Department of Rare Skeletal Disorders, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Ortopedico Rizzoli, Bologna, Italy.
Front Endocrinol (Lausanne). 2022 Jun 28;13:874126. doi: 10.3389/fendo.2022.874126. eCollection 2022.
Despite 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.
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.
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 patients with MO but negative for the presence of pathogenic variants in or genes. Sensitivity, specificity, and accuracy were evaluated.
All the 11 known CNVs (exon and multi-exon deletions) have been detected with a sensitivity of 97.5%. A novel 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).
In 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.
尽管新一代测序（NGS）分子方法已应用于临床诊断中的基因检测，但 NGS 数据的拷贝数变异（CNV）检测仍然很困难，主要原因是缺乏生物信息学人员（并非实验室资源中总是具备），以及使用的基因小panel 不符合商业软件标准。此外，由于方法学的限制以及大多数基因没有可用的试剂盒，并非所有的大片段缺失/重复都可以通过多重连接依赖性探针扩增（MLPA）技术检测到。
我们提出了一种新的方法，用于在罕见骨骼疾病多发性骨软骨瘤（MO）的背景下识别新型大片段缺失，该方法使用并验证了一种基于 NGS 覆盖数据的用户友好方法，该方法不需要任何专用软件或专业人员。
该方法使用一个简单的算法，将每个扩增子的归一化覆盖与同一扩增子在一组“野生型”样本（代表基线）的平均归一化覆盖进行比较。该方法已在之前通过 MLPA 分析的 11 个样本上进行了验证，然后应用于 20 名 MO 患者，但在 或 基因中未发现致病性变异。评估了敏感性、特异性和准确性。
所有 11 个已知的 CNV（外显子和多外显子缺失）均被检测到，敏感性为 97.5%。发现了一种新型的 外显子缺失 c. (744-122)-?_804+?del -超出 MLPA 靶区域-。该变体通过实时定量聚合酶链反应（qPCR）得到证实。
除了提高 MO 分子诊断中的变异检测率外，这种易于使用的 CNV 检测方法还可以很容易地扩展到许多其他诊断领域-特别是在资源有限的环境或非常小的基因panel 中。值得注意的是，它还允许检测部分外显子缺失。
