Combining genetic and physical trait data improves measurement of base editing variant effects

An integrated experimental and computational pipeline enhances the precision of variant effect assessments in CRISPR base editing screens.

• Variable efficiency and precision in CRISPR base editing complicate the analysis of disease-associated variants.
• A new Bayesian network approach, called BEAN, improves estimation of variant impacts by using editing outcomes and chromatin accessibility.
• BEAN outperforms existing tools in quantifying variant effects.
• The method identifies common regulatory variants affecting low-density lipoprotein (LDL) uptake and implicates previously unreported genes.
• Saturation base editing of LDLR allows accurate quantification of missense variant pathogenicity, aligned with UK Biobank patient data.

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