Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene–Gene Interactions

Abstract

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < .001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

“…However, we found that some SNP combinations (dominated solutions) that are eliminated during the search by the nondominated solutions are the true disease-causing SNP combinations, which results in an increase in the number of false negative errors. In FHSA-SED [40], NHSA-DHSC [41] and MCDA-MDR [73], the experimental results indicate that multicriteria algorithms are superior to multi-objective optimization algorithms. In future studies, lightweight and complementary evaluation criteria should also be considered to screen for suspected SNP interactions that have a strong association with disease status using any SIS in the first search phase.…”

EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection

“…We model the epistasis process as a one-step Markov Decision Process (MDP) (Figure 1). The state S is a latent representation encoded from genome data; The action space is all the SNPs, where highly interacted SNPs are selected by a probability threshold so that it poses no constraint to fix the size of interaction; the reward is efficient interaction measurements like MDR correct classification rate (CCR) and Rule Utility (Yang et al, 2018). A reinforcement learning agent will learn to select SNPs that have high rewards, i.e., high interaction, by using the policy gradient method.…”

“…Now, we can calculate the two measures. These two measures together are shown to be effective in measuring epistasis (Yang et al, 2018). MDR CCR is the correct classification rate and Rule Utility U derives from the chi-square statistics of rule relevance, which measures the interaction:…”

“…There has been a substantial amount of work on epistasis detection. Exhaustive combinatorial search methods like Multifactor Dimensionality Reduction (MDR) (Yang et al, 2018) have been shown successful, but only in small genome-scale due to computational complexity. Later, attempts to reduce search spaces exhibit efficiency, like ReliefF and Spatially Uniform ReliefF (Niel et al, 2015).…”

Higher-order clinical risk factor interaction analysis for overall mortality in maintenance hemodialysis patients

“…The multifactor dimensionality reduction (MDR) method is a novel computational approach initially developed for detecting complex multifactor interactions. 12 Several new MDR-based methods, such as generalized MDR, 13 classification based MDR, 14 balanced MDR, 15 multi-objective MDR, 6,17 and other approaches have been proposed for improving the performance and applicability of the general MDR method. Evenly distributed case-control data sets are required for general MDR-based analyses.…”