High Detection Rate of Copy Number Variations Using Capture Sequencing Data: A Retrospective Study
High Detection Rate of Copy Number Variations Using Capture Sequencing Data: A Retrospective Study
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使用捕获测序数据实现拷贝数变异的高检出率：一项回顾性研究
DOI：
10.1093/clinchem/hvz033
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发表时间：
2020-03-01
影响因子：
9.3
通讯作者：
Yu, Yongguo
中科院分区：
文献类型：
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作者：
Sun, Yu;Ye, Xiantao;Yu, Yongguo
背景：捕获测序（CS）被广泛应用于检测小的遗传变异，如单核苷酸变异或插入缺失。基于深度比较的算法正变得可用于从CS数据检测拷贝数变异（CNV）。然而，一个大样本量的系统性评价尚未进行评估的有效性CS为基础的CNV检测在临床diagnosis.METHODS：我们回顾性研究了3010例样品提到我们的诊断实验室CS测试。我们使用68个染色体微阵列分析阳性样本（真集[TS]）和1520个参考样本来构建一个强大的CS-CNV管道。该管道用于检测1422个未诊断样本（未诊断集[UDS]）中的候选临床相关CNV。结果：CS-CNV pipeline检测到TS样本中79个临床相关CNV中的78个，分析灵敏度为98.7%，阳性预测值为49.4%。在106份UDS样本中鉴定了候选临床相关CNV。在96例患者（90.6%）中证实了CNV。诊断率为6.8%。分子病因包括非整倍体（n = 7）、微缺失/微重复综合征（n = 40）和孟德尔遗传病（n = 49）。随着我们的CS-CNV管道的进一步改进，该方法可能具有临床实用性，用于同时评价CNV和产前或产后分析的样品中的小变化。
BACKGROUND: Capture sequencing (CS) is widely applied to detect small genetic variations such as single nucleotide variants or indels. Algorithms based on depth comparison are becoming available for detecting copy number variation (CNV) from CS data. However, a systematic evaluation with a large sample size has not been conducted to evaluate the efficacy of CS-based CNV detection in clinical diagnosis.METHODS: We retrospectively studied 3010 samples referred to our diagnostic laboratory for CS testing. We used 68 chromosomal microarray analysis-positive samples (true set [TS]) and 1520 reference samples to build a robust CS-CNV pipeline. The pipeline was used to detect candidate clinically relevant CNVs in 1422 undiagnosed samples (undiagnosed set [UDS]). The candidate CNVs were confirmed by an alternative method.RESULTS: The CS-CNV pipeline detected 78 of 79 clinically relevant CNVs in TS samples, with analytical sensitivity of 98.7% and positive predictive value of 49.4%. Candidate clinically relevant CNVs were identified in 106 UDS samples. CNVs were confirmed in 96 patients (90.6%). The diagnostic yield was 6.8%. The molecular etiology includes aneuploid (n = 7), microdeletion/microduplication syndrome (n = 40), and Mendelian disorders (n = 49).CONCLUSIONS: These findings demonstrate the high yield of CS-based CNV. With further improvement of our CS-CNV pipeline, the method may have clinical utility for simultaneous evaluation of CNVs and small variations in samples referred for pre- or postnatal analysis.