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regionalpcs: improved discovery of DNA methylation associations with complex traits.
Eulalio, Tiffany; Sun, Min Woo; Gevaert, Olivier; Greicius, Michael D; Montine, Thomas J; Nachun, Daniel; Montgomery, Stephen B.
Afiliación
  • Eulalio T; Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
  • Sun MW; Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
  • Gevaert O; Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
  • Greicius MD; Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA.
  • Montine TJ; Department of Pathology, Stanford University, Stanford, CA, 94305, USA.
  • Nachun D; Department of Pathology, Stanford University, Stanford, CA, 94305, USA.
  • Montgomery SB; Department of Pathology, Stanford University, Stanford, CA, 94305, USA.
bioRxiv ; 2024 May 01.
Article en En | MEDLINE | ID: mdl-38746367
ABSTRACT
We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: BioRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos