| Title: | A novel explainable deep-learning approach for network analysis of epistatic interactions |
| Journal: | NAR Genomics and Bioinformatics |
| Published: | 6 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41625145/ |
| DOI: | https://doi.org/10.1093/nargab/lqag004 |
Publication 18731
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Abstract
Epistatic interactions of gene loci often determine complex trait phenotypes and may indicate the underlying molecular mechanisms of traits and diseases. Yet, the inference of epistatic interactions and gene-gene networks remains challenging. Neural networks have become successful in classifying complex data, revolutionizing various fields. However, their complexity does not reveal how they combine input features, and this lack of interpretability limits their use with genetic data. We thus introduce EpiDetect, a novel framework for discovering interactions between input features (single-nucleotide polymorphisms-SNPs-in our setting). EpiDetect neural-network-based classifiers detect interactions in systolic, diastolic, and pulse pressure genome-wide association datasets. Central to EpiDetect is EpiCID, a novel explainability algorithm for neural networks. Using EpiCID, we identified a network of highly interactive SNPs, performed centrality analysis to pinpoint central SNPs and genes, and outperformed established epistatic-interaction-detection algorithms. Moreover, pathway analysis uncovered well-known and novel pathways that could play a significant role in blood pressure traits, opening up new research directions.</p>
9 Keywords
- Algorithms
- Blood Pressure
- Deep Learning
- Epistasis, Genetic
- Gene Regulatory Networks
- Genome-Wide Association Study
- Humans
- Neural Networks, Computer
- Polymorphism, Single Nucleotide
4 Authors
- Andrea Mastropietro
- Georgios Markopoulos
- Evangelos Evangelou
- Aris Anagnostopoulos
1 Application
| Application ID | Title |
|---|---|
| 22102 | Multivariate approaches for the identification of novel environmental and genetic risk factors for neuropsychiatric disorders, skin cancers and bone-related outcomes. |
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