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利用自动化重新分析提高阴性全基因组切片面板的诊断产量。

Increased diagnostic yield from negative whole genome-slice panels using automated reanalysis.

机构信息

Children's National Rare Disease Institute, Division of Genetics and Metabolism, Washington, DC, USA.

Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA.

出版信息

Clin Genet. 2023 Sep;104(3):377-383. doi: 10.1111/cge.14360. Epub 2023 May 17.

Abstract

We evaluated the diagnostic yield using genome-slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI-funded GREGoR Consortium site. Genome-wide reanalysis was performed using Moon™, a machine-learning-based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome-wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting.

摘要

我们使用自动化表型/基因排序系统,在临床环境中通过基因组切片面板重新分析来评估诊断产量。我们分析了从临床订购的生物信息切片构建的临床多样化、未确诊的 16 个病例的全基因组测序 (WGS) 数据,这些病例被转介到儿科孟德尔基因组学研究中心,这是一个由 NHGRI 资助的 GREGoR 联盟站点。使用基于机器学习的变异优先级排序工具 Moon™ 对全基因组进行了重新分析。在 16 例中的 5 例中,我们发现了一个潜在的具有临床意义的变异。在其中 4 例中,由于疾病表型扩展或对患者的初始表型分析不完整,该变异出现在原始面板中未包含的基因中。在第 5 例中,包含该变异的基因包含在原始面板中,但由于其为具有内含子断裂的复杂结构重排,且断裂位于临床分析区域之外,因此最初未被识别。对靶向面板测试过程中生成的临床 WGS 数据进行自动化全基因组重新分析,使诊断结果增加了 25%,并在另一个病例中发现了一个可能具有临床意义的发现,这突显了与临床环境中常规分析相比,分析的附加价值。

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