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Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

Nature genetics | Apr 30, 2009

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Pubmed ID: 19270708 RIS Download

Mesh terms: Abdomen | Adipose Tissue | Animals | Carrier Proteins | Disease Models, Animal | Female | Gene Expression Profiling | Genetic Variation | Glutathione Peroxidase | Glycoproteins | Humans | Liver | Male | Mice | Mice, Knockout | Mice, Transgenic | Muscle, Skeletal | Nerve Tissue Proteins | Obesity | Phenotype | Reproducibility of Results | Transcription, Genetic

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Associated grants

  • Agency: NHLBI NIH HHS, Id: HL28481
  • Agency: NHLBI NIH HHS, Id: P01 HL028481
  • Agency: NIDDK NIH HHS, Id: R01 DK072206-04
  • Agency: NHLBI NIH HHS, Id: HL30568
  • Agency: NHLBI NIH HHS, Id: P01 HL030568
  • Agency: NIDDK NIH HHS, Id: DK072206
  • Agency: NIDDK NIH HHS, Id: R01 DK072206

Comparative Toxicogenomics Database (Data, Disease Annotation)

Mouse Genome Informatics (Data, Gene Annotation)

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Classification system that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in the absence of direct experimental evidence. Proteins are classified by expert biologists according to: * Gene families and subfamilies, including annotated phylogenetic trees * Gene Ontology classes: molecular function, biological process, cellular component * PANTHER Protein Classes * Pathways, including diagrams The PANTHER Classifications are the result of human curation as well as sophisticated bioinformatics algorithms. Details of the methods can be found in (Thomas et al., Genome Research 2003; Mi et al. NAR 2005). Version 8.1 contains 7729 protein families, each with a phylogenetic tree relating modern-day genes in 48 organisms.) PANTHER contains the complete sets of protein coding genes for 48 organisms, obtained from definitive sources. PANTHER uses the Gene Ontology for classifications by molecular function, biological process and cellular component. The PANTHER Protein Class ontology was adapted from the PANTHER/X molecular function ontology, and includes commonly used classes of protein functions, many of which are not covered by GO molecular function. You may download the classes and relationship information. PANTHER uses only a subset of GO terms (GO slim) to facilitate browsing. You may download the PANTHER GO slim. You may Score proteins against the PANTHER HMM library and download PANTHER tools and data. PANTHER Pathway consists of over 176, primarily signaling, pathways, each with subfamilies and protein sequences mapped to individual pathway components. Pathways are drawn using CellDesigner software, capturing molecular level events in both signaling and metabolic pathways, and can be exported in SBML format. The SBGN view of the diagram can also be exported. Pathway diagrams are interactive and include tools for visualizing gene expression data in the context of the diagrams.


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