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AutoPM3: enhancing variant interpretation via LLM-driven PM3 evidence extraction from scientific literature
Shumin Li1,2,3, Yiding Wang1, Chi-Man Liu1
1Department of Computer Science, School of Computing and Data Science, University of Hong Kong, Hong Kong, 999077, China.
Motivation:
Rare diseases affect over 300 million people worldwide and are often caused by genetic variants. While variant detection has become cost-effective, interpreting these variants-particularly collecting literature-based evidence like ACMG/AMP PM3-remains complex and time-consuming.
Results:
We present AutoPM3, a method that automates PM3 evidence extraction from literatures using open-source large language models (LLMs). AutoPM3 combines a Text2SQL-based variant extractor and a retrieval-augmented generation (RAG) module, enhanced by a variant-specific retriever and fine-tuned LLM, to separately process tables and text. We curated PM3-Bench, a dataset of 1027 variant-publication evidence pairs from ClinGen. On openly accessible pairs, AutoPM3 achieved 86.1% accuracy for variant hits and 72.5% recall for in trans variants-outperforming other methods, including those using larger models. We uncovered the effectiveness of AutoPM3's key modules, especially for variant-specific retriever and Text2SQL, through the sequential ablation study. AutoPM3 located evidence in 76 s, demonstrating that open-source LLMs can offer an efficient, cost-effective solution for rare disease diagnosis.
Availability And Implementation:
AutoPM3 is implemented and freely available under the MIT license at https://github.com/HKU-BAL/AutoPM3.
Insights
AutoPM3 automates the extraction of crucial genetic variant evidence from scientific literature, significantly speeding up rare disease diagnosis. This method uses open-source large language models for efficient and cost-effective analysis.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Rare diseases impact over 300 million individuals globally, frequently stemming from genetic variations.
- Interpreting genetic variants, particularly gathering literature-based evidence (e.g., ACMG/AMP PM3), is a significant bottleneck in rare disease diagnosis.
- Current methods for evidence extraction are often complex and time-consuming.
Purpose of the Study:
- To develop an automated method for extracting ACMG/AMP PM3 evidence from scientific literature.
- To leverage open-source large language models (LLMs) for efficient and cost-effective variant interpretation.
- To accelerate the diagnostic process for rare diseases.
Main Methods:
- Introduction of AutoPM3, a novel method employing open-source LLMs for automated PM3 evidence extraction.
- Integration of a Text2SQL-based variant extractor and a retrieval-augmented generation (RAG) module.
- Enhancement of the RAG module with a variant-specific retriever and a fine-tuned LLM for processing tables and text separately.
- Creation of PM3-Bench, a curated dataset of 1027 variant-publication evidence pairs from ClinGen.
Main Results:
- AutoPM3 achieved 86.1% accuracy for variant hits and 72.5% recall for in trans variants on openly accessible data.
- The method outperformed existing approaches, including those utilizing larger models.
- A sequential ablation study confirmed the effectiveness of AutoPM3's core components, particularly the variant-specific retriever and Text2SQL.
- Evidence location was achieved in as little as 76 seconds, demonstrating significant efficiency gains.
Conclusions:
- Open-source LLMs provide an efficient and cost-effective solution for automating genetic variant evidence extraction.
- AutoPM3 significantly reduces the time required for variant interpretation, thereby accelerating rare disease diagnosis.
- The developed method has the potential to improve diagnostic yields and streamline clinical workflows for rare genetic disorders.
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