GWGGI statistics

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Citations per year

Number of citations per year for the bioinformatics software tool GWGGI
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Tool usage distribution map

This map represents all the scientific publications referring to GWGGI per scientific context
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Protocols

GWGGI specifications

Information


Unique identifier OMICS_25315
Name GWGGI
Alternative name genome-wide gene-gene interaction analyses
Software type Application/Script
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Output data Statistical significance of detected gene-gene interactions and the marginal contribution of each genetic variant.
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++
Computer skills Medium
Version 1.0
Stability Stable
Source code URL https://msu.edu/~qlu/doc/gwggi.code.zip
Registration required Yes
Maintained Yes

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Maintainer


  • person_outline Qing Lu

Additional information


https://msu.edu/~changs18/software.html#GWGGI

Publication for genome-wide gene-gene interaction analyses

GWGGI citations

 (2)
library_books

A survey about methods dedicated to epistasis detection

2015
Front Genet
PMCID: 4564769
PMID: 26442103
DOI: 10.3389/fgene.2015.00285

[…] several independent SNPs additively contributing to the phenotype. As a result, random forests are lacking clear interpretation.More recently, another tree assembling software program was developed: GWGGI (Wei and Lu, ). It differs from the previous methods in two points. First, it uses a tree-growing algorithm which is more computationally efficient (Lu et al., ): the standard variable selection […]

call_split

Analysis pipeline for the epistasis search – statistical versus biological filtering

2014
Front Genet
PMCID: 4012196
PMID: 24817878
DOI: 10.3389/fgene.2014.00106
call_split See protocol

[…] n. They use a multi-locus Mann–Whitney statistic to evaluate the joint association of a SNP combination. Using a computationally efficient forward selection algorithm makes these methods feasible for genome-wide gene–gene interaction analyses. Nevertheless, they require at least one SNP in the combination to have a significant marginal association. The non-parametric approaches do not suffer from […]


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GWGGI institution(s)
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
GWGGI funding source(s)
Supported by the National Institute on Drug Abuse under Award Number K01DA033346 and by the National Institute of Dental & Craniofacial Research under Award Number R03DE022379.

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