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Identification of SNP-SNP Interaction Using Entropy-Based Multifactor Dimensionality Reduction in Case-Control Studies

Cheng-Hong Yang 1, Cheng-Han Wu 1, and Li-Yeh Chuang 2
1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
2. Department of Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
Abstract—Diseases susceptibility plays an important role in genome-wide association study (GWAS). There are complex relationships between genotypes and environment factors in diseases. Due to the nonlinear relationship, the identification methods are met a challenge to detect gene-gene interaction or gene-environment interactions. In this study, Entropy-Based Multifactor Dimensionality Reduction (EMDR) was used for identification of the single nucleotide polymorphisms (SNPs) interaction effects. MDR method is able to identify the interaction by trying n-locus interaction brute force. The proposed method uses K-way entropy based information gain as the filter for preprocessing, and then picks the suggested percentage of n-locus SNP combinations. Entropy-based interaction was compared with the searching way of MDR based on the ranking of interaction gain value. The Gametes simulation datasets were used to test the top percentage chosen for MDR, and the real kidney data was used to proof the ability of EMDR.

Index Terms—entropy-based interaction gain, SNP-SNP interaction, multifactor dimensionality reduction, Gametes

Cite: Cheng-Hong Yang, Cheng-Han Wu, and Li-Yeh Chuang, "Identification of SNP-SNP Interaction Using Entropy-Based Multifactor Dimensionality Reduction in Case-Control Studies," Journal of Life Sciences and Technologies, vol. 4, no. 2, pp. 60-65, December 2016. doi: 10.18178/jolst.4.2.60-65
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