Epistasis analysis using multifactor dimensionality reduction.

Here we introduce the multifactor dimensionality reduction (MDR) methodology and software package for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the key functions of the open-source MDR software package that is freely distributed. We end with a few examples of published studies of complex human diseases that have used MDR.

[1]  Choon-Sik Park,et al.  Genetic interactions model among Eotaxin gene polymorphisms in asthma , 2008, Journal of Human Genetics.

[2]  Jiang Gui,et al.  A Robust Multifactor Dimensionality Reduction Method for Detecting Gene–Gene Interactions with Application to the Genetic Analysis of Bladder Cancer Susceptibility , 2011, Annals of human genetics.

[3]  Scott M. Williams,et al.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.

[4]  Jason H. Moore,et al.  Ant Colony Optimization for Genome-Wide Genetic Analysis , 2008, ANTS Conference.

[5]  J. Steven Leeder,et al.  Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes , 2012, BioData Mining.

[6]  Jason H Moore,et al.  Computational analysis of gene-gene interactions using multifactor dimensionality reduction , 2004, Expert review of molecular diagnostics.

[7]  Kristel Van Steen,et al.  mbmdr: an R package for exploring gene-gene interactions associated with binary or quantitative traits , 2010, Bioinform..

[8]  Marylyn D. Ritchie,et al.  Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions , 2006, Bioinform..

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

[10]  Leonid Kruglyak,et al.  More Than the Sum of Its Parts: A Complex Epistatic Network Underlies Natural Variation in Thermal Preference Behavior in Caenorhabditis elegans , 2012, Genetics.

[11]  Jun Zhu,et al.  A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. , 2007, American journal of human genetics.

[12]  Jason H. Moore,et al.  Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS , 2010, Bioinform..

[13]  Angeline S. Andrew,et al.  A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis , 2010, Human Heredity.

[14]  Lorenzo Beretta,et al.  Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data , 2010, BMC Bioinformatics.

[15]  Marylyn D. Ritchie,et al.  Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction , 2008, BMC Bioinformatics.

[16]  Ting Hu,et al.  Epistasis, complexity, and multifactor dimensionality reduction. , 2013, Methods in molecular biology.

[17]  Jason H. Moore,et al.  A global view of epistasis , 2005, Nature Genetics.

[18]  Jason H. Moore,et al.  Layers of epistasis: genome‐wide regulatory networks and network approaches to genome‐wide association studies , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[19]  H. Cordell.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. , 2002, Human molecular genetics.

[20]  Fabio Cancare,et al.  Accelerating epistasis analysis in human genetics with consumer graphics hardware , 2009, BMC Research Notes.

[21]  J H Moore,et al.  The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies. , 2008, Current pharmacogenomics and personalized medicine.

[22]  Angeline S. Andrew,et al.  A novel survival multifactor dimensionality reduction method for detecting gene–gene interactions with application to bladder cancer prognosis , 2010, Human Genetics.

[23]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[24]  Jason H. Moore,et al.  Tuning ReliefF for Genome-Wide Genetic Analysis , 2007, EvoBIO.

[25]  Jason H. Moore,et al.  Genome-Wide Analysis of Epistasis Using Multifactor Dimensionality Reduction: Feature Selection and Construction in the Domain of Human Genetics , 2009 .

[26]  R. Gibbs,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:Epistasis dominates the genetic architecture of Drosophila quantitative traits , 2012 .

[27]  Alison A. Motsinger-Reif,et al.  A comparison of internal validation techniques for multifactor dimensionality reduction , 2010, BMC Bioinformatics.

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

[29]  Casey S. Greene,et al.  Failure to Replicate a Genetic Association May Provide Important Clues About Genetic Architecture , 2009, PloS one.

[30]  J. Steven Leeder,et al.  Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction , 2013, BioData Mining.

[31]  Ting Hu,et al.  Statistical Epistasis Networks Reduce the Computational Complexity of Searching Three-Locus Genetic Models , 2012, Pacific Symposium on Biocomputing.

[32]  P. Phillips.  The language of gene interaction. , 1998, Genetics.

[33]  Ryszard S. Michalski,et al.  A Theory and Methodology of Inductive Learning , 1983, Artificial Intelligence.

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

[35]  Paolo Vineis,et al.  DNA Repair Polymorphisms Modify Bladder Cancer Risk: A Multi-factor Analytic Strategy , 2007, Human Heredity.

[36]  Marylyn D. Ritchie,et al.  Biofilter: A Knowledge-Integration System for the Multi-Locus Analysis of Genome-Wide Association Studies , 2008, Pacific Symposium on Biocomputing.

[37]  J. H. Moore,et al.  A novel method to identify gene–gene effects in nuclear families: the MDR‐PDT , 2006, Genetic epidemiology.

[38]  Jason H. Moore,et al.  An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing , 2009, ECAL.

[39]  Jason H. Moore,et al.  Ideal discrimination of discrete clinical endpoints using multilocus genotypes , 2004, Silico Biol..

[40]  Jason H. Moore,et al.  The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics , 2010, EvoBIO.

[41]  M. L. Calle,et al.  FAM-MDR: A Flexible Family-Based Multifactor Dimensionality Reduction Technique to Detect Epistasis Using Related Individuals , 2010, PloS one.

[42]  Todd Holden,et al.  A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. , 2006, Journal of theoretical biology.

[43]  Jason H. Moore,et al.  Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS , 2012, BioData Mining.

[44]  Margaret R Karagas,et al.  Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. , 2006, Carcinogenesis.

[45]  Scott M. Williams,et al.  Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. , 2005, BioEssays : news and reviews in molecular, cellular and developmental biology.

[46]  Jason H. Moore,et al.  Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies , 2003, EvoWorkshops.

[47]  Jason H. Moore,et al.  Pacific Symposium on Biocomputing 15:327-336(2010) ENABLING PERSONAL GENOMICS WITH AN EXPLICIT TEST OF EPISTASIS , 2022 .

[48]  Alison A. Motsinger-Reif,et al.  An R package implementation of multifactor dimensionality reduction , 2011, BioData Mining.

[49]  Jason H. Moore,et al.  Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction. , 2010, Advances in genetics.

[50]  Jason H. Moore,et al.  An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene Interactions on risk of myocardial infarction: The importance of model validation , 2004, BMC Bioinformatics.

[51]  Jason H. Moore,et al.  Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity , 2003, Genetic epidemiology.

[52]  Ting Hu,et al.  ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks , 2013, Genetic epidemiology.

[53]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[54]  Scott M. Williams,et al.  Shadows of complexity: what biological networks reveal about epistasis and pleiotropy , 2009, BioEssays : news and reviews in molecular, cellular and developmental biology.

[55]  Jiang Gui,et al.  A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction , 2009, Genetic epidemiology.

[56]  Jason H. Moore,et al.  The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases , 2003, Human Heredity.

[57]  Jason H. Moore,et al.  Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions , 2009, BioData Mining.

[58]  Casey S. Greene,et al.  IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks , 2012, Nucleic Acids Res..

[59]  Jason H. Moore,et al.  Exploiting graphics processing units for computational biology and bioinformatics , 2010, Interdisciplinary Sciences: Computational Life Sciences.

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

[61]  Scott M. Williams,et al.  A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits , 2013, PloS one.