mutual information for testing gene-environment interaction互信息为测试基因-环境相互作用.pdfVIP

Mutual Information for Testing Gene-Environment Interaction Xuesen Wu1,4, Li Jin1,2, Momiao Xiong1,3* 1 School of Life Science, Theoretic Systems Biology Laboratory and Center for Evolutionary Biology, Fudan University, Shanghai, China, 2 CAS-MPG Partner Institute of Computational Biology, SIBS, CAS, Shanghai, China, 3 Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 4 Department of Epidemiology and Statistics, Bengbu Medical College at Bengbu, Anhui, China 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 perfor