Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Abstract:Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.19569 [q-bio.GN]

(or arXiv:2609.19569v1 [q-bio.GN] for this version)

https://doi.org/10.48550/arXiv.2609.19569 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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