Disease and phenotype gene set analysis of disease-based gene expression in mouse and human

被引:16
作者
De, Supriyo [1 ]
Zhang, Yongqing [1 ]
Garner, John R. [1 ]
Wang, S. Alex [2 ]
Becker, Kevin G. [1 ]
机构
[1] NIA, Biomed Res Ctr, NIH, Gene Express & Genom Unit, Baltimore, MD 21224 USA
[2] NIH, Div Computat Biosci, Ctr Informat Technol, Bethesda, MD 20892 USA
基金
美国国家卫生研究院;
关键词
genome-wide association; microarray; common disease; disease ontology; data integration; GENOME-WIDE ASSOCIATION; ENRICHMENT ANALYSIS; PATHWAYS; DATABASE; ARCHIVE; BIOLOGY; ATLAS;
D O I
10.1152/physiolgenomics.00008.2010
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
De S, Zhang Y, Garner JR, Wang SA, Becker KG. Disease and phenotype gene set analysis of disease-based gene expression in mouse and human. Physiol Genomics 42A: 162-167, 2010. First published August 3, 2010; doi:10.1152/physiolgenomics.00008.2010.-The genetic contributions to common disease and complex disease phenotypes are pleiotropic, multifactorial, and combinatorial. Gene set analysis is a computational approach used in the analysis of microarray data to rapidly query gene combinations and multifactorial processes. Here we use novel gene sets based on population-based human genetic associations in common human disease or experimental genetic mouse models to analyze disease-related microarray studies. We developed a web-based analysis tool that uses these novel disease-and phenotype-related gene sets to analyze microarray-based gene expression data. These gene sets show disease and phenotype specificity in a species-specific and cross-species fashion. In this way, we integrate population-based common human disease genetics, mouse genetically determined phenotypes, and disease or phenotype structured ontologies, with gene expression studies relevant to human disease. This may aid in the translation of large-scale high-throughput datasets into the context of clinically relevant disease phenotypes.
引用
收藏
页码:162 / 167
页数:6
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