Fuzzy Logic System Application for Detecting SNP-SNP Interaction

The identification of interactions between single-nucleotide polymorphisms (SNP–SNP interactions) is crucial for determining human genetic disease susceptibility. With rapid technological advancements, multiobjective multifactor dimensionality reduction (MOMDR) measurements have achieved high detection success rates. However, the classification of high- or low-risk groups is central to MOMDR and has yet to be extensively studied. To address limitations in binary classification, we propose an improved fuzzy sigmoid (FS) approach that uses membership degrees in MOMDR, thus denoting it as FSMOMDR. For determining the interval of membership, our improved FS approach assesses the distance between the $i^{\mathrm {th}}$ multifactor class and outcome (cases and controls). Thus, the improved FS approach enables MOMDR algorithms to determine the membership degrees of high- and low-risk groups in each multifactor class because the two-element set is extended to a specified membership interval. Moreover, the improved FS approach can handle uncertain information, which thus enables the effective detection of the $m$ -locus combinations with similar distributions. FSMOMDR measurements can also distinguish similar frequencies among genotype combinations, thus enabling the detection of more significant SNP–SNP interactions. On the basis of the classification accuracy rate of MOMDR and results obtained from the analysis of several test data sets, we determined FSMOMDR to be superior to other MDR-based methods with respect to detection success rate. The results indicate that binary and fuzzy classifications involving MOMDR can provide insight into uncertainty in risk classification. Thus, FSMOMDR could successfully detect SNP–SNP interactions in coronary artery disease in a large data set obtained from the Wellcome Trust Case Control Consortium. We could successfully reduce uncertain information in MDR and thus suggest that membership based on the improved sigmoid function can be used to identify SNP–SNP interactions as well as obtain content knowledge.