Deep Agentic Variant Prioritisation for Expert Level Genetic Diagnosis Fast at Scale

Abstract

Abstract Genetic diagnosis remains a formidable challenge characterized by a diagnostic odyssey that spans years, with over half of rare disease patients remaining undiagnosed affecting more than 300 million people on earth. Clinicians must navigate through thousands of candidate variants against a noisy and fragmented literature landscape, a task that overwhelms human cognitive capacity and conventional decision-making approaches. Recent advances in agentic artificial intelligence systems have demonstrated superior performance in complex, multi-step reasoning tasks by systematically evaluating vast amounts of information, breaking down problems into manageable components, and adapting dynamically to new evidence. These capabilities align precisely with the requirements of genetic variant prioritization. Here we present DAVP (Deep Agentic Variant Prioritisation), a hierarchical agentic AI system that represents a major step forward in genetic diagnosis through patient-specific variant evaluation. Unlike traditional approaches that apply generic pathogenicity scores, DAVP evaluates each variant within the full context of the patient’s clinical presentation, phenotypic profile, and genomic background. The system comprises three interconnected algorithmic components: prelimin8 , a gene pre-screening algorithm that rapidly filters the genomic search space; inGeneTopMatch , a semantic knowledge graph algorithm that captures complex gene-phenotype-disease relationships; and elimin8 , an in-context learning prioritization algorithm that dynamically ranks variants through iterative knowledge sorting and evidence synthesis. We conducted comprehensive benchmarks measuring diagnostic cumulative distribution function (CDF) recall based on top-k variant recommendations using simulation cases constructed with 1000 Genomes as healthy background genomes and variants from ClinVar as positive controls. DAVP demonstrates strong diagnostic performance superior to expert genetic clinicians while operating at orders of magnitude greater speed and scale. Our results demonstrate that agentic AI systems can transform rare disease diagnostics by combining the systematic evaluation capabilities of artificial intelligence with the nuanced clinical reasoning required for complex genetic diagnosis. This work lays the foundation for a new paradigm in AI-driven genetic medicine that could accelerate diagnosis, reduce healthcare costs, and improve patient outcomes worldwide. The source code and data to reproduce this work are available at https://github.com/Muti-Kara/davp .

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