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Detection of mosaicism for segmental and whole chromosome imbalances by targeted sequencing
Annals of Human Genetics ( IF 1.9 ) Pub Date : 2020-08-06 , DOI: 10.1111/ahg.12402
Darine Villela 1 , Juliana Sobral de Barros 1 , Silvia Souza da Costa 1 , Talita F M Aguiar 1 , Francine Campagnari 2 , Angela M Vianna-Morgante 1 , Ana C V Krepischi 1 , Carla Rosenberg 1
Affiliation  

Mosaic segmental and whole chromosome copy number alterations are postzygotic variations known to be associated with several disorders. We have previously presented an efficient targeted sequencing approach to simultaneously detect point mutations and copy number variations (CNVs). In this study, we evaluated the efficiency of this approach to detect mosaic CNVs, using seven postnatal and 19 tumor samples, previously characterized by chromosomal microarray analyses (CMA). These samples harbored a total of 28 genomic imbalances ranging in size from 0.68 to 171 Mb, and present in 10–80% of the cells. All CNV regions covered by the platform were correctly identified in postnatal samples, and only seven out of 19 CNVs from tumor samples were not identified either because of a lack of target probes in the affected genomic regions or an absence of minimum reads for an alteration call. These results demonstrate that, in a research setting, this is a robust approach for detecting mosaicism in cases of segmental and whole chromosome alterations. Although the current sequencing platform presented a resolution similar to genomic microarrays, it is still necessary to further validate this approach in a clinical setting in order to replace CMA and sequencing analyses by a single test.

中文翻译:

通过靶向测序检测片段和全染色体不平衡的嵌合体

镶嵌片段和全染色体拷贝数改变是已知与多种疾病相关的合子后变异。我们之前已经提出了一种有效的靶向测序方法,可以同时检测点突变和拷贝数变异 (CNV)。在这项研究中,我们使用 7 个出生后和 19 个肿瘤样本评估了这种方法检测嵌合 CNV 的效率,这些样本之前通过染色体微阵列分析 (CMA) 进行了表征。这些样本共有 28 个基因组失衡,大小从 0.68 到 171 Mb,存在于 10-80% 的细胞中。平台覆盖的所有 CNV 区域都在产后样本中被正确识别,并且由于受影响的基因组区域中缺少目标探针或缺少改变调用的最小读数,因此肿瘤样本中的 19 个 CNV 中只有 7 个未被识别。这些结果表明,在研究环境中,这是在片段和整个染色体改变的情况下检测镶嵌现象的有效方法。尽管当前的测序平台提供了类似于基因组微阵列的分辨率,但仍需要在临床环境中进一步验证这种方法,以通过单一测试取代 CMA 和测序分析。
更新日期:2020-08-06
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