Genetic high throughput screening in Retinitis Pigmentosa based on high resolution melting (HRM) analysis

Retinitis Pigmentosa (RP) involves a group of genetically determined retinal diseases caused by a large number of mutations that result in rod photoreceptor cell death followed by gradual death of cone cells. Most cases of RP are monogenic, with more than 80 associated genes identified so far. The high number of genes and variants involved in RP, among other factors, is making the molecular characterization of RP a real challenge for many patients. Although HRM has been used for the analysis of isolated variants or single RP genes, as far as we are concerned, this is the first study that uses HRM analysis for a high-throughput screening of several RP genes. Our main goal was to test the suitability of HRM analysis as a genetic screening technique in RP, and to compare its performance with two of the most widely used NGS platforms, Illumina and PGM-Ion Torrent technologies. RP patients (n = 96) were clinically diagnosed at the Ophthalmology Department of Donostia University Hospital, Spain. We analyzed a total of 16 RP genes that meet the following inclusion criteria: 1) size: genes with transcripts of less than 4 kb; 2) number of exons: genes with up to 22 exons; and 3) prevalence: genes reported to account for, at least, 0.4% of total RP cases worldwide. For comparison purposes, RHO gene was also sequenced with Illumina (GAII; Illumina), Ion semiconductor technologies (PGM; Life Technologies) and Sanger sequencing (ABI 3130xl platform; Applied Biosystems). Detected variants were confirmed in all cases by Sanger sequencing and tested for co-segregation in the family of affected probands. We identified a total of 65 genetic variants, 15 of which (23%) were novel, in 49 out of 96 patients. Among them, 14 (4 novel) are probable disease-causing genetic variants in 7 RP genes, affecting 15 patients. Our HRM analysis-based study, proved to be a cost-effective and rapid method that provides an accurate identification of genetic RP variants. This approach is effective for medium sized (<4 kb transcript) RP genes, which constitute over 80% of the total of known RP genes.