EINVis: A visualization tool for analyzing and exploring genetic interactions in large-scale association studies

Yubao Wu, Xiaofeng Zhu, Jian Chen, Xiang Zhang

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/.

Original languageEnglish (US)
Pages (from-to)675-685
Number of pages11
JournalGenetic Epidemiology
Volume37
Issue number7
DOIs
StatePublished - Nov 1 2013

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Single Nucleotide Polymorphism
Chromosome Structures
Gene Regulatory Networks
Linkage Disequilibrium
Genetic Association Studies
Proxy
Genetic Markers
Documentation
Genes
Research

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)

Cite this

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abstract = "Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/.",
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EINVis : A visualization tool for analyzing and exploring genetic interactions in large-scale association studies. / Wu, Yubao; Zhu, Xiaofeng; Chen, Jian; Zhang, Xiang.

In: Genetic Epidemiology, Vol. 37, No. 7, 01.11.2013, p. 675-685.

Research output: Contribution to journalArticle

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