Abstract

Meta-analyses of genome-wide association studies, which dominate genetic discovery, are based on data from diverse historical time periods and populations. Genetic scores derived from genome-wide association studies explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the ‘hidden heritability’ puzzle. Using seven sampling populations (n = 35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller across populations compared with within populations. We show that the hidden heritability varies substantially: from zero for height to 20% for body mass index, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results are more likely to reflect heterogeneity in phenotypic measurement or gene–environment interactions than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene–environment interaction may be a central challenge for genetic discovery.

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Acknowledgements

Funding was provided by the European Research Council Consolidator Grant SOCIOGENOME (615603) and the UK Economic and Social Research Council/National Centre for Research Methods SOCGEN grant (ES/N011856/1), as well as the Wellcome Trust Institutional Strategic Support Fund and John Fell Fund (awarded to M.C.M.). The ARIC study is carried out as a collaborative study supported by the United States National Heart, Lung, and Blood Institute (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C). The EGCUT study was supported by European Union Horizon 2020 grants 692145, 676550 and 654248, Estonian Research Council Grant IUT20-60, the Nordic Information for Action e-Science Center, the European Institute of Innovation and Technology–Health, National Institutes of 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 United States National Institute on Aging (U01AG009740). The genotyping was funded separately by the United States National Institute on Aging (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 Organisation for Scientific Research (NWO 175.010.2007.006), Economic Structure Enhancing Fund of the Dutch government, Ministry of Economic Affairs, Ministry of Education, Culture and Science, Ministry for Health, Welfare and Sports, Northern Netherlands Collaboration of Provinces, Province of Groningen, University Medical Center Groningen, University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. The Swedish Twin Registry (TWINGENE) was supported by the Swedish Research Council (M-2005-1112), GenomEUtwin (EU/QLRT-2001-01254 and QLG2-CT-2002-01254), NIH DK U01-066134, Swedish Foundation for Strategic Research and Heart and Lung Foundation (20070481). The TwinsUK study was funded by the Wellcome Trust and European Community’s Seventh Framework Programme (FP7/2007–2013). The study also received support from the National Institute for Health Research-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via the NIH CIDR. For the QIMR data, funding was provided by the Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485 and 552498), Australian Research Council (A7960034, A79906588, A79801419, DP0770096, DP0212016 and DP0343921), FP-5 GenomEUtwin Project (QLG2-CT-2002-01254) and NIH (grants AA07535, AA10248, AA13320, AA13321, AA13326, AA14041, DA12854 and MH66206). A portion of the genotyping on which the QIMR study was based (the Illumina 370 K scans) was carried out at the CIDR through an access award to the authors’ late colleague R. Todd (Psychiatry, Washington University School of Medicine, St Louis). Imputation was carried out on the Genetic Cluster Computer, which is financially supported by the Netherlands Organisation for Scientific Research (NWO 480-05-003). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. Department of Sociology/Nuffield College, University of Oxford, Oxford, OX1 3UQ, UK

    • Felix C. Tropf
    • , Charles Rahal
    • , Robert Hellpap
    • , Nicola Barban
    •  & Melinda C. Mills
  2. School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia

    • S. Hong Lee
  3. Department of Sociology/Interuniversity Center for Social Science Theory and Methodology, University of Groningen, Groningen, 9712 TG, The Netherlands

    • Renske M. Verweij
    •  & Gert Stulp
  4. Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, 9700 RB, The Netherlands

    • Peter J. van der Most
    •  & Harold Snieder
  5. Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, 3062 PA, The Netherlands

    • Ronald de Vlaming
  6. Department of Complex Trait Genetics, University Amsterdam, Amsterdam, The Netherlands

    • Ronald de Vlaming
  7. Institute of Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia

    • Andrew Bakshi
    •  & Matthew R. Robinson
  8. Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, 61820-9998, USA

    • Daniel A. Briley
  9. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, Stockholm, SE-171 77, Sweden

    • Anastasia N. Iliadou
  10. Estonian Genome Center, University of Tartu, 51010, Tartu, Estonia

    • Tõnu Esko
    •  & Andres Metspalu
  11. Quantitative Genetics Laboratory, Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia

    • Sarah E. Medland
    •  & Nicholas G. Martin
  12. Department of Computational Biology, University of Lausanne, Lausanne, CH-1015, Switzerland

    • Matthew R. Robinson

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Contributions

F.C.T., S.H.L. and M.C.M. developed the study concept and design. M.R.R. and F.C.T. developed the concept for and performed the simulation studies. F.C.T., R.M.V., G.S. and C.R. performed the data analysis and visualization. T.E., A.M., S.E.M., N.G.M., A.N., S.H.L. and A.B. provided data and input on data analysis and interpretation, and imputed the data. F.C.T., M.C.M., S.H.L., M.R.R., H.S., G.S. and R.d.V. drafted the manuscript. F.C.T., M.C.M., M.R.R., R.M.V., G.S., P.J.v.d.M., R.d.V., N.G.M., N.B., D.A.B., C.R. and R.H. revised the manuscript. All authors approved the final version of the manuscript for submission.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Felix C. Tropf.

Electronic supplementary material

  1. Supplementary Information

    Supplementary Tables 1–8, Supplementary Notes 1–4, Supplementary References 1–9

  2. Life Sciences Reporting Summary