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Assortative mating biases marker-based heritability estimators

Author

Listed:
  • Richard Border

    (University of California
    University of Colorado Boulder
    Harvard T.H. Chan School of Public Health)

  • Sean O’Rourke

    (University of Colorado Boulder)

  • Teresa de Candia

    (University of Colorado Boulder)

  • Michael E. Goddard

    (University of Melbourne
    Biosciences Research Division)

  • Peter M. Visscher

    (University of Queensland)

  • Loic Yengo

    (University of Queensland)

  • Matt Jones

    (University of Colorado Boulder)

  • Matthew C. Keller

    (University of Colorado Boulder
    University of Colorado Boulder)

Abstract

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% – 23% higher than corrected estimates using UK Biobank data.

Suggested Citation

  • Richard Border & Sean O’Rourke & Teresa de Candia & Michael E. Goddard & Peter M. Visscher & Loic Yengo & Matt Jones & Matthew C. Keller, 2022. "Assortative mating biases marker-based heritability estimators," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28294-9
    DOI: 10.1038/s41467-022-28294-9
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    References listed on IDEAS

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    1. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
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    Cited by:

    1. Tabea Schoeler & Doug Speed & Eleonora Porcu & Nicola Pirastu & Jean-Baptiste Pingault & Zoltán Kutalik, 2023. "Participation bias in the UK Biobank distorts genetic associations and downstream analyses," Nature Human Behaviour, Nature, vol. 7(7), pages 1216-1227, July.
    2. Jennifer Sjaarda & Zoltán Kutalik, 2023. "Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity," Nature Human Behaviour, Nature, vol. 7(5), pages 776-789, May.
    3. Hans Fredrik Sunde & Nikolai Haahjem Eftedal & Rosa Cheesman & Elizabeth C. Corfield & Thomas H. Kleppesto & Anne Caroline Seierstad & Eivind Ystrom & Espen Moen Eilertsen & Fartein Ask Torvik, 2024. "Genetic similarity between relatives provides evidence on the presence and history of assortative mating," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    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.

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