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Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics
Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step appro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143689/ https://www.ncbi.nlm.nih.gov/pubmed/25519396 http://dx.doi.org/10.1186/1753-6561-8-S1-S62 |
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author | Wang, Maggie Haitian Huang, Chien-Hsun Zheng, Tian Lo, Shaw-Hwa Hu, Inchi |
author_facet | Wang, Maggie Haitian Huang, Chien-Hsun Zheng, Tian Lo, Shaw-Hwa Hu, Inchi |
author_sort | Wang, Maggie Haitian |
collection | PubMed |
description | Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step approach incorporating an influence measure that capturespure gene-environment effect. We found that pure gene-age interaction has a stronger association than considering the genetic effect alone for systolic blood pressure, measured by counting the number of single-nucleotide polymorphisms (SNPs)reaching a certain significance level. We analyzed the subjects by dividing them into two age groups and found no overlap in the top identified SNPs between them. This suggested that age might have a nonlinear effect on genetic association. Furthermore, the scores of the top SNPs for the two age subgroups were about 3times those obtained when using all subjects for systolic blood pressure. In addition, the scores of the older age subgroup were much higher than those for the younger group. The results suggest that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially age. |
format | Online Article Text |
id | pubmed-4143689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41436892014-09-02 Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics Wang, Maggie Haitian Huang, Chien-Hsun Zheng, Tian Lo, Shaw-Hwa Hu, Inchi BMC Proc Proceedings Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step approach incorporating an influence measure that capturespure gene-environment effect. We found that pure gene-age interaction has a stronger association than considering the genetic effect alone for systolic blood pressure, measured by counting the number of single-nucleotide polymorphisms (SNPs)reaching a certain significance level. We analyzed the subjects by dividing them into two age groups and found no overlap in the top identified SNPs between them. This suggested that age might have a nonlinear effect on genetic association. Furthermore, the scores of the top SNPs for the two age subgroups were about 3times those obtained when using all subjects for systolic blood pressure. In addition, the scores of the older age subgroup were much higher than those for the younger group. The results suggest that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially age. BioMed Central 2014-06-17 /pmc/articles/PMC4143689/ /pubmed/25519396 http://dx.doi.org/10.1186/1753-6561-8-S1-S62 Text en Copyright © 2014 Wang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Proceedings Wang, Maggie Haitian Huang, Chien-Hsun Zheng, Tian Lo, Shaw-Hwa Hu, Inchi Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title | Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title_full | Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title_fullStr | Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title_full_unstemmed | Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title_short | Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
title_sort | discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143689/ https://www.ncbi.nlm.nih.gov/pubmed/25519396 http://dx.doi.org/10.1186/1753-6561-8-S1-S62 |
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