Reference-free cell mixture adjustments in analysis of DNA methylation data
Open accessAbstract
Motivation: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. Results: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. Availability and implementation: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981. Contact: andres.houseman@oregonstate.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Some clinical studies using peripheral blood to examine DNA methylation changes in MDD corrected for cell composition.
“some clinical studies using peripheral blood to examine DNA methylation changes in MDD corrected for cell composition,101,102”
From Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential (2023) · cites this paper as [101]
The Introduction (page unavailable) discusses how cell mixtures can affect blood DNA methylation measurements, and Data Examples (page unavailable) describes adjusting peripheral-blood analyses for cell composition. The studies described there concern rheumatoid arthritis and other outcomes, not MDD; the source does not support the MDD-specific part of the claim. (LLM, conf=0.85)
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- Authors
- Houseman, E. A., Molitor, J., & Marsit, C. J.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Houseman, Eugene Andres
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-01-21
- Volume
- 30
- Issue
- 10
- Pages
- 1431-1439
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4016702
- OpenAlex
- W2170324063
- Cited by
- 441
- References
- 5
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 15 · 2023: 29 · 2022: 47 · 2021: 46 · 2020: 41 · 2019: 43 · 2018: 33 · 2017: 47 · 2016: 50 · 2015: 47 · 2014: 21
- FWCI
- 22.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Oregon State University
- National Institutes of Health (NIH-R01)
- National Institute of Mental Health (R01MH094609)
- National Cancer Institute (P30CA023108)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- DNA methylation, cell mixtures, epigenome-wide association study, cell type proportions, bootstrap standard errors
- MeSH terms
- Humans, Software, Gene Expression, Sequence Analysis, DNA, DNA Methylation, High-Throughput Nucleotide Sequencing
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