A Multifactor Dimensionality Reduction Based Associative Classification for Detecting SNP Interactions

Abstract

Identification and characterization of interactions between genes have been increasingly explored in current Genome-wide association studies (GWAS). Several machine learning and data mining approaches have been proposed to identify the multi-locus interactions in higher order genomic data. However, detecting these interactions is challenging due to bio-molecular complexities and computational limitations. In this paper, a multifactor dimensionality reduction based associative classifier is proposed for detecting SNP interactions in genetic epidemiological studies. The approach is evaluated for one to six loci models by varying heritability, minor allele frequency, case-control ratios and sample size. The experimental results demonstrated significant improvements in accuracy for detecting interacting single nucleotide polymorphisms (SNPs) responsible for complex diseases when compared to the previous approaches. Further, the approach was successfully evaluated by using sporadic breast cancer data. The results show interactions among five polymorphisms in three different estrogen-metabolism genes.