High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation

TL;DR

This paper introduces a novel method called FMQA for detecting high-order epistasis in genetic data, addressing computational challenges of traditional MDR approaches by framing the problem as a black-box optimization task.

Contribution

The paper proposes an efficient, optimization-based approach using factorization machines with quadratic annealing to improve high-order epistasis detection in genetic studies.

Findings

Successfully identified ground-truth epistasis in simulated datasets

Demonstrated effectiveness across various interaction orders and loci counts

Achieved detection with limited iterations, showing computational efficiency

Abstract

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.