Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype–covariate interaction effects at common SNP loci. We find evidence for genotype–age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10−18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype–environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10−5 and LRT = 30.80, P = 1.42 × 10−8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    , & Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9, 255–266 (2008).

  2. 2.

    & Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).

  3. 3.

    , , , & Estimation and partition of heritability in human populations using whole-genome analysis methods. Annu. Rev. Genet. 47, 75–95 (2013).

  4. 4.

    , , & Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

  5. 5.

    et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

  6. 6.

    et al. Sex differences in heritability of BMI: a comparative study of results from twin studies in eight countries. Twin Res. 6, 409–421 (2003).

  7. 7.

    et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front. Endocrinol. (Lausanne) 3, 29 (2012).

  8. 8.

    et al. Genetic variability of adult body mass index: a longitudinal assessment in Framingham families. Obes. Res. 10, 675–681 (2002).

  9. 9.

    , & Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 8, e1002637 (2012).

  10. 10.

    et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013).

  11. 11.

    et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

  12. 12.

    et al. Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs. Am. J. Hum. Genet. 93, 865–875 (2013).

  13. 13.

    et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

  14. 14.

    et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

  15. 15.

    et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

  16. 16.

    et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

  17. 17.

    et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

  18. 18.

    et al. Dominance genetic variation contributes little to the missing heritability for human complex traits. Am. J. Hum. Genet. 96, 377–385 (2015).

  19. 19.

    & Familial resemblance of body mass index and familial risk of high and low body mass index. A study of young men in Sweden. Int. J. Obes. Relat. Metab. Disord. 26, 1225–1231 (2002).

  20. 20.

    et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

  21. 21.

    , , , & Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism–derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

  22. 22.

    , & Estimating the covariance structure of traits during growth and ageing, illustrated with lactation in dairy cattle. Genet. Res. 64, 57–69 (1994).

  23. 23.

    , & Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993 (1990).

  24. 24.

    Estimating covariance functions for longitudinal data using a random regression model. Genet. Sel. Evol. 30, 221 (1998).

  25. 25.

    & Up hill, down dale: quantitative genetics of curvaceous traits. Phil. Trans. R. Soc. Lond. B 360, 1443–1455 (2005).

  26. 26.

    , , , & Environmental heterogeneity generates fluctuating selection on a secondary sexual trait. Curr. Biol. 18, 751–757 (2008).

  27. 27.

    , , & GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  28. 28.

    et al. Stable genes and changing environments: body mass index across adolescence and young adulthood. Behav. Genet. 40, 495–504 (2010).

  29. 29.

    , & A twin study of human obesity. JAMA 256, 51–54 (1986).

  30. 30.

    , & Genetic and environmental contributions to the association between body height and educational attainment: a study of adult Finnish twins. Behav. Genet. 30, 477–485 (2000).

  31. 31.

    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. 11, e1005378 (2015).

  32. 32.

    et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781 (2014).

  33. 33.

    & Genetics of obesity: what have we learned? Curr. Genomics 12, 169–179 (2011).

  34. 34.

    et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 367, 1387–1396 (2012).

  35. 35.

    et al. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation 126, 1821–1827 (2012).

  36. 36.

    , & Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat. Commun. 7, 12724 (2016).

  37. 37.

    et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012).

  38. 38.

    et al. Cohort of birth modifies the association between FTO genotype and BMI. Proc. Natl. Acad. Sci. USA 112, 354–359 (2015).

  39. 39.

    et al. Gene–obesogenic environment interactions in the UK Biobank study. Int. J. Epidemiol. 46, 559–575 (2017).

  40. 40.

    , , & Warped linear mixed models for the genetic analysis of transformed phenotypes. Nat. Commun. 5, 4890 (2014).

  41. 41.

    et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 11, e1004969 (2015).

  42. 42.

    et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 16 (2017).

  43. 43.

    , & Sex-specific genetic architecture of human disease. Nat. Rev. Genet. 9, 911–922 (2008).

  44. 44.

    , , & The sex-specific genetic architecture of quantitative traits in humans. Nat. Genet. 38, 218–222 (2006).

  45. 45.

    et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).

  46. 46.

    et al. Genome-wide genetic homogeneity between sexes and populations for human height and body mass index. Hum. Mol. Genet. 24, 7445–7449 (2015).

  47. 47.

    et al. Genetic mechanisms leading to sex differences across common diseases and anthropometric traits. Genetics 205, 979–992 (2017).

  48. 48.

    , & in Unequal Chances: Family Background and Economic Success (eds. Bowles, S., Gintis, H. & Osborne-Grave, M.) 145–164 (Princeton University Press, 2005).

  49. 49.

    et al. The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res. Hum. Genet. 16, 317–329 (2013).

  50. 50.

    & MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information. Bioinformatics 32, 1420–1422 (2016).

  51. 51.

    , & Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML. Genet. Sel. Evol. 36, 363–369 (2004).

  52. 52.

    & Variance components testing in the longitudinal mixed effects model. Biometrics 50, 1171–1177 (1994).

  53. 53.

    et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J. Dairy Sci. 95, 4114–4129 (2012).

  54. 54.

    Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257 (2009).

  55. 55.

    , & Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

Download references


We thank the anonymous reviewers for their insightful comments; the participants of the cohort studies; our colleagues at the Program in Complex Trait Genomics and M. Goddard for comments and suggestions. The authors also wish to thank the staff, contributing research centers and the participants of all studies. The UK Biobank research was conducted using the UK Biobank Resource under project 12505. The University of Queensland group is supported by the Australian Research Council (Discovery Project 160103860), the Australian National Health and Medical Research Council (1080157, 1078037, 1048853, 1050218, and 1113400), and the NIH (R21ESO25052-01 and PO1GMO99568). J.Y. is supported by a Charles and Sylvia Viertel Senior Medical Research Fellowship. M.R.R. is supported by the University of Lausanne. D.C. and M.J. were supported by the Swedish Research Council (421-2013-1061), the Ragnar Söderberg Foundation (E9/11), and the Jan Wallander and Tom Hedelius Foundation (P2015-0001:1). The ARIC study is carried out as a collaborative study supported by the US National Heart, Lung, and Blood Institute (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The Estonian Genome Centre of University of Tartu Study was supported by EU Horizon 2020 grants 692145, 676550, and 654248; Estonian Research Council Grant IUT20-60, NIASC, EIT–Health; NIH BMI grant 2R01DK075787-06A1; and the European Regional Development Fund (project 2014-2020.4.01.15-0012 GENTRANSMED). The Health and Retirement Study is supported by the US National Institute on Aging (NIA; U01AG009740). The genotyping was funded separately by the NIA (RC2 AG036495 and RC4 AG039029) and was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the HRS data were performed by the Genetics Coordinating Center at the University of Washington. The LifeLines Cohort Study and generation and management of GWAS genotype data for the LifeLines Cohort Study were supported by the Netherlands Organization of Scientific Research NWO (175.010.2007.006); the Economic Structure Enhancing Fund of the Dutch government; the Ministry of Economic Affairs; the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the Northern Netherlands Collaboration of Provinces; the Province of Groningen; University Medical Center Groningen; the University of Groningen; the Dutch Kidney Foundation; and the Dutch Diabetes Research Foundation. The Nurses Health Study (NHS) and Health Professionals Follow-up Studies (HPFS) received funding support for the GWAS of Gene and Environment Initiatives in Type 2 Diabetes through the NIH Genes, Environment and Health Initiative (GEI) (U01HG004399). The human subjects participating in the GWAS derived from NHS and HPFS and these studies are supported by National Institutes of Health grants CA87969, CA55075 and DK58845. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the Gene Environment Association Studies, GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH GEI (U01HG004424). The Swedish Twin Registry (TWINGENE) was supported by the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254), NIH DK U01-066134, the Swedish Foundation for Strategic Research (SSF), and the Heart and Lung Foundation (20070481).

Author information


  1. Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.

    • Matthew R Robinson
    • , Geoffrey English
    • , Gerhard Moser
    • , Luke R Lloyd-Jones
    • , Zhihong Zhu
    • , Jian Yang
    •  & Peter M Visscher
  2. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.

    • Matthew R Robinson
  3. Queensland Brain Institute, The University of Queensland, Brisbane, Australia.

    • Marcus A Triplett
  4. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

    • Ilja M Nolte
    •  & Harold Snieder
  5. Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

    • Jana V van Vliet-Ostaptchouk
  6. Estonian Genome Center, University of Tartu, Tartu, Estonia.

    • Tonu Esko
    • , Lili Milani
    • , Reedik Mägi
    •  & Andres Metspalu
  7. Division of Endocrinology, Boston Children's Hospital, Cambridge, Massachusetts, USA.

    • Tonu Esko
  8. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    • Tonu Esko
  9. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Tonu Esko
  10. Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.

    • Andres Metspalu
  11. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

    • Patrik K E Magnusson
    •  & Nancy L Pedersen
  12. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.

    • Erik Ingelsson
  13. Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.

    • Erik Ingelsson
  14. Stockholm School of Economics, Stockholm, Sweden.

    • Magnus Johannesson
  15. Center for Experimental Social Science, Department of Economics, New York University, New York, New York, USA.

    • David Cesarini


  1. The LifeLines Cohort Study

    A full list of members and affiliations appears in the Supplementary Note.


  1. Search for Matthew R Robinson in:

  2. Search for Geoffrey English in:

  3. Search for Gerhard Moser in:

  4. Search for Luke R Lloyd-Jones in:

  5. Search for Marcus A Triplett in:

  6. Search for Zhihong Zhu in:

  7. Search for Ilja M Nolte in:

  8. Search for Jana V van Vliet-Ostaptchouk in:

  9. Search for Harold Snieder in:

  10. Search for Tonu Esko in:

  11. Search for Lili Milani in:

  12. Search for Reedik Mägi in:

  13. Search for Andres Metspalu in:

  14. Search for Patrik K E Magnusson in:

  15. Search for Nancy L Pedersen in:

  16. Search for Erik Ingelsson in:

  17. Search for Magnus Johannesson in:

  18. Search for Jian Yang in:

  19. Search for David Cesarini in:

  20. Search for Peter M Visscher in:


M.R.R. and P.M.V. conceived and designed the study. M.R.R. conducted all analysis, with contributions from G.E., G.M., L.R.L.-J., D.C., and M.A.T. G.M. developed the BayesR software, and J.Y. developed the GCTA software. The LifeLines Cohort Study, Z.Z., I.M.N., J.V.v.V.-O., H.S., T.E., L.M., R.M., A.M., P.K.E.M., N.L.P., E.I., M.J., J.Y., and D.C. provided study oversight, sample collection, and management. M.R.R. and P.M.V. wrote the manuscript. All authors reviewed and approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Matthew R Robinson or Peter M Visscher.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–6, Supplementary Tables 1–6 and Supplementary Note.

About this article

Publication history






Further reading