regionalpcs improve discovery of DNA methylation associations with complex traits

Tiffany Eulalio (), Min Woo Sun, Olivier Gevaert, Michael D. Greicius, Thomas J. Montine, Daniel Nachun () and Stephen B. Montgomery ()
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Tiffany Eulalio: Stanford University
Min Woo Sun: Stanford University
Olivier Gevaert: Stanford University
Michael D. Greicius: Stanford University
Thomas J. Montine: Stanford University
Daniel Nachun: Stanford University
Stephen B. Montgomery: Stanford University

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-024-55698-6

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