Frontiers in Endocrinology (Jun 2022)
An Easy-to-Use Approach to Detect CNV From Targeted NGS Data: Identification of a Novel Pathogenic Variant in MO Disease
Abstract
BackgroundDespite the new next-generation sequencing (NGS) molecular approaches implemented the genetic testing in clinical diagnosis, copy number variation (CNV) detection from NGS data remains difficult mainly in the absence of bioinformatics personnel (not always available among laboratory 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 Multiplex Ligation-dependent Probe Amplification (MLPA) technique due to both the limitations of the methodology and no kits available for the most of genes.AimWe 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.MethodsThe 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 patients with MO but negative for the presence of pathogenic variants in EXT1 or EXT2 genes. Sensitivity, specificity, and accuracy were evaluated.ResultsAll 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 quantitative Polymerase Chain Reaction (qPCR).ConclusionIn addition to 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. Notably, it also allows partial-exon deletion detection.
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