High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation
This addresses a computational bottleneck in genetic research for detecting complex gene interactions, but it is incremental as it builds on existing MDR and optimization techniques.
The paper tackled the problem of detecting high-order epistasis in genetic association studies, which is computationally challenging due to combinatorial explosion, by proposing a method using factorization machine with quadratic optimization annealing and MDR-based evaluation; the results showed it successfully identified ground-truth epistasis across various interaction orders and locus numbers within limited iterations, indicating effectiveness and computational efficiency.
Detecting high-order epistasis is a fundamental challenge in genetic association studies due to the combinatorial explosion of candidate locus combinations. Although multifactor dimensionality reduction (MDR) is a widely used method for evaluating epistasis, exhaustive MDR-based searches become computationally infeasible as the number of loci or the interaction order increases. In this paper, we define the epistasis detection problem as a black-box optimization problem and solve it with a factorization machine with quadratic optimization annealing (FMQA). We propose an efficient epistasis detection method based on FMQA, in which the classification error rate (CER) computed by MDR is used as a black-box objective function. Experimental evaluations were conducted using simulated case-control datasets with predefined high-order epistasis. The results demonstrate that the proposed method successfully identified ground-truth epistasis across various interaction orders and the numbers of genetic loci within a limited number of iterations. These results indicate that the proposed method is effective and computationally efficient for high-order epistasis detection.