title: ViSEN: methodology and software for visualization of statistical epistasis networks.
abstract: The nonlinear interaction effect among multiple genetic factors, i.e. epistasis, has been recognized as a key component in understanding the underlying genetic basis of complex human diseases and phenotypic traits. Due to the statistical and computational complexity, most epistasis studies are limited to interactions with an order of two. We developed ViSEN to analyze and visualize epistatic interactions of both two-way and three-way. ViSEN not only identifies strong interactions among pairs or trios of genetic attributes, but also provides a global interaction map that shows neighborhood and clustering structures. This visualized information could be very helpful to infer the underlying genetic architecture of complex diseases and to generate plausible hypotheses for further biological validations. ViSEN is implemented in Java and freely available at https://sourceforge.net/projects/visen/.
authors.affiliation: Institute for Quantitative Biomedical Sciences, Dartmouth College, New Hampshire, USA.
authors.forename: Ting
authors.initials: T
authors.lastname: Hu
authors.forename: Yuanzhu
authors.initials: Y
authors.lastname: Chen
authors.forename: Jeff W
authors.initials: JW
authors.lastname: Kiralis
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D003196:Computer Graphics; D004843:Epistasis, Genetic; D006801:Humans; D015233:Models, Statistical; D010641:Phenotype; D011381:Programming Languages; D012984:Software