Meta-analysis of the heritability of human traits based on fifty years of twin studies (original) (raw)

Nature Genetics volume 47, pages 702–709 (2015)Cite this article

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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.

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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).

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

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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; _h_2, heritability; _c_2, shared environment; _h_2 same sex; _c_2 same sex; _h_2 males; _c_2 males; _h_2 female; _c_2 females.

Supplementary Figure 3 Funnel plots for _r_MZ across the major trait domains.

The plots denote the relationship between the _Z_-transformed _r_MZ and its standard error. SE, standard error.

Supplementary Figure 4 Funnel plots for _r_DZ across the major trait domains.

The plots denote the relationship between the _Z_-transformed _r_DZ and its standard error. SE, standard error.

Supplementary Figure 5 Funnel plots for _h_2 across the major trait domains.

The plots denote the relationship between the _Z_-transformed _h_2 and its standard error. SE, standard error.

Supplementary Figure 6 Funnel plots for _c_2 across the major trait domains.

The plots denote the relationship between the _Z_-transformed _c_2 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.

_r_MZ, monozygotic twin correlation; _r_DZ, dizygotic twin correlation; _r_DZSS, DZ same-sex twin correlation; _r_MZM, MZ male twin correlation; _r_MZF, MZ female twin correlation; _r_DZM, DZ male twin correlation; _r_DZF, DZ female twin correlation; _r_DOS, DZ opposite-sex twin correlation; _h_2, heritability; _c_2, shared environment; _h_2 same sex;_c_2 same sex; _h_2 males; _c_2 males; _h_2 females; _c_2 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.

_r_MZ, monozygotic twin correlation; _r_DZ, dizygotic twin correlation; _r_DZSS, DZ same-sex twin correlation; _r_MZM, MZ male twin correlation; _r_MZF, MZ female twin correlation; _r_DZM, DZ male twin correlation; _r_DZF, DZ female twin correlation; _r_DOS, DZ opposite-sex twin correlation.

Supplementary Figure 9 The correlation between variance component estimates (_h_2 or _c_2) from maximum-likelihood (BEST or FULL models) (x axis) compared to the least-squares estimates (y axis).

Supplementary Figure 10 The difference between variance components estimates from maximum-likelihood (BEST model) and least-squares (_h_2, left panel; _c_2, right panel) for given sample size.

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.

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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 Genet 47, 702–709 (2015). https://doi.org/10.1038/ng.3285

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