Journal Article

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

Jankowiak, Martin, Pinello, Luca, Brown, Lara, Ascher, David B., Zhou, Yunzhuo, Tognon, Manuel, Lettre, Guillaume, Sherwood, Richard I., Barkal, Sam, Love, Michael I., Phan, Quang Vinh, Yu, Tian, Ryu, Jayoung, Francoeur, Matthew, Li, Zhijian, Bhat, Vineel, Cassa, Christopher A.

2024

Overview

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. BEAN is a Bayesian approach for analyzing base editing screens with improved effect size quantification and variant classification. Applied to low-density lipoprotein (LDL)-associated common variants and saturation base editing of LDLR , BEAN identifies new LDL uptake genes and offers insights into variant structure–pathogenicity mechanisms.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/2163 / 631/114/794 / 631/1647/1511 / 631/208/191/1908 / Agriculture / Animal Genetics and Genomics / Bayes Theorem / Bayesian analysis / Beans / Biobanks / Biomedical and Life Sciences / Biomedicine / Cancer Research / Cardiovascular disease / Chromatin / CRISPR / CRISPR-Cas Systems / Editing / Editors / Efficiency / Gene Editing - methods / Gene Function / Genotype / gRNA / HEK293 Cells / Human Genetics / Humans / Low density lipoprotein / Pathogenicity / Pathogens / Phenotype / Phenotypes / Receptors, LDL - genetics / RNA editing / RNA, Guide, CRISPR-Cas Systems - genetics / Sieve analysis