Phenotype Driven Analysis of Whole Genome Sequencing Identifies Deep Intronic Variants that Cause Retinal Dystrophies by Aberrant Exonization Journal Articles uri icon

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  • PURPOSE: To demonstrate the effectiveness of combining retinal phenotyping and focused variant filtering from genome sequencing (GS) in identifying deep intronic disease causing variants in inherited retinal dystrophies. METHODS: Affected members from three pedigrees with classical enhanced S-cone syndrome (ESCS; Pedigree 1), congenital stationary night blindness (CSNB; Pedigree 2), and achromatopsia (ACHM; Pedigree 3), respectively, underwent detailed ophthalmologic evaluation, optical coherence tomography, and electroretinography. The probands underwent panel-based genetic testing followed by GS analysis. Minigene constructs (NR2E3, GPR179 and CNGB3) and patient-derived cDNA experiments (NR2E3 and GPR179) were performed to assess the functional effect of the deep intronic variants. RESULTS: The electrophysiological findings confirmed the clinical diagnosis of ESCS, CSNB, and ACHM in the respective pedigrees. Panel-based testing revealed heterozygous pathogenic variants in NR2E3 (NM_014249.3; c.119-2A>C; Pedigree 1) and CNGB3 (NM_019098.4; c.1148delC/p.Thr383Ilefs*13; Pedigree 3). The GS revealed heterozygous deep intronic variants in Pedigrees 1 (NR2E3; c.1100+1124G>A) and 3 (CNGB3; c.852+4751A>T), and a homozygous GPR179 variant in Pedigree 2 (NM_001004334.3; c.903+343G>A). The identified variants segregated with the phenotype in all pedigrees. All deep intronic variants were predicted to generate a splice acceptor gain causing aberrant exonization in NR2E3 [89 base pairs (bp)], GPR179 (197 bp), and CNGB3 (73 bp); splicing defects were validated through patient-derived cDNA experiments and/or minigene constructs and rescued by antisense oligonucleotide treatment. CONCLUSIONS: Deep intronic mutations contribute to missing heritability in retinal dystrophies. Combining results from phenotype-directed gene panel testing, GS, and in silico splice prediction tools can help identify these difficult-to-detect pathogenic deep intronic variants.


  • DiScipio, Matteo
  • Di Scipio, Matteo
  • Tavares, Erika
  • Deshmukh, Shriya
  • Audo, Isabelle
  • Green-Sanderson, Kit
  • Zubak, Yuliya
  • Zine-Eddine, Fayçal
  • Pearson, Alexander
  • Vig, Anjali
  • Tang, Chen Yu
  • Mollica, Antonio
  • Karas, Jonathan
  • Tumber, Anupreet
  • Yu, Caberry W
  • Billingsley, Gail
  • Wilson, Michael D
  • Zeitz, Christina
  • Héon, Elise
  • Vincent, Ajoy

publication date

  • August 20, 2020

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