LDLR variant classification through activity-normalized prime editing screening

Abstract

Background: 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 lipid-lowering therapy and cascade testing of at-risk relatives; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, leaving many patients without actionable information. Methods: We developed the first activity-normalized prime editing screening pipeline to measure the impact of 5,184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Each prime editing guide RNA (pegRNA) is paired with a genotypic outcome reporter to correct for variable editing efficiency, overcoming a key limitation of prior pooled genome editing screens. A statistical framework further improves variant effect estimates by jointly analyzing all missense variants at each amino acid position. Results: We show that prime editing of the reporter construct correlates with endogenous variant installation frequency, validating the activity normalization approach. The resulting scores capture a continuous spectrum of functional effects, robustly separate pathogenic vs. benign ClinVar variants, and show concordance with LDL-C levels in UK Biobank participants. We calibrate functional evidence strengths to the ACMG/AMP variant interpretation framework, enabling integration into a clinical variant classification workflow. By combining functional, computational, population, and contextual evidence, 322 of 434 LDLR variants currently classified as VUS, conflicting, or absent from ClinVar appears to meet evidence thresholds for reclassification and can be prioritized for expert review, substantially expanding the pool of actionable variant classifications. The screen also reveals a cluster of gain-of-function variants in LDLR class A repeat 5, at least some of which enhance LDL-C uptake through increased apolipoprotein B interaction, with implications for therapeutic genome editing. Finally, prime editing uniquely detects splice-altering coding variants missed by cDNA-based screens and pathogenicity predictors, revealing an advantage of endogenous variant installation. Conclusions: Altogether, activity-normalized prime editing provides a scalable framework for LDLR variant classification that substantially expands the proportion of variants with evidence for genetic diagnosis and reveals novel biology with therapeutic relevance.