Evidence map›Paper›PMID 23157493›Full record

ArticleBMC genomics2012

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects.

Kristel R van Eijk, Simone de Jong, Marco P M Boks, Terry Langeveld, Fabrice Colas, Jan H Veldink, Carolien G F de Kovel, Esther Janson, Eric Strengman, Peter Langfelder and 4 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02976714 (Identification of Predictive Epigenetic Biomarkers of Lung Disease Severity in Cystic Fibrosis), which is not on this map. Cited by 135 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 1 pooled it
8.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

NCT02976714 nacompletednot on this mapstarted 2016, after this paper: background citation

Identification of Predictive Epigenetic Biomarkers of Lung Disease Severity in Cystic Fibrosis

TypeinterventionalSponsorUniversity Hospital, MontpellierRan2016 to 2021Enrolled50ConditionsCystic FibrosisArmsspontaneous sputum
3 · Its place in the literature

Who cites it

135 citing papers in PubMed, 1 synthesis or guideline pooled it, 240 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Genetic-epigenetic interactions (meQTLs) in orofacial clefts etiology.medRxiv : the preprint server for health sciences · 2025
    Article
  9. Article
  10. Article
  11. Editorial: Methylation in the human brain.Frontiers in genetics · 2025
    Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

75 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 2 countries.

Kristel R van EijkDepartment of Medical Genetics, University Medical Center Utrecht, Utrecht 3584, CG, The Netherlands.
Simone de Jong
Marco P M Boks
Terry Langeveld
Fabrice Colas
Jan H Veldink
Carolien G F de Kovel
Esther Janson
Eric Strengman
Peter Langfelder
René S Kahn
Leonard H van den Berg
Steve Horvath
Roel A Ophoff
University Medical Center Utrecht · NLUniversity of California, Los Angeles · USUtrecht University · NLLeiden University Medical Center · NL

Funding

Epigenetics and disease: the role of DNA methylation in schizophrenia susceptibilR01DA028526 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI OPHOFF, ROEL A · 2009 to 2013
$2.5M
Genetics of Gene Expression and Gene Mapping for Amytrophic Lateral SclerosisR01NS058980 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI OPHOFF, ROEL A · 2008 to 2012
$1.6M
NIDA NIH HHS DA028526NIDA NIH HHS R01 DA028526NINDS NIH HHS R01 NS058980
6 · The paper itself

Abstract

backgroundThe predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex.

resultsSystems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels.

conclusionsOur results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated.

Indexed as

Genetic VariationBlood CellsCpG IslandsDNA MethylationGene Expression RegulationGenotypeHumansLinear ModelsModels, GeneticPolymorphism, Single NucleotideTranscriptome

Identifiers

PMID23157493
PMCPMC3583143
OpenAlexW2102893853

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.