Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic Diseases

Abstract Background Identifying causative variants is crucial for the diagnosis of rare genetic diseases. Over the past two decades, the application of genome sequencing technologies in the field has significantly improved diagnostic outcomes. However, the complexity of data analysis and interpretation continues to limit the efficiency and accuracy of these applications. Various genotype and phenotype-driven filtering and prioritization strategies are used to generate a candidate list of variants for expert curation, with the final report variants determined through knowledge-intensive and labor-intensive expert review. Despite these efforts, the current methods fall short of meeting the growing demand for accurate and efficient diagnosis of rare disease. Recent developments in large language models (LLMs) suggest that LLMs possess the potential to augment or even supplant human labor in this context. Methods In this study, we have developed Genetic Transformer (GeneT), an innovative large language model (LLM) driven approach to accelerate identification of candidate causative variants for rare genetic disease. A comprehensive evaluation was conducted between the fine-tuned large language models and four phenotype-driven methods, including Xrare, Exomiser, PhenIX and PHIVE, alongside six pre-trained LLMs (Qwen1.5-0.5B, Qwen1.5-1.8B, Qwen1.5-4B, Mistral-7B, Meta-Llama-3-8B, Meta-Llama-3-70B). This evaluation focused on performance and hallucinations. Results Genetic Transformer (GeneT) as an innovative LLM-driven approach demonstrated outstanding performance on identification of candidate causative variants, identified the average number of candidate causative variants reduced from an average of 418 to 8, achieving recall rate of 99% in synthetic datasets. Application in real-world clinical setting demonstrated the potential for a 20-fold increase in processing speed, reducing the time required to analyze each sample from approximately 60 minutes to around 3 minutes. Concurrently, the recall rate has improved from 94.36% to 97.85%. An online analysis platform iGeneT was developed to integrate GeneT into the workflow of rare genetic disease analysis. Conclusion Our study represents the inaugural application of fine-tuned LLMs for identifying candidate causative variants, introducing GeneT as an innovative LLM-driven approach, demonstrating its superiority in both simulated data and real-world clinical setting. The study is unique in that it represents a paradigm shift in addressing the complexity of variant filtering and prioritization of whole exome or genome sequencing data, effectively resolving the challenge akin to finding a needle in a haystack.