title: Disease gene characterization through large-scale co-expression analysis.
abstract: BACKGROUND
  In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET).
  
  
  RESULTS
  Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.
  
  
  CONCLUSIONS
  UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.
authors.affiliation: Department of Human Genetics, Molecular Biology Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.
authors.forename: Allen
authors.initials: A
authors.lastname: Day
authors.forename: Jun
authors.initials: J
authors.lastname: Dong
authors.forename: Vincent A
authors.initials: VA
authors.lastname: Funari
authors.forename: Bret
authors.initials: B
authors.lastname: Harry
authors.forename: Samuel P
authors.initials: SP
authors.lastname: Strom
authors.forename: Dan H
authors.initials: DH
authors.lastname: Cohn
authors.forename: Stanley F
authors.initials: SF
authors.lastname: Nelson
mesh_terms: D000818:Animals; D020029:Base Pairing / Q000235:genetics; D016000:Cluster Analysis; D004194:Disease / Q000235:genetics*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D012044:Regression Analysis; D020133:Reverse Transcriptase Polymerase Chain Reaction