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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Complex diseases are multifactorial traits caused by both genetic and environmental factors. Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason could be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. To address this problem, a number of novel methods have been developed. In the current study, we compare the performance of five analytical approaches based on the odd ratios of risk factors to detect Gene environment interactions in a range of genetic models. Mutual information, crossover analysis, dummy variable based on crossover analysis, interaction based on the linkage disequilibrium and explicit logistic regression were compared.