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

Area of Science:

• Genetics and Genomics
• Molecular Biology
• Bioinformatics

Background:

• CRISPR base editing screens are powerful for analyzing disease variants.
• Current methods struggle with variable efficiency and precision, confounding phenotype assessment.
• Accurate variant effect quantification is crucial for understanding genetic diseases.

Purpose of the Study:

• To develop an integrated experimental and computational pipeline to improve the estimation of variant effects in base editing screens.
• To enhance the precision and scalability of CRISPR base editing screens for disease variant characterization.
• To identify novel genes and mechanisms involved in lipid metabolism regulation.

Main Methods:

• Development of a reporter construct to simultaneously measure guide RNA editing efficiency and phenotypic consequences.
• Introduction of Base Editor Screen Analysis with Activity Normalization (BEAN), a Bayesian network for variant impact estimation.
• Integration of per-guide editing outcomes and target site chromatin accessibility data within BEAN.
• Application of BEAN to analyze CRISPR screens for low-density lipoprotein (LDL) uptake and LDLR variant pathogenicity.

Main Results:

• BEAN significantly outperforms existing tools in quantifying variant effects.
• Identified common regulatory variants impacting LDL uptake, implicating novel genes.
• Accurately quantified missense variant pathogenicity in LDLR, consistent with clinical data.
• Uncovered underlying structural mechanisms of variant pathogenicity.

Conclusions:

• The developed pipeline and BEAN provide a widely applicable approach to enhance CRISPR base editing screens.
• This methodology improves the power to characterize disease-associated variants and their functional impacts.
• The findings offer new insights into the genetic regulation of lipid metabolism and cardiovascular disease risk.