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Analysis of Asthma Related SNP Genotype Data Using Normalized Mutual Information and Support Vector Machines  

Lee, Jung-Seob (아주대학교 자연과학부)
Kim, Seung-Hyun (아주대학교 의과대학)
Shin, Ki-Seob (아주대학교 수학과)
Lim, Kyu-Cheol (아주대학교 수학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.9, 2009 , pp. 691-696 More about this Journal
Abstract
Introduction: There are two types of asthma according to aspirin hypersensitivity: aspirin intolerant asthma (AIA) and aspirin tolerant asthma (ATA). The genetic risk factors that are related with asthma have been investigated intensively and extensively. However the combinatory effects of single nucleotide polymorphisms (SNPs) have hardly been evaluated. In this paper we searched the best set of SNPs that are useful to diagnose the two types of asthma. Methods: We examined 246 asthmatic patients (94 having aspirin intolerant asthma and 152 having aspirin tolerant asthma) and analyzed 25 SNPs typed in them, which are suspected to be associated with asthma. Normalized mutual information values of combinations of typed SNPs are calculated, and those with high normalized mutual information values are selected. We use support vector machines to evaluate the prediction accuracy of the selected combinations. Results: The best combination model turns out four-locus and consists of ALOX5_p1_1708, B2ADR_q1_46, CCR3_p1_520, CysLTR1_p1_634. Its normalized mutual information value is 0.053 and the accuracy in predicting ATA disease risk among asthmatic patients is 71.14%.
Keywords
asthma; single nucleotide polymorphism; mutual information; support vector machine;
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