June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Vision Variation Database (VVD)
Author Affiliations & Notes
  • Adam DeLuca
    Biomedical Engineering, University of Iowa, Iowa City, IA
  • Sean Ephraim
    Biomedical Engineering, University of Iowa, Iowa City, IA
  • Todd Scheetz
    Biomedical Engineering, University of Iowa, Iowa City, IA
    Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
  • Edwin Stone
    Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
    Howard Hughes Medical Institute, Iowa City, IA
  • Terry Braun
    Biomedical Engineering, University of Iowa, Iowa City, IA
  • Footnotes
    Commercial Relationships Adam DeLuca, None; Sean Ephraim, None; Todd Scheetz, None; Edwin Stone, None; Terry Braun, Alcon Research, LTD (F)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3382. doi:
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      Adam DeLuca, Sean Ephraim, Todd Scheetz, Edwin Stone, Terry Braun; Vision Variation Database (VVD). Invest. Ophthalmol. Vis. Sci. 2013;54(15):3382.

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

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Purpose: Genetic testing has dramatically changed with the introduction of clinical exome and targeted exon sequencing. The burden has shifted from generating genotypes to interpreting variations. There are vast amounts of publically available data to aid this interpretation. However, currently there are no centralized resources to capture and annotate variations observed in patients with inherited eye diseases. To address these issues in clinical genetic testing of retinal disease genes, we have developed the Vision Variation Database (VVD) that incorporates 1) reported and unpublished disease-causing variants, 2) publically available allele frequency data from control and diseased populations, and 3) pathogenicity prediction scores.

Methods: A manually curated list of retinal disease-causing variants has been collected from the literature and from unpublished data to provide interpretation for clinical genetic testing. Allele frequencies were obtained from the snp135 table in the UCSC genome database and the NHLBI GO Exome Sequencing Project. Pathogenicity predictions from SIFT, Polyphen2, MutationTaster, and LRT for coding single nucleotide variations were acquired from dbNSFP. Sequence reads from the 1000 genomes project, the Ciliopathies Exome Sequencing Initiative (phs000288.v1.p1), and local exome samples were aligned using BWA. Variants were called using the GATK UnifiedGenotyper and annotated. Summary data from these sets are provided including allele frequencies and frequencies of compound heterozygotes. To reduce false positives in interpreting exome results minimal filtering is performed on these recalled datasets.

Results: Access to these data is provided via a public website available at: VVD.eng.uiowa.edu The interface allows users to navigate to a variant by gene and an API is provided. The interface allows users to view pathogenicity predictions, and allele frequencies for disease-causing variants and polymorphisms in retinal disease genes. The site currently lists 1522 causative variants in 49 retinal disease genes.

Conclusions: We have created a public resource to catalogue and aid in the interpretation of variants in retinal disease genes. As more exome and genome sequence data becomes available, data on specific disease causing alleles and variants will accumulate and will enable the refinement of disease sub-types.

Keywords: 539 genetics • 537 gene screening • 688 retina  

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