PDF(Pubmed)
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：
我们使用自动表型/基因排名系统在临床环境中使用基因组切片面板再分析评估了诊断产量。我们分析了全基因组测序（WGS）数据，这些数据是从临床订购的面板构建为16个临床多样性的生物信息学切片，未确诊病例转诊至小儿孟德尔基因组学研究中心，NHGRI资助的GREGoR财团网站。使用Moon™进行全基因组再分析，一种基于机器学习的工具，用于对变体进行优先级排序。16例中有5例,我们发现了一个潜在的临床显著变异.在其中四个案例中，由于疾病的表型扩增或患者的初始表型分析不完全,该变异体出现在原始组的基因中.在第五种情况下，包含变异的基因包含在原始面板中，但是在临床分析区域之外具有内含子断点的复杂结构重排，最初没有确定。在靶向小组测试过程中产生的临床WGS数据的自动化全基因组再分析产生了25%的诊断结果和在另外一个病例中可能的临床相关发现。强调分析相对于在临床环境中常规进行的分析的附加值.
