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Hidden heritability due to heterogeneity across seven populations

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 popula...

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Autores principales: Tropf, Felix C., Lee, S. Hong, Verweij, Renske M., Stulp, Gert, van der Most, Peter J., de Vlaming, Ronald, Bakshi, Andrew, Briley, Daniel A., Rahal, Charles, Hellpap, Robert, Nyman, Anastasia, Esko, Tõnu, Metspalu, Andres, Medland, Sarah E., Martin, Nicholas G., Barban, Nicola, Snieder, Harold, Robinson, Matthew R., Mills, Melinda C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642946/
https://www.ncbi.nlm.nih.gov/pubmed/29051922
http://dx.doi.org/10.1038/s41562-017-0195-1
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author Tropf, Felix C.
Lee, S. Hong
Verweij, Renske M.
Stulp, Gert
van der Most, Peter J.
de Vlaming, Ronald
Bakshi, Andrew
Briley, Daniel A.
Rahal, Charles
Hellpap, Robert
Nyman, Anastasia
Esko, Tõnu
Metspalu, Andres
Medland, Sarah E.
Martin, Nicholas G.
Barban, Nicola
Snieder, Harold
Robinson, Matthew R.
Mills, Melinda C.
author_facet Tropf, Felix C.
Lee, S. Hong
Verweij, Renske M.
Stulp, Gert
van der Most, Peter J.
de Vlaming, Ronald
Bakshi, Andrew
Briley, Daniel A.
Rahal, Charles
Hellpap, Robert
Nyman, Anastasia
Esko, Tõnu
Metspalu, Andres
Medland, Sarah E.
Martin, Nicholas G.
Barban, Nicola
Snieder, Harold
Robinson, Matthew R.
Mills, Melinda C.
author_sort Tropf, Felix C.
collection PubMed
description 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.
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spelling pubmed-56429462018-03-11 Hidden heritability due to heterogeneity across seven populations Tropf, Felix C. Lee, S. Hong Verweij, Renske M. Stulp, Gert van der Most, Peter J. de Vlaming, Ronald Bakshi, Andrew Briley, Daniel A. Rahal, Charles Hellpap, Robert Nyman, Anastasia Esko, Tõnu Metspalu, Andres Medland, Sarah E. Martin, Nicholas G. Barban, Nicola Snieder, Harold Robinson, Matthew R. Mills, Melinda C. Nat Hum Behav Article 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. 2017-09-11 2017-10 /pmc/articles/PMC5642946/ /pubmed/29051922 http://dx.doi.org/10.1038/s41562-017-0195-1 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Tropf, Felix C.
Lee, S. Hong
Verweij, Renske M.
Stulp, Gert
van der Most, Peter J.
de Vlaming, Ronald
Bakshi, Andrew
Briley, Daniel A.
Rahal, Charles
Hellpap, Robert
Nyman, Anastasia
Esko, Tõnu
Metspalu, Andres
Medland, Sarah E.
Martin, Nicholas G.
Barban, Nicola
Snieder, Harold
Robinson, Matthew R.
Mills, Melinda C.
Hidden heritability due to heterogeneity across seven populations
title Hidden heritability due to heterogeneity across seven populations
title_full Hidden heritability due to heterogeneity across seven populations
title_fullStr Hidden heritability due to heterogeneity across seven populations
title_full_unstemmed Hidden heritability due to heterogeneity across seven populations
title_short Hidden heritability due to heterogeneity across seven populations
title_sort hidden heritability due to heterogeneity across seven populations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642946/
https://www.ncbi.nlm.nih.gov/pubmed/29051922
http://dx.doi.org/10.1038/s41562-017-0195-1
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