Hidden heritability due to heterogeneity across seven populations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-10

AUTHORS

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, Tõnu Esko, Andres Metspalu, Sarah E. Medland, Nicholas G. Martin, Nicola Barban, Harold Snieder, Matthew R. Robinson, Melinda C. Mills

ABSTRACT

Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from diverse historical time periods and populations. Genetic scores derived from GWAS 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 from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction 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. More... »

PAGES

757-765

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41562-017-0195-1

    DOI

    http://dx.doi.org/10.1038/s41562-017-0195-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091539794

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29051922


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