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Background <p>Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures.</p> Results <p>To bridge this gap, we develop a novel large language model-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on Alzheimer's disease as a case study, we integrate epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn Alzheimer's disease -associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of Alzheimer's disease -relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing Alzheimer's disease-associated genetic variants, which show significant enrichment in disease-relevant pathways.</p> Conclusions <p>Our framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.</p>

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Predicting disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Xiaoyu Wang,
  • Tong Pan,
  • Sihan Chen,
  • Geoffrey I. Webb,
  • Yunzhe Jiang,
  • Joel Rozowsky,
  • Mark Gerstein,
  • Jiangning Song

摘要

Background

Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheimer's disease, where dysregulated histone modifications are strongly implicated in disease mechanisms. While recent advances underscore the importance of accurately identifying these modifications to elucidate their contribution to Alzheimer's disease pathology, existing computational methods remain limited by their generic approaches that overlook disease-specific epigenetic signatures.

Results

To bridge this gap, we develop a novel large language model-based deep learning framework tailored for disease-contextual prediction of histone modifications and variant effects. Focusing on Alzheimer's disease as a case study, we integrate epigenomic data from multiple patient samples to construct a comprehensive, disease-specific histone modification dataset, enabling our model to learn Alzheimer's disease -associated molecular signatures. A key innovation of our approach is the incorporation of a Mixture of Experts architecture, which effectively distinguishes between disease and healthy epigenetic states, allowing for precise identification of Alzheimer's disease -relevant epigenetic modification patterns. Our model demonstrates robust performance in disease-specific histone modification prediction, significantly outperforming existing state-of-the-art methods that lack disease context. Beyond accurate modification site prediction, our framework provides important biological insights by successfully prioritizing Alzheimer's disease-associated genetic variants, which show significant enrichment in disease-relevant pathways.

Conclusions

Our framework establishes a powerful new paradigm for epigenetic research that can be extended to other complex diseases, offering both a valuable tool for variant effect interpretation and a promising strategy for uncovering novel disease mechanisms through epigenetic profiling.