These authors contributed equally to this work.
ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks
Version of Record online: 6 MAR 2013
© 2013 Wiley Periodicals, Inc.
Volume 37, Issue 3, pages 283–285, April 2013
How to Cite
Hu, T., Chen, Y., Kiralis, J. W. and Moore, J. H. (2013), ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks. Genet. Epidemiol., 37: 283–285. doi: 10.1002/gepi.21718
Contract grant sponsor: JHM; Contact grant sponsor: NIH; Contract grant numbers: R01-LM009012, R01-LM010098, R01-AI59694, R01-EY022300; Contract grant sponsor: YC; Contact grant sponsor: NSERC; Contract grant number: 327667-2010.
- Issue online: 25 MAR 2013
- Version of Record online: 6 MAR 2013
- Manuscript Accepted: 5 FEB 2013
- Manuscript Revised: 20 DEC 2012
- Manuscript Received: 13 SEP 2012
- NIH. Grant Numbers: R01-LM009012, R01-LM010098, R01-AI59694, R01-EY022300
- NSERC. Grant Number: 327667-2010
- complex diseases;
- gene–gene interaction;
- genome-wide association;
- high-order interaction;
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/.