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Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma

Background: Non-hodgkin lymphoma (NHL) is one of the most common and deadly cancers. There is limited analysis of gene-environment interactions for the risk of NHL. This study intends to explore the interactions between genetic variants and environmental factors, and how they contribute to NHL risk....

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Autores principales: Zhang, Jiahui, Ye, Xibiao, Wu, Cuie, Fu, Hua, Xu, Wei, Hu, Pingzhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6340069/
https://www.ncbi.nlm.nih.gov/pubmed/30693270
http://dx.doi.org/10.3389/fonc.2018.00657
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author Zhang, Jiahui
Ye, Xibiao
Wu, Cuie
Fu, Hua
Xu, Wei
Hu, Pingzhao
author_facet Zhang, Jiahui
Ye, Xibiao
Wu, Cuie
Fu, Hua
Xu, Wei
Hu, Pingzhao
author_sort Zhang, Jiahui
collection PubMed
description Background: Non-hodgkin lymphoma (NHL) is one of the most common and deadly cancers. There is limited analysis of gene-environment interactions for the risk of NHL. This study intends to explore the interactions between genetic variants and environmental factors, and how they contribute to NHL risk. Methods: A case-control study was performed in Shanghai, China. The cases were diagnosed between 2003 and 2008 with patients aged 18 years or older. Samples and SNPs which did not satisfy quality control were excluded from the analysis. Weighted and unweighted genetic risk scores (GRS) and environmental risk scores were generated using clustering analysis algorithm. Univariate and multivariable logistic regression analyses were conducted. Moreover, genetics and environment interactions (G × E) were tested on the NHL cases and controls. Results: After quality control, there are 22 SNPs, 11 environmental variables and 5 demographical variables to be explored. For logistic regression analyses, 5 SNPs (rs1800893, rs4251961, rs1800630, rs13306698, rs1799931) and environmental tobacco smoking showed statistically significant associations with the risk of NHL. Odds ratio (OR) and 95% confidence interval (CI) was 10.82 (4.34–28.88) for rs13306698, 2.84 (1.66–4.95) for rs1800893, and 2.54 (1.43–4.58) for rs4251961. For G × E analysis, the interaction between smoking and dichotomized weighted GRS showed statistically significant association with NHL (OR = 0.23, 95% CI = [0.09, 0.61]). Conclusions: Several genetic and environmental risk factors and their interactions associated with the risk of NHL have been identified. Replication in other cohorts is needed to validate the results.
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spelling pubmed-63400692019-01-28 Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma Zhang, Jiahui Ye, Xibiao Wu, Cuie Fu, Hua Xu, Wei Hu, Pingzhao Front Oncol Oncology Background: Non-hodgkin lymphoma (NHL) is one of the most common and deadly cancers. There is limited analysis of gene-environment interactions for the risk of NHL. This study intends to explore the interactions between genetic variants and environmental factors, and how they contribute to NHL risk. Methods: A case-control study was performed in Shanghai, China. The cases were diagnosed between 2003 and 2008 with patients aged 18 years or older. Samples and SNPs which did not satisfy quality control were excluded from the analysis. Weighted and unweighted genetic risk scores (GRS) and environmental risk scores were generated using clustering analysis algorithm. Univariate and multivariable logistic regression analyses were conducted. Moreover, genetics and environment interactions (G × E) were tested on the NHL cases and controls. Results: After quality control, there are 22 SNPs, 11 environmental variables and 5 demographical variables to be explored. For logistic regression analyses, 5 SNPs (rs1800893, rs4251961, rs1800630, rs13306698, rs1799931) and environmental tobacco smoking showed statistically significant associations with the risk of NHL. Odds ratio (OR) and 95% confidence interval (CI) was 10.82 (4.34–28.88) for rs13306698, 2.84 (1.66–4.95) for rs1800893, and 2.54 (1.43–4.58) for rs4251961. For G × E analysis, the interaction between smoking and dichotomized weighted GRS showed statistically significant association with NHL (OR = 0.23, 95% CI = [0.09, 0.61]). Conclusions: Several genetic and environmental risk factors and their interactions associated with the risk of NHL have been identified. Replication in other cohorts is needed to validate the results. Frontiers Media S.A. 2019-01-14 /pmc/articles/PMC6340069/ /pubmed/30693270 http://dx.doi.org/10.3389/fonc.2018.00657 Text en Copyright © 2019 Zhang, Ye, Wu, Fu, Xu and Hu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhang, Jiahui
Ye, Xibiao
Wu, Cuie
Fu, Hua
Xu, Wei
Hu, Pingzhao
Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title_full Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title_fullStr Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title_full_unstemmed Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title_short Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
title_sort modeling gene-environment interaction for the risk of non-hodgkin lymphoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6340069/
https://www.ncbi.nlm.nih.gov/pubmed/30693270
http://dx.doi.org/10.3389/fonc.2018.00657
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