title: A novel method for identifying SNP disease association based on maximal information coefficient.
abstract: To improve single-nucleotide polymorphism (SNP) association studies, we developed a method referred to as maximal information coefficient (MIC)-based SNP searching (MICSNPs) by employing a novel statistical approach known as the MIC to identify SNP disease associations. MIC values varied with minor allele frequencies of SNPs and the odds ratios for disease. We used a Monte Carlo-based permutation test to eliminate the effects of fluctuating MIC values and included a sliding-window-based binary search whose time-cost was 0.58% that of a sequential search to save time. The experiments examining both simulation and actual data demonstrated that our method is computationally and statistically feasible after reducing the resampling count to 4 times the number of markers and applying a sliding-window-based binary search to the method. We found that our method outperforms existing approaches.
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
authors.forename: H M
authors.initials: HM
authors.lastname: Liu
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China raonn@uestc.edu.cn.
authors.forename: N
authors.initials: N
authors.lastname: Rao
authors.affiliation: School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, China.
authors.forename: D
authors.initials: D
authors.lastname: Yang
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
authors.forename: L
authors.initials: L
authors.lastname: Yang
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
authors.forename: F
authors.initials: F
authors.lastname: Ou
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D004198:Disease Susceptibility; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results