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.

CRISPR 碱基编辑筛选能够大规模分析疾病相关变异；然而，可变的效率和精度混淆了对变异诱导表型的评估。在这里，我们提供了一个集成的实验和计算管道，可以改进碱基编辑屏幕中变异效应的估计。我们使用报告基因构建体来测量引导 RNA (gRNA) 编辑结果及其表型后果，并引入具有活性标准化 (BEAN) 的碱基编辑器筛选分析 (BEAN)，这是一种贝叶斯网络，使用报告基因和目标位点染色质可访问性提供的每个引导编辑结果估计变量的影响。 BEAN 在变异效应量化方面优于现有工具。我们使用 BEAN 来查明改变低密度脂蛋白 (LDL) 摄取的常见调控变异，从而涉及以前未报告的基因。此外，通过LDLR的饱和碱基编辑，我们准确地量化了错义变异的致病性，这与英国生物银行患者的测量结果一致，并确定了潜在的结构机制。这项工作提供了一种广泛适用的方法来提高碱基编辑筛选的能力，以进行与疾病相关的变异表征。