Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Ambry Genetics, Aliso Viejo, CA 92656, USA.
Am J Hum Genet. 2021 Dec 2;108(12):2248-2258. doi: 10.1016/j.ajhg.2021.11.001. Epub 2021 Nov 17.
Clinical interpretation of missense variants is challenging because the majority identified by genetic testing are rare and their functional effects are unknown. Consequently, most variants are of uncertain significance and cannot be used for clinical diagnosis or management. Although not much can be done to ameliorate variant rarity, multiplexed assays of variant effect (MAVEs), where thousands of single-nucleotide variant effects are simultaneously measured experimentally, provide functional evidence that can help resolve variants of unknown significance (VUSs). However, a rigorous assessment of the clinical value of multiplexed functional data for variant interpretation is lacking. Thus, we systematically combined previously published BRCA1, TP53, and PTEN multiplexed functional data with phenotype and family history data for 324 VUSs identified by a single diagnostic testing laboratory. We curated 49,281 variant functional scores from MAVEs for these three genes and integrated four different TP53 multiplexed functional datasets into a single functional prediction for each variant by using machine learning. We then determined the strength of evidence provided by each multiplexed functional dataset and reevaluated 324 VUSs. Multiplexed functional data were effective in driving variant reclassification when combined with clinical data, eliminating 49% of VUSs for BRCA1, 69% for TP53, and 15% for PTEN. Thus, multiplexed functional data, which are being generated for numerous genes, are poised to have a major impact on clinical variant interpretation.
临床对同义突变变异的解读具有挑战性,因为大多数经基因检测发现的同义突变变异较为罕见,其功能影响未知。因此,大多数变异的意义不确定,无法用于临床诊断或管理。虽然无法改变变异的罕见性,但可以同时对数千个单核苷酸变异的影响进行实验测量的多重变异效应分析(MAVE)提供了有助于解决意义不明的变异(VUS)的功能证据。然而,对于用于变异解读的多重功能数据的临床价值,还缺乏严格的评估。因此,我们系统地将之前发表的 BRCA1、TP53 和 PTEN 的多重功能数据与一个诊断检测实验室确定的 324 个 VUS 的表型和家族史数据相结合。我们从这三个基因的 MAVEs 中整理了 49,281 个变异功能评分,并通过机器学习将四个不同的 TP53 多重功能数据集整合为每个变体的单一功能预测。然后,我们确定了每个多重功能数据集提供的证据强度,并重新评估了 324 个 VUS。当与临床数据结合使用时,多重功能数据在推动变体重新分类方面非常有效,消除了 BRCA1 中 49%、TP53 中 69%和 PTEN 中 15%的 VUS。因此,正在为众多基因生成的多重功能数据有望对临床变异解读产生重大影响。