Detecting SNP-SNP Interactions in Imbalanced Case-Control Study

SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of disease risk. However, SNP-SNP interaction identifications are yet limited in imbalanced case-control study. In this study, we proposed a multiobjective multifactor dimensionality reduction (MOMDR) based on three balancing approaches (BMOMDR), including (1) stratified K-fold cross-validation; (2) balanced estimation of ratio between cases and controls; (3) balanced measures of SNP-SNP interactions, to effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR was evaluated by extensive experiments on both simulated imbalanced case-control datasets and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC). For the simulated datasets, the results indicated that three balancing approaches can enhance the detection success rate of SNP-SNP interaction by MOMDR in imbalanced datasets. For WTCCC datasets, the results of SNP-SNP interaction detection obtained from BMOMDR revealed statistically significant (p<0.0001), revealing that BMOMDR can effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR is freely available at http://shorturl.at/bluJS.