Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

Informing variant assessment using structured evidence from prior classifications (PS1, PM5, and PVS1 sequence variant interpretation criteria).

PURPOSE: This study aimed to explore whether evidence of pathogenicity from prior variant classifications in ClinVar could be used to inform variant interpretation using the American College of Medical Genetics and Genomics/Association for Molecular Pathology clinical guidelines. METHODS: We identified distinct single-nucleotide variants (SNVs) that are either similar in location or in functional consequence to pathogenic variants in ClinVar and analyzed evidence in support of pathogenicity using 3 interpretation criteria. RESULTS: Thousands of variants, including many in clinically actionable disease genes (American College of Medical Genetics and Genomics secondary findings v3.0), have evidence of pathogenicity from existing variant classifications, accounting for 2.5% of nonsynonymous SNVs within ClinVar. Notably, there are many variants with uncertain or conflicting classifications that cause the same amino acid substitution as other pathogenic variants (PS1, N = 323), variants that are predicted to cause different amino acid substitutions in the same codon as pathogenic variants (PM5, N = 7692), and loss-of-function variants that are present in genes in which many loss-of-function variants are classified as pathogenic (PVS1, N = 3635). Most of these variants have similar computational predictions of pathogenicity and splicing effect as their associated pathogenic variants. CONCLUSION: Broadly, for >1.4 million SNVs exome wide, information from previously classified variants could be used to provide evidence of pathogenicity. We have developed a pipeline to identify variants meeting these criteria that may inform interpretation efforts.