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ChatTogoVar: a TogoVar-based retrieval-augmented generation system for precise genomic variant interpretation.

Nobutaka Mitsuhashi1,2, Toyofumi Fujiwara3, Atsuko Yamaguchi4

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Human Genome Variation
|April 9, 2026
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Summary
This summary is machine-generated.

Large language models (LLMs) can now aid genomic variant interpretation. A new system, ChatTogoVar, uses a variant database to improve LLM accuracy, enhancing genomic medicine applications.

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Area of Science:

  • Genomics
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Large language models (LLMs) are increasingly used for human genomic variant interpretation.
  • General-purpose LLMs exhibit 'hallucinations,' impacting reliability in specialized genomic queries.
  • Accurate variant interpretation is crucial for genomic medicine and personalized healthcare.

Purpose of the Study:

  • To develop a reliable LLM-based system for human genomic variant interpretation.
  • To mitigate LLM hallucinations in genomic variant analysis.
  • To enhance the accuracy and practical utility of AI tools in genomic medicine.

Main Methods:

  • Developed ChatTogoVar, a retrieval-augmented generation system querying the TogoVar database.
  • Integrated retrieved variant information (allele frequency, clinical significance) into LLM prompts.
  • Constructed and utilized a benchmark of 1500 template-variant combinations for assessment.

Main Results:

  • ChatTogoVar outperformed a general-purpose LLM and an existing specialized system on a 150-question benchmark.
  • Automated LLM-based scoring of 1500 questions confirmed ChatTogoVar's superior performance.
  • The system demonstrated improved accuracy in genomic variant interpretation.

Conclusions:

  • Integrating a reliable variant database with LLMs enhances genomic variant interpretation accuracy.
  • ChatTogoVar shows promise as a practical tool for supporting genomic medicine.
  • The approach mitigates LLM hallucinations, improving reliability for clinical applications.