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Quantification of the overall contribution of gene-environment interaction for obesity-related traits
The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070002/ https://www.ncbi.nlm.nih.gov/pubmed/32170055 http://dx.doi.org/10.1038/s41467-020-15107-0 |
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author | Sulc, Jonathan Mounier, Ninon Günther, Felix Winkler, Thomas Wood, Andrew R. Frayling, Timothy M. Heid, Iris M. Robinson, Matthew R. Kutalik, Zoltán |
author_facet | Sulc, Jonathan Mounier, Ninon Günther, Felix Winkler, Thomas Wood, Andrew R. Frayling, Timothy M. Heid, Iris M. Robinson, Matthew R. Kutalik, Zoltán |
author_sort | Sulc, Jonathan |
collection | PubMed |
description | The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). |
format | Online Article Text |
id | pubmed-7070002 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70700022020-03-18 Quantification of the overall contribution of gene-environment interaction for obesity-related traits Sulc, Jonathan Mounier, Ninon Günther, Felix Winkler, Thomas Wood, Andrew R. Frayling, Timothy M. Heid, Iris M. Robinson, Matthew R. Kutalik, Zoltán Nat Commun Article The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass). Nature Publishing Group UK 2020-03-13 /pmc/articles/PMC7070002/ /pubmed/32170055 http://dx.doi.org/10.1038/s41467-020-15107-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sulc, Jonathan Mounier, Ninon Günther, Felix Winkler, Thomas Wood, Andrew R. Frayling, Timothy M. Heid, Iris M. Robinson, Matthew R. Kutalik, Zoltán Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title | Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title_full | Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title_fullStr | Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title_full_unstemmed | Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title_short | Quantification of the overall contribution of gene-environment interaction for obesity-related traits |
title_sort | quantification of the overall contribution of gene-environment interaction for obesity-related traits |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070002/ https://www.ncbi.nlm.nih.gov/pubmed/32170055 http://dx.doi.org/10.1038/s41467-020-15107-0 |
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