Rationale: Genetic testing in epilepsy frequently reveals variants of uncertain significance (VUS). In community-based practices, due to the lack of access specialized neurogenetics expertise and functional testing laboratories, the clinical utility of VUS is limited. This creates interpretive challenges for neurologists/epileptologists, particularly in adult-onset cases where phenotypic variability and non-monogenic inheritance are common. Large language models (LLMs), such as GPT-4o, offer a novel, open-access solution to enhance variant interpretation by integrating genomic, structural, and phenotypic data. We evaluated the utility of an open-access, LLM-assisted framework for systematic VUS reclassification in epilepsy patients, aiming to improve diagnostic yield, elucidate complex inheritance models, and guide clinical decision-making in resource-constrained settings.
Methods: We retrospectively reviewed 64 epilepsy patients who underwent genetic testing (targeted gene panel or whole exome sequencing) in our epilepsy center (2015–2025). After excluding cases with definitive pathogenic findings, 47 patients with 258 reported VUS were reanalyzed using an AI LLM-powered framework (Fig 1). The workflow incorporated splice prediction (SpliceAI), pathogenicity scoring (CADD, REVEL, AlphaMissense), structural modeling (AlphaFold2, Missense3D, HOPE etc), phenotype-genotype mapping (OMIM, HPO), and LLM-based literature synthesis. The LLM assisted in variant prioritization, exclusion of non-contributory findings, and ACMG/AMP-guided reclassification, reviewed by a clinical genetics team. For unresolved or non-monogenic cases, additional analyses such as gene co-expression networks and multi-gene interaction modeling were applied.
Results:
Of the 258 VUS, 194 were prioritized for in-depth review. Reclassification was achieved in 60% of patients: 24% had variants upgraded to likely pathogenic/pathogenic, and 36% downgraded to likely benign/benign. Among the 47 cases: 17 (36%) were reclassified as monogenic epilepsy; 6 (13%) followed an oligogenic model involving multiple contributory variants; 10 (21%) supported a modifier model, with variants influencing phenotype expressivity and severity; 9 (19%) remained unresolved but were recommended for further testing due to strong phenotypic-genetic alignment; 5 (11%) were determined to have non-genetic etiologies. LLM-assisted reanalysis improved genotype-phenotype correlation, resolved conflicting variant annotations, and directly influenced patient care—including antiseizure medication changes, epilepsy surgery evaluations, and genetic counseling.
Conclusions: This study demonstrates the feasibility and clinical impact of an open-access, LLM-assisted VUS reclassification framework in real-world epilepsy care. By uncovering complex and multifactorial genetic architectures, this approach enhances diagnostic precision and supports equitable implementation of genomic medicine in resource-limited settings. It also represents a pioneering integration of advanced AI tools into neurology workflows, opening new frontiers for elucidating epilepsy pathogenesis.
Funding: none