 (2010).

    Google Scholar 

  27. Wellcome Trust Case Control Consortium. et al. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature 464, 713–720 (2010).

  28. Pennacchio, L.A., Loots, G.G., Nobrega, M.A. & Ovcharenko, I. Predicting tissue-specific enhancers in the human genome. Genome Res. 17, 201–211 (2007).

    Google Scholar 

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

    Google Scholar 

  30. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    Google Scholar 

  31. Dixon, A.L. et al. A genome-wide association study of global gene expression. Nat. Genet. 39, 1202–1207 (2007).

    Google Scholar 

  32. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. advance online publication, doi:10.1038/ng.686 (10 October 2010).

  33. Wells, J.C. Sexual dimorphism of body composition. Best Pract. Res. Clin. Endocrinol. Metab. 21, 415–430 (2007).

    Google Scholar 

  34. Aulchenko, Y.S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 41, 47–55 (2009).

    Google Scholar 

  35. Döring, A. et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat. Genet. 40, 430–436 (2008).

    Google Scholar 

  36. Shifman, S. et al. Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women. PLoS Genet. 4, e28 (2008).

    Google Scholar 

  37. Ridker, P.M. et al. Polymorphism in the CETP gene region, HDL cholesterol, and risk of future myocardial infarction: genomewide analysis among 18,245 initially healthy women from the Women's Genome Health Study. Circ. Cardiovasc. Genet. 2, 26–33 (2009).

    Google Scholar 

  38. Cooney, G.J. et al. Improved glucose homeostasis and enhanced insulin signalling in Grb14-deficient mice. EMBO J. 23, 582–593 (2004).

    Google Scholar 

  39. Zeggini, E. et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638–645 (2008).

    Google Scholar 

  40. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    Google Scholar 

  41. Boesgaard, T.W. et al. Variant near ADAMTS9 known to associate with type 2 diabetes is related to insulin resistance in offspring of type 2 diabetes patients–EUGENE2 study. PLoS ONE 4, e7236 (2009).

    Google Scholar 

  42. Nishimura, S. et al. Adipogenesis in obesity requires close interplay between differentiating adipocytes, stromal cells, and blood vessels. Diabetes 56, 1517–1526 (2007).

    Google Scholar 

  43. Silha, J.V., Krsek, M., Sucharda, P. & Murphy, L.J. Angiogenic factors are elevated in overweight and obese individuals. Int. J. Obes. (Lond) 29, 1308–1314 (2005).

    Google Scholar 

  44. García de la Torre, N. et al. Effects of weight loss after bariatric surgery for morbid obesity on vascular endothelial growth factor-A, adipocytokines, and insulin. J. Clin. Endocrinol. Metab. 93, 4276–4281 (2008).

    Google Scholar 

  45. Gesta, S. et al. Evidence for a role of developmental genes in the origin of obesity and body fat distribution. Proc. Natl. Acad. Sci. USA 103, 6676–6681 (2006).

    Google Scholar 

  46. Gesta, S., Tseng, Y.H. & Kahn, C.R. Developmental origin of fat: tracking obesity to its source. Cell 131, 242–256 (2007).

    Google Scholar 

  47. Lanctôt, C., Kaspar, C. & Cremer, T. Positioning of the mouse Hox gene clusters in the nuclei of developing embryos and differentiating embryoid bodies. Exp. Cell Res. 313, 1449–1459 (2007).

    Google Scholar 

  48. Candille, S.I. et al. Dorsoventral patterning of the mouse coat by Tbx15. PLoS Biol. 2, E3 (2004).

    Google Scholar 

  49. O'Rahilly, S. Human genetics illuminates the paths to metabolic disease. Nature 462, 307–314 (2009).

    Google Scholar 

  50. Krotkiewski, M., Bjorntorp, P., Sjostrom, L. & Smith, U. Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J. Clin. Invest. 72, 1150–1162 (1983).

    Google Scholar 

  51. Fox, C.S. et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116, 39–48 (2007).

    Google Scholar 

  52. Scherzer, R. et al. Simple anthropometric measures correlate with metabolic risk indicators as strongly as magnetic resonance imaging-measured adipose tissue depots in both HIV-infected and control subjects. Am. J. Clin. Nutr. 87, 1809–1817 (2008).

    Google Scholar 

  53. Vega, G.L. et al. Influence of body fat content and distribution on variation in metabolic risk. J. Clin. Endocrinol. Metab. 91, 4459–4466 (2006).

    Google Scholar 

  54. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).

    Google Scholar 

  55. 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).

    Google Scholar 

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

    Google Scholar 

  57. Cox, D.R. & Hinkley, D.V. Theoretical Statistics (Chapman and Hall, London, 1979).

  58. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Google Scholar 

  59. Stouffer, S.A., Suchman, E.A., De Vinney, L.C., Star, S.A. & Williams, R.M. Adjustment During Army Life (Princeton University Press, Princeton, New Jersey, USA, 1949).

  60. Whitlock, M.C. Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J. Evol. Biol. 18, 1368–1373 (2005).

    Google Scholar 

  61. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate–a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

Download references

Acknowledgements

Funding for this study was provided by the Academy of Finland (grants 104781, 120315, 129269, 117797, 121584, 126925, 129418, 129568, 77299, 124243, 213506, 129680, 129494, 10404, 213506, 129680, 114382, 126775, 127437, 129255, 129306, 130326, 209072, 210595, 213225 and 216374); an ADA Mentor-Based Postdoctoral Fellowship grant; Affymetrix, Inc., for genotyping services (N02-HL-6-4278); ALF/LUA Gothenburg; Althingi (the Icelandic Parliament); Amgen; AstraZeneca AB; Augustinus Foundation; Becket Foundation; Biocentrum Helsinki; Biomedicum Helsinki Foundation; Boston Obesity Nutrition Research Center (DK46200); British Diabetes Association (1192); British Diabetic Association Research; British Heart Foundation (97020, PG/02/128); Busselton Population Medical Research Foundation; Cambridge NIHR Comprehensive Biomedical Research Centre; CamStrad; Chief Scientist Office of the Scottish Government; Contrat Plan Etat Région de France; Danish Centre for Health Technology Assessment; Danish Diabetes Association; Danish Ministry of Internal Affairs and Health; Danish Heart Foundation; Danish Pharmaceutical Association; Danish Research Council; DIAB Core (German Network of Diabetes); Diabetes UK; Donald W. Reynolds Foundation; Dresden University of Technology Funding Grant, Med Drive; EMGO+ institute; Emil and Vera Cornell Foundation; Erasmus Medical Center and Erasmus University, Rotterdam, The Netherlands; Estonian Government SF0180142s08; European Commission (2004310, 212111, 205419, 245536, DG XII, HEALTH-F4-2007-201413, FP7/2007-2013, QLG1-CT-2000-01643, QLG2-CT-2002-01254, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHG-CT-2004-512066, LSHM-CT-2007-037273, EU/WLRT-2001-01254, LSHG-CT-2004-518153, SOC 95201408 05F02, Marie Curie Intra-European Fellowship); Federal Ministry of Education and Research, Germany (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 01 EA 9401); Federal State of Mecklenburg-West Pomerania; Finnish Diabetes Research Foundation; Finnish Diabetes Research Society; Finnish Foundation for Pediatric Research; Finnish Foundation of Cardiovascular Research; Finnish Medical Society; Finska Läkaresällskapet; Finnish Ministry of Education; Folkhälsan Research Foundation; Fond Européen pour le Développement Régional; Fondation LeDucq; Foundation for Life and Health in Finland; GEN-AU 'GOLD' from Austria; German Bundesministerium fuer Forschung und Technology (# 01 AK 803 A-H, # 01 IG 07015 G); German National Genome Research Net NGFN2 and NGFNplus (01GS0823, FKZ 01GS0823); German Research Council (KFO-152); GlaxoSmithKline; Göteborg Medical Society; Gyllenberg Foundation; Health Care Centers in Vasa, Närpes and Korsholm; Healthway, Western Australia; Helmholtz Center Munich; Helsinki University Central Hospital; Hjartavernd (the Icelandic Heart Association); Ib Henriksen Foundation; IZKF (B27); Jalmari and Rauha Ahokas Foundation; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International (JDRF); Karolinska Institute and the Stockholm County Council (560183); Knut and Alice Wallenberg Foundation; Lundbeck Foundation Centre of Applied Medical Genomics for Personalized Disease Prediction, Prevention and Care; Knut Krohn, Microarray Core Facility of the Interdisciplinary Centre for Clinical Research (IZKF), University of Leipzig, Germany; Lundberg Foundation; MC Health; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania, Germany; South Tyrol Ministry of Health; Ministry of Science, Education and Sport of the Republic of Croatia (216-1080315-0302); Medical Research Council UK (G0000649, G0601261, G9521010D, G0000934, G0500539, G0600331, PrevMetSyn); Montreal Heart Institute Foundation; MRC Centre for Obesity-Related Metabolic Disease; Municipal Health Care Center and Hospital in Jakobstad; Municipality of Rotterdam; Närpes Health Care Foundation; National Health and Medical Research Council of Australia and the Great Wine Estates Auctions; Netherlands Centre for Medical Systems Biology (SPI 56-464-1419); Netherlands Ministry for Health, Welfare and Sports; Netherlands Ministry of Education, Culture and Science; Netherlands Genomics Initiative; Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Organisation of Scientific Research Netherlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Investments (175.010.2005.011, 911-03-012, 904-61-090, 904-61-193, 480-04-004, 400-05-717); National Institute on Aging Intramural Research Program; US National Institutes of Health (CA047988, CA65725, CA87969, CA49449, CA67262, CA50385, DK075787, DK062370, DK58845, DK072193, K23-DK080145, K99HL094535, N01-HC85079 through N01-HC85086, N01-HG-65403, N01-AG-12100, N01-HC-25195, N01-HC35129, N01-HC15103, N01-HC55222, N01-HC75150, N01-HC45133, N01-HC55015, N01-HC55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, NO1-AG-1-2109, HL71981, HG005581, HG002651, HL084729, HL043851, HHSN268200625226C, K23-DK080145, MH084698, P30-DK072488, R01-DK075787, R01 HL087652, R01-HL087641, R01-HL59367, R01-HL086694, R01-HL087647, R01-HL087679, R01-HL087700, R01-AG031890, R01-HL088119, R01-DK068336, R01-DK075681, R01-DK-073490, R01-DK075787, R01-MH63706, U01-HL72515, U01-GM074518, U01-HL084756, U01-HG004399, UO1-CA098233, UL1-RR025005, UL1-RR025005, U01-HG004402, U01-DK062418, U01 HL080295, T32-HG00040, 263-MA-410953, 1RL1-MH083268-01, intramural project 1Z01-HG000024); National Institute for Health Research (NIHR); Neuroscience Campus Amsterdam; Novo Nordisk Foundation; Novo Nordisk Inc., Research Foundation of Copenhagen County; Ollqvist Foundation; Paavo Nurmi Foundation; Päivikki and Sakari Sohlberg Foundation; Pew Scholarship for the Biomedical Sciences; Perklén Foundation; Petrus and Augusta Hedlunds Foundation; Research Institute for Diseases in the Elderly (014-93-015, RIDE, RIDE2); Sahlgrenska Center for Cardiovascular and Metabolic Research (CMR, A305:188); Siemens Healthcare, Erlangen, Germany; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; Social Ministry of the Federal State of Mecklenburg-West Pomerania, Germany; South Tyrolean Sparkasse Foundation; State of Bavaria, Germany; Support for Science Funding programme; Swedish Cultural Foundation in Finland; Swedish Foundation for Strategic Research (SSF); Swedish Heart-Lung Foundation; Swedish Medical Research Council (8691, K2007-66X-20270-01-3, K2010-54X-09894-19-3); Swedish Society of Medicine; Swiss National Science Foundation (33CSCO-122661); the Royal Society; the Royal Swedish Academy of Science; Torsten and Ragnar Söderberg's Foundation; Turku University Hospitals; UK Department of Health Policy Research Programme; University and Research of the Autonomous Province of Bolzano; University Hospital Medical funds to Tampere; University Hospital Oulu, Biocenter, University of Oulu, Finland (75617); Västra Götaland Foundation; Wellcome Trust (077016/Z/05/Z, 068545/Z/02, 072960, 076113, 083270, 085301, 079557, 081682, 075491, 076113/B/04/Z, 091746/Z/10/Z, 079895, WT086596/Z/08/Z, WT Research Career Development Fellowship; WT Career Development Award); Western Australian Genetic Epidemiology Resource and the Western Australian DNA Bank (both National Health and Medical Research Council of Australia Enabling Facilities); Yrjö Jahnsson Foundation.

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Writing group: I.B., C.S.F., I.M.H. (lead), C.M.L. (lead), M.I.M., K.L. Mohlke, L.Q., V. Steinthorsdottir, G.T., M.C.Z.

Waist phenotype working group: T.L.A., N.B., I.B., L.A.C., C.M.D., C.S.F., T.B.H., I.M.H., A.U.J., C.M.L. (lead), R.J.F.L., R.M., M.I.M., K.L. Mohlke, L.Q., J.C.R., E.K.S., V. Steinthorsdottir, K. Stefansson, G.T., U.T., C.C.W., T.W., T.W.W., H.E.W., M.C.Z.

Data cleaning and analysis: S.I.B., I.M.H. (lead), E.I., A.U.J., H.L., C.M.L. (lead), R.J.F.L. (lead), J.L., R.M., L.Q., J.C.R., E.K.S., G.T., S.V., M.N.W., E.W., C.J.W., T.W.W., T.W.

Sex-specific analyses: S.I.B., T.E., I.M.H., A.U.J., T.O.K., Z.K., S.L., C.M.L., R.J.F.L., R.M., K.L. Monda, K.E.N., L.Q., J.C.R. (lead), V. Steinthorsdottir, G.T., T.W.W. (lead).

eQTL and expression analyses: S.I.B., A.L.D., C.C.H., J.N.H., F.K., L.M.K., C.M.L., L.L., R.J.F.L., J.L., M.F.M., J.L.M., C.M., G.N., E.E.S., E.K.S., V. Steinthorsdottir, G.T., K.T.Z.

Pathway and CNV analyses: C.M.L., S.A.M., M.I.M., J.N., V. Steinthorsdottir, G.T., B.F.V.

Secondary analyses: S.I.B., I.B.B., N.C., K.E., T.M.F., M.F.F., T.F., M.E.G., J.N.H., E.I., G.L., C.M.L., H.L., R.M., M. Mangino, M.I.M., K.L. Mohlke, D.R.N., J.R.O., S.P., J.R.B.P., J.C.R., A.V.S., E.K.S., P.M.V., M.N.W., C.J.W., R.J.W., E.W., A.R.W., J.Y.

Study-specific analyses: G.R.A., D.A., N.A., T.A., T.L.A., N.B., C.C., P.S.C., L.C., L.A.C., D.I.C., M.N.C., C.M.D., T.E., K.E., E.F., M.F.F., T.F., A.P.G., N.L.G., M.E.G., C. Hayward, N.L.H., I.M.H., J.J.H., A.U.J., Å.J., T. Johnson, J.O.J., J.R.K., M. Kaakinen, K. Kapur, S. Ketkar, J.W.K., P. Kraft, A.T.K., Z.K., J. Kettunen, C. Lamina, R.J.F.L., C. Lecoeur, H.L., M.F.L., C.M.L., J.L., R.W.L., R.M., M. Mangino, B.M., K.L. Monda, A.P.M., N.N., K.E.N., D.R.N., J.R.O., K.K.O., C.O., M.J.P., O. Polasek, I. Prokopenko, N.P., M.P., L.Q., J.C.R., N.W.R., S.R., F.R., N.R.R., C.S., L.J.S., K. Silander, E.K.S., K. Stark, S.S., A.V.S., N.S., U.S., V. Steinthorsdottir, D.P.S., I.S., M.L.T., T.M.T., N.J.T., A.T., G.T., A.U., S.V., V. Vitart, L.V., P.M.V., R.M.W., R.W., R.J.W., S.W., M.N.W., C.C.W., C.J.W., T.W.W., A.R.W., J.Y., J.H.Z., M.C.Z.

Study-specific genotyping: D.A., T.L.A., L.D.A., N.B., I.B., A.J.B., E.B., L.L.B., I.B.B., H.C., D.I.C., I.N.M.D., M. Dei, M.R.E., P.E., K.E., N.B.F., M.F., A.P.G., H.G., C.G., E.J.C.G., C.J.G., T. Hansen, A.L.H., N.H., C. Hayward, A.A.H., J.J.H., F.B.H., D.J.H., J.H., W.I., M.R.J., Å.J., J.O.J., J.W.K., P. Kovacs, A.T.K., H.K.K., J. Kettunen, P. Kraft, R.N.L., C.M.L., R.J.F.L., J.L., M.L.L., M.A.M., M. Mangino, W.L.M., M.I.M., J.B.J.M., M.J.N., M.N., D.R.N., K.K.O., C.O., O. Pedersen, L.P., M.J.P., G.P., A.N.P., N.P., L.Q., N.W.R., F.R., N.R.R., C.S., A.J.S., N.S., A.C.S., M.T., B.T., A.U., G.U., V. Vatin, P.M.V., H.W., P.Z.

Study-specific phenotyping: H.A., P.A., D.A., A.M.A., T.L.A., B.B., S.R.B., R.B., E.B., I.B.B., J.P.B., M. Dörr, C.M.D., P.E., M.F.F., C.S.F., T.M.F., M.F., S.G., J.G., L.C.G., T. Hansen, A.S.H., C. Hengstenberg, A.L.H., A.T.H., K.H.H., A. Hofman, F.B.H., D.J.H., B.I., T.I., T. Jørgensen, P.J., M.R.J., Å.J., A.J., A.L.J., J.O.J., F.K., L.K., J. Kuusisto, K. Kvaloy, R.K., S. Ketkar, J.W.K., I.K., S. Koskinen, V.K., M. Kähönen, P. Kovacs, O.L., R.N.L., B.L., J.L., G.M.L., R.J.F.L., T.L., M. Mangino, M.I.M., C.O., B.M.P., O. Pedersen, C.G.P.P., J.F.P., I. Pichler, K.P., O. Polasek, A.P., L.Q., M.R., I.R., O.R., V. Salomaa, J. Saramies, P.E.H.S., K. Silander, N.J.S., J.H.S., T.D.S., D.P.S., R.S., H.M.S., J. Sinisalo, T.T., A.T., M.U., P.V., C.B.V., L.V., J.V., D.R.W., G.B.W., S.H.W., G.W., J.C.W., A.F.W., L.Z., P.Z.

Study-specific management: G.R.A., A.M.A., B.B., Y.B.S., R.N.B., H.B., J.S.B., S.B., M.B., E.B., D.I.B., I.B.B., J.P.B., M.J.C., F.S.C., L.A.C., G.D., C.M.D., S.E., G.E., P.F., C.S.F., T.M.F., L.C.G., V.G., U.G., M.E.G., T. Hansen, C. Hengstenberg, K.H., A. Hamsten, T.B.H., A.T.H., A. Hofman, F.B.H., D.J.H., B.I., T.I., C.I., T. Jørgensen, M.R.J., A.L.J., F.K., K.T.K., W.H.L.K., R.K., J. Kaprio, M. Kähönen, M.L., D.A.L., L.J.L., C.M.L., R.J.F.L., T.L., M. Marre, T.M., A.M.E.T., K.M., M.I.M., K.L. Mohlke, P.B.M., K.E.N., M.S.N., D.R.N., B.O., C.O., O. Pedersen, L.P., B.W.P., P.P.P., B.M.P., L.J.P., T.Q., A.R., I.R., O.R., P.M.R., V. Salomaa, P.S., D.S., A.R.S., N.S., T.D.S., K. Stefansson, D.P.S., A.C.S., M.S., T.T., J.T., U.T., A.T., M.U., A.U., T.T.V., P.V., H.V., J.V., P.M.V., N.J.W., H.E.W., J.F.W., J.C.W., A.F.W.

Steering committee: G.R.A., T.L.A., I.B., S.I.B., M.B., I.B.B., P.D., C.M.D., C.S.F., T.M.F., L.C.G., T. Haritunians, J.N.H. (chair), D.J.H., E.I., R.K., R.J.F.L., M.I.M., K.L. Mohlke, K.E.N., J.R.O., L.P., D.S., D.P.S., U.T., H.E.W.

Corresponding authors

Correspondence to Iris M Heid, Mark I McCarthy, Caroline S Fox, Karen L Mohlke or Cecilia M Lindgren.

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Competing interests

I.B. and spouse own stock in Incyte Ltd and GlaxoSmithKline. J.H. is a member of the Scientific Advisory Board, Correlagen, Inc. A.P. is employed by Amgen. K.S., V.S., G.T., U.T. and G.B.W. are employed by deCODE Genetics.

Additional information

On behalf of the MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium) investigators.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–11, Supplementary Figure 1 and Supplementary Note. (PDF 1108 kb)

Supplementary Table 11

3,113 SNPs tagging the 856 CNVs in the HapMap 3 catalog across all HapMap3 populations (XLS 217 kb)

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Heid, I., Jackson, A., Randall, J. 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). https://doi.org/10.1038/ng.685

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  • DOI: https://doi.org/10.1038/ng.685

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