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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Main Authors: Jiahui Zhang, Xibiao Ye, Cuie Wu, Hua Fu, Wei Xu, Pingzhao Hu
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-01-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2018.00657/full
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spelling doaj-b40aa6a58cc74c9aa6bc19e8f9de23c42020-11-25T00:53:40ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-01-01810.3389/fonc.2018.00657421127Modeling Gene-Environment Interaction for the Risk of Non-hodgkin LymphomaJiahui Zhang0Xibiao Ye1Cuie Wu2Hua Fu3Wei Xu4Wei Xu5Pingzhao Hu6Pingzhao Hu7Pingzhao Hu8Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaDepartment of Community Health Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, CanadaSchool of Public Health, Fudan University, Shanghai, ChinaSchool of Public Health, Fudan University, Shanghai, ChinaDivision of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaDepartment of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, CanadaDivision of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaDepartment of Biochemistry and Medical Genetics, Faculty of Health Sciences, College of Medicine, University of Manitoba, Winnipeg, MB, CanadaResearch Institute in Oncology and Hematology, Winnipeg, MB, CanadaBackground: 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.https://www.frontiersin.org/article/10.3389/fonc.2018.00657/fullgene-environment interaction (G × E)genetic risk score (GRS)clustering (unsupervised) algorithmsnon-hodgkin lymphomacandidate genes
collection DOAJ
language English
format Article
sources DOAJ
author Jiahui Zhang
Xibiao Ye
Cuie Wu
Hua Fu
Wei Xu
Wei Xu
Pingzhao Hu
Pingzhao Hu
Pingzhao Hu
spellingShingle Jiahui Zhang
Xibiao Ye
Cuie Wu
Hua Fu
Wei Xu
Wei Xu
Pingzhao Hu
Pingzhao Hu
Pingzhao Hu
Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma
Frontiers in Oncology
gene-environment interaction (G × E)
genetic risk score (GRS)
clustering (unsupervised) algorithms
non-hodgkin lymphoma
candidate genes
author_facet Jiahui Zhang
Xibiao Ye
Cuie Wu
Hua Fu
Wei Xu
Wei Xu
Pingzhao Hu
Pingzhao Hu
Pingzhao Hu
author_sort Jiahui Zhang
title 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_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_sort modeling gene-environment interaction for the risk of non-hodgkin lymphoma
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2019-01-01
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.
topic gene-environment interaction (G × E)
genetic risk score (GRS)
clustering (unsupervised) algorithms
non-hodgkin lymphoma
candidate genes
url https://www.frontiersin.org/article/10.3389/fonc.2018.00657/full
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