Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Wu, Ya-Zhou; Yang, Huan; Zhang, Ling; Zhang, Yan-Qi; Liu, Ling; Yi, Dong; Cao, Jia

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자료유형: 학술저널

저자정보: Wu, Ya-Zhou (Department of Health Statistics, Third Military Medical University) Yang, Huan (Department of Hygienic Toxicology, Third Military Medical University) Zhang, Ling (Department of Health Education & Medical Humanities, Third Military Medical University) Zhang, Yan-Qi (Department of Health Statistics, Third Military Medical University) Liu, Ling (Department of Health Statistics, Third Military Medical University) Yi, Dong (Department of Health Statistics, Third Military Medical University) Cao, Jia (Department of Hygienic Toxicology, Third Military Medical University)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제13권 제5호

발행연도: 2012.1

수록면: 2,031 - 2,037 (7page)

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Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

#Colorectal cancer #crossover analysis #logistic regression #polymorphisms #gene-environmental interaction

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