A Geometric Perspective on Zero-Shot Variant Effect Prediction Across the Central Dogma

Log-likelihood ratios (LLR) have emerged as a standard probe of biological founda- tion models’ ability to predict variant effects. However, it remains unclear whether the latent manifold of these models already encodes the relevant geometric structure of variant effects. We investigate this question across the central dogma by com- puting local intrinsic dimensionality (LID) around reference and alternative variant embeddings generated by EVO2 (DNA), ORTHRUS (RNA), and ESM3 (protein). This allows us to compare the geometric neighbourhoods of pathogenic and benign variants in each model’s embedding space. On 105,224 ClinVar missense variants, we find that the optimal scoring method is modality-dependent: ESM3 protein achieves the highest LID-based AUROC (0.738), exceeding its own LLR (0.629), while EVO2-7B DNA achieves the highest LLR (0.878). Dense per-layer analy- sis reveals three distinct information-processing regimes: stable geometric signal from the first layer onward (ESM3), monotonic buildup (ORTHRUS), and cyclic build-and-flush phases tied to the convolution–attention architecture (EVO2). The geometric signal persists after controlling for evolutionary conservation (57–61% retained) and is positive across all conservation quartiles, indicating that foundation models learn constraint structure beyond what conservation scores capture.