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


RESULTS
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.


CONCLUSIONS
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.
Ala
Ugo
U
Molecular Biotechnology Center, Department of Genetics, Biology and Biochemistry, University of Turin, Turin, Italy.
Piro
Rosario Michael
RM
Grassi
Elena
E
Damasco
Christian
C
Silengo
Lorenzo
L
Oti
Martin
M
Provero
Paolo
P
Di Cunto
Ferdinando
F
D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000032:analysis; D002874:Chromosome Mapping / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D030342:Genetic Diseases, Inborn / Q000175:diagnosis* / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D051379:Mice; D020543:Proteome / Q000032:analysis / Q000235:genetics*
D015415:Biomarkers; D020543:Proteome