Deep Agentic Variant Prioritisation for Expert Level Genetic Diagnosis

Abstract Diagnosing rare genetic disease hinges on identifying the causal variant among thousands of candidates, a task that leaves most of the 300 million affected patients undiagnosed despite the availability of genome sequencing. Here we present DAVP (Deep Agentic Variant Prioritisation), a hierarchical agentic-LLM pipeline combining gene pre-filtering, knowledge-graph-grounded variant reports, and iterative LLM tournaments to rank variants against each patient’s phenotype. We benchmark DAVP on 709 diagnosis cases across four datasets, including LIRDB-47—a new cohort of 47 Turkish rare-disease patients with 63 clinically diagnosed causal variants. DAVP achieves 85.5% variant-level top-3 recall on the full benchmark; on LIRDB-47, it places the causal variant in the top-3 in 76.2% of patients (top-20 in 88.9%), a 1.6-fold improvement over the best phenotype-driven baseline (Exomiser, 47.6%). Our work suggests that agentic LLM systems combining evidence synthesis with patient-specific reasoning have the potential to reshape clinical genomic workflows.