MONDAY, March 31 (HealthDay News) -- Investigating genes with similar expressions in humans and mice provides a strong method of predicting disease-related relationships in human genes, even when genetic disease loci comprise hundreds of genes, according to research published in the March PLoS Computational Biology.
Ugo Ala, of the University of Turin in Italy, and colleagues created two human-mouse conserved coexpression networks based on cDNA and oligonucleotide microarray platforms. They used these networks to construct coexpression clusters, with one cluster for each gene containing the given gene and its nearest neighbors in the network. The researchers considered a cluster relevant to a disease if it contained more than one gene known to cause phenotypes similar to the disease.
By combining conserved coexpression with phenotype correlation data, the researchers proposed high-probability candidates for 81 genetic diseases. Four candidates were found that were common to both networks -- for Ehlers-Danlos syndrome, Welander distal myopathy, spinal muscular atrophy and muscular dystrophy -- and should get the highest priority for experimental validation, according to the authors.
"We think that these results strongly underscore two critical points. The first is the importance of using a restrictive filter to select biologically relevant coexpression links, such as our phylogenetic filter selecting links which are under selective pressure and therefore more likely to imply functional relationships. The second is the usefulness of systematic phenotype analysis methods, which may capture disease similarities that could easily escape human operator-based approaches," Ala and colleagues write.
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