Movatterモバイル変換

[0]ホーム
URL:

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.

Advertisement

Nature Genetics
  • Analysis
  • Published:

Meta-analysis of the heritability of human traits based on fifty years of twin studies

Nature Geneticsvolume 47, pages702–709 (2015)Cite this article

Subjects

Abstract

Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.

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

Access options

Access through your institution

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

￥ 4,980

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

Figure 1: Distribution of the investigated traits in virtually all twin studies published between 1958 and 2012.
Figure 2: Twin correlations and heritabilities for all human traits studied.
Figure 3: Twin correlations for the top 20 most investigated specific traits by age and sex.

Similar content being viewed by others

References

  1. Moore, J.H. Analysis of gene-gene interactions.Curr. Protoc. Hum. Genet.Chapter 1, Unit 1.14 (2004).

  2. Hill, W.G., Goddard, M.E. & Visscher, P.M. Data and theory point to mainly additive genetic variance for complex traits.PLoS Genet.4, e1000008 (2008).

    Article  Google Scholar 

  3. Traynor, B.J. & Singleton, A.B. Nature versus nurture: death of a dogma, and the road ahead.Neuron68, 196–200 (2010).

    Article CAS  Google Scholar 

  4. Zuk, O., Hechter, E., Sunyaev, S.R. & Lander, E.S. The mystery of missing heritability: genetic interactions create phantom heritability.Proc. Natl. Acad. Sci. USA109, 1193–1198 (2012).

    Article CAS  Google Scholar 

  5. Phillips, P.C. Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems.Nat. Rev. Genet.9, 855–867 (2008).

    Article CAS  Google Scholar 

  6. Visscher, P.M., Brown, M.A., McCarthy, M.I. & Yang, J. Five years of GWAS discovery.Am. J. Hum. Genet.90, 7–24 (2012).

    Article CAS  Google Scholar 

  7. Manolio, T.A. et al. Finding the missing heritability of complex diseases.Nature461, 747–753 (2009).

    Article CAS  Google Scholar 

  8. Stranger, B.E., Stahl, E.A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics.Genetics187, 367–383 (2011).

    Article CAS  Google Scholar 

  9. Maher, B. Personal genomes: the case of the missing heritability.Nature456, 18–21 (2008).

    Article CAS  Google Scholar 

  10. Eichler, E.E. et al. Missing heritability and strategies for finding the underlying causes of complex disease.Nat. Rev. Genet.11, 446–450 (2010).

    Article CAS  Google Scholar 

  11. Nelson, R.M., Pettersson, M.E. & Carlborg, Ö. A century after Fisher: time for a new paradigm in quantitative genetics.Trends Genet.29, 669–676 (2013).

    Article CAS  Google Scholar 

  12. Barker, J.S. Inter-locus interactions: a review of experimental evidence.Theor. Popul. Biol.16, 323–346 (1979).

    Article CAS  Google Scholar 

  13. Cockerham, C.C. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present.Genetics39, 859–882 (1954).

    CAS PubMed PubMed Central  Google Scholar 

  14. Cockerham, C.C. inStatistical Genetics and Plant Breeding 53–94 (Nat. Acad. Sci. Nat. Res. Council Publ., 1963).

  15. Kempthorne, O. On the covariances between relatives under selfing with general epistacy.Proc. R. Soc. Lond. B Biol. Sci.145, 100–108 (1956).

    Article  Google Scholar 

  16. Crow, J.F. & Kimura, M.An Introduction To Population Genetics Theory (Harper and Row, 1970).

  17. Carlborg, O. & Haley, C.S. Epistasis: too often neglected in complex trait studies?Nat. Rev. Genet.5, 618–625 (2004).

    Article CAS  Google Scholar 

  18. Falconer, D.S. & Mackay, T.F.C.Quantitative Genetics (Longman Group, 1996).

  19. Lynch, M. & Walsch, B.Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).

  20. Begg, C.B. & Mazumdar, M. Operating characteristics of a rank correlation test for publication bias.Biometrics50, 1088–1101 (1994).

    Article CAS  Google Scholar 

  21. Egger, M., Davey Smith, G., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test.Br. Med. J.315, 629–634 (1997).

    Article CAS  Google Scholar 

  22. Rosenthal, R. The file drawer problem and tolerance for null results.Psychol. Bull.86, 91–106 (1979).

    Google Scholar 

  23. Schulze, R.Meta-Analysis: A Comparison Of Approaches (Hogrefe & Huber, 2004).

  24. Drasgow, F. inEncyclopedia of Statistical Sciences (eds. Kotz, S., Read, C.B., Balakrishnan, N. & Vidakovic, B.) Vol. 7, 68–74 (John Wiley & Sons, 2006).

  25. Olsson, U. Maximum likelihood estimation of the polychoric correlation coefficient.Psychometrika44, 443–460 (1979).

    Article  Google Scholar 

  26. Falconer, D.S. The inheritance of liability to certain diseases, estimated from the incidence among relatives.Ann. Hum. Genet.29, 51–76 (1965).

    Article  Google Scholar 

  27. Smith, C. Concordance in twins: methods and interpretation.Am. J. Hum. Genet.26, 454–466 (1974).

    CAS PubMed PubMed Central  Google Scholar 

  28. Jiang, H. & Doerge, R.W. Estimating the proportion of true null hypotheses for multiple comparisons.Cancer Inform.6, 25–32 (2008).

    Article  Google Scholar 

  29. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies.Proc. Natl. Acad. Sci. USA100, 9440–9445 (2003).

    Article CAS  Google Scholar 

  30. Bulik-Sullivan, B.K. & Sullivan, P.F. The authorship network of genome-wide association studies.Nat. Genet.44, 113 (2012).

    Article CAS  Google Scholar 

  31. Blondel, V., Guillaume, J., Lambiotte, R. & Lefebvre, E. Fast unfolding of community hierarchies in large networks.J. Stat. Mech.10, P10008 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank M. Frantsen, M.P. Roeling, R. Lee and D.M. DeCristo for their contribution to collecting the full texts of selected twin studies and data entry. This work was funded by the Netherlands Organization for Scientific Research (NWO VICI 453-14-005, NWO Complexity 645-000-003), by the Australian Research Council (DP130102666) and by the Australian National Health and Medical Research Council (APP613601).

Author information

Author notes

  1. Tinca J C Polderman and Beben Benyamin: These authors contributed equally to this work.

  2. Peter M Visscher and Danielle Posthuma: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands

    Tinca J C Polderman, Christiaan A de Leeuw & Danielle Posthuma

  2. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Beben Benyamin & Peter M Visscher

  3. Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, the Netherlands

    Christiaan A de Leeuw

  4. Department of Genetics, Center for Psychiatric Genomics, University of North Carolina, Chapel Hill, North Carolina, USA

    Patrick F Sullivan

  5. Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA

    Patrick F Sullivan

  6. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Patrick F Sullivan

  7. Faculty of Sciences, VU University, Amsterdam, the Netherlands

    Arjen van Bochoven

  8. University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia

    Peter M Visscher

  9. Department of Clinical Genetics, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands

    Danielle Posthuma

Authors
  1. Tinca J C Polderman
  2. Beben Benyamin
  3. Christiaan A de Leeuw
  4. Patrick F Sullivan
  5. Arjen van Bochoven
  6. Peter M Visscher
  7. Danielle Posthuma

Contributions

D.P., B.B., P.F.S. and P.M.V. performed the analyses. D.P. conceived the study. D.P., T.J.C.P. and P.M.V. designed the study. T.J.C.P. and D.P. collected and entered the data. D.P. and P.F.S. categorized traits according to standard classifications. A.v.B. and C.A.d.L. checked data entries, and checked and wrote statistical scripts. A.v.B. designed and programmed the webtool. D.P., T.J.C.P. and P.M.V. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence toDanielle Posthuma.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Authorship co-occurrence matrix on 2,748 twin studies published between 1958 and 2012.

Each colored cell represents two authors who appeared on the same paper; darker cells indicate authors that co-published more frequently. The filter of at least 25 papers per author was set for readability. The web application MaTCH has an interactive version of this matrix.

Supplementary Figure 2 Funnel plots across all traits for twin correlations and variance components.

Z,Z-converted correlation; MZ, monozygotic twins; DZ, dizygotic twins; DZSS, DZ same-sex twins; MZM, MZ male twins; MZF, MZ female twins; DZM, DZ male twins; DZF, DZ female twins; DOS, DZ opposite-sex twins;h2, heritability;c2, shared environment;h2 same sex;c2 same sex;h2 males;c2 males;h2 female;c2 females.

Supplementary Figure 3 Funnel plots forrMZ across the major trait domains.

The plots denote the relationship between theZ-transformedrMZ and its standard error. SE, standard error.

Supplementary Figure 4 Funnel plots forrDZ across the major trait domains.

The plots denote the relationship between theZ-transformedrDZ and its standard error. SE, standard error.

Supplementary Figure 5 Funnel plots forh2 across the major trait domains.

The plots denote the relationship between theZ-transformedh2 and its standard error. SE, standard error.

Supplementary Figure 6 Funnel plots forc2 across the major trait domains.

The plots denote the relationship between theZ-transformedc2 and its standard error. SE, standard error.

Supplementary Figure 7 Distribution of twin correlations and variance components in full and best models across all traits from 2,748 studies.

rMZ, monozygotic twin correlation;rDZ, dizygotic twin correlation;rDZSS, DZ same-sex twin correlation;rMZM, MZ male twin correlation;rMZF, MZ female twin correlation;rDZM, DZ male twin correlation;rDZF, DZ female twin correlation;rDOS, DZ opposite-sex twin correlation;h2, heritability;c2, shared environment;h2 same sex;c2 same sex;h2 males;c2 males;h2 females;c2 females. “BEST” denotes estimates from the most parsimonious models per study. All other estimates are from “FULL” models.

Supplementary Figure 8 Distribution of differences between MZ and DZ correlations.

rMZ, monozygotic twin correlation;rDZ, dizygotic twin correlation;rDZSS, DZ same-sex twin correlation;rMZM, MZ male twin correlation;rMZF, MZ female twin correlation;rDZM, DZ male twin correlation;rDZF, DZ female twin correlation;rDOS, DZ opposite-sex twin correlation.

Supplementary Figure 11 Scatterplots of all MZ versus DZ correlations.

Contour lines indicate the density of the data in that region. The lines are ‘heat’ colored from blue to red, indicating increasing data density.

Supplementary Figure 12 QQ plots of theχ2 test statistics for testing the null hypothesis that 2(rMZ –rDZ) = 0 and 2rDZ –rMZ = 0 and relationship with sample size.

(a) The deviation from the null hypotheses is quantified with the inflationλ in the QQ plots. (b) Effects as a function of sample size.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Note and Supplementary Tables 1–19, 22–24 and 26–31. (PDF 4786 kb)

Supplementary Tables 20, 21, 25, 32 and 33.

Supplementary Tables 20, 21, 25, 32 and 33. (XLSX 609 kb)

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polderman, T., Benyamin, B., de Leeuw, C.et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies.Nat Genet47, 702–709 (2015). https://doi.org/10.1038/ng.3285

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Advertisement

Search

Advanced search

Quick links

Nature Briefing

Sign up for theNature 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
[8]ページ先頭
©2009-2026 Movatter.jp