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Open Access Research article

Improved human disease candidate gene prioritization using mouse phenotype

Jing Chen12, Huan Xu1, Bruce J Aronow123 and Anil G Jegga13*

Author Affiliations

1 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, USA

2 Department of Biomedical Engineering, University of Cincinnati, Cincinnati, USA

3 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, USA

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BMC Bioinformatics 2007, 8:392  doi:10.1186/1471-2105-8-392

Published: 16 October 2007



The majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.


Extending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene webcite, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.


The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.