Background:Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of "missing heritability".However,related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges.Results:We develop CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for the detection and visualization of epistatic interactions of their orders from 1 to n (n≥2).CINOEDV is composed of two stages,namely,detecting stage and visualizing stage.In detecting stage,co-information based measures are employed to quantify association effects of n-order SNP combinations to the phenotype,and two types of search strategies are introduced to identify n-order epistatic interactions: an exhaustive search and a particle swarm optimization based search.In visualizing stage,all detected n-order epistatic interactions are used to construct a hypergraph,where a real vertex represents the main effect of a SNP and a virtual vertex denotes the interaction effect of an n-order epistatic interaction.By deeply analyzing the constructed hypergraph,some hidden clues for better understanding the underlying genetic architecture of complex diseases could be revealed.