Whole-exome sequencing revealed two novel mutations in Usher syndrome

Next-generation sequencing reveals the mutational landscape of clinically diagnosed Usher syndrome: copy number variations, phenocopies, a predominant target for translational read-through, and PEX26 mutated in Heimler syndrome

Background

Combined retinal degeneration and sensorineural hearing impairment is mostly due to autosomal recessive Usher syndrome (USH1: congenital deafness, early retinitis pigmentosa (RP); USH2: progressive hearing impairment, RP).

Methods

Sanger sequencing and NGS of 112 genes (Usher syndrome, nonsyndromic deafness, overlapping conditions), MLPA, and array‐CGH were conducted in 138 patients clinically diagnosed with Usher syndrome.

Results

A molecular diagnosis was achieved in 97% of both USH1 and USH2 patients, with biallelic mutations in 97% (USH1) and 90% (USH2), respectively. Quantitative readout reliably detected CNVs (confirmed by MLPA or array‐CGH), qualifying targeted NGS as one tool for detecting point mutations and CNVs. CNVs accounted for 10% of identified USH2A alleles, often in trans to seemingly monoallelic point mutations. We demonstrate PTC124‐induced read‐through of the common p.Trp3955* nonsense mutation (13% of detected USH2A alleles), a potential therapy target. Usher gene mutations were found in most patients with atypical Usher syndrome, but the diagnosis was adjusted in case of double homozygosity for mutations in OTOA and NR2E3, genes implicated in isolated deafness and RP. Two patients with additional enamel dysplasia had biallelic PEX26 mutations, for the first time linking this gene to Heimler syndrome.

Conclusion

Targeted NGS not restricted to Usher genes proved beneficial in uncovering conditions mimicking Usher syndrome.

FMFilter: A fast model based variant filtering tool

The availability of whole exome and genome sequencing has completely changed the structure of genetic disease studies. It is now possible to solve the disease causing mechanisms within shorter time and budgets. For this reason, mining out the valuable information from the huge amount of data produced by next generation techniques becomes a challenging task. Current tools analyze sequencing data in various methods. However, there is still need for fast, easy to use and efficacious tools. Considering genetic disease studies, there is a lack of publicly available tools which support compound heterozygous and de novo models. Also, existing tools either require advanced IT expertise or are inefficient for handling large variant files. In this work, we provide FMFilter, an efficient sieving tool for next generation sequencing data produced by genetic disease studies. We develop a software which allows to choose the inheritance model (recessive, dominant, compound heterozygous and de novo), the affected and control individuals. The program provides a user friendly Graphical User Interface which eliminates the requirement of advanced computer techniques. It has various filtering options which enable to eliminate the majority of the false alarms. FMFilter requires negligible memory, therefore it can easily handle very large variant files like multiple whole genomes with ordinary computers. We demonstrate the variant reduction capability and effectiveness of the proposed tool with public and in-house data for different inheritance models. We also compare FMFilter with the existing filtering software. We conclude that FMFilter provides an effective and easy to use environment for analyzing next generation sequencing data from Mendelian diseases.