June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Discovery of novel disease-causing mutations in inherited retinal dystrophies – an application to retinitis pigmentosa
Author Affiliations & Notes
  • Caberry W. Yu
    Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
    Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
  • Erika Tavares
    Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
  • Vaishnavi Batmanabane
    Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
  • Ajoy Vincent
    Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
    Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
  • Elise Heon
    Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
    Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
  • Footnotes
    Commercial Relationships   Caberry Yu, None; Erika Tavares, None; Vaishnavi Batmanabane, None; Ajoy Vincent, None; Elise Heon, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4475. doi:
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      Caberry W. Yu, Erika Tavares, Vaishnavi Batmanabane, Ajoy Vincent, Elise Heon; Discovery of novel disease-causing mutations in inherited retinal dystrophies – an application to retinitis pigmentosa. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4475.

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

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Abstract

Purpose : Inherited retinal dystrophies (IRDs) implicate >250 genes, however, ~50% of patients do not have a genetic diagnosis. These patients are offered Whole Genome Sequencing (WGS) to identify the genetic cause. This project aims to identify novel disease-causing variants in patients with autosomal dominant retinitis pigmentosa (RP) who failed standard-of-care panel-based genetic testing. It also aims to refine a streamlined variant analysis pipeline of WGS data that is applicable for other IRDs to identify novel disease-causing mutations.

Methods : WGS was performed on DNA of two family members with RP whereby standard of care genetic testing failed to identify a disease-causing mutation. Copy number variations (CNVs), transposable elements (TEs), and single nucleotide polymorphisms (SNPs) were examined. These variants underwent quality filter to ensure low frequency in controls. The remaining variants were validated manually through software visualization, and examination of gene expression in retina, and in silico prediction of gene splicing and pathogenicity.

Results : The filter strategy identified 3 CNVs, 8 TEs, and 59 SNPs of interest (Figure 1). Variants were then prioritized using conservation in vertebrates, relation to retina, and mouse phenotype data. Segregation analyses were performed on variants of interest. A CNV that is a 86 000 base pair tandem duplication of a region on chromosome 8 was identified to be potentially disease-causing. The variant underwent the analysis pipeline and was found to be shared between the affected individuals and not found in controls. It contains the gene KLF10 which is highly expressed in the retina and is not previously linked to IRDs (Figure 2).

Conclusions : The genetic characterization of IRDs is particularly important and timely as there is an emergence of novel and personalized gene-specific treatment opportunities. The variant identified on chromosome 8 contributes to genetic diagnosis certainty for autosomal dominant RP patients and for optimal gene-specific management of patients. The variant analysis pipeline based on WGS data created for this case is a streamlined way to discover novel variants for patients without a genetic diagnosis.

This is a 2020 ARVO Annual Meeting abstract.

 

Figure 1: Pipeline for filtering variants for Case 1 and Case 2. RPE = retinal pigment epithelium.

Figure 1: Pipeline for filtering variants for Case 1 and Case 2. RPE = retinal pigment epithelium.

 

Figure 2: KLF10 (Kruppel-Like Factor 10) Relevance

Figure 2: KLF10 (Kruppel-Like Factor 10) Relevance

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