New evaluation measures for multifactor dimensionality reduction classifiers in gene-gene interaction analysis

Motivation: Gene-gene interactions are important contributors to complex biological traits. Multifactor dimensionality reduction (MDR) is a method to analyze gene-gene interactions and has been applied to many genetics studies of complex diseases. In order to identify the best interaction model associated with disease susceptibility, MDR classifiers corresponding to interaction models has been constructed and evaluated as a predictor of disease status via a certain measure such as balanced accuracy (BA). It has been shown that the performance of MDR tends to depend on the choice of the evaluation measures. Results: In this article, we introduce two types of new evaluation measures. First, we develop weighted BA (wBA) that utilizes the quantitative information on the effect size of each multi-locus genotype on a trait. Second, we employ ordinal association measures to assess the performance of MDR classifiers. Simulation studies were conducted to compare the proposed measures with BA, a current measure. Our results showed that the wBA and tau(b) improved the power of MDR in detecting gene-gene interactions. Noticeably, the power increment was higher when data contains the greater number of genetic markers. Finally, we applied the proposed evaluation measures to real data.