ArticleBMC bioinformatics2016
A statistical model for the analysis of beta values in DNA methylation studies.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.Epigenetics · 2026Article
- Maternal Obesity Induces DNA Methylation Changes in the Kidneys and Blood of Rodent Offspring at Adulthood.Nutrients · 2026Article
- Genome-wide DNA methylation profiling in COVID-19 positive patients reveals alterations in pathways linked to neurological dysfunction.Clinical epigenetics · 2026Article
- Promoter Methylation-Expression Coupling of Gliogenesis Genes inInternational journal of molecular sciences · 2026Article
- CoxMDS: multiple data splitting for high-dimensional mediation analysis with survival outcomes in epigenome-wide studies.Briefings in bioinformatics · 2026Article
- Methods for analysing wildlife DNA methylation data.Conservation physiology · 2026Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- RASGEF1C methylation for the distinguishment and classification of benign and malignant thyroid tumors.Clinical epigenetics · 2025Article
- cfMethylPre: deep transfer learning enhances cancer detection based on circulating cell-free DNA methylation profiling.Briefings in bioinformatics · 2025Article
- High expression of COL8A1 predicts poor prognosis and promotes EMT in papillary thyroid cancer.Endocrine connections · 2024Article
- Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions.BMC bioinformatics · 2024Article
- DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues.Nature communications · 2023Article
- Flexible copula model for integrating correlated multi-omics data from single-cell experiments.Biometrics · 2023Article
- Maternal Periconceptional Folic Acid Supplementation and DNA Methylation Patterns in Adolescent Offspring.The Journal of nutrition · 2023Article
- Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity.Science advances · 2022Article
- Low-dose hydralazine reduces albuminuria and glomerulosclerosis in a mouse model of obesity-related chronic kidney disease.Diabetes, obesity & metabolism · 2022Article
- EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses.BMC bioinformatics · 2022Article
- DNA methylation profile in beef cattle is influenced by additive genetics and age.Scientific reports · 2022Article
- Blood DNA Methylation Predicts Diabetic Kidney Disease Progression in High Fat Diet-Fed Mice.Nutrients · 2022Article
- Microarray Data Preprocessing: From Experimental Design to Differential Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M/(M+U) that are generated by Illumina's 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as M-value regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values.
resultsWe develop a statistical model for the analysis of beta values that is derived from a bivariate gamma distribution for the signal intensities M and U. By allowing for possible correlations between M and U, the proposed model explicitly takes into account the data-generating process underlying the calculation of beta values. Using simulated data and a real sample of DNA methylation data from the Heinz Nixdorf Recall cohort study, we demonstrate that the proposed model fits our data significantly better than beta regression and M-value regression.
conclusionThe proposed model contributes to an improved identification of associations between beta values and covariates such as clinical variables and lifestyle factors in epigenome-wide association studies. It is as easy to apply to a sample of beta values as beta regression and M-value regression.
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.