ArticleNature methods2018
Identification of differentially methylated cell types in epigenome-wide association studies.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 209 papers, 8 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
209 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- A cell-type-resolved meta-analysis reveals glial DNA methylation changes associated with aging and Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Pooled it
- DNA methylation of genes involved in lipid metabolism drives adiponectin levels and metabolic disease.Diabetologia · 2026Pooled it
- Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.PLoS pathogens · 2024Pooled it
- Altered methylation pattern in EXOC4 is associated with stroke outcome: an epigenome-wide association study.Clinical epigenetics · 2022Pooled it
- Distinct sex-specific DNA methylation differences in Alzheimer's disease.Alzheimer's research & therapy · 2022Pooled it
- Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease.Nature communications · 2022Pooled it
- A methylation study implicates the rewiring of brain neural circuits during puberty in the emergence of sex differences in depression symptoms.Journal of child psychology and psychiatry, and allied disciplines · 2022Pooled it
- A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes.Nature communications · 2020Pooled it
- Semaglutide slows epigenetic aging in a randomized trial of HIV-associated lipohypertrophy.Nature communications · 2026Trial
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- Article
- Blood cell composition reveals distinct biological interpretation of DNA methylation age and age acceleration.Genome medicine · 2026Article
- Epigenome-Wide Association Study of Abdominal Aortic Aneurysm Identifies Central Role for Inflammatory Pathways.Circulation · 2026Article
- Distinct multi-omics signatures of clinical subgroups of type 2 diabetes define heterogeneous responses to an insulin sensitizer.Nature communications · 2026Article
- DNA methylation changes following rapid maxillary expansion in children.Scientific reports · 2026Article
- Genome-wide DNA methylation analysis revealed epigenetic mechanism underlying end-stage renal disease.Nature communications · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Article
- Mapping DNA Methylation Signatures to Identify Epigenetic Variation Across Subcortical Regions of the Human Posttraumatic Stress Disorder Brain.Biological psychiatry · 2026Article
- Distinct Regulatory DNA Methylation Signatures Across Multiple Sclerosis, Neuromyelitis Optica, and Neurological Post-Acute Sequelae of COVID-19.Journal of clinical medicine · 2026Article
149 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. We validated CellDMC on in silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide association and cancer epigenome studies. CellDMC achieved over 90% sensitivity and specificity in scenarios where current state-of-the-art methods did not identify differential methylation. By applying CellDMC to an EWAS performed in buccal swabs, we identified smoking-associated differentially methylated positions occurring in the epithelial compartment, which we validated in smoking-related lung cancer. CellDMC may be useful in the identification of causal DNA-methylation alterations in disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.