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An R package implementation of multifactor dimensionality reduction.

Stacey J Winham1, Alison A Motsinger-Reif

  • 1Department of Statistics, North Carolina State University, Raleigh NC 27695, USA. winham.stacey@mayo.edu.

Biodata Mining
|August 18, 2011
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Summary
This summary is machine-generated.

A new R package implements Multifactor Dimensionality Reduction (MDR) for identifying gene-gene interactions from high-dimensional genetic data. This flexible tool enhances the utility of MDR for researchers analyzing complex genetic associations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-dimensional genetic data requires advanced variable selection methods.
  • Multifactor Dimensionality Reduction (MDR) is a key data-mining technique for identifying gene-gene and gene-environment interactions.
  • Expanding software availability enhances MDR's accessibility and application.

Purpose of the Study:

  • To introduce a new software package for the R statistical language.
  • To implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection.
  • To provide R users with a flexible and useful tool for genetic data analysis.

Main Methods:

  • Development of an R package implementing the Multifactor Dimensionality Reduction (MDR) algorithm.
  • Nonparametric variable selection for identifying interaction effects.
  • Provision of example datasets for package demonstration.

Main Results:

  • A new 'MDR' package is available for the R statistical language.
  • The package offers a flexible implementation of MDR for genetic association studies.
  • Example data illustrates the package's functionality for users.

Conclusions:

  • The new R package increases the accessibility and utility of Multifactor Dimensionality Reduction (MDR).
  • This implementation will further promote the use of MDR for identifying gene-gene interactions.
  • The package provides a valuable resource for researchers in statistical genetics.