July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Towards comprehensive identification and functional characterization of deep-intronic ABCA4 variants in 1000 Stargardt disease cases
Author Affiliations & Notes
  • Mubeen Khan
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
    Donders institute for Brain,Cognition and Behaviour, Netherlands
  • Stephanie Cornelis
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
    Donders institute for Brain,Cognition and Behaviour, Netherlands
  • Marta del Pozo Valero
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
    Fundación Jiménez Díaz, CIBERER, Department of Genetics, Madrid, Spain
  • Muhammad Imran Khan
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
    Donders institute for Brain,Cognition and Behaviour, Netherlands
  • Heidi Stohr
    Institut für Humangenetik, Universität Regensburg, Regensburg, Germany
  • Felix Grassmann
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  • Marloes Steehouwer
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
  • Alexander Hoischen
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
  • Carmen Ayuso
    Fundación Jiménez Díaz, CIBERER, Department of Genetics, Madrid, Spain
  • Raj Ramesar
    Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa
  • Isabelle Anne Meunier
    Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, France
  • Sabine defoort
    Service des explorations de la fonction visuelle, CHRU de Lille, France
  • Bernhard HF Weber
    Institut für Humangenetik, Universität Regensburg, Regensburg, Germany
  • Claire-Marie Dhaenens
    University Lille, Inserm UMR-S 1172, CHU Lille., Biochemistry and Molecular Biology Department - UF Génopathies, Lille, France
  • Frans P Cremers
    Department of Human genetics, Radboud university medical center, Nijmegen, Netherlands
  • Footnotes
    Commercial Relationships   Mubeen Khan, None; Stephanie Cornelis, None; Marta del Pozo Valero, None; Muhammad Imran Khan, None; Heidi Stohr, None; Felix Grassmann, None; Marloes Steehouwer, None; Alexander Hoischen, None; Carmen Ayuso, None; Raj Ramesar, None; Isabelle Meunier, None; Sabine defoort, None; Bernhard Weber, None; Claire-Marie Dhaenens, None; Frans Cremers, None
  • Footnotes
    Support  Retina UK grant GR591
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2813. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Mubeen Khan, Stephanie Cornelis, Marta del Pozo Valero, Muhammad Imran Khan, Heidi Stohr, Felix Grassmann, Marloes Steehouwer, Alexander Hoischen, Carmen Ayuso, Raj Ramesar, Isabelle Anne Meunier, Sabine defoort, Bernhard HF Weber, Claire-Marie Dhaenens, Frans P Cremers; Towards comprehensive identification and functional characterization of deep-intronic ABCA4 variants in 1000 Stargardt disease cases. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2813.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Although the gene underlying Stargardt disease (STGD1), ABCA4, has been identified 22 years ago, for many cases the underlying mutations are unknown. This in part is due to the intronic location of many causal variants. In this study, we aim to develop a cost-effective whole ABCA4 gene sequencing technique to analyze >1000 genetically unsolved STGD1 probands.

Methods : We designed 3866 single molecule molecular inversion probes (smMIPs), each capturing 110 nt of the 128-kb ABCA4 gene proper or ~40 kb of flanking sequences. Sense and antisense strands were targeted using overlapping smMIPs. Rebalancing of the smMIPs spanning the coding sequences was performed. We collected STGD1 probands from 21 collaborators worldwide. NextSeq 500 sequence analysis was performed for 20 test cases (16 STGD1, 4 healthy controls) and 200 probands in each run. Novel deep-intronic variants were tested for splice defects using in vitro splice assays employing ABCA4 midigenes.

Results : smMIPs were designed for 99% of the ABCA4 gene proper and ~50% of flanking sequences as repeats outside the gene were refractory to smMIPs design. Sequence analysis of 16 previously genetically solved STGD1 patients revealed all known alleles with an average smMIP coverage of 933x. Single or double coverage (>100 reads) was achieved for 98.5% of the ABCA4 gene proper and all reported 17 causal deep-intronic variants were covered with smMIPs. We collected 1000 genetically unsolved STGD1 samples, ~900 of which previously were scanned for coding variants and ~100 probands lacked genotyping. In the first 200 STGD1 cases analyzed in a single run, the average smMIP coverage was 377x. We identified 7 different known deep-intronic variants in 23 alleles and 27 different novel deep-intronic variants in 55 alleles. The latter are being tested using midigene-based splice assays.

Conclusions : Due to the paucity of sizeable repeats, the introns of ABCA4 could readily be captured using smMIPs. Apart from design and synthesis costs of smMIPs, the sequencing costs for the entire ABCA4 gene are USD25 per sample, rendering this the most cost-effective ABCA4 sequence analysis for coding and non-coding sequences. We will generate a comprehensive list of causal deep-intronic variants for ABCA4, which then can be used to design a minimal diagnostic set of smMIPs that capture 95% of causal ABCA4 alleles in each patient.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×