Result: ViSEN: methodology and software for visualization of statistical epistasis networks.

Title:
ViSEN: methodology and software for visualization of statistical epistasis networks.
Authors:
Hu T; Institute for Quantitative Biomedical Sciences, Dartmouth College, New Hampshire, USA., Chen Y, Kiralis JW, Moore JH
Source:
Genetic epidemiology [Genet Epidemiol] 2013 Apr; Vol. 37 (3), pp. 283-5. Date of Electronic Publication: 2013 Mar 06.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8411723 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1098-2272 (Electronic) Linking ISSN: 07410395 NLM ISO Abbreviation: Genet Epidemiol Subsets: MEDLINE
Imprint Name(s):
Publication: New York, NY : Wiley-Liss
Original Publication: New York, N.Y. : Alan R. Liss, c1984-
MeSH Terms:
Computer Graphics* , Epistasis, Genetic* , Models, Statistical* , Software*, Humans ; Phenotype ; Programming Languages
References:
Nat Rev Genet. 2009 Jun;10(6):392-404. (PMID: 19434077)
Nat Genet. 2005 Jan;37(1):13-4. (PMID: 15624016)
Genet Epidemiol. 2011 Nov;35(7):706-21. (PMID: 22009792)
J Am Med Inform Assoc. 2013 Jul-Aug;20(4):630-6. (PMID: 23396514)
BMC Bioinformatics. 2011 Sep 12;12:364. (PMID: 21910885)
J Theor Biol. 2006 Jul 21;241(2):252-61. (PMID: 16457852)
Nat Rev Genet. 2005 Feb;6(2):95-108. (PMID: 15716906)
N Engl J Med. 2009 Apr 23;360(17):1759-68. (PMID: 19369657)
Eur J Hum Genet. 2009 Oct;17(10):1274-86. (PMID: 19293841)
Grant Information:
R01 EY022300 United States EY NEI NIH HHS; P20 GM103534 United States GM NIGMS NIH HHS; R01-AI59694 United States AI NIAID NIH HHS; R01-LM010098 United States LM NLM NIH HHS; R01-EY022300 United States EY NEI NIH HHS; R01 LM010098 United States LM NLM NIH HHS; R01-LM009012 United States LM NLM NIH HHS; R01 AI059694 United States AI NIAID NIH HHS; R01 LM009012 United States LM NLM NIH HHS
Entry Date(s):
Date Created: 20130308 Date Completed: 20130830 Latest Revision: 20211021
Update Code:
20250114
PubMed Central ID:
PMC3758133
DOI:
10.1002/gepi.21718
PMID:
23468157
Database:
MEDLINE

Further Information

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/.
(© 2013 Wiley Periodicals, Inc.)