Automating the Clinical Interpretation of LDLR, APOB , and PCSK9 Variants: A Web-Based Platform for FH Diagnosis

Abstract Background Clinical interpretation of LDLR, APOB , and PCSK9 variants is essential for diagnosing familial hypercholesterolemia (FH), yet manual evidence curation is labor-intensive and prone to variability. Methods We developed a web-based platform ( https://fh-interpret.shinyapps.io/beta/ ) to automate the synthesis of multi-dimensional evidence. The tool integrates population frequencies (gnomAD), phenotype associations (UK Biobank), AI-based pathogenicity scores (AlphaMissense), high-throughput functional data from saturation mutagenesis, and established clinical classifications (ClinVar, HGMD). Results The application streamlines the diagnostic workflow by automatically synthesizing: (1) population frequencies from gnomAD to assess variant rarity; (2) statin-adjusted LDL-cholesterol association analyses in ∼470,000 UK Biobank participants; (3) AlphaMissense pathogenicity predictions; (4) functional scores for ∼17,000 LDLR variants quantifying impact on LDL uptake and LDLR cell-surface abundance; (5) existing clinical evidence from ClinVar and HGMD; and (6) identification of pathogenic variants at identical amino acid positions (ACMG PM5). The tool generates narrative evidence summaries to facilitate standardized ACMG/AMP classification. Conclusions By providing real-time access to massive-scale genomic, phenotypic, and functional datasets within a single platform, this tool streamlines the FH diagnostic workflow and supports more consistent and efficient clinical variant interpretation. Graphical Abstract