Open-Source MDR 0.6.1

The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 0.6.1 BETA of our open-source Multifactor Dimensionality Reduction (MDR) software package.

Download information can be found here.

New features in MDR 0.6.1 include:

1) Search or wrapper algorithms

We have implemented the first of several different computational search or wrapper algorithms for identifying gene-gene interaction models when there are too many attributes (i.e. SNPs) to exhaustively evaluate. The first wrapper available is a random search. Future additions will include simulated annealing and a genetic algorithm, for example. A deterministic search strategy such as best first will also be added.

2) Additional filter algorithmsWe have added the cross-product odds ratio (OR) to the filter list. This new metric computes the OR for each pairwise combination of levels within an attribute. It also computes the OR comparing each level to all others combined. The largest OR is returned and can be used to filter SNPs for further analysis with MDR. Note that OR of infinity are not returned. Rather, a cell with a zero count is changed to 1 when the OR is computed. This puts the OR on a scale that is easier to plot and easier to compare across other attributes for the purpose of filtering. Additional filter metrics such as information gain will be added soon.

Open-Source MDR 0.5.1

The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 0.5.1 BETA of our open-source Multifactor Dimensionality Reduction (MDR) software package.

Download information can be found here.

New features in MDR 0.5.1 include:

1) Filter methods

This new feature provides the ability to 'filter' or select from your list of SNPs or other attributes a subset that can be analyzed using MDR. We provide ReliefF and chi-square statistics for filtering. We also provide tools for visualizing the fitness landscape in the form of a line plot, histogram, or raw text. This approach is useful when the number of attributes or variables exceeds the number that is practical for an exhaustive MDR analysis of attribute combinations. For a recent review of the ReliefF algorithm see Robnik-Sikonja M, Kononenko I. 2003. Theoretical and empirical analysis of reliefF and rreliefF. Machine Learning 53:23-69. We are preparing a manuscript reporting results that show ReliefF works much better than chi-square when gene-gene interactions are present. We think this approach will be useful for MDR analysis of genome-wide data.

2) New configuration option

We have included the option to disable the fitness landscape in the configuration tab. Computing and saving the fitness landscape consumes a lot of memory when there are many attribute combinations to evaluate.

3) New statistics

The output for the Best Model tab includes new statistics such as balanced accuracy, chi-square, precision, kappa, and F-measure. Are there others you would like to see? Let us know here!

4) Minor interface changes

We have also implemented some minor interface changes that we think will make MDR easier to use. For example, the Configuration options are now in a tab instead of a new window. We have removed the Raw Results tab due to its redundancy.

5) Source code redesigned

The source code for the new version of MDR has been redsigned for easier use and reuse.

6) Documentation

The documentation we distribute with the MDR software has been updated. The docs describe each of the features and their use.

Plans for the Future:

The next major release of MDR will include several wrapper algorithms such as simulated annealing and genetic algorithms for stochastic searching when an exhaustive search is not possible, as is the case in a genome-wide association studies. We plan to have these new features ready sometime this fall.Remember! The MDR software is still in the beta testing phase. Your feedback is very important to us. Questions, criticisms, and suggestions can be directed to me (jason DOT h DOT moore {at} Dartmouth DOT edu). Feel free to post your feedback at the MDR project on Sourceforge.net.