ArticleGenetics2017
A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Epigenomic insights into common human disease pathology.Cellular and molecular life sciences : CMLS · 2024Review
- Mitochondrial dysfunction in neurodegenerative disorders.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024Review
- A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.Biometrics · 2021Article
- LuxUS: DNA methylation analysis using generalized linear mixed model with spatial correlation.Bioinformatics (Oxford, England) · 2020Article
- Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application.Briefings in bioinformatics · 2019Review
- MethylCal: Bayesian calibration of methylation levels.Nucleic acids research · 2019Article
- Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function.Genetics · 2017Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS). We also show how this approach can be leveraged to identify disease-associated changes in DNA methylation, suggesting mechanisms through which these alterations might affect disease. From a data modeling perspective, ABBA has the distinctive feature of automatically adapting to different correlation structures in CpG methylation levels across the genome while taking into account the distance between CpG sites as a covariate. Our simulation study shows that ABBA has greater power to detect DMRs than existing methods, providing an accurate identification of DMRs in the large majority of simulated cases. To empirically demonstrate the method's efficacy in generating biological hypotheses, we performed WGBS of primary macrophages derived from an experimental rat system of glomerulonephritis and used ABBA to identify >1000 disease-associated DMRs. Investigation of these DMRs revealed differential DNA methylation localized to a 600 bp region in the promoter of the
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