ArticleEpigenomics2016
Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.
Article in Epigenomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors.Nature communications · 2024Article
- Epigenomic profiling of isolated blood cell types reveals highly specific B cell smoking signatures and links to disease risk.Clinical epigenetics · 2023Article
- Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes.Research square · 2023Article
- Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling.Nature communications · 2022Article
- Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Review
- Article
- Leveraging cell-specific differentially methylated regions to identify leukocyte infiltration in adipose tissue.Genetic epidemiology · 2019Article
- Immunomethylomics: A Novel Cancer Risk Prediction Tool.Annals of the American Thoracic Society · 2018Review
- Cell-type deconvolution from DNA methylation: a review of recent applications.Human molecular genetics · 2017Review
- Distinct Epigenetic Effects of Tobacco Smoking in Whole Blood and among Leukocyte Subtypes.PloS one · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
aimWe examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries. MATERIALS &
methodsWe compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.
resultsCalculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)).
conclusionPathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.
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