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.
| Comments: | 6 pages, 2 figures |
| Subjects: | Machine Learning (cs.LG); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2601.01860 [cs.LG] |
| (or arXiv:2601.01860v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2601.01860 arXiv-issued DOI via DataCite |
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| Journal reference: | 2026 International Conference on Quantum Communications, Networking, and Computing (QCNC), pp. 924-929 |
| Related DOI: | https://doi.org/10.1109/QCNC69040.2026.00152
DOI(s) linking to related resources |
Submission history
From: Shuta Kikuchi [view email]
[v1]
Mon, 5 Jan 2026 07:41:34 UTC (174 KB)
[v2]
Wed, 13 May 2026 06:27:59 UTC (177 KB)