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- Title
regionalpcs improve discovery of DNA methylation associations with complex traits.
- Authors
Eulalio, Tiffany; Sun, Min Woo; Gevaert, Olivier; Greicius, Michael D.; Montine, Thomas J.; Nachun, Daniel; Montgomery, Stephen B.
- 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. Understanding gene-level methylation is key to studying diseases like Alzheimer's disease. Here, the authors introduce regionalpcs, a method that improves sensitivity by analysing methylation patterns with principal components, revealing genes linked to Alzheimer's disease risk.
- Subjects
DISEASE risk factors; LOCUS (Genetics); ALZHEIMER'S disease; GENOME-wide association studies; AMYLOID plaque
- Publication
Nature Communications, 2025, Vol 16, Issue 1, p1
- ISSN
2041-1723
- Publication type
Academic Journal
- DOI
10.1038/s41467-024-55698-6