Genotek Ltd., Nastavnicheskii pereulok 17/1, 105120, Moscow, Russia.
Sci Rep. 2020 Nov 23;10(1):20375. doi: 10.1038/s41598-020-76425-3.
Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV's performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV .
拷贝数变异 (CNVs) 是人类遗传变异的重要组成部分。它们可以是良性的,也可以通过造成剂量失衡和破坏基因及调控元件在人类疾病中发挥作用。然而,准确识别和临床注释 CNVs 至关重要,但是临床医生大规模手动评估个体 CNVs 具有挑战性。在这里,我们展示了 ClassifyCNV,这是一个易于使用的工具,它实现了 2019 年 ACMG 分类指南,以评估 CNV 的致病性。ClassifyCNV 使用基因组坐标和 CNV 类型作为输入,为每个变体报告临床分类、分类评分细目以及对变体解释具有潜在重要性的基因列表。我们使用一组已知的临床 CNVs 和一组手动评估的变体来验证 ClassifyCNV 的性能。ClassifyCNV 与 81%的手动评估变体的致病性类别相匹配,其余致病性和良性变体的显著性自动确定为不确定,需要临床医生进一步评估。ClassifyCNV 有助于在高通量 CNV 分析中实施最新的 ACMG 指南,适合集成到 NGS 分析管道中,并可以缩短诊断时间。该工具可在 https://github.com/Genotek/ClassifyCNV 上获得。