MAGI: Mechanistic Consequences of Genetic Variants via Genomic Foundation Models
Clinical variant interpretation requires mechanism-aware evidence to guide diagnosis and clarify the biological consequences of mutations. However, existing computational predictors and genomic foundation models largely function as black boxes, providing pathogenicity labels with limited mechanistic insight or clinical actionability. Here, we present MAGI (Mechanistic Annotation of Genomic Impacts), a novel method that bridges this interpretability gap by unifying clinically relevant variant interpretation with mechanistic genomic analysis. MAGI pipeline leverages a genomic transformer model to quantify the effects of DNA variants across 3,623 functional tracks, encompassing regulatory features, multi-omics datasets, including tissue specificity and chromatin states, and 21 additional molecular annotations of genes and transcripts. These signals are integrated through a deterministic logic layer that maps single-nucleotide variants and indels to explicit molecular consequences. We benchmark MAGI-derived consequences against clinical rationales curated from ClinVar and observe strong concordance that scales with the magnitude of functional disruption. MAGI accurately recapitulates canonical pathogenic mechanisms, including start codon loss, splice site disruption, and regulatory element perturbation, consistent with ClinVar annotations. We further present case studies addressing conflicting or incomplete mechanistic interpretations, as well as variants requiring complex inference. Notably, MAGI is also applicable to non-human genomes and was evaluated on multispecies OMIA pathogenic variants. Collectively, MAGI establishes a generalizable framework that extends beyond clinical diagnostics to enable mechanistic discovery in functional genomics, generating mechanistically grounded, testable hypotheses for variants of uncertain significance (VUS) and variants with discordant clinical interpretations. In several cases, MAGI proposes alternative explanations that challenge existing annotations, providing transparent rationales and experimentally tractable predictions.