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Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank
Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693916/ https://www.ncbi.nlm.nih.gov/pubmed/31453325 http://dx.doi.org/10.1126/sciadv.aaw3538 |
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author | Wang, Huanwei Zhang, Futao Zeng, Jian Wu, Yang Kemper, Kathryn E. Xue, Angli Zhang, Min Powell, Joseph E. Goddard, Michael E. Wray, Naomi R. Visscher, Peter M. McRae, Allan F. Yang, Jian |
author_facet | Wang, Huanwei Zhang, Futao Zeng, Jian Wu, Yang Kemper, Kathryn E. Xue, Angli Zhang, Min Powell, Joseph E. Goddard, Michael E. Wray, Naomi R. Visscher, Peter M. McRae, Allan F. Yang, Jian |
author_sort | Wang, Huanwei |
collection | PubMed |
description | Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10(−9) for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data. |
format | Online Article Text |
id | pubmed-6693916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66939162019-08-26 Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank Wang, Huanwei Zhang, Futao Zeng, Jian Wu, Yang Kemper, Kathryn E. Xue, Angli Zhang, Min Powell, Joseph E. Goddard, Michael E. Wray, Naomi R. Visscher, Peter M. McRae, Allan F. Yang, Jian Sci Adv Research Articles Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10(−9) for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data. American Association for the Advancement of Science 2019-08-14 /pmc/articles/PMC6693916/ /pubmed/31453325 http://dx.doi.org/10.1126/sciadv.aaw3538 Text en Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Wang, Huanwei Zhang, Futao Zeng, Jian Wu, Yang Kemper, Kathryn E. Xue, Angli Zhang, Min Powell, Joseph E. Goddard, Michael E. Wray, Naomi R. Visscher, Peter M. McRae, Allan F. Yang, Jian Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title | Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title_full | Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title_fullStr | Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title_full_unstemmed | Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title_short | Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank |
title_sort | genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the uk biobank |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693916/ https://www.ncbi.nlm.nih.gov/pubmed/31453325 http://dx.doi.org/10.1126/sciadv.aaw3538 |
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