Variants of uncertain significance represent a massive challenge to medical genetics. Multiplexed functional assays, in which the functional effects of thousands of genomic variants are assessed simultaneously, are increasingly generating data that can be used as additional evidence for or against variant pathogenicity. Such assays have the potential to resolve variants of uncertain significance, thereby increasing the clinical utility of genomic testing. Existing standards from the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) and new guidelines from the Clinical Genome Resource (ClinGen) establish the role of functional data in variant interpretation, but do not address the specific challenges or advantages of using functional data derived from multiplexed assays. Here, we build on these existing guidelines to provide recommendations to experimentalists for the production and reporting of multiplexed functional data and to clinicians for the evaluation and use of such data. By following these recommendations, experimentalists can produce transparent, complete, and well-validated datasets that are primed for clinical uptake. Our recommendations to clinicians and diagnostic labs on how to evaluate the quality of multiplexed functional datasets, and how different datasets could be incorporated into the ACMG/AMP variant-interpretation framework, will hopefully clarify whether and how such data should be used. The recommendations that we provide are designed to enhance the quality and utility of multiplexed functional data, and to promote their judicious use.

中文翻译：

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关于收集和使用多重功能数据以进行临床变异解释的建议。

意义不确定的变异对医学遗传学来说是一个巨大的挑战。多重功能测定同时评估数千个基因组变异的功能效应，越来越多地生成可用作支持或反对变异致病性的额外证据的数​​据。此类测定有可能解决不确定意义的变异，从而增加基因组测试的临床实用性。美国医学遗传学和基因组学学院 (ACMG)/分子病理学协会 (AMP) 的现有标准和临床基因组资源 (ClinGen) 的新指南确立了功能数据在变异解释中的作用，但没有解决具体的挑战或使用来自多重测定的功能数据的优点。在这里，我们以这些现有指南为基础，为实验人员提供多重功能数据的生成和报告以及临床医生评估和使用此类数据的建议。通过遵循这些建议，实验人员可以生成透明、完整且经过充分验证的数据集，为临床应用做好准备。我们向临床医生和诊断实验室提出的关于如何评估多重功能数据集的质量以及如何将不同数据集纳入 ACMG/AMP 变异解释框架的建议，有望澄清是否以及如何使用此类数据。我们提供的建议旨在提高多重功能数据的质量和实用性，并促进其明智的使用。