Literature DB >> 16809395

Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions.

William S Bush1, Scott M Dudek, Marylyn D Ritchie.   

Abstract

UNLABELLED: Parallel multifactor dimensionality reduction is a tool for large-scale analysis of gene-gene and gene-environment interactions. The MDR algorithm was redesigned to allow an unlimited number of study subjects, total variables and variable states, and to remove restrictions on the order of interactions being analyzed. In addition, the algorithm is markedly more efficient, with approximately 150-fold decrease in runtime for equivalent analyses. To facilitate the processing of large datasets, the algorithm was made parallel. AVAILABILITY: Parallel MDR is freely available for non-commercial research institutions. For full details see http://chgr.mc.vanderbilt.edu/ritchielab/pMDR. An open-source version of MDR software is available at http://www.epistasis.org.

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Year:  2006        PMID: 16809395      PMCID: PMC4939609          DOI: 10.1093/bioinformatics/btl347

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  2 in total

1.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

Authors:  Lance W Hahn; Marylyn D Ritchie; Jason H Moore
Journal:  Bioinformatics       Date:  2003-02-12       Impact factor: 6.937

2.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.

Authors:  M D Ritchie; L W Hahn; N Roodi; L R Bailey; W D Dupont; F F Parl; J H Moore
Journal:  Am J Hum Genet       Date:  2001-06-11       Impact factor: 11.025

  2 in total
  27 in total

1.  A robust multifactor dimensionality reduction method for detecting gene-gene interactions with application to the genetic analysis of bladder cancer susceptibility.

Authors:  Jiang Gui; Angeline S Andrew; Peter Andrews; Heather M Nelson; Karl T Kelsey; Margaret R Karagas; Jason H Moore
Journal:  Ann Hum Genet       Date:  2010-11-22       Impact factor: 1.670

2.  AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes.

Authors:  Pritam Chanda; Lara Sucheston; Aidong Zhang; Daniel Brazeau; Jo L Freudenheim; Christine Ambrosone; Murali Ramanathan
Journal:  Genetics       Date:  2008-09-09       Impact factor: 4.562

3.  Information-theoretic metrics for visualizing gene-environment interactions.

Authors:  Pritam Chanda; Aidong Zhang; Daniel Brazeau; Lara Sucheston; Jo L Freudenheim; Christine Ambrosone; Murali Ramanathan
Journal:  Am J Hum Genet       Date:  2007-10-03       Impact factor: 11.025

4.  A general framework for formal tests of interaction after exhaustive search methods with applications to MDR and MDR-PDT.

Authors:  Todd L Edwards; Stephen D Turner; Eric S Torstenson; Scott M Dudek; Eden R Martin; Marylyn D Ritchie
Journal:  PLoS One       Date:  2010-02-23       Impact factor: 3.240

5.  SYMPHONY, an information-theoretic method for gene-gene and gene-environment interaction analysis of disease syndromes.

Authors:  J Knights; J Yang; P Chanda; A Zhang; M Ramanathan
Journal:  Heredity (Edinb)       Date:  2013-02-20       Impact factor: 3.821

6.  Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits.

Authors:  Pritam Chanda; Lara Sucheston; Song Liu; Aidong Zhang; Murali Ramanathan
Journal:  BMC Genomics       Date:  2009-11-04       Impact factor: 3.969

7.  Mutual information for testing gene-environment interaction.

Authors:  Xuesen Wu; Li Jin; Momiao Xiong
Journal:  PLoS One       Date:  2009-02-24       Impact factor: 3.240

8.  Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.

Authors:  Waranyu Wongseree; Anunchai Assawamakin; Theera Piroonratana; Saravudh Sinsomros; Chanin Limwongse; Nachol Chaiyaratana
Journal:  BMC Bioinformatics       Date:  2009-09-17       Impact factor: 3.169

9.  Accelerating epistasis analysis in human genetics with consumer graphics hardware.

Authors:  Nicholas A Sinnott-Armstrong; Casey S Greene; Fabio Cancare; Jason H Moore
Journal:  BMC Res Notes       Date:  2009-07-24

Review 10.  Bioinformatics challenges for genome-wide association studies.

Authors:  Jason H Moore; Folkert W Asselbergs; Scott M Williams
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

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