Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples. Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests. With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

中文翻译：

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检测来自诊断基因组的下一代测序数据中的拷贝数变异


检测与疾病相关的基因中的拷贝数变异（CNV）对于遗传诊断非常重要，下一代测序（NGS）技术提供了可用于CNV检测的数据。然而，基于 NGS 数据的 CNV 检测通常不常用于诊断实验室，因为数据分析具有挑战性，尤其是来自目标基因组的数据。 MLPA（MRC Holland）等湿实验室方法被广泛使用，但昂贵、耗时且具有基因特异性限制。我们的目标是开发一种生物信息学工具，用于根据医学遗传诊断样本中的 NGS 数据检测 CNV。我们用于检测来自目标基因组的 NGS 数据中的 CNV 的计算管道利用捕获区域的覆盖深度并计算每个区域的拷贝数比率得分。这是通过将样本的平均覆盖率与其他样本（定义为池）中相同区域的平均覆盖率进行比较来计算的。该管道使用平均覆盖深度最接近样本之一的池，从先前测序的样本中动态选择池进行比较。基于滑动窗口的方法用于分析每个区域，其中可以动态选择滑动窗口的长度和滑动距离以增加或减少分辨率。这有助于检测小或部分外显子中的 CNV。通过该流程，我们正确识别了 36 个阳性对照样本中的 CNV，灵敏度为 100%，特异性为 91%。我们检测到全基因水平缺失/重复、单/多外显子水平缺失/重复、部分外显子缺失和嵌合缺失。自 2018 年中期实施以来，它在常规测试中通过超过 45 个 CNV 结果证明了其诊断价值。 将此管道作为我们诊断实践的一部分，现在可以检测我们目标组中所有基因的部分、单或多外显子和基因内 CNV。这帮助我们的诊断实验室扩大了提供 CNV 检测的基因组合，而此前该检测受到 MLPA 试剂盒可用性的限制。