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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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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