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Testing the key assumption of heritability estimates based on genome-wide genetic relatedness

Journal of Human Genetics 59, 342–345 (2014) | Download Citation

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  • A Corrigendum to this article was published on 25 June 2014

This article has been updated

Abstract

Comparing genetic and phenotypic similarity among unrelated individuals seems a promising way to quantify the genetic component of traits while avoiding the problematic assumptions plaguing twin- and other kin-based estimates of heritability. One approach uses a Genetic Relatedness Estimation through Maximum Likelihood (GREML) model for individuals who are related at less than 0.025 to predict their phenotypic similarity by their genetic similarity. Here we test the key underlying assumption of this approach: that genetic relatedness is orthogonal to environmental similarity. Using data from the Health and Retirement Study (and two other surveys), we show two unrelated individuals may be more likely to have been reared in a similar environment (urban versus nonurban setting) if they are genetically similar. This effect is not eliminated by controls for population structure. However, when we include this environmental confound in GREML models, heritabilities do not change substantially and thus potential bias in estimates of most biological phenotypes is probably minimal.

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Change history

  • 25 June 2014

    This article has been corrected since Advance Online Publication, and a corrigendum is also printed in this issue.

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Acknowledgements

This research uses data from The National Longitudinal Study of Adolescent Health (Add Health), a program project directed by Kathleen Mullan Harris and designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). The Framingham Heart Study (FHS; accession #7909-7) was supported by the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine, and the National Heart, Lung and Blood Institute's Framingham Heart Study. The Health and Retirement Study (HRS; accession number 0925-0670) is sponsored by the National Institute on Aging (grant numbers NIA U01AG009740, RC2AG036495, and RC4AG039029) and is conducted by the University of Michigan. Additional funding support for genotyping and analysis were provided by NIH/NICHD R01 HD060726.

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Affiliations

  1. Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA

    • Dalton Conley
    •  & Mark L Siegal

  2. Institute for Behavioral Science, University of Colorado, Boulder, CO, USA

    • Benjamin W Domingue
    •  & Jason D Boardman

  3. Department of Sociology, University of North Carolina, Chapel Hill, NC, USA

    • Kathleen Mullan Harris

  4. Department of Integrative Physiology, University of Colorado, Boulder, CO, USA

    • Matthew B McQueen

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Correspondence to Dalton Conley.

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DOI

https://doi.org/10.1038/jhg.2014.14

Supplementary Information accompanies the paper on Journal of Human Genetics website (http://www.nature.com/jhg)

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