High-throughput genome-wide studies like linkage analysis and gene expression profiling, although useful for classification and characterization, do not provide sufficient information to identify specific disease causal genes. Both of these approaches typically result in hundreds of potential candidate genes, often failing to help researchers in reducing the target genes to a manageable number for further validation. To overcome these limitations, several gene prioritization methods have been developed (1–10). While all of these tools are based on the assumption that similar phenotypes are caused by genes with similar or related functions (2,11–13), they differ by the strategy they adopt in calculating similarity and by the data sources they use (14). Except for ENDEAVOUR (5,14) and ToppGene (10), most of the existing approaches mainly focus on the combination of few data sources. Interestingly, none of these approaches utilize mouse phenotype data explicitly in their prioritization approaches even though the mouse is the key model organism for the analysis of mammalian developmental, physiological and disease processes (15). Additionally, previous reports (16,17) have shown that a direct comparison of human and mouse phenotypes allowed
