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Hidden heritability due to heterogeneity across seven populations

Author

Listed:
  • Felix C. Tropf

    (University of Oxford)

  • S. Hong Lee

    (University of New England)

  • Renske M. Verweij

    (University of Groningen)

  • Gert Stulp

    (University of Groningen)

  • Peter J. van der Most

    (University Medical Center Groningen, University of Groningen)

  • Ronald de Vlaming

    (Erasmus School of Economics
    University Amsterdam)

  • Andrew Bakshi

    (The University of Queensland)

  • Daniel A. Briley

    (University of Illinois at Urbana-Champaign)

  • Charles Rahal

    (University of Oxford)

  • Robert Hellpap

    (University of Oxford)

  • Anastasia N. Iliadou

    (Karolinska Institutet)

  • Tõnu Esko

    (University of Tartu)

  • Andres Metspalu

    (University of Tartu)

  • Sarah E. Medland

    (Queensland Institute of Medical Research Berghofer Medical Research Institute)

  • Nicholas G. Martin

    (Queensland Institute of Medical Research Berghofer Medical Research Institute)

  • Nicola Barban

    (University of Oxford)

  • Harold Snieder

    (University Medical Center Groningen, University of Groningen)

  • Matthew R. Robinson

    (The University of Queensland
    University of Lausanne)

  • Melinda C. Mills

    (University of Oxford)

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.

Suggested Citation

  • Felix C. Tropf & S. Hong Lee & Renske M. Verweij & Gert Stulp & Peter J. van der Most & Ronald de Vlaming & Andrew Bakshi & Daniel A. Briley & Charles Rahal & Robert Hellpap & Anastasia N. Iliadou & T, 2017. "Hidden heritability due to heterogeneity across seven populations," Nature Human Behaviour, Nature, vol. 1(10), pages 757-765, October.
  • Handle: RePEc:nat:nathum:v:1:y:2017:i:10:d:10.1038_s41562-017-0195-1
    DOI: 10.1038/s41562-017-0195-1
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    Citations

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    Cited by:

    1. Rita Dias Pereira & Pietro Biroli & Titus Galama & Stephanie von Hinke & Hans van Kippersluis & Cornelius A. Rietveld & Kevin Thom, 2022. "Gene-Environment Interplay in the Social Sciences," Papers 2203.02198, arXiv.org, revised Aug 2022.
    2. Domingue, Benjamin & Trejo, Sam & Armstrong-Carter, Emma & Tucker-Drob, Elliot M., 2020. "Interactions between polygenic scores and environments: Methodological and conceptual challenges," SocArXiv u7sh4, Center for Open Science.
    3. Nicola Barban & Elisabetta De Cao & Marco Francesconi, 2021. "Gene-Environment Effects on Female Fertility," CESifo Working Paper Series 9337, CESifo.
    4. Hans Kippersluis & Pietro Biroli & Rita Dias Pereira & Titus J. Galama & Stephanie Hinke & S. Fleur W. Meddens & Dilnoza Muslimova & Eric A. W. Slob & Ronald Vlaming & Cornelius A. Rietveld, 2023. "Overcoming attenuation bias in regressions using polygenic indices," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Kailaheimo-Lönnqvist, Sanna & Kuja-Halkola, Ralf & Larsson, Henrik & Lichtenstein, Paul & Latvala, Antti, 2022. "Parental criminality and children's educational attainment: A population-based extended family study," Journal of Criminal Justice, Elsevier, vol. 81(C).
    6. Md. Moksedul Momin & Jisu Shin & Soohyun Lee & Buu Truong & Beben Benyamin & S. Hong Lee, 2023. "A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. Lauren Gaydosh & Daniel W. Belsky & Benjamin W. Domingue & Jason D. Boardman & Kathleen Mullan Harris, 2018. "Father Absence and Accelerated Reproductive Development in Non-Hispanic White Women in the United States," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1245-1267, August.

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