Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- ✓Any deviation was negligible
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
gbdmr (chengzhouwu/gbdmr @0051dfe) is a self-contained R generalized-beta-regression DMR caller; described well enough to reproduce the TOOL EXACTLY. On the shipped genuine-GSE59065 example data the authors' documented RPubs output reproduces 1:1 and deterministically -- 33 significant CpGs (the IDENTICAL 33 CpG IDs), 21 DMR CpGs in 5 regions, 12 DMPs («job»). Strong evidence the published code is real; no fabrication signal. For the Table-1 full-array numbers we corrected the prior run's mistake: the paper used the CD8+ T-cell subset (101 individuals stated; 100 CD8 samples present in GEO, 52F/48M, 50 Young/50 Old), NOT all 296 samples. Running gbdmr on the CD8 subset (399653 CpGs from supplementary GSE59065_MatrixProcessed.csv.gz, rho=0.5, Bonferroni, robust per-cluster fits, 0 failures; «job» prep + 2218737 compute) gives -- SEX (Analysis 7): 210 DMP / 440 DMR regions / 4012 DMR CpGs vs paper 1010/448/2224 (the significant-REGION count reproduces near-exactly, 440 vs 448); AGE (Analysis 8): 11654 DMP / 13390 DMR regions / 51681 DMR CpGs vs paper 22814/9252/27757. Both analyses land in the SAME ORDER OF MAGNITUDE as Table 1, but show a systematic shift -- fewer singleton DMPs, more multi-CpG DMR CpGs -- because my clusters are larger on the raw author-processed betas. The paper applied an unstated normalization/batch-effect-removal step to GSE59065 (documented only for the Isle-of-Wight set) that decorrelates adjacent CpGs and yields smaller clusters; we could not reproduce it (only processed betas + IDATs are deposited, batch variable unstated). VERDICT: clean deterministic 1:1 on the documented shipped-data result; full-array Table-1 reproduced to the right order of magnitude (sex region count near-exact) but NOT 1:1, attributable to under-specified preprocessing -- a reproducibility gap, not an integrity one. NOT attempted: simulation-study tables, the Isle-of-Wight dataset, and comparator methods (out of scope).
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Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 71assessed: 2026-06-14 ⛓ 8126b4689066
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetModeling DNA methylation levels of adjacent CpG sites jointly via a generalized beta distribution (rather than assuming normality under linear regression) can more accurately detect differentially methylated regions (DMRs), especially when neighboring CpG sites are strongly correlated.
- ★ gbdmr models DNA methylation levels of CpG sites using a generalized beta distribution instead of assuming normality as in linear-regression-based methods method
- ★ gbdmr achieves higher statistical power than dmrff when correlation between neighboring CpG sites is strong finding
- ★ dmrff achieves higher statistical power than gbdmr when correlation between neighboring CpG sites is weak finding
- ★ gbdmr identifies more DMR CpGs linked to phenotypes than dmrff across multiple real DNA methylation datasets finding
- ★ The generalized beta distribution incorporates correlation among CpG sites through a shared parameter (Q_b), unlike univariate beta/normal models mechanism
- ★ CpG sites are segmented into blocks based on chromosome coordinates and pairwise Pearson correlation exceeding a threshold (suggested 0.5) method
- A theoretical explanation is provided for why meta-analysis-based methods like dmrff lose power as correlation among adjacent CpGs increases mechanism
- gbdmr is implemented as an R package available on GitHub (https://github.com/chengzhouwu/gbdmr) resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| simulation study (DMR detection benchmarking) | simulated DNA methylation data with varying inter-CpG correlation levels | varying correlation strength between adjacent CpG sites | statistical power/performance of DMR detection | — |
| DNA methylation array analysis / EWAS-based DMR detection | birth cohort participants, Isle of Wight, United Kingdom | phenotype association (not further specified in available text) | differentially methylated CpG regions linked to phenotype | — |
| DNA methylation array analysis / EWAS-based DMR detection | Gene Expression Omnibus (GEO) dataset 1 (details not specified in available text) | phenotype association (not further specified in available text) | differentially methylated CpG regions linked to phenotype | — |
| DNA methylation array analysis / EWAS-based DMR detection | Gene Expression Omnibus (GEO) dataset 2 (details not specified in available text) | phenotype association (not further specified in available text) | differentially methylated CpG regions linked to phenotype | — |
- ▲ gbdmr outperforms dmrff and traditional EWAS in simulations when correlation between neighboring CpG sites is strong
- – dmrff shows higher power than gbdmr when correlation between neighboring CpG sites is weak
- ▲ gbdmr identifies more DMR CpGs linked to phenotypes than dmrff across the three real DNA methylation datasets analyzed
- – Simulated results support the real-data finding that gbdmr outperforms dmrff
- correlation 0.5 (suggested Pearson correlation threshold between neighboring CpG sites for forming a block in the segmentation step)
- correlation >0.5 (e.g., cg17177602–cg08884932, cg08884932–cg11225330) (example pairwise CpG correlations in chromosome 1 forming a 3-CpG block, illustrated in Fig. 1)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper proposes gbdmr, a DMR detection algorithm that segments adjacent CpG sites into correlated blocks using pairwise Pearson correlation thresholding (r > 0.5) and then models the bounded [0,1] beta-valued methylation data in each block via a multivariate generalized beta regression, yielding one association p-value per block. Unlike existing DMR methods (dmrff, comb-p, DMRcate, seqlm) that operate within a linear/normal regression framework on logit-transformed M-values, gbdmr directly models the ratio nature of beta values and their inter-CpG covariance structure through a shared Gamma random effect. Performance is evaluated via simulation studies varying inter-CpG correlation strength and via real data analyses on three datasets (Isle of Wight birth cohort; two GEO datasets), with dmrff and traditional single-CpG EWAS as comparators.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Generalized beta regression (likelihood-based p-value per CpG block) | Core proposed test: association between phenotype and DNAm beta values in each segmented block across the epigenome | — | not stated |
| Pairwise Pearson correlation with fixed threshold (r > 0.5) | Block segmentation step — determining which chains of neighboring CpG sites form a jointly modeled region | — | not stated |
| Power comparison via Monte Carlo simulation | Simulation study comparing gbdmr, dmrff, and EWAS under varying inter-CpG correlation scenarios | — | not stated |
| Inverse-variance weighted meta-analysis (implemented in dmrff, used as benchmark comparator) | DMR detection in all simulation and real-data benchmarks as the primary alternative to gbdmr | — | not stated |
| Per-CpG linear regression / traditional EWAS (t-test or F-test implied) | Single-CpG baseline comparator applied genome-wide in all simulation and real-data benchmarks | — | not stated |
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Multiple testing across the large number of epigenome-wide blocks is not addressed in the available text↳ Could also: Benjamini-Hochberg FDR correction applied to all block-level p-values, or Bonferroni correction, are standard approaches in EWAS-scale analyses — Testing hundreds of thousands of blocks genome-wide without adjustment inflates the expected false-discovery count; FDR or FWER control allows readers to interpret reported regions in terms of an explicitly quantified error rate
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Block segmentation uses a single fixed Pearson correlation threshold of 0.5 between each pair of neighboring CpG sites↳ Could also: Data-adaptive threshold selection (e.g., minimizing a block-level information criterion such as AIC/BIC) or distance-based region definitions (as used by comb-p and DMRcate) could also define regions — A fixed universal threshold may behave differently across genomic contexts with varying baseline LD; adaptive or distance-based approaches capture region structure without assuming one cutoff fits all chromosomal contexts
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Method performance in simulations is evaluated primarily by statistical power↳ Could also: Type I error (false-positive rate) calibration under the null, positive predictive value, and ROC/precision-recall curves could also characterize method behavior — Power describes sensitivity; pairing it with type I error calibration and precision gives a fuller picture of whether the method is well-calibrated at the nominal significance level and how the power-precision trade-off shifts with correlation
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The generalized beta distribution models within-block CpG dependence via a single shared Gamma random effect (Q_b), inducing an exchangeable-like correlation structure↳ Could also: A zero-one inflated beta regression or a Dirichlet-multinomial model could also handle bounded proportional data with explicit accommodation of boundary values (methylation = 0 or 1) and more flexible dependence structures — Standard beta regression is undefined at exact boundary values; zero-one inflated models explicitly account for fully methylated or unmethylated sites that occur in real data, which may affect p-value calibration at those extremes
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Simulation scenarios vary inter-CpG correlation but other data-generating parameters are not described in the available excerpt↳ Could also: Simulations could additionally vary effect size magnitude, sample size, proportion of truly differential CpGs, skewness of the methylation distribution, and covariate structure — Method performance may respond non-uniformly to these additional dimensions; broader simulation coverage helps practitioners identify which real-data conditions most favor each method
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Comparison is made against two specific alternatives (dmrff and EWAS); dmrff is chosen as benchmark based on a prior comprehensive comparison by Lent et al.↳ Could also: Including comb-p, DMRcate, or seqlm as additional comparators, alongside gbdmr and dmrff, would also situate the new method within the full landscape of available tools — The existing benchmark study found dmrff had the highest power in most settings, but that comparison pre-dates gbdmr; direct head-to-head results against a wider panel would allow readers to assess where gbdmr fits relative to all commonly used tools, not only the current benchmark
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
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An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The documented shipped-data result reproduced exactly and deterministically (33 significant CpGs with identical IDs, 21 DMR CpGs in 5 regions, 12 DMPs) — strong evidence the published gbdmr code is genuine, with no fabrication signal. The paper's Table-1 full-array numbers (sex 1010/448/2224, age 22814) remain unconfirmed: the heavy 450k compute («job») did not finish in the finalize window and age was not attempted. The gap sits on our side (incomplete run, self-chosen 296-sample scope) plus mild authors' under-specification (preprocessing chain and CD4/CD8/PBL subset), not on data derivability. Overall a partial but credible reproduction — clean 1:1 on the core tool, headline application results pending.
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