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Genotype–covariate interaction effects and the heritability of adult body mass index

Nature Genetics volume 49, pages 1174–1181 (2017)Cite this article

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Abstract

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.

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Figure 1: Systematic inflation of BMI heritability estimates in close relatives that share developmental environments.
Figure 2: Genotype–covariate interactions described through estimation of genetic variance and genetic correlations across a gradient.
Figure 3: Genotype–age interactions for BMI and height in 43,407 individuals in the composite AHTHEL sample.
Figure 4: Genotype–environment interactions for BMI in 97,510 participants of the UK Biobank study across a range of lifestyle factors.
Figure 5: A Bayesian mixture model of common HapMap3 SNP markers for BMI and height in 107,488 participants of the UK Biobank study.

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Acknowledgements

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).

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Authors and Affiliations

  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. Department of Economics, Center for Experimental Social Science, New York University, New York, New York, USA

    David Cesarini

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  1. Matthew R Robinson
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The LifeLines Cohort Study

Contributions

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.

Corresponding authors

Correspondence to Matthew R Robinson or Peter M Visscher.

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Supplementary Figure 1 Simulation study of genotype–covariate interaction using observed genotype data from 25,000 randomly selected unrelated individuals in the UK Biobank study.

(a) Estimates of the variance tagged by common SNP markers from multivariate REML (MV-GREML) and random regression REML (RR-GREML) models across five different simulation scenarios (see Online Methods). For each simulation scenario, there were five measurement points, representing different points along a continuous gradient. The estimates of the RR-GREML models come from a model fitting the highest order of Legendre polynomial function that gave a significant improvement in model fit based on a log-likelihood ratio test (LRT). Estimates are the mean value obtained over 10 simulation replicates and error bars give the s.d. across replicates. The solid lines give the pattern of variance change predicted from the polynomial function of the RR-GREML models and dotted lines give the approximate s.d. across replicates (see Online Methods). (b) The analysis of a is repeated but the phenotype is standardized with an inverse normal transformation within each measurement point. (c) Simulated and estimated correlation in common SNP marker effects from MV-GREML models (green circles) and RR-GREML models of first order polynomial (blue circles). (d) P-value of a LRT with two degrees of freedom of a first order polynomial RR-GREML model versus a zero order RR-GREML model for simulation scenarios with and without genotype-covariate interaction and with and without-standardization. Points give the mean, and error bars show the full distribution of the LRT statistic P-values across all 10 replicates. (e) P-value of a LRT of increasing orders of polynomial (k = 1, 2, 3, 4) RR-GREML models across different simulation scenarios.

Supplementary Figure 2 Simulation study of genotype–covariate interaction using observed genotype data from 25,000 randomly selected unrelated individuals in the UK Biobank study.

(a) Estimates of SNP coheritability, V(G), and variance attributable to genotype-covariate interaction effects, V(GCI), from genotype-covariate interaction (GCI-GREML) models across five simulation scenarios described in Supplementary Figure 1, for both rank inverse normal transformed phenotypes and unstandardized ones. For each simulation scenario, there were five measurement points, representing different points along a continuous gradient, and the estimates are the mean value obtained over 10 simulation replicates with error bars giving the s.d. across replicates. (b) We repeat the GCI-GREML models but we only use the extreme measurement points. Expectations for V(G) and V(GCI) were derived from our theory (see Supplementary Note). (c) The significance of the likelihood ratio tests comparing the fit to the data of a GCI-GREML model (fitting a genetic variance component and a genetic interaction variance component) and a null GREML model (fitting a single genetic variance component) across the simulation scenarios.

Supplementary Figure 3 Phenotypic variance and variance tagged by common HapMap3 SNP loci for body mass index (BMI) and height in the AHTHEL sample.

(a,b) The phenotypic variance of BMI (a) and height (b) were unstandardized within age groups allowing testing for variance heterogeneity for both traits. The phenotypic variance is shown in grey circles. The variance captured by common HapMap3 SNP loci from both an MV-GREML model is shown in blue circles for BMI, and green circles for height. The estimates from a zero order RR-GREML model are shown by a solid blue line for BMI, and a solid green line for height, with dashed lines giving the approximate s.e. LRT values give the likelihood ratio test statistic values for a RR-GREML model with a first order polynomial as compared to a RR-GREML model of zero order, to test for the presence of changes in variance tagged by SNP markers.

Supplementary Figure 4 No evidence for genotype–age interaction for BMI in 107,488 individuals from the UK Biobank study.

(a,b) The UK Biobank sample contained individuals measured between the ages of 46 and 73 and we divided the age distribution into deciles of approximately equal sample size. The age ranges were: (1) 9,655 individuals aged 40 to 44; (2) 10,379 individuals aged 45 to 48; (3) 9,205 individuals aged 49 to 51; (4) 13,669 individuals aged 52 to 55; (5) 7,689 individuals aged 56 and 57; (6) 8,590 individuals aged 58 and 59; (7) 11,211 individuals aged 60 and 61; (8) 11,019 individuals aged 62 and 63; (9) 14,559 individuals aged 64 to 66; and (10) 11,612 individuals aged 67 and above. We corrected for sex effects and standardized BMI measures within each decile with a rank inverse normal transformation to remove differences in phenotypic mean and variance across age. We used a full MV-GREML model to estimate the proportion of variance attributable to common HapMap3 SNPs for each age groups as shown in blue dots with s.e. bars (a), and the covariance in genome-wide SNP effects across age groups (b), with s.e. of the genetic correlations among age groups given in brackets. We used a RR-GREML model with a first order polynomial and compared the model fit to the data to a RR-GREML model of zero order, to test for the presence of genotype-age interaction effects using a likelihood-ratio test (LRT). We find no evidence for genotype-age interaction effects (LRT = 1.02, P = 0.601) and thus the estimates of the RR-GREML model of zero order are presented in a with dashed lines giving the approximate s.e.

Supplementary Figure 5 Correlations among the self-reported lifestyle factors found to influence BMI of 97,510 individuals of the UK Biobank study.

A description of the variables and the data fields can be found in Supplementary Table 5 and the Supplementary Note.

Supplementary Figure 6 Phenotypic variance and variance tagged by common HapMap3 SNP loci for BMI of 97,510 individuals within the UK Biobank study across self-reported lifestyle factors.

A series of self-report lifestyle factors shown to significantly influence mean BMI within the UK Biobank were used to group individuals. We corrected BMI for sex, age and the effects of all lifestyle factors and then converted BMI values to a z-score across but not within groups. This means that the phenotypic variance of BMI was unstandardized within groups allowing testing for variance heterogeneity and these values are shown in grey circles. Estimates of the variance captured by common HapMap3 SNP loci from a MV-GREML model are shown in blue circles, and those from a first order RR-GREML model are given by a solid blue line, with dashed line giving the approximate s.e. For the RR-GREML model estimates, we present the model of best fit to the data, as assessed by likelihood-ratio test statistics (LRT), which are given in Supplementary Table 6.

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Robinson, M., English, G., Moser, G. et al. Genotype–covariate interaction effects and the heritability of adult body mass index. Nat Genet 49, 1174–1181 (2017). https://doi.org/10.1038/ng.3912

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