AlphaGenome informed machine learning for pathogenicity prediction of germline mutations

Authors

Kumar, P., Singh, S., Garai, S., Ramesh, M., Goli, R. C., KC, N., Sahu, T. K., Naskar, S., Bhadana, V. P., Nandineni, M. R., KK, K.

Abstract

Accurate interpretation of germline variants remains a central challenge in clinical genomics. Existing in silico pathogenicity predictors often provide incomplete coverage or restricted to missense variants from the coding region, leaving the variants from splice sites, -UTR, and intronic regions under-scored. We present AGVarPred, a gradient-boosted classifier designed to predict the pathogenicity of germline mutants, with a broad consequence-class coverage across SNVs and indels. Here, the population allele frequency from gnomAD, functional consequence annotations from Ensembl VEP, and cell-type-specific functional genomic scores from the AlphaGenome platform were integrated to develop the classifier. Using a gene-level split of 134,002 high-confidence ClinVar variants across 9,222 genes, AGVarPred achieved an AUC-ROC of 0.9753 (95% CI 0.9731-0.9775) and a AUC-PR of 0.9606 on 12,385 scored internal test variants. A paired DeLong analysis showed that the full model significantly outperformed an allele-frequency-plus-VEP model ({Delta}AUC = 0.1539, 95% CI 0.1489-0.1589; p < 1 x 10-300), supporting a substantial incremental contribution from AlphaGenome-derived features. Performance remained strong when restricted to SNVs (AUC-ROC 0.9558) and was also high for indels (AUC-ROC 0.9532). On five external benchmarks, AUC-ROCs ranged from 0.528 on a functional-assay-derived benchmark to 0.977 on a clinically curated deafness database, reflecting its dependence on benchmark composition and label source. A separate analysis of the VIP Database, whose pathogenic labels are ClinVar-derived, showed consistent performance (AUC-ROC of 0.892) in held-out genes. AGVarPred scored 100% of evaluated variants in the external benchmarks, whereas AlphaMissense and REVEL covered only small fractions of non-missense variants. Model interpretation indicated that gnomAD allele frequency was the dominant individual predictor; the 111 AlphaGenome features provided complementary functional signal, with splicing and chromatin assays contributing to the largest share of the AlphaGenome SHAP mass. A web-based interface for AGVarPred is available at https://pankaj357.github.io/AGVarPred/ to facilitate the submission of normalized GRCh38 germline variants and retrieval of pathogenicity predictions using the frozen AGVarPred model.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 26 Oct 2026.

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