Detecting genetic interactions with visible neural networks

Non-linear interactions among single nucleotide polymorphisms (SNPs), genes, and pathways play an important role in human diseases, but identifying these interactions is a challenging task. Neural networks are state-of-the-art predictors in many domains due to their ability to analyze big data and model complex patterns, including non-linear interactions. In genetics, visible neural networks are popular as they provide insight into the most important SNPs, genes, and pathways for prediction. Visible neural networks use prior knowledge (e.g., gene and pathway annotations) to define node connections in the network, making them sparse and interpretable. Currently, most of these networks provide measures for the importance of SNPs, genes, and pathways but do not provide information about interactions. In this paper, we explore different methods to detect non-linear interactions with visible neural networks. We adapt and speed up existing methods, create a comprehensive benchmark with simulated data from GAMETES and EpiGEN, and demonstrate that these methods can extract multiple types of interactions from trained neural networks. Finally, we apply these methods to a genome-wide case-control study of inflammatory bowel disease and find high consistency of the epistasis pairs candidates between interpretation methods. The follow-up association test on these candidates identifies seven significant epistasis pairs.