Prediction of human disease genes by human-mouse conserved coexpression analysis |
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Authors: | Ala Ugo Piro Rosario Michael Grassi Elena Damasco Christian Silengo Lorenzo Oti Martin Provero Paolo Di Cunto Ferdinando |
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Affiliation: | 1Molecular Biotechnology Center, Department of Genetics, Biology and Biochemistry, University of Turin, Turin, Italy;2Department of Human Genetics and Centre for Molecular and Biomolecular Informatics, University Medical Centre Nijmegen, Nijmegen, The Netherlands;Lilly Singapore Centre for Drug Discovery, Singapore |
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Abstract: | BackgroundEven in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.Methodology/Principal FindingsWe show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.ConclusionOur results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. |
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