TY - JOUR T1 - Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis A1 - Ala, Ugo A1 - Piro, Rosario Michael A1 - Grassi, Elena A1 - Damasco, Christian A1 - Silengo, Lorenzo A1 - Oti, Martin A1 - Provero, Paolo A1 - Di Cunto, Ferdinando Y1 - 2008/03/28 N2 - Author SummaryOne of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 4 IS - 3 UR - https://doi.org/10.1371/journal.pcbi.1000043 SP - e1000043 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000043 ER -