FUSE: Improving the estimation and imputation of variant impacts in functional screening

Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening. [Display omitted] •FUSE reduces noise in functional screening data by analyzing observations collectively•Imputes the impact of unscreened variants, broadening the utility of assays•Increases the classification of clinically actionable disease variants within ClinVar•Improves prediction of disease status in patients with rare variants in the UK Biobank Yu et al. introduce FUSE, a framework that reduces statistical noise in functional screening datasets by analyzing experimental results collectively. By leveraging data from 115 functional assays, FUSE improves estimates of functional effects and variant classification accuracy, providing valuable insights for clinical and research applications in genomics.