Gene–gene interaction detection with deep learning

Funding Information: The work used computer resources of the Aalto University School of Science Science-IT project. This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI, and grants 319264, 292334, 286607, 294015, 336033, 321356), the EU Horizon 2020 (grant no. 101016775), and UKRI Turing AI World-Leading Researcher Fellowship, EP/W002973/1. This research has been conducted using the UK Biobank Resource under application number 46791. | openaire: EC/H2020/101016775/EU//INTERVENEThe extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independentFINRISK dataset.Peer reviewe