Browsing by Subject "Neuromuscular disorder"

High-throughput sequencing techniques make it possible to identify DNA variants at a reasonable cost, representing a first-tier diagnostic test for rare mendelian diseases. However, a substantial number of variants identified through the analysis of sequencing data are frequently classified as variants of uncertain significance (VUS). Accordingly, only 30–60% of individuals receive a conclusive molecular diagnosis depending on the clinical phenotype. Reanalysis of older sequencing data has been encouraged by recently developed and improved methodologies for analysis and more robust bioinformatic pipelines to enhance variant interpretation and raise the diagnostic/detection rate. This study focused on reanalyzing data from a targeted gene panel, MYOcap, a targeted gene panel for patients with neuromuscular disorders. The aims were to find elusive (i.e., previously undetected/misinterpreted) variants in patients still missing a molecular diagnosis and, by using novel bioinformatic tools, focusing on pathogenic and likely pathogenic variants (according to ACMG guidelines) in Varsome as well as on variants affecting the splicing as predicted by SpliceAI. With this setting, the detection rate of solved cases increased by 2,7% in the first cohort and 0,5% in the third. This study suggests that additional data, such as segregation data or transcriptomic and proteomic data are essential for reducing the number of VUS and increase the detection rate. Notably, this study represents an essential first step of a larger reanalysis project, aiming at providing a diagnosis to an increasing number of myopathy patients.