Improved human disease candidate gene prioritization using mouse phenotype

By Jing Chen, Huan Xu, Bruce J. Aronow, Anil G. Jegga. BMC Bioinformatics, 2007

300 citations recorded by the source corpus.

BackgroundThe 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.ResultsExtending 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 http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.ConclusionThe incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/958620f2d25da676e7d16faca13b95cbaca20d19

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Citations in the corpus

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization (2009, methodology): “The back end of ToppGene Suite is a scripted process that automatically downloads data from publicly available data sources [see (10,27) for more details].”
Disease candidate gene identification and prioritization using protein interaction networks (2009, methodology): “Surprisingly, we found that networkbased methods are better than all annotations (see [6] for details).”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, methodology): “For instance, GeneWanderer uses random walks or a diffusion kernel on a PPI network 75 , and ToppNet (from the ToppGene suite) uses Web and social network methods on a PPI network 69,76 .”