ArticleClinical epigenetics2021
Estimands in epigenome-wide association studies.
Article in Clinical epigenetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Novel epigenetic marks of insulin resistance trajectories in a longitudinal study of childhood obesity.Cardiovascular diabetology · 2026Article
- Sjögren's patient subgroups identified through whole genome DNA methylation profiling.Arthritis research & therapy · 2026Article
- ReMeDy: A Flexible Statistical Framework For Region-based Detection of DNA Methylation Dysregulation.bioRxiv : the preprint server for biology · 2026Article
- Methods for analysing wildlife DNA methylation data.Conservation physiology · 2026Article
- Article
- Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.bioRxiv : the preprint server for biology · 2025Article
- Immediate and durable effects of maternal tobacco consumption on placental DNA methylation: a replication and discovery study.Environmental epigenetics · 2025Article
- Transcriptomics and epigenetic data integration learning module on Google Cloud.Briefings in bioinformatics · 2024Article
- Article
- Epigenetic biomarkers for smoking cessation.Addiction neuroscience · 2023Article
- Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing.Epigenetics · 2022Article
- Maternal blood pressure associates with placental DNA methylation both directly and through alterations in cell-type composition.BMC medicine · 2022Article
- Epigenetic adaptations of the masticatory mucosa to periodontal inflammation.Clinical epigenetics · 2021Article
- The relationship of smoking to cg05575921 methylation in blood and saliva DNA samples from several studies.Scientific reports · 2021Article
- External validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease.Epigenomics · 2021Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.
resultsWe present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.
conclusionsThe usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.
Indexed as
Identifiers
What OpenQuestion holds
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.