Joint estimation and imputation of variant functional effects using high throughput assay data

Summary Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE ( Fu nctional S ubstitution E stimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1 , p=2.24×10 −51 ), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53 , FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10 −6 ). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays. Graphical Abstract Highlights Uses functional assay data collectively to improve the estimation of allelic variant effects Infers the impact of variants not experimentally screened, broadening the utility of assays Improves the discrimination of clinically actionable variants within ClinVar Significantly separates patients at risk for cancer syndromes in the UK Biobank