Mutual Information for Testing Gene-Environment Interaction

Authors

Xuesen Wu, School of Life Science, Theoretic Systems Biology Laboratory and Center for Evolutionary Biology, Fudan University, Shanghai, China; Department of Epidemiology and Statistics, Bengbu Medical College at Bengbu, Anhui, China
Li Jiin, School of Life Science, Theoretic Systems Biology Laboratory and Center for Evolutionary Biology, Fudan University, Shanghai, China; CAS-MPG Partner Institute of Computational Biology, SIBS, CAS, Shanghai, China
Momiao Xiong, School of Life Science, Theoretic Systems Biology Laboratory and Center for Evolutionary Biology, Fudan University, Shanghai, China; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America

Publication Date

2-2009

Journal

PLoS ONE 4(2): e4578

Abstract

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

Keywords

Genetics and Genomics

Comments

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