Nature geneticsApr 24, 2024

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

CRISPR Gene Editing Weekly Brief ↗PubMed ↗DOI ↗

Updated Mar 30, 2026

Abstract

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.

Simplified

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.

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Competing interests

Competing interests. L.P. has financial interests in Edilytics and SeQure Dx. L.P.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict-of-interest policies. The remaining authors declare no competing interests.

PubMed

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

Abstract

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