Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation

Abstract

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.

Fig. 1

Overview of the steps required to produce, validate and use multiplexed functional data for variant interpretation. Multiplexed assays for variant effect. a A DNA variant library is generated and introduced into cells before being b subjected to a functional assay. c Variants from a sample of each cell population are sequenced and d functional scores that reflect their change in frequency are calculated for each variant. Data quality control. e The dynamic range of the functional score distribution of the entire library (gray) is benchmarked by the observed scores for known functionally normal (blue) or abnormal (red) variants; here, synonymous and nonsense protein variants are used as an example. f. Comparison of functional scores across two or more replicate experiments generates an overall metric of reproducibility (R²). Biological replicates have different input populations that result from separate introductions of the variant library and they provide a better characterization of variation than technical replicates, which use the same starting population. g Confidence intervals for each variant functional score are calculated from replicate experiments. h Multiplexed functional scores are benchmarked against other measurements of molecular function. Reporting. i Sharing of data and analyses facilitates data reuse and enhances data utility. Validation for clinical utility. j Functional score ranges are divided into categorical bins. Here, the cutoff between bins is determined by the measured functional score distributions of known interpreted variants. k A precision-recall curve is used to assess the assay’s sensitivity and specificity. Multiplexed functional data as evidence for variant interpretation. l Clinicians and diagnostic laboratories assess the overall quality of the assay and of specific variant information to determine the weighting, in terms of strength of evidence that should be assigned to the functional information