CoNVict: An Agentic AI System for Copy Number Variation Prioritization in Rare Disease Diagnosis

Journal: medRxiv
Published Date:

Abstract

Copy number variants (CNVs) are established contributors to rare genetic disorders, yet their clinical interpretation remains challenging in diagnostic genomics. Large CNVs frequently encompass multiple functional regions whose clinical significance can only be resolved in the context of the patient's phenotype. Effective prioritization demands variant-level scoring of dosage sensitivity, structural consequences, and disease associations, and systematic comparison of candidates within the same clinical context. Current computational tools only partially address these requirements: they automate variant-level scoring but leave phenotype-guided evidence integration and cross-variant ranking to the clinician, creating a gap between annotation throughput and diagnostic decision-making. Agentic AI systems coordinate large language model-driven reasoning across structured multi-step pipelines and have shown strong performance on biomedical tasks requiring iterative evidence evaluation and contextual judgement, making them well suited to patient-specific variant interpretation where rigid scoring functions fall short. Here, we present CoNVict, a two-stage agentic AI system for patient-specific CNV prioritization. The system ranks CNVs through verdict classification that triages candidates and tournament ranking that performs pairwise comparisons via structured, in-context reasoning. Evaluated on simulated diagnostic cases spanning multiple clinical subspecialties, CoNVict substantially outperforms existing computational methods in identifying the causal CNV and maintains robust performance on variants of uncertain significance and non-coding variants without retraining. Our results demonstrate that agentic AI can bridge the gap between automated variant-level annotation and the patient-specific clinical reasoning required for CNV-driven genetic diagnosis. Availability and Implementation: Source code and data are available at https://github.com/Muti-Kara/CoNVict.

Authors

  • Gencturk
  • M. M.; Kara
  • M.; Ozden
  • F.