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Estimating the regulatory potential of DNA methylation in Alzheimer's disease
Author(s) -
Wang Lily,
Silva Tiago C,
Young Juan,
Martin Eden R,
Kunkle Brian W.,
Chen Xi
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.049365
Subject(s) - dna methylation , cpg site , biology , transcription factor , methylation , gene , genetics , computational biology , dna binding site , promoter , regulation of gene expression , gene expression
Background Epigenome‐wide association studies (EWAS) often detect a large number of differentially methylated CpGs, many are located far from genes, complicating the interpretation of their functionalities. Therefore, there is a critical need to better understand the functional impact of these CpGs. Recent studies demonstrated methylated CpGs can affect gene transcription by either increase or decrease transcription factor binding strengths. To prioritize significant CpGs from EWAS, an integrative analysis that assesses the impact of CpG methylation on TF regulatory activities is proposed. Method We developed a new method and software, MethReg, that analyzes matched DNA‐methylation and gene‐expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize, and annotate CpG sites with high regulatory potential. By simultaneous modeling three key elements that contribute to gene transcription (CpG methylation, target gene expression and TF activity), MethReg identifies TF‐target gene associations that are present only in a subset of samples with high (or low) methylation levels at the CpG that influences TF activities, which can be missed in analyses that use all samples. Result We performed a MethReg analysis for the ROSMAP Alzheimer's Disease (AD) dataset with DNA methylation and RNA‐seq data for 529 independent subjects. MethReg identified 60 methylation sensitive transcription factors, many of which are well‐known regulators for AD such as TCF12, SPI1, NR3C1, CEBPB, GABPA, and others. Conclusion Although many of these significant TFs have been previously implicated in AD pathology, their specific roles in transcription regulation and the identification of their targets in AD remain to be investigated. Currently available tools only identify the TFs but do not consider CpGs or provide detailed information on the relevant target genes. In contrast, MethReg fills this critical gap by nominating plausible TF‐target associations that are mediated by DNA methylation. Therefore, MethReg analysis, which leverages additional gene expression data and provides more comprehensive information on transcription regulation for the TFs, complements existing approaches.