A novel information theoretic method for detecting gene-gene and gene-environment interactions in complex diseases

Gene-gene and gene-environment interactions play important roles in the etiology of complex multi-factorial diseases. With the advancements in genotyping technology, large genetic association studies based on hundreds of thousands of single-nucleotide polymorphisms are a popular option for the study of complex diseases. In this paper we use information theoretic concepts to develop a novel method for detecting statistical gene-gene and gene-environment interactions in complex disease models. We explore the effectiveness of our method with extensive simulations using different gene-gene interaction models and the rheumatoid arthritis dataset from genetic analysis workshop-15. The performance of the method was compared to the well known multi-factor dimensionality reduction (MDR) and generalized MDR (GMDR) methods. We demonstrate that our method is capable of analyzing a diverse range of epidemiological data sets containing evidences for gene-gene interactions.

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