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通过全外显子组测序（WES）数据分析进行胚系 CNV 检测可提高罕见遗传病的分辨率。

Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases.

机构信息

Laboratory of Medical Genetics, St. Sophia's Children's Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.

Research University Institute for the Study and Prevention of Genetic and Malignant Disease of Childhood, St. Sophia's Children's Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.

出版信息

Genes (Basel). 2023 Jul 21;14(7):1490. doi: 10.3390/genes14071490.

DOI:10.3390/genes14071490
PMID:37510394
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10379589/
Abstract

Whole-Exome Sequencing (WES) has proven valuable in the characterization of underlying genetic defects in most rare diseases (RDs). Copy Number Variants (CNVs) were initially thought to escape detection. Recent technological advances enabled CNV calling from WES data with the use of accurate and highly sensitive bioinformatic tools. Amongst 920 patients referred for WES, 454 unresolved cases were further analysed using the ExomeDepth algorithm. CNVs were called, evaluated and categorized according to ACMG/ClinGen recommendations. Causative CNVs were identified in 40 patients, increasing the diagnostic yield of WES from 50.7% (466/920) to 55% (506/920). Twenty-two CNVs were available for validation and were all confirmed; of these, five were novel. Implementation of the ExomeDepth tool promoted effective identification of phenotype-relevant and/or novel CNVs. Among the advantages of calling CNVs from WES data, characterization of complex genotypes comprising both CNVs and SNVs minimizes cost and time to final diagnosis, while allowing differentiation between true or false homozygosity, as well as compound heterozygosity of variants in AR genes. The use of a specific algorithm for calling CNVs from WES data enables ancillary detection of different types of causative genetic variants, making WES a critical first-tier diagnostic test for patients with RDs.

摘要

外显子组测序 (WES) 在鉴定大多数罕见病 (RD) 的潜在遗传缺陷方面已被证明具有价值。拷贝数变异 (CNV) 最初被认为无法检测到。最近的技术进步使得可以使用准确且高度敏感的生物信息学工具从 WES 数据中调用 CNV。在 920 名接受 WES 检查的患者中，有 454 例未解决的病例进一步使用 ExomeDepth 算法进行了分析。根据 ACMG/ClinGen 建议对 CNV 进行了调用、评估和分类。在 40 名患者中鉴定出了致病 CNV，使 WES 的诊断率从 50.7%（466/920）提高到 55%（506/920）。有 22 个 CNV 可用于验证，均得到确认；其中 5 个是新的。实施 ExomeDepth 工具促进了对表型相关和/或新型 CNV 的有效识别。从 WES 数据中调用 CNV 的优点之一是，对包含 CNV 和 SNV 的复杂基因型进行特征描述可最大限度地降低最终诊断的成本和时间，同时允许区分真正或假同型性以及 AR 基因中变异的复合杂合性。使用特定的算法从 WES 数据中调用 CNV 可辅助检测不同类型的致病遗传变异，使 WES 成为 RD 患者的关键一线诊断测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb9/10379589/ff2a3d21f721/genes-14-01490-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb9/10379589/0529bbff19a0/genes-14-01490-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb9/10379589/ff2a3d21f721/genes-14-01490-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb9/10379589/0529bbff19a0/genes-14-01490-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bb9/10379589/ff2a3d21f721/genes-14-01490-g002.jpg

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