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Prediction of human disease genes by human-mouse conserved coexpression analysis.

Ala U, Piro RM, Grassi E, Damasco C, Silengo L, Oti M, Provero P, Di Cunto F - PLoS Comput. Biol. (2008)

Bottom Line: However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.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.Our 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.

View Article: PubMed Central - PubMed

Affiliation: Molecular Biotechnology Center, Department of Genetics, Biology and Biochemistry, University of Turin, Turin, Italy.

ABSTRACT

Background: Even 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 findings: We 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.

Conclusion: Our 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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Related in: MedlinePlus

Identification of candidate disease genes by means of conserved co-expression clusters.A locus with positional candidates (green bar) associated with a disease of unknown molecular basis (yellow) is screened for one or more candidate genes (green sphere) that appear in a conserved coexpression cluster (purple spheres) together with at least two other genes known to be involved in similar phenotypes (yellow spheres), as defined by the MimMiner similarity score.
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pcbi-1000043-g001: Identification of candidate disease genes by means of conserved co-expression clusters.A locus with positional candidates (green bar) associated with a disease of unknown molecular basis (yellow) is screened for one or more candidate genes (green sphere) that appear in a conserved coexpression cluster (purple spheres) together with at least two other genes known to be involved in similar phenotypes (yellow spheres), as defined by the MimMiner similarity score.

Mentions: Figure 1 schematically illustrates our approach.


Prediction of human disease genes by human-mouse conserved coexpression analysis.

Ala U, Piro RM, Grassi E, Damasco C, Silengo L, Oti M, Provero P, Di Cunto F - PLoS Comput. Biol. (2008)

Identification of candidate disease genes by means of conserved co-expression clusters.A locus with positional candidates (green bar) associated with a disease of unknown molecular basis (yellow) is screened for one or more candidate genes (green sphere) that appear in a conserved coexpression cluster (purple spheres) together with at least two other genes known to be involved in similar phenotypes (yellow spheres), as defined by the MimMiner similarity score.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2268251&req=5

pcbi-1000043-g001: Identification of candidate disease genes by means of conserved co-expression clusters.A locus with positional candidates (green bar) associated with a disease of unknown molecular basis (yellow) is screened for one or more candidate genes (green sphere) that appear in a conserved coexpression cluster (purple spheres) together with at least two other genes known to be involved in similar phenotypes (yellow spheres), as defined by the MimMiner similarity score.
Mentions: Figure 1 schematically illustrates our approach.

Bottom Line: However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.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.Our 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.

View Article: PubMed Central - PubMed

Affiliation: Molecular Biotechnology Center, Department of Genetics, Biology and Biochemistry, University of Turin, Turin, Italy.

ABSTRACT

Background: Even 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 findings: We 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.

Conclusion: Our 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.

Show MeSH
Related in: MedlinePlus