title: CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.
abstract: 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.
  
  
  CONCLUSIONS
  Experiments of CINOEDV and its comparison with existing state-of-the-art methods are performed on both simulation data sets and a real data set of age-related macular degeneration. Results demonstrate that CINOEDV is promising in detecting and visualizing n-order epistatic interactions. CINOEDV is implemented in R and is freely available from R CRAN: http://cran.r-project.org and https://sourceforge.net/projects/cinoedv/files/ .
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. shangjunliang110@163.com.|Institute of Network Computing, Qufu Normal University, Rizhao, 276826, China. shangjunliang110@163.com.
authors.forename: Junliang
authors.initials: J
authors.lastname: Shang
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
authors.forename: Yingxia
authors.initials: Y
authors.lastname: Sun
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.|Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, China.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.affiliation: Institute of Health Sciences, Anhui University, Hefei, Anhui, 230601, China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Xia
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
authors.forename: Junying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, 230039, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics; D020641:Polymorphism, Single Nucleotide
keywords: Co-information; Epistatic interactions; Hypergraph; Particle swarm optimization; Single nucleotide polymorphisms