EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection

Epistasis (gene-gene interaction) is crucial to predicting genetic disease. Our work tackles the computational challenges faced by previous works in epistasis detection by modeling it as a one-step Markov Decision Process where the state is genome data, the actions are the interacted genes, and the reward is an interaction measurement for the selected actions. A reinforcement learning agent using policy gradient method then learns to discover a set of highly interacted genes. The fundamental goal for studying genetics is to understand how certain genes can incur disease and traits. Since the advent of Genome-Wide Association Studies (GWAS) (Burton et al., 2007), thousands of SNP (Single Nucleotide Polymorphism)s have been identified and associated with genetic diseases and traits.