Combining multiplexed functional data to improve variant classification
Atlas of Variant Effects Alliance, :, Jeffrey D. Calhoun, Moez Dawood, Charlie F. Rowlands, Shawn Fayer, Elizabeth J. Radford, Abbye E. McEwen, Clare Turnbull, Amanda B. Spurdle, Lea M. Starita, Sujatha Jagannathan, Ken, Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois, Human Genome Sequencing Center, Department of Molecular, Human Genetics, Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, Brotman Baty Institute for Precision Medicine, Department of Genome Sciences, WA, Wellcome Sanger Institute, Hinxton, CB10 1SA, Department of Pediatrics, University of Cambridge, Level 8, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK., Department of Laboratory Medicine, Pathology, University of Washington, Seattle, Division of Genetics, Epidemiology, The Institute of Cancer Research, London, UK, Population Health Program, QIMR Berghofer Medical Research Institute, Herston, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia, Department of Biochemistry, Molecular Genetics, USA., RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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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 TP53. By following these steps, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, reclassify more VUS, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.