PDF(Pubmed)
Abstract：
As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.
摘要：
随着基因检测的采用和范围不断扩大，大规模解释DNA序列变异的临床意义仍然是一个巨大的挑战，被分类为不确定显著性变异(VUS)的比例很高。基因检测实验室历来依赖，在某种程度上,关于学术文献中的功能数据，以支持变体分类。高通量功能测定或变异效应(MAVEs)的多重测定，旨在评估DNA变体对蛋白质稳定性和功能的影响，代表了变体分类的重要且日益可用的证据来源，但是他们的潜力才刚刚开始在临床实验室中实现。这里,我们描述了一个生成的框架，验证并将MAVE的数据纳入应用于临床基因检测的半定量变异分类方法。使用单细胞基因表达测量，建立细胞证据模型来评估44个临床感兴趣基因中DNA变异的影响。该框架还应用于具有先前公布的MAVE数据集的另外22个基因的模型。总的来说,将来自24个基因的建模数据纳入我们的变异分类方法.这些数据为在超过57,000个个体中对4043个观察到的变异进行分类提供了证据。基因检测实验室具有独特的优势，分析,验证,并纳入高通量功能数据的证据，最终能够使用这些数据为更多患者提供明确的临床变异分类。
