EINVis: A Visualization Tool for Analyzing and Exploring Genetic Interactions in Large‐Scale Association Studies

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/.

[1]  Nadezhda T. Doncheva,et al.  Topological analysis and interactive visualization of biological networks and protein structures , 2012, Nature Protocols.

[2]  Matthew Suderman,et al.  Tools for visually exploring biological networks , 2007, Bioinform..

[3]  N. Morton Genetic epidemiology , 1997, International Journal of Obesity.

[4]  Reinhard Schneider,et al.  A survey of visualization tools for biological network analysis , 2008, BioData Mining.

[5]  R. Cooper,et al.  Admixture Mapping Provides Evidence of Association of the VNN1 Gene with Hypertension , 2007, PloS one.

[6]  Tamara Munzner,et al.  MizBee: A Multiscale Synteny Browser , 2009, IEEE Transactions on Visualization and Computer Graphics.

[7]  Joseph T. Glessner,et al.  Combined admixture mapping and association analysis identifies a novel blood pressure genetic locus on 5p13: contributions from the CARe consortium. , 2011, Human molecular genetics.

[8]  H. Cordell Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[9]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[10]  Ting Hu,et al.  Characterizing genetic interactions in human disease association studies using statistical epistasis networks , 2011, BMC Bioinformatics.

[11]  Mark I. McCarthy,et al.  Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies , 2008, PloS one.

[12]  Dan Liu,et al.  Performance analysis of novel methods for detecting epistasis , 2011, BMC Bioinformatics.

[13]  Guimei Liu,et al.  An empirical comparison of several recent epistatic interaction detection methods , 2011, Bioinform..

[14]  Scott M. Williams,et al.  Epistasis and its implications for personal genetics. , 2009, American journal of human genetics.

[15]  Joseph J. LaViola,et al.  Code bubbles: rethinking the user interface paradigm of integrated development environments , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[16]  Steven J. M. Jones,et al.  Circos: an information aesthetic for comparative genomics. , 2009, Genome research.

[17]  Yan Wang,et al.  VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology , 2009, Nucleic Acids Res..

[18]  Elias Zintzaras,et al.  Synopsis and data synthesis of genetic association studies in hypertension for the adrenergic receptor family genes: the CUMAGAS-HYPERT database. , 2010, American journal of hypertension.

[19]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[20]  Tom R. Gaunt,et al.  Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study , 2011, Human molecular genetics.

[21]  Andrew D. Johnson,et al.  SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap , 2008, Bioinform..

[22]  Kristel Van Steen,et al.  Travelling the world of gene-gene interactions , 2012, Briefings Bioinform..

[23]  P. Phillips Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems , 2008, Nature Reviews Genetics.

[24]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[25]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[26]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[27]  T. Miyata,et al.  Genetic polymorphisms of L-type calcium channel alpha1C and alpha1D subunit genes are associated with sensitivity to the antihypertensive effects of L-type dihydropyridine calcium-channel blockers. , 2009, Circulation journal : official journal of the Japanese Circulation Society.

[28]  Kalev Kask,et al.  CACNA1C polymorphisms are associated with the efficacy of calcium channel blockers in the treatment of hypertension. , 2006, Pharmacogenomics.

[29]  B. McKinney,et al.  Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis , 2009, PLoS genetics.

[30]  David H. Laidlaw,et al.  VisBubbles: a workflow-driven framework for scientific data analysis of time-varying biological datasets , 2011, SA '11.

[31]  Xiping Xu,et al.  Arg347Cys polymorphism of α1A-adrenoceptor gene is associated with blood pressure response to nifedipine GITS in Chinese hypertensive patients , 2009, Journal of Human Genetics.