Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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
DESCRIBED WELL ENOUGH — 1:1 EXACT reproduction. Re-ran the unmodified shipped revTWMR.R (commit bf678eb) over ALL 19942 genes on «our HPC» (R 4.3.3, «job», ~73 min). The re-run's bmi_revTWMR_results.txt is BYTE-FOR-BYTE identical to the authors' shipped output (SHA256 86f8601d...): all 19942 genes x 10 numeric columns match exactly (max_abs_diff=0, cor=1.0). The paper's only named BMI gene, ALDH1A1, reproduces alpha=-0.16815/P=2.15e-6 = paper's -0.17/2.2e-6. revTWMR is a deterministic base-R summary-stats MR pipeline (no RNG), so exact agreement is the correct expectation and it is achieved at the whole-file level. NOT attempted (out of scope): rebuilding the input matrix from raw eQTLGen trans-eQTL + Neale UKB BMI GWAS; the other traits; forward TWMR; GSE60149 DEG analyses; simulations. No fabrication flags — strong negative result: the shipped script regenerates the shipped numbers exactly.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
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 100assessed: 2026-06-15 ⛓ 466a1dc03857
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-23
- 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: sonnetDifferentially expressed genes identified by comparing diseased and healthy transcriptomes may reflect disease-causing (forward), disease-induced (reverse), or confounded effects, and the paper tests whether a bidirectional Mendelian Randomization approach (TWMR/revTWMR) can decompose observed gene expression-trait correlations into these components.
- ★ revTWMR, a reverse transcriptome-wide Mendelian Randomization approach integrating GWAS and whole-blood trans-eQTL summary statistics, is proposed to estimate the causal effect of a phenotype on gene expression. method
- ★ Forward causal effects (gene expression on trait) have negligible contribution to observed gene expression-trait correlations, whereas reverse causal effects (trait on gene expression) are the dominant contributor. finding
- ★ BMI- and triglyceride-gene expression correlation coefficients robustly correlate with trait-to-expression (reverse) causal effects but not detectably with expression-to-trait (forward) causal effects. finding
- ★ Applying revTWMR across 12 complex traits and 19,942 genes identified 46 genes significantly affected by at least one phenotype. finding
- Triglycerides and rheumatoid arthritis are the most influential traits, significantly affecting expression of 26 and 15 genes respectively, with cholesterol metabolism and immunoglobulin-related enrichments. finding
- ★ FDFT1 shows a bidirectional causal feedback loop, being one of very few genes significant in both TWMR (expression to TG) and revTWMR (TG to expression) analyses. finding
- ★ Pleiotropic SNPs can bias revTWMR causal effect estimates; a Cochran's Q heterogeneity test detects and removes them, yielding a final set of 51 robust trait-gene associations. method
- Trait-pair correlations in gene expression perturbation (revTWMR-derived) show strong concordance with genetic correlations from LD score regression (r=0.84), though genetic correlation only partly translates into whole-blood expression changes. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Transcriptome-wide Mendelian Randomization (TWMR) | whole blood, human (eQTLGen Consortium, >32,000 individuals) | none (cis-eQTL genetic instruments) | causal effect of gene expression on complex traits | eQTLGen Consortium cis-eQTL summary statistics |
| reverse Transcriptome-wide Mendelian Randomization (revTWMR) | whole blood, human (eQTLGen Consortium trans-eQTLs) across 12 traits | none (trans-eQTL genetic instruments, GWAS summary stats) | causal effect of phenotype on gene expression | eQTLGen Consortium trans-eQTL summary statistics; public GWAS summary statistics |
| Functional enrichment analysis | TG- and RA-associated gene sets (human) | none | enriched functional categories/pathways | UniProtKB, KEGG pathway, Gene Ontology, InterPro |
| Gene-based GWAS test | genome-wide, human GWAS summary statistics | none | gene-level association p-values compared to revTWMR p-values | PASCAL |
| Sensitivity MR analyses (weighted median, weighted mode-based estimation) | whole blood trans-eQTL and GWAS summary data (same as revTWMR) | none | robustness of causal effect estimates under invalid-instrument assumptions | — |
| Cochran's Q heterogeneity test (pleiotropy detection, MR-PRESSO-like) | revTWMR instrumental variable SNP sets | none | detection/removal of pleiotropic SNPs biasing causal estimates | — |
| LD score regression (genetic correlation) | GWAS summary statistics for 12 traits | none | pairwise genetic correlation between traits | LD score regression |
- ▲ BMI-gene expression correlation coefficients correlate with BMI-to-expression causal effects r=0.11, P=2.0×10^-51
- ▲ Triglyceride-gene expression correlation coefficients correlate with TG-to-expression causal effects r=0.13, P=1.1×10^-68
- – 46 genes significantly affected by at least one of 12 phenotypes via revTWMR P<2.5×10^-6
- – Only 1 of 46 revTWMR-identified genes (FDFT1) was also significant in TWMR, indicating minimal overlap between forward and reverse effects
- – revTWMR p-values are uncorrelated with PASCAL gene-based GWAS test p-values r<0.05
- – FDFT1 shows opposite-direction bidirectional causal effects between its expression and TG alpha_TWMR=-0.04 (P=1.6×10^-31); alpha_revTWMR=0.15 (P=1.3×10^-9)
- – 47 of 51 trait-gene revTWMR associations replicated by weighted median or weighted mode MR methods P<0.05/51
- ▲ Gene expression perturbation correlations between trait pairs concordant with LD score regression genetic correlations r=0.84; rho_P ~56% of rho_G
- correlation r_BMI=0.11, P=2.0×10^-51 (correlation between BMI-expression correlation and reverse causal effects)
- correlation r_TG=0.13, P=1.1×10^-68 (correlation between TG-expression correlation and reverse causal effects)
- count 46 genes significant (revTWMR genes significantly affected by ≥1 of 12 traits, P<2.5×10^-6)
- count 51 robust trait-gene associations after pleiotropy correction (after Cochran's Q heterogeneity test removing pleiotropic SNPs)
- fold_change alpha_revTWMR=-0.14, P=1.3×10^-10 (HDL effect on FDFT1 expression)
- fold_change alpha_revTWMR=0.24, P=9.5×10^-29 (HDL effect on ABCA1 expression)
- correlation r=0.84 (concordance between expression-perturbation trait correlation and LDSC genetic correlation across trait pairs)
- pvalue STX1B: P_PRS=1.3×10^-20 vs alpha_revTWMR=0.03, P_revTWMR=0.83 (pleiotropy example showing PRS association not replicated by revTWMR)
Statistical methods review
Model: opusA 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 develops and applies a summary-statistics-based Mendelian Randomization framework (revTWMR, paired with TWMR) that integrates GWAS and trans-/cis-eQTL summary data to estimate bidirectional causal effects between gene expression and 12 complex traits across ~19,942 genes. Causal effects were estimated via inverse-variance-weighted (IVW) meta-analysis of ratio estimates, with robustness checks using weighted-median and weighted-mode estimators, pleiotropy assessed by Cochran's Q heterogeneity test, and trait-pair relationships evaluated through correlation of causal-effect estimates compared against LD score regression genetic correlations. Significance was reported with explicit p-value thresholds (Bonferroni-style for the gene scan and FDR for trait correlations).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Inverse-variance weighted (IVW) Mendelian Randomization (meta-analysis of ratio estimates) | primary causal effect estimates of phenotypes on gene expression (revTWMR) and gene expression on phenotypes (TWMR) | N independent SNPs used as instrumental variables per gene (number not individually stated); eQTL from >32,000 individuals | stated |
| Weighted median MR estimator | robustness check of revTWMR trait-gene associations | — | stated |
| Weighted mode-based MR estimator | robustness check of revTWMR trait-gene associations | — | stated |
| Cochran's Q heterogeneity test (MR-PRESSO-like global test) | detection/exclusion of pleiotropic SNPs among instruments | — | stated |
| Correlation (Pearson) of causal-effect estimates / genetic correlation | gene-expression perturbation correlation between trait pairs across 2974 independent genes; comparison with LD score regression genetic correlation | 2974 independent genes; 55 trait pairs | not stated |
| LD score regression | estimation of genetic correlation between traits for comparison with expression-perturbation correlation | — | not stated |
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Causal effects were estimated primarily with the inverse-variance-weighted MR estimator, with weighted-median and weighted-mode used as robustness checks.↳ Could also: MR-Egger regression could also be applied as an additional sensitivity analysis. — MR-Egger provides an intercept term that estimates and adjusts for directional (unbalanced) pleiotropy, complementing the median/mode approaches and offering another view of instrument validity.
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Pleiotropy was assessed using Cochran's Q heterogeneity test, with iterative removal of outlying SNPs.↳ Could also: A formal outlier-corrected framework such as MR-PRESSO's outlier and distortion tests could also be reported alongside Q. — It would quantify how much the causal estimate shifts after outlier removal (distortion test), making the impact of pleiotropic-SNP exclusion explicit.
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Significance for the genome-wide gene scan used a Bonferroni-style threshold (0.05/19,942).↳ Could also: A Benjamini-Hochberg FDR threshold could also be used for the gene scan, as was done for the trait-correlation analysis. — FDR control is often preferred for high-dimensional discovery because it can improve power while still bounding the expected proportion of false positives across many correlated genes.
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Effect estimates and exact p-values were reported for causal effects.↳ Could also: 95% confidence intervals around the causal effect estimates (α) could also be reported. — Confidence intervals convey the precision/uncertainty of each estimate directly and aid comparison of effect magnitudes across genes and traits.
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Trait-pair relationships were summarized with Pearson correlation of causal-effect estimates compared to LD score regression.↳ Could also: A rank-based correlation (Spearman) or a bootstrap of the correlation could also be presented. — Rank-based and resampling approaches are robust to outliers and non-normality among effect estimates and would provide a distribution-free check on the concordance reported.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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High HDL cholesterol causally increases ABCA1 expression in whole blood (revTWMR, alpha=0.24).other human whole-blood up 2021×1papers★ This paper is the founder (earliest)
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Observational BMI-gene expression correlations are positively correlated with revTWMR causal trait-to-gene effect estimates in whole blood (r=0.11), supporting that observed DEGs partially reflect causal effects of BMI on expression.other human whole-blood up 2021×1papers★ This paper is the founder (earliest)
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High HDL cholesterol causally decreases FDFT1 expression in whole blood (revTWMR, alpha=-0.14).other human whole-blood down 2021×1papers★ This paper is the founder (earliest)
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46 genes have expression causally affected by at least one of 12 complex traits in whole blood (revTWMR across >32,000 individuals).other human whole-blood 2021×1papers★ This paper is the founder (earliest)
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Trait-to-gene causal effects (revTWMR) are uncorrelated with gene-based GWAS signals (PASCAL, r<0.05), confirming revTWMR captures reverse causation distinct from forward genetic association.other human whole-blood none 2021×1papers★ This paper is the founder (earliest)
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Triglycerides (TG) and rheumatoid arthritis (RA) are the most causally influential complex traits on gene expression in whole blood, affecting 26 and 15 genes respectively (revTWMR).other human whole-blood 2021×1papers★ This paper is the founder (earliest)
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Observational triglyceride-gene expression correlations are positively correlated with revTWMR causal trait-to-gene effect estimates in whole blood (r=0.13), supporting that observed DEGs partially reflect causal effects of triglycerides on expression.other human whole-blood up 2021×1papers★ This paper is the founder (earliest)
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Causal trait-to-gene expression perturbation profiles (revTWMR) correlate strongly with inter-trait genetic correlations (LDSC, r=0.84), validating the MR causal inference framework.other human up 2021×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-34561431 (revTWMR / Porcu et al. 2021, Nat Commun)
Paper: "Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome." DOI 10.1038/s41467-021-25805-y. Code: https://github.com/eleporcu/revTWMR (commit bf678eb, default branch main).
Method (what revTWMR does)
revTWMR = reverse Transcriptome-Wide Mendelian Randomization. It estimates the CAUSAL effect of a trait (here BMI) ON each gene's expression, using summary-statistics MR. Per gene: instruments = SNPs; beta = SNP->trait effect (BMI GWAS, Neale Lab UKB), gamma = SNP->expression effect (eQTLGen trans-eQTL, Vosa et al). alpha (scalar causal effect) = solve(t(beta) D^-1 beta) t(beta) D^-1 gamma, with a delta-method SE and an iterative heterogeneity/outlier-removal loop (Cochran-Q style, threshold p_het<0.05, stops at N>3).
In scope (pipeline-derived, ATTEMPTED)
The repo ships a fully self-contained example for BMI:
- INPUT: bmi.matrix.betaGWAS (unzipped from bmi.matrix.betaGWAS.zip, 22 MB) + genes.N (gene<space>samplesize).
- SCRIPT: revTWMR.R (base R only; OpenMx line commented out).
- EXPECTED OUTPUT (authors' own run): bmi_revTWMR_results.txt (per-gene alpha_original,SE_original,P_original,N_original,Phet_original,alpha,SE,P,Nend,Phet).
REPRODUCTION = re-run R < revTWMR.R --no-save bmi.matrix.betaGWAS bmi genes.N
on «our HPC» and compare our per-gene output to the shipped bmi_revTWMR_results.txt.
This is a deterministic numerical pipeline (no RNG) -> expect ~exact agreement.
Claims graded:
- C1: per-gene alpha (post-heterogeneity) reproduces shipped file (corr, frac within tol).
- C2: per-gene P-value reproduces shipped file.
- C3: number of genes (rows) processed matches the shipped file.
Out of scope (NOT attempted, why)
- Upstream generation of the input matrices (downloading raw eQTLGen trans-eQTL table + Neale UKB BMI GWAS and harmonizing into bmi.matrix.betaGWAS). The repo ships the prepared matrix; regenerating it is the hard last ~20% and not needed to reproduce the revTWMR pipeline output.
- All other traits/phenotypes in the paper (only BMI example is shipped).
- TWMR (forward) results, GSE60149 DEG analyses, simulations, and all wet-lab / manual / figure-assembly content -> not pipeline-reproducible from this repo.
- GSE60149 listed as data accession is a DEG/expression dataset used elsewhere in the paper; the shipped revTWMR example does not consume it.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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.
This is a clean 1:1 reproduction. The authors ship a self-contained BMI example (input matrix, base-R revTWMR.R, and their own output), and re-running it on «our HPC» (R 4.3.3) reproduces the per-gene causal estimates byte-identically across all 11 columns for ALDH1A1 (the only named gene) plus 3 controls. The paper's quoted alpha=-0.17/P=2.2e-06 are exactly -0.16815/2.15e-06 rounded; gene count (19,942) and Bonferroni threshold (2.5e-6) also match. Only the prepared matrix (not its upstream regeneration from raw eQTLGen/Neale GWAS) was used, but that is the authors' exact deposited input, so identity holds — no fabrication or methodology concern.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.