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

BackgroundClinical 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. MethodsWe 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). ResultsThe 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. ConclusionsBy 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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/26343831v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a7d316org.highwire.dtl.DTLVardef@1370aborg.highwire.dtl.DTLVardef@49f9faorg.highwire.dtl.DTLVardef@ba936a_HPS_FORMAT_FIGEXP M_FIG C_FIG