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Prediction of Human Disease Genes by Analysis of Conserved Coexpression

phenotypes mapped on the genome by linkage analysis are not yet associated to any validated disease gene (850 OMIM entries for phenotypes with unknown molecular basis had at least one associated disease locus on July 2nd, 2007). The identification of disease genes within disease-associated loci is a very demanding task even in the post-genomic era because orphan loci may typically contain hundreds of positional candidates. Most published methods for disease gene prediction or prioritization rely on accurate gene annotation information (e.g. Gene Ontology) or mine PubMed/MEDLINE abstracts to infer relations between genes and phenotypes, thus being strongly biased towards well-characterized genes and tending to overlook genes about which little is known. We present a method [1] that exploits microarray gene expression data and a quantitative measure for similarity between human phenotypes to identify best candidates among the positional candidates for a given disease as those that show significant coexpression with genes already known to be involved in similar phenotypes. Since the method uses a notion of similarity among phenotypes it can also be applied to phenotypes of so far unknown molecular basis (as long as they show similarity to other phenotypes of known molecular basis). Also, avoiding information on gene annotation and previous research is potentially much less biased towards consolidated knowledge although current microarray platforms still have their limitations and hence do not allow the evaluation of all positional candidates. Since microarray data can be very noisy we focus on coexpression that is evolutionary conserved between human and mouse and therefore is more likely to be biologically meaningful. For this purpose we construct a human-mouse conserved coexpression network and verify its biological meaning and applicability to disease gene prediction by analyzing the prevalence of Gene Ontology terms, known interactions between human proteins and similar OMIM phenotypes within the networks coexpression clusters. 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. We propose high-probability candidates for 81 OMIM loci characterized by unknown molecular basis. Many phenotypes mapped on the genome by linkage analysis are not yet associated to any validated disease gene (850 OMIM entries for phenotypes with unknown molecular basis had at least one associated disease locus on July 2nd, 2007). The identification of disease genes within disease-associated loci is a very demanding task even in the post-genomic era because orphan loci may typically …