Su, Y.; Lin, Y.-J.

Missense variant interpretation remains a central challenge in clinical genomics. Missense pathogenicity predictors achieve strong performance, but many emphasize protein-level consequences or overlapping annotation priors. Whether genomic language models add non-redundant nucleotide-context signal to missense interpretation remains unclear. Here, we systematically adapted genomic language models to ClinVar missense pathogenicity prediction across backbone architectures, representation strategies, classifier heads, and adaptation regimes. In our analysis, variant-position embeddings consistently outperformed pooled sequence representations, multi-species pretraining provided the strongest backbone-level advantage, and low-rank adaptation generalized better than full fine-tuning. The resulting fine-tuned model, GLM-Missense, substantially outperformed zero-shot scoring from the same pretrained model. To test whether GLM-Missense contributes information beyond existing methods, we built MetaMissense, an XGBoost ensemble combining GLM-Missense with AlphaMissense, ESM1b, REVEL, CADD, SIFT, and PolyPhen-2. GLM-Missense showed the lowest concordance with other predictors, retained the strongest partial association with pathogenicity after controlling for the other predictors, and ranked as the most informative non-ensemble input to MetaMissense. MetaMissense achieved the best performance in both cross-validation and held-out testing. Analyses of variants correctly classified by GLM-Missense but misclassified by several established predictors suggested two patterns. First, part of the GLM-Missense signal may reflect splice-relevant exonic context. Second, GLM-Missense appears to add value in settings where other predictors may overweight allele frequency, gene-level constraint, or amino-acid-change severity. However, these features explained only about 10% of the distinction between the GLM-Missense-correct subset from the background. Together, our results demonstrate that fine-tuned genomic language models contribute complementary nucleotide-context information to missense variant interpretation.