A novel method for identifying SNP disease association based on maximal information coefficient
, “A novel method for identifying SNP disease association based on maximal information coefficient”, vol. 13, pp. 10863-10877, 2014.
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.
