An entropy-based approach for testing genetic epistasis underlying complex diseases |
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Authors: | Kang Guolian Yue Weihua Zhang Jifeng Cui Yuehua Zuo Yijun Zhang Dai |
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Institution: | a Department of Statistics and Probability, East Lansing, Michigan State University, MI 48824, USA b Key Laboratory for Mental Health, Ministry of Health, Institute of Mental Health, Peking University, Beijing 100083, China c Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China |
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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 χ2 distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard χ2 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. |
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Keywords: | Case-only design Complex diseases Entropy Genetic epistasis Genetic network |
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