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A novel explainable deep-learning approach for network analysis of epistatic interactions
Andrea Mastropietro1,2, Georgios Markopoulos3, Evangelos Evangelou4,5,6
1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.
NAR Genomics and Bioinformatics
|February 2, 2026
Summary
EpiDetect, a new framework using neural networks, identifies gene interactions (epistasis) for complex traits. It overcomes neural network limitations by revealing how single-nucleotide polymorphisms (SNPs) interact, aiding disease research.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Epistatic interactions between gene loci influence complex traits and diseases, but their inference is challenging.
- Neural networks excel at complex data classification but lack interpretability, hindering their application in genetic studies.
- Understanding gene-gene networks is crucial for elucidating the molecular basis of traits and diseases.
Purpose of the Study:
- To introduce EpiDetect, a novel framework for discovering epistatic interactions among single-nucleotide polymorphisms (SNPs).
- To develop EpiCID, an explainability algorithm for neural networks, to interpret feature interactions in genetic data.
- To apply EpiDetect and EpiCID to genome-wide association datasets for blood pressure traits.
Main Methods:
- Developed EpiDetect, a neural network-based framework for identifying SNP-SNP interactions.
- Integrated EpiCID, a novel explainability algorithm, to interpret the neural network's decision-making process.
- Applied the framework to genome-wide association study (GWAS) datasets for systolic, diastolic, and pulse pressure.
Main Results:
- EpiDetect successfully identified epistatic interactions in blood pressure GWAS data.
- EpiCID enabled the pinpointing of central SNPs and genes within an interactive SNP network through centrality analysis.
- The novel framework outperformed existing algorithms for detecting epistatic interactions.
- Pathway analysis revealed significant known and novel pathways implicated in blood pressure regulation.
Conclusions:
- EpiDetect provides an interpretable approach to discovering gene-gene interactions, overcoming limitations of traditional neural networks in genetics.
- The identification of interactive SNP networks and key genes offers new insights into the genetic architecture of blood pressure traits.
- This framework opens avenues for further research into the molecular mechanisms underlying complex traits and diseases.
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