Semantic embedding of variant effect annotations enables rapid and accurate pathogenicity prediction with VUS.Life.

Interpreting the pathogenicity of genetic variants remains a critical bottleneck in genomic medicine. Millions of variants of uncertain significance (VUS) hinder the clinical application of genetic findings. Traditional computational approaches often rely on hand-engineered features and fail to capture the complexity of multidimensional genomic annotations fully. We developed VUS.Life, a multi-modal framework that synergizes semantic text embeddings of biological and clinical annotations with protein language modeling. We transformed variant annotations from Variant Effect Predictor (VEP) into natural language descriptions which are then converted into vector embeddings via established Large Language Models (LLMs), namely all- mpnet-base-v2, MedEmbed-large-v0.1, and text-embedding-004. Pathogenicity of a variant of interest is predicted by its proximity in the vector embedding space with variants of known pathogenicity. We further extended VUS.Life by employing residue-level delta embeddings from the ESMC-600 M model to capture both clinical context and biophysical constraints. We evaluated the framework on > 10,000 variants across eight ACMG Tier 1 disease genes (BRCA1, BRCA2, FBN1, ATM, PALB2, MYH7, USH2A, and PAH), achieving Leave-One-Out Cross-Validation (LOO-CV) MCC of 0.895-0.989 and F1 ≥ 0.94 across all genes evaluated. Additionally, our unsupervised FBN1 structural analysis using ESMC-600M revealed that delta embeddings disentangled distinct pathogenic mechanisms, topologically separating disulfide bond disruptions from calcium- binding defects. These structural clusters correlated strongly with Zero-Shot Log-Likelihood Ratio (LLR) scores, validating evolutionary fitness as a proxy for pathogenicity. An ablation study removing all pre-computed pathogenicity scores demonstrated that MPNet embeddings retain full discriminative power, confirming that the classifier captures biological signal independent of existing scoring tools. This semantic embedding framework, VUS.Life, accurately captures pathogenicity-relevant features from complex variant annotations, enabling robust automated classification across eight ACMG Tier 1 disease genes and three embedding models. The approach generalizes beyond well-curated genes and supports scalable, interpretable, and representation-based classification of VUS. It holds significant promise for alleviating the variant interpretation bottleneck in clinical genomics.