The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Blood-based profiles of DNA methylation predict the underlying distribution of cell types: A validation analysis
Open accessAbstract
The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.
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“we used established methods to estimate the proportion (Houseman et al., 2012; Koestler et al., 2013) and abundance (Horvath, 2013) of specific cell types in whole blood”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as Koestler et al., 2013
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“we were only able to generate the six cellular composition variables using the Houseman algorithm (Houseman et al., 2012; Koestler et al., 2013), which were included as covariates.”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as Koestler et al., 2013
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Self-citationCapturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
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Self-citationvan Dijk, S. J., Molloy, P. L., Varinli, H., Morrison, J. L., Mühlhäusler, B. S., EpiSCOPE, M. O., Buckley, M. J., Clark, S. J., McMillen, I. C., Noakes, M., et al. (2014) · International Journal of Obesity · doi:10.1038/ijo.2014.34 · Cited by 364
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Metadata
- Authors
- Koestler, D. C., Christensen, B. C., Karagas, M. R., Marsit, C. J., Langevin, S. M., Kelsey, K. T., Wiencke, J. K., & Houseman, E. A.
- Affiliations
- Dartmouth College (US)
- Brown University (US)
- University of California, San Francisco (US)
- Oregon State University (US)
- Corresponding author
- Houseman, E Andres
- ORCID
- Venue
- Epigenetics
- Publisher
- Informa UK Limited
- Published
- 2013-08
- Volume
- 8
- Issue
- 8
- Pages
- 816-826
- ISSN
- 1559-2294 (ISSN-L)
- 1559-2308
- PMC
- PMC3883785
- OpenAlex
- W1984689638
- Cited by
- 251
- References
- 39
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 15 · 2023: 16 · 2022: 15 · 2021: 17 · 2020: 19 · 2019: 17 · 2018: 19 · 2017: 27 · 2016: 23 · 2015: 24 · 2014: 27 · 2013: 6
- FWCI
- 11.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Epigenetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (RD83459901)
- National Institutes of Health
- National Institute of Mental Health (R01MH094609)
- National Cancer Institute (R25CA134286, R01CA126831, R01CA057494, R01CA078609)
- National Institute of Environmental Health Sciences (P20ES018175, ES018175)
- 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, monocytes, lymphocytes, granulocytes, complete blood count, cell type prediction, epigenetic biomarkers
- MeSH terms
- Blood Cell Count, Humans, Leukocytes, Lymphocytes, Monocytes, CpG Islands, DNA Methylation
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