May 2005
Volume 46, Issue 13
ARVO Annual Meeting Abstract  |   May 2005
Using the Human Retina and RPE Transcriptomes to Identify Candidate Disease Genes for Retinal Dystrophies
Author Affiliations & Notes
  • J.N. Ebright
    Duke Univ Med Ctr, Durham, NC
  • K. Boon
    Johns Hopkins University, Baltimore, MD
  • M.A. Hauser
    Medicine and Ophthalmology,
    Duke Univ Med Ctr, Durham, NC
  • S.P. Daiger
    University of Texas, Houston, TX
  • C. Bowes Rickman
    Ophthalmology and Cell Biology,
    Duke Univ Med Ctr, Durham, NC
  • Footnotes
    Commercial Relationships  J.N. Ebright, None; K. Boon, None; M.A. Hauser, None; S.P. Daiger, None; C. Bowes Rickman, None.
  • Footnotes
    Support  NEI RO1 EY11286, NEI P30 EY05722, and RPB CDA (cbr)
Investigative Ophthalmology & Visual Science May 2005, Vol.46, 3098. doi:
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      J.N. Ebright, K. Boon, M.A. Hauser, S.P. Daiger, C. Bowes Rickman; Using the Human Retina and RPE Transcriptomes to Identify Candidate Disease Genes for Retinal Dystrophies . Invest. Ophthalmol. Vis. Sci. 2005;46(13):3098.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract: : Purpose: To develop a large–scale, high–throughput means to identify and prioritize human candidate genes for inherited retinal dystrophies based on eye expression profiles using Serial Analysis of Gene Expression (SAGE). Methods: An 'EyeSage' relational database was generated containing: 8 eye SAGE libraries (five retina and three RPE), differential microarray analysis of macular retina, and 39 other normal human tissue SAGE libraries. Retinal disease loci for which a disease gene has not yet been identified were obtained from the RetNet website ( The narrowest mapping range was determined from the most recent publication listed on RetNet, and the delineating sequence tagged sites (STSs) were translated into their numerical chromosome position using Ensembl Genome Browser. Microsoft Access was used to run a query of the EyeSage dataset to generate expression information for each disease locus as a table, containing a list of all genes found within the mapped range that are expressed in retina or RPE and sorted by total eye expression level. Results:Gene expression tables were generated for 39 mapped–but–not–yet–cloned retinal disease loci on RetNet. Limiting candidate genes by eye expression served to greatly reduce the number of candidate genes that should be initially considered within each locus, and overall expression throughout the body served as a further filter. This analysis was performed on the autosomal recessive Bardet–Biedl syndrome (BBS5) disease region for which the BBS5 disease gene was recently identified. The analysis yielded 21 candidates with a total of at least 15 tags in the eye SAGE libraries. The top candidate, with the highest sum of tag counts in the eye and expected ubiquitous expression, was the BBS5 disease gene. Conclusions: The BBS5 example provides proof of principle for this approach to identify candidate genes. Thus the expression profiling information contained in the EyeSAGE dataset can be used in the way described here to provide a rational prioritization of candidate genes within mapped retinal disease regions where the disease gene has not yet been identified and cloned.

Keywords: candidate gene analysis • degenerations/dystrophies • retinal degenerations: hereditary 

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