title: An entropy-based approach for testing genetic epistasis underlying complex diseases.
abstract: The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a chi(2) distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard chi(2) test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.
authors.affiliation: Department of Statistics and Probability, East Lansing, Michigan State University, MI 48824, USA.
authors.forename: Guolian
authors.initials: G
authors.lastname: Kang
authors.forename: Weihua
authors.initials: W
authors.lastname: Yue
authors.forename: Jifeng
authors.initials: J
authors.lastname: Zhang
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Cui
authors.forename: Yijun
authors.initials: Y
authors.lastname: Zuo
authors.forename: Dai
authors.initials: D
authors.lastname: Zhang
mesh_terms: D019277:Entropy; D004843:Epistasis, Genetic; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D006454:Hemoglobins / Q000235:genetics; D006801:Humans; D008288:Malaria / Q000150:complications; D008957:Models, Genetic; D009010:Monte Carlo Method; D010641:Phenotype; D012559:Schizophrenia / Q000235:genetics; D017085:alpha-Thalassemia / Q000150:complications / Q000235:genetics
chemical_list: D006454:Hemoglobins