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<p>We have developed the <i>regionalpcs</i> method, an approach for summarizing gene-level methylation. <i>regionalpcs</i> addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer’s disease. In contrast to averaging, <i>regionalpcs</i> uses principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrates a 54% improvement in sensitivity over averaging in simulations, providing a robust framework for identifying subtle epigenetic variations. Applying <i>regionalpcs</i> to Alzheimer’s disease brain methylation data, combined with cell type deconvolution, we uncover 838 differentially methylated genes associated with neuritic plaque burden—significantly outperforming conventional methods. Integrating methylation quantitative trait loci with genome-wide association studies identified 17 genes with potential causal roles in Alzheimer’s disease risk, including <i>MS4A4A</i> and <i>PICALM</i>. Available in the Bioconductor package <i>regionalpcs</i>, our approach facilitates a deeper understanding of the epigenetic landscape in Alzheimer’s disease and opens avenues for research into complex diseases.</p>

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regionalpcs improve discovery of DNA methylation associations with complex traits

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月3日

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  • Tiffany Eulalio,
  • Min Woo Sun,
  • Olivier Gevaert,
  • Michael D. Greicius,
  • Thomas J. Montine,
  • Daniel Nachun,
  • Stephen B. Montgomery

摘要

We have developed the regionalpcs method, an approach for summarizing gene-level methylation. regionalpcs addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer’s disease. In contrast to averaging, regionalpcs uses principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrates a 54% improvement in sensitivity over averaging in simulations, providing a robust framework for identifying subtle epigenetic variations. Applying regionalpcs to Alzheimer’s disease brain methylation data, combined with cell type deconvolution, we uncover 838 differentially methylated genes associated with neuritic plaque burden—significantly outperforming conventional methods. Integrating methylation quantitative trait loci with genome-wide association studies identified 17 genes with potential causal roles in Alzheimer’s disease risk, including MS4A4A and PICALM. Available in the Bioconductor package regionalpcs, our approach facilitates a deeper understanding of the epigenetic landscape in Alzheimer’s disease and opens avenues for research into complex diseases.