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. 2010 Oct;42A(2):162-7.
doi: 10.1152/physiolgenomics.00008.2010. Epub 2010 Aug 3.

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

Supriyo De  1 , Yongqing Zhang, John R Garner, S Alex Wang, Kevin G Becker
Affiliations

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

Supriyo De et al. Physiol Genomics. 2010 Oct.

Abstract

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.

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Figures

Fig. 1.
Fig. 1.
Human gene expression vs. human gene sets. Ribbon graphs of the top 30 highest Z-scores of the statistically significant (P < 0.05) parametric analysis of gene expression (PAGE) results in human Type 2 diabetes -ArrayExpress #E-CBIL-30 (A), sepsis - Gene Expression Omnibus (GEO) #GSE8182 (B), and smoking - GEO #GSE994 (C), using the human Genetic Association Database (GAD) disease gene sets. *Gene sets with ontological relevance to the disease of reference.
Fig. 2.
Fig. 2.
Mouse gene expression vs. mouse gene sets. Ribbon graphs of the top 30 highest Z-scores of the statistically significant (P < 0.05) PAGE results in mouse sepsis - GEO #GSE4479 (A), neural crest/development - GEO #GSE11356 (B), and cerebral malaria - GEO #GSE7814 (C) using the mouse mammalian phenotype (MP) gene sets. *Gene sets with ontological relevance to the disease of reference.
Fig. 3.
Fig. 3.
Mouse gene expression vs. mouse gene sets. Heat map of mouse B6 cerebral malaria GEO #GSE7814. B6 day 1 vs. B6 day 0; B6 day 3 vs. B6 day 0; B6 day 6 vs. B6 day 0.
Fig. 4.
Fig. 4.
Human gene expression vs. mouse gene sets. Ribbon graphs of the top 30 highest Z-scores of the statistically significant (P < 0.05) PAGE results in human Type 2 diabetes ArrayExpress #E-CBIL-30 (A), Alzheimer's disease GEO #GSE 1297 (B), and human sepsis GEO #GSE 8121 (C) using the mouse MP gene sets. *Gene sets with clear ontological relevance to the disease of reference.
See this image and copyright information in PMC

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