Improved human disease candidate gene prioritization using mouse phenotype Export

@article{citeulike:1778376, abstract = {BACKGROUND: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.RESULTS: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 http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.CONCLUSION:The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.}, author = {Chen, Jing and Xu, Huan and Aronow, Bruce and Jegga, Anil}, citeulike-article-id = {1778376}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-8-392}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17939863}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17939863}, doi = {10.1186/1471-2105-8-392}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {abstracts, candidate, candidate\_gene\_analysis, data\_integration, disease, disease\_gene, prioritization}, number = {1}, pages = {392+}, posted-at = {2009-11-03 11:42:53}, priority = {3}, title = {Improved human disease candidate gene prioritization using mouse phenotype}, url = {http://dx.doi.org/10.1186/1471-2105-8-392}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1778376 L3 - citeulike-article-id:1778376 N2 - BACKGROUND: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.RESULTS: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 http://toppgene.cchmc.org, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.CONCLUSION:The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - Improved human disease candidate gene prioritization using mouse phenotype VL - 8 SP - 392 KW - abstracts KW - candidate KW - candidate_gene_analysis KW - data_integration KW - disease KW - disease_gene KW - prioritization AU - Chen, Jing AU - Xu, Huan AU - Aronow, Bruce AU - Jegga, Anil PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-392 ER -