Mutation Detection in Patients with Retinal Dystrophies Using Targeted Next Generation Sequencing

Retinal dystrophies (RD) constitute a group of blinding diseases that are characterized by clinical variability and pronounced genetic heterogeneity. The different nonsyndromic and syndromic forms of RD can be attributed to mutations in more than 200 genes. Consequently, next generation sequencing (NGS) technologies are among the most promising approaches to identify mutations in RD. We screened a large cohort of patients comprising 89 independent cases and families with various subforms of RD applying different NGS platforms. While mutation screening in 50 cases was performed using a RD gene capture panel, 47 cases were analyzed using whole exome sequencing. One family was analyzed using whole genome sequencing. A detection rate of 61% was achieved including mutations in 34 known and two novel RD genes. A total of 69 distinct mutations were identified, including 39 novel mutations. Notably, genetic findings in several families were not consistent with the initial clinical diagnosis. Clinical reassessment resulted in refinement of the clinical diagnosis in some of these families and confirmed the broad clinical spectrum associated with mutations in RD genes

Mapping the genomic landscape of inherited retinal disease genes prioritizes genes prone to coding and noncoding copy-number variations

Purpose Part of the hidden genetic variation in heterogeneous genetic conditions such as inherited retinal diseases (IRDs) can be explained by copy-number variations (CNVs). Here, we explored the genomic landscape of IRD genes listed in RetNet to identify and prioritize those genes susceptible to CNV formation. Methods: RetNet genes underwent an assessment of genomic features and of CNV occurrence in the Database of Genomic Variants and literature. CNVs identified in an IRD cohort were characterized using targeted locus amplification (TLA) on extracted genomic DNA. Results: Exhaustive literature mining revealed 1,345 reported CNVs in 81 different IRD genes. Correlation analysis between rankings of genomic features and CNV occurrence demonstrated the strongest correlation between gene size and CNV occurrence of IRD genes. Moreover, we identified and delineated 30 new CNVs in IRD cases, 13 of which are novel and three of which affect noncoding, putative cis-regulatory regions. Finally, the breakpoints of six complex CNVs were determined using TLA in a hypothesis-neutral manner. Conclusion: We propose a ranking of CNV-prone IRD genes and demonstrate the efficacy of TLA for the characterization of CNVs on extracted DNA. Finally, this IRD-oriented CNV study can serve as a paradigm for other genetically heterogeneous Mendelian diseases with hidden genetic variation