A novel explainable deep-learning approach for network analysis of epistatic interactions

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.