MDR 0.4 Released

The Dartmouth Computational Genetics Laboratory is pleased to announce the release of version 0.4 of our open-source multifactor dimensionality reduction (MDR) software package.

MDR 0.4 has been posted to sourceforge.net which can be accessed from here.

New features in MDR 0.4 include:

1) Threading to take advantage of multi-processor computers.

MDR will now automatically detect if your computer has multiple processors and will parallelize the algorithm accordingly. Thus, if you have two processors with threading turned on, MDR will run 4x faster.

2) Batch/command line mode to allow MDR to be run from scripts.

This new feature allows MDR to be run from the command line with a Perl script, for example. This makes it possible to run MDR on a grid or parallel computer for simulation studies.

3) Visualization of the fitness landscape.

This new feature plots the training accuracy for every model evaluated by MDR. Line plots or histograms can be selected. A zoom feature permits 'drilling down' on a particular region of the landscape. At a fine resolution, mousing over points reveals the model and the training accuracy of that model.

4) Odds ratios.

This statistic and its 95% confidence interval have been added to the MDR output to facilitate an epidemiological interpretation of MDR models.

The next major additions to the MDR software will include computation search or wrapper algorithms for variable or attribute selection when the number of combinations to be evaluated is not computationally feasible. Random, greedy, and stochastic search algorithms will be added. These are necessary for genome-wide association studies. This feature will be available later in the summer.

Is there something you would like to see added to MDR? Request it here.

Note that MDR will be in beta testing for another 2-3 months. Please send us your feedback so we can roll out a polished MDR 1.0 later this summer.

Systematic interpretation of genetic interactions using protein networks

A recent paper by Kelly and Ideker in Nature Biotechnology discusses how genetic and physical interactions can be integrated to reveal pathway organization and function. These 'systems biology' studies have great potential for the study of human health (see Moore, Nat Genet. 2005 Jan;37(1):13-4 [PubMed]; Moore and Williams, BioEssays. 2005 Jun;27(6):637-46 [PubMed]).

Kelley R, Ideker T. Systematic interpretation of genetic interactions using protein networks. Nat Biotechnol. 2005 May;23(5):561-6. [PubMed]

Abstract:

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.

Recent CGL Press

The Spring 2005 issue of Dartmouth's Skylight newsletter highlights the research on epistasis being carried out by Dr. Jason H. Moore and the Computational Genetics Laboratory. The pdf can be found here.

The Winter 2004 issue of Dartmouth Medicine also highlights our work. The pdf can be found here.

Nuclear-mitochondrial epistasis

A new paper by Zeyl et al. in the journal Evolution documents nulcear-mitochondrial epistasis in yeast:

Zeyl C, Andreson B, Weninck E. Nuclear-mitochondrial epistasis for fitness in Saccharomyces cerevisiae. Evolution Int J Org Evolution. 2005 Apr;59(4):910-4. [PubMed]

Abstract:

In addition to the familiar possibility of epistasis between nuclear loci, interactions may evolve between the mitochondrial and nuclear genomes in eukaryotic cells. We looked for such interactions in Saccharomyces cerevisiae genotypes evolved independently and asexually in the laboratory for 2000 generations, and in an ecologically distinct pathogenic S. cerevisiae strain. From these strains we constructed derivatives entirely lacking mitochondrial DNA and then used crosses to construct matched and unmatched pairings of nuclear and mitochondrial genomes. We detected fitness effects of such interactions in an evolved laboratory strain and in crosses between the laboratory and pathogen strains. In both cases, there were significant contributions to progeny fitness of both nuclear and mitochondrial genomes and of their interaction. A second evolved genotype showed incompatibility with the first evolved genotype, but the nuclear and mitochondrial contributions to this incompatibility could not be resolved. These results indicate that cytonuclear interactions analogous to those already known from plants and animals can evolve rapidly on an evolutionary timescale.