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Title: Hidden heritability due to heterogeneity across seven populations
Contributor(s): Tropf, Felix C (author); Lee, S Hong  (author); Verweij, Renske M (author); Stulp, Gert (author); van der Most, Peter J (author); de Vlaming, Ronald (author); Bakshi, Andrew (author); Briley, Daniel A (author); Rahal, Charles (author); Hellpap, Robert (author); Nyman, Anastasia (author); Esko, Tõnu (author); Metspalu, Andres (author); Medland, Sarah E (author); Martin, Nicholas G (author); Barban, Nicola (author); Snieder, Harold (author); Robinson, Matthew R (author); Mills, Melinda C (author)
Publication Date: 2017-10
Early Online Version: 2017-09-11
DOI: 10.1038/s41562-017-0195-1
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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.

Publication Type: Journal Article
Source of Publication: Nature Human Behaviour, 1(10), p. 757-765
Publisher: Nature Publishing Group
Place of Publication: United Kingdom
ISSN: 2397-3374
Fields of Research (FoR) 2020: 310207 Statistical and quantitative genetics
Socio-Economic Objective (SEO) 2020: 280102 Expanding knowledge in the biological sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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