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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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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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. |
format | Online Article Text |
id | pubmed-5642946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
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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