LDLR variant classification through activity-normalized prime editing screening

Inherited variants in the LDL receptor (LDLR) gene are the most common cause of familial hypercholesterolemia (FH), significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt intervention with lipid-lowering therapies; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, limiting the clinical utility of genetic testing for definitive FH diagnosis, cascade testing of at-risk relatives, and timely lipid-lowering intervention. We developed an innovative, activity-normalized prime editing screening pipeline to measure the impact of 5,184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Through pairing a genotypic outcome reporter with every prime editing guide RNA (pegRNA), we adjust phenotypic measurements to account for variable editing efficiency. Further, we use a statistical estimation approach that leverages measurements for all missense variants at a given position to denoise the resulting scores. We show that prime editing-mediated reporter editing correlates with endogenous variant installation frequency, allowing activity normalization to improve imputation of LDLR variant effect. We achieve robust separation of pathogenic vs. benign ClinVar variants and concordance between experimentally derived functional scores and LDL-C levels measured in UK Biobank participants. Further, we calibrate the strength of this functional evidence to align with the ACMG/AMP variant interpretation guidelines. By integrating additional sources of evidence, a majority of currently unclassified rare LDLR variants appear to meet computational evidence thresholds for reclassification and can be prioritized for expert review. We use the broad coverage of this screen to gain insight into how apolipoproteins bind to LDLR. In particular, we identify and characterize rare LDLR variants that enhance LDL-C uptake through increased interaction with apolipoprotein B. Finally, we compare prime editing-based functional scores with those derived from recent base editing and cDNA-based LDLR variant screens, and find that all approaches show robust correlation with clinically observed LDL-C levels and computational scores, while prime editing identifies splice-altering coding variants that are not modeled by cDNA screening.This table serves as supplementary material for this study, containing the functional data from each of our LDLR prime editing screens, variant ACMG evidence scores, a list of plasmids, and the primer sequences used to perform the experiments described. The preprint is linked here: https://www.biorxiv.org/content/10.64898/2025.12.16.694467v1.