An Easy-to-Use Approach to Detect CNV From Targeted NGS Data: Identification of a Novel Pathogenic Variant in MO Disease - Corsini, Serena; Pedrini, Elena; Patavino, Claudio; Gnoli, Maria; Lanza, Marcella; Sangiorgi, Luca (Frontiers in Endocrinology, 2022)
Background: Despite the new NGS molecular approaches implemented the genetic testing in clinical diagnosis, copy number variations (CNV) detection from NGS data remain difficult mainly in absence of bioinformatics personnel (not always available among labs resources) and when using very small gene panels that do not meet commercial software criteria. Furthermore, not all large deletions/duplications can be detected with the MLPA technique due to both the limitations of the methodology and no kits available for the most of genes. Aim: We propose our experience regarding the identification of a novel large deletion in the context of a rare skeletal disease, Multiple Osteochondromas (MO), using and validating a user-friendly approach based on NGS coverage data, which does not require any dedicated software or specialized personnel. Methods: The pipeline uses a simple algorithm comparing the normalized coverage of each amplicon with the mean normalized coverage of the same amplicon in a group of ‘wild-type’ samples representing the baseline. It has been validated on 11 samples previously analyzed by MLPA and then applied on 20 MO patients negative for the presence of pathogenic variants in EXT1 or EXT2 genes. Sensitivity, specificity, and accuracy were evaluated. Results: All the 11 known CNVs (exon and multi-exon deletions) have been detected with a sensitivity of 97.5%. A novel EXT2 partial exonic deletion c. (744-122)-?_804+?del – out of the MLPA target regions - has been identified. The variant was confirmed by Real-Time qPCR. Conclusion: Besides enhancing the variant detection rate in MO molecular diagnosis, this easy-to-use approach for CNV detection can be easily extended to many other diagnostic fields - especially in resource-limited settings or very small gene panels. Of note, it also allows partial-exon deletion detection.