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

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial humandisease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations ofparametric-statistical methods for detection of gene effects that are dependent solely or partially on interactionswith 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 locusrs1329149 and known homozygous AA at locus rs671.
Methods: In this study, we proposed statistical methodcrossoveranalysis in combination with logistic regression model, to further analyze our data and focus on assessinggene-environmental interactions for colorectal cancer.
Results: The results of the crossover analysis showed thatthere are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactoriallogistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicativeinteraction with alcohol consumption. Moreover, we also found additive interactions between any pair of twofactors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through thecrossover analysis, which was not evident on logistic regression.
Conclusions: In conclusion, the method basedon crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environmentinteractions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption inthe pathogenesis and development of colorectal cancer.