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

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.

SUBMITTER: Ala U

PROVIDER: S-EPMC2268251 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature

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

Ala Ugo U Piro Rosario Michael RM Grassi Elena E Damasco Christian C Silengo Lorenzo L Oti Martin M Provero Paolo P Di Cunto Ferdinando F

PLoS computational biology 20080328 3

<h4>Background</h4>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 coexpressio ...[more]

PMID: 18369433

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Project description:BackgroundMacronutrients are pivotal elements for proper plant growth and development. Although extensive gene expression profiling revealed a large number of genes differentially expressed under various nutrient deprivation, characterization of these genes has never been fully explored especially in rice. Coexpression network analysis is a useful tool to elucidate the functional relationships of genes based on common expression. Therefore, we performed microarray analysis of rice shoot under nitrogen (N), phosphorus (P), and potassium (K) deficiency conditions. Moreover, we conducted a large scale coexpression analysis by integrating the data with previously generated gene expression profiles of organs and tissues at different developmental stages to obtain a global view of gene networks associated with plant response to nutrient deficiency.ResultsWe statistically identified 5400 differentially expressed genes under the nutrient deficiency treatments. Subsequent coexpression analysis resulted in the extraction of 6 modules (groups of highly interconnected genes) with distinct gene expression signatures. Three of these modules comprise mostly of downregulated genes under N deficiency associated with distinct functions such as development of immature organs, protein biosynthesis and photosynthesis in chloroplast of green tissues, and fundamental cellular processes in all organs and tissues. Furthermore, we identified one module containing upregulated genes under N and K deficiency conditions, and a number of genes encoding protein kinase, kinase-like domain containing protein and nutrient transporters. This module might be particularly involved in adaptation to nutrient deficiency via phosphorylation-mediated signal transduction and/or post-transcriptional regulation.ConclusionsOur study demonstrated that large scale coexpression analysis is an efficient approach in characterizing the nutrient response genes based on biological functions and could provide new insights in understanding plant response to nutrient deficiency.

Dataset Information

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

Publications

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

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