Combining multiplexed functional data to improve variant classification.

With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a source of evidence which can be used for clinical gene variant classification. Increasingly, there are multiple published MAVEs for the same gene, sometimes measuring different aspects of variant impact. Where multiple functional consequences may need to be considered to get a more complete understanding of variant effects for a given gene, combining data from multiple MAVEs may lead to the assignment of increased evidence strength which could impact variant classifications. Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We illustrate the potential of this approach by showing the integration of multiplexed functional data from four MAVEs for the gene