Cell Specific eQTL Analysis without Sorting Cells.
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.
- ✓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
- 🟡Could not use the authors’ exact input 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
Described well enough to reproduce the publicly-checkable parts 1:1. The paper's core output is a multi-cohort genotype x expression INTERACTION eQTL meta-analysis; it is NOT reproducible from scratch on public data because 6/7 discovery cohorts (n=5863) are controlled-access and the one public accession (ArrayExpress E-TABM-1036 = DILGOM) is expression-only (no genotypes, no measured cell counts). What we reproduced on «our HPC»/«infra»: (C1) re-derived the headline interaction tallies directly from the shipped per-eQTL S3 Table using the paper's stated FDR<0.05 + interaction-Z-sign rule -> 13124 tested / 1117 significant / 909 neutrophil-mediated / 208 lymphocyte-mediated, ALL EXACT, and independently confirmed by the table's own 'Mediated by cell type' column (generic 12007, neutrophils 909, lymphocytes 208). (C2) re-implemented the neutrophil-proxy construction (S1's 58 HT12v3 probes -> mapped Array_Address_Id to ILMN via GPL6947 -> PCA) on the public DILGOM expression matrix: PC1 explains 64.3% of variance with all-positive coherent loadings, reproducing the premise that these probes form a single neutrophil axis. NOT attempted (and why): the from-scratch interaction-eQTL discovery (controlled raw data), purified-cell replication Fig 3 (wet-lab/controlled), proxy external-validation correlations R=0.75/0.81 (need measured counts absent from public data), and Crohn's enrichment S5 (depends on full discovery). Fabrication check: no discrepancy -- the abstract's tallies are fully internally consistent with the shipped supplementary data, verified two independent ways. Verdict provisional; human audit sheet in AUDIT.md.
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 90assessed: 2026-06-14 ⛓ 3a61b4230861
✎ 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
- 2026-06-14
- 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
- 2026-09-19
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: sonnetCell-type specific (e.g., neutrophil- or lymphocyte-specific) cis-eQTL effects can be inferred from whole blood gene expression data, without physically sorting cells, by using predicted cell-type proportions as an interaction term in a genotype-by-environment (GxE) meta-analysis.
- ★ A genotype x predicted-cell-count interaction (GxE) meta-analysis across whole blood datasets can detect neutrophil-specific cis-eQTLs without cell sorting method
- ★ The same approach can predict lymphocyte-specific cis-eQTLs finding
- ★ Predicted cell-type-specific eQTL effects replicate in independent cell-type specific datasets finding
- ★ Crohn's disease-associated SNPs preferentially affect gene expression within neutrophils, including at the NOD2 locus finding
- ★ A 58-probe expression signature, summarized via PCA, provides a proxy for neutrophil percentage in datasets lacking direct cell counts resource
- 95% of the 58 neutrophil-correlated probes show much higher expression in purified neutrophils than in 13 other purified blood cell types (BLUEPRINT RNA-seq) finding
- Neutrophils comprise ~60% of white blood cells but had no published eQTL dataset prior to this work due to purification difficulty finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| cis-eQTL GxE meta-analysis (genotype x cell-count-proxy interaction, linear model) | whole blood, human, 7 cohorts (EGCUT, InCHIANTI, Rotterdam Study, Fehrmann, SHIP-TREND, KORA F4, DILGOM) | none (natural genetic variation, interaction with inferred neutrophil proportion) | cell-type-mediated vs non-cell-type-mediated cis-eQTL effects on gene expression | Illumina HT12v3 expression array (genotyping/array platform per cohort) |
| Cell-count proxy derivation via correlation + PCA of expression probes | whole blood, human, EGCUT cohort (training) and SHIP-TREND (validation), both with actual neutrophil counts | none | neutrophil percentage proxy (first principal component of 58 probes) | Illumina HT12v3 expression array |
| RNA-seq expression comparison across purified cell types | purified neutrophils vs. 13 other purified blood cell types, BLUEPRINT epigenome project | none (cell sorting/purification) | relative expression level of the 58 neutrophil-correlated probes/genes | RNA-seq (BLUEPRINT) |
| cis-eQTL lookup/annotation | whole blood, human | none | 13,124 previously identified cis-eQTLs used as the eQTL set for cell-type-specificity testing | — |
- ▲ 58 Illumina HT12v3 probes identified as positively correlated with neutrophil percentage in EGCUT training data Spearman R > 0.57
- ▲ 95% of the 58 probes show much higher expression in purified neutrophils vs. 13 other purified blood cell types 95%
- ▲ Predicted neutrophil percentage (proxy) strongly correlates with actual neutrophil percentage in EGCUT Spearman R=0.75, Pearson R=0.76
- ▲ Predicted neutrophil percentage (proxy) strongly correlates with actual neutrophil percentage in SHIP-TREND (independent validation) Spearman R=0.81, Pearson R=0.82
- ▲ Actual and proxy neutrophil percentage both show weak positive correlation with age in EGCUT actual: Pearson R=0.08, P=0.02; proxy: Pearson R=0.14, P=6x10^-5
- – Neither actual nor proxy neutrophil percentage is associated with gender in EGCUT actual P=0.31; proxy P=0.11
- – Including more or fewer than 58 top probes gives similar proxy-to-actual correlations
- correlation Spearman R > 0.57 (threshold for selecting 58 neutrophil-correlated probes in EGCUT)
- correlation Spearman R = 0.75, Pearson R = 0.76 (proxy vs actual neutrophil percentage, EGCUT training cohort)
- correlation Spearman R = 0.81, Pearson R = 0.82 (proxy vs actual neutrophil percentage, SHIP-TREND validation cohort)
- correlation Pearson R = 0.08, P = 0.02 (actual neutrophil percentage vs age, EGCUT)
- correlation Pearson R = 0.14, P = 6x10^-5 (proxy neutrophil percentage vs age, EGCUT)
- pvalue P = 0.31 (actual), P = 0.11 (proxy) (neutrophil percentage vs gender association test, EGCUT)
- count 5,863 samples (total unrelated whole blood samples across 7 discovery cohorts)
- count 13,124 cis-eQTLs (previously discovered whole blood cis-eQTLs used as basis for cell-type-specificity testing)
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 study is a genome-environment interaction (GxE) meta-analysis across 5,683 (reported elsewhere as 5,863) unrelated whole-blood samples from seven cohorts, designed to infer cell-type-specific cis-eQTLs without sorting cells. A cell-count proxy was built from expression probes via principal component analysis and validated against measured cell counts using correlation, and cell-type specificity was tested by fitting a linear model containing a SNP-by-proxy interaction term for a predefined set of 13,124 previously discovered cis-eQTLs. Predictions were replicated in independent cell-type-specific datasets, and disease-associated SNPs were tested for enrichment of cell-type-specific effects.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Spearman rank correlation | selecting 58 probes correlated with neutrophil percentage (R>0.57) and validating the neutrophil proxy vs. actual neutrophil percentage (EGCUT R=0.75, SHIP-TREND R=0.81) | — | not stated |
| Pearson correlation | proxy/neutrophil percentage vs. age (EGCUT R=0.08, P=0.02; proxy R=0.14, P=6x10^-5) and proxy vs. actual neutrophil percentage (EGCUT R=0.76, SHIP-TREND R=0.82) | — | not stated |
| Student's t-test | testing association of neutrophil percentage and of the proxy with gender (P=0.31; P=0.11) | — | not stated |
| Linear regression with a SNP-by-cell-count-proxy interaction term | distinguishing cell-type-mediated/specific from generic cis-eQTL effects across the 13,124 cis-eQTLs | 5,683 whole blood samples (text also states 5,863) | not stated |
| Principal component analysis (first PC as proxy) | summarizing the 58 neutrophil-marker probes into a single neutrophil-percentage estimate per cohort | — | na |
| Meta-analysis across cohorts | combining the seven whole-blood cohorts to detect cell-type-specific interaction effects | — | not stated |
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Cell-type specificity was assessed by adding a single SNP-by-proxy interaction term in a linear model.↳ Could also: A formal likelihood-ratio or nested-model comparison (full model with interaction vs. reduced model without it), or a mixed-effects model accounting for cohort structure, could also be used. — An explicit nested-model test makes the contribution of the interaction term transparent and a mixed model can pool cohorts while modeling between-cohort heterogeneity directly.
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The cell-count proxy was derived as the first principal component of selected marker probes.↳ Could also: Reference-based deconvolution methods (e.g., CIBERSORT-style regression or constrained least-squares with a cell-type signature matrix) could also estimate cell-type proportions. — Reference-based deconvolution yields proportions on an interpretable scale for multiple cell types simultaneously, which can complement a single-component proxy.
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Proxy validation relied on Spearman and Pearson correlation coefficients with p-values.↳ Could also: Reporting agreement metrics such as a Bland-Altman analysis, concordance correlation, or root-mean-square error with confidence intervals could also summarize prediction quality. — Agreement statistics capture systematic bias and absolute prediction error, which correlation alone does not convey, and CIs would quantify uncertainty in the estimates.
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Probe selection used a Spearman correlation threshold (R>0.57) to pick 58 marker probes.↳ Could also: A cross-validated or penalized feature-selection approach (e.g., elastic net) on the training cohort could also define the marker set. — Cross-validation gives an out-of-sample performance estimate and penalized selection can reduce sensitivity of the proxy to the specific threshold chosen.
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Several univariate tests (correlation with age, t-test for gender) were reported with individual exact p-values.↳ Could also: A single multivariable model including age and gender together, optionally with multiple-testing adjustment across the reported associations, could also be used. — A joint model accounts for covariate correlation simultaneously, and an explicit adjustment communicates the family of comparisons considered.
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Disease-variant enrichment for cell-type-specific eQTLs is framed as a downstream test (details beyond the available excerpt).↳ Could also: Standard enrichment frameworks such as permutation-based null distributions, Fisher's exact tests with FDR control, or matched-SNP background sampling could also be applied. — These approaches provide a calibrated null that accounts for properties like minor allele frequency and gene density when assessing enrichment significance.
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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Crohn's disease GWAS loci including NOD2 show enrichment for neutrophil-specific cis-eQTL effects detected in whole-blood microarray data.microarray human whole-blood 2015×1papers★ This paper is the founder (earliest)
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Neutrophil expression proxy correlates weakly with age (Pearson R=0.14) and shows no correlation with sex.other human whole-blood mixed 2015×1papers★ This paper is the founder (earliest)
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Neutrophil proportion predicted from whole-blood expression correlates with measured neutrophil percentage in validation cohort SHIP-TREND (Spearman R=0.81).other human whole-blood up 2015×1papers★ This paper is the founder (earliest)
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95% of 58 neutrophil signature genes are specifically upregulated in purified neutrophils compared with 13 other purified blood cell types.RNA-seq human neutrophil up 2015×1papers★ 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 — pmid-25955312
Paper: Westra HJ et al. (2015) Cell Specific eQTL Analysis without Sorting Cells. PLoS Genet 11(5):e1005223. PMID 25955312 · PMCID PMC4425538 · DOI 10.1371/journal.pgen.1005223.
Code: https://github.com/molgenis/systemsgenetics (eQTL meta-analysis pipeline) — pinned d9f0387c89531c1fd31513b448498a024707f216 (master, not archived).
Data accession (brief): ArrayExpress E-TABM-1036 = DILGOM cohort, whole-blood Illumina HT12 expression, quantile-normalized matrix Files/test.tab (310 MB, ~518 samples). PUBLIC, expression-only (no genotypes, no measured cell counts).
Method (what the paper does)
Cell-type-specific eQTLs are detected with an interaction model on whole-blood data:
- base:
Y ≈ I + β1·G + e - interaction:
Y ≈ I + β1·G + β2·P + β3·(P·G) + e
where Y=expression, G=genotype, P=neutrophil-percentage proxy. The interaction term β3 (P·G) tests cell-type specificity; interaction Z-scores are meta-analyzed across cohorts (sample-size–weighted Z method). The neutrophil proxy P is built purely from expression: 58 HT12v3 probes correlated with measured neutrophil% (Spearman R>0.57 in EGCUT), then PC1 of those probes is used as the proxy phenotype.
In scope (pipeline-derived, attempted)
| id | result | pipeline step | reproducibility from public data |
|---|---|---|---|
| C1 | Aggregate interaction tallies: 13,124 cis-eQTLs tested → 1,117 significant at FDR<0.05 (909 neutrophil-mediated/positive + 208 lymphocyte-mediated/negative) | FDR threshold + sign classification applied to the shipped per-eQTL interaction table (S3 Table) | YES — re-derive from shipped S3 results; a consistency/fabrication check of the headline numbers against the data behind them |
| C2 | Neutrophil proxy is a single coherent expression axis (PC1 of the 58 S1 probes) | PCA proxy construction (S1 Table probes) applied to public DILGOM expression (E-TABM-1036) | PARTIAL — method re-run on the paper's own public cohort; PC1 variance-explained + sign coherence checkable, but external validation R=0.75/0.81 needs measured counts not in public data |
Out of scope (not attempted — and why)
- Full interaction-eQTL discovery from raw data (the actual β3 Z-scores in S3). Requires individual-level genotype + expression for 7 discovery cohorts (EGCUT, InCHIANTI, Rotterdam, Fehrmann, SHIP-TREND, KORA F4, DILGOM; n=5,863). Six are controlled-access (EGA/dbGaP/on-request); the one public accession (E-TABM-1036/DILGOM) is expression-only — no genotypes. →
data_restrictedfor the full pipeline. - Replication in purified cells (neutrophils, CD4/CD8 T, B-cells, monocytes, LCLs; Fig 3) — wet-lab–generated / controlled data. Out of scope.
- Proxy external-validation correlations R=0.75 (EGCUT)/0.81 (SHIP-TREND) — need measured neutrophil counts paired with expression; not in public E-TABM-1036. Not attempted.
- Crohn's disease enrichment (S5) — depends on the full discovery output. Not attempted.
Honest framing
The full multi-cohort genotype×expression interaction meta-analysis is not reproducible from publicly obtainable data. What is reproducible and auditable: (C1) re-deriving the reported aggregate counts from the shipped supplementary result table, and (C2) re-running the neutrophil-proxy construction method on the paper's own public DILGOM expression. This is a partial reproduction by design (80/20).
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.
A strong partial with no fabrication: the headline interaction-eQTL tallies (13124 tested -> 1117 significant = 909 neutrophil + 208 lymphocyte) re-derived exactly from the shipped S3 table two independent ways, and the neutrophil-proxy PC1 reproduced on public DILGOM (64.3% variance, coherent loadings). The limitations are data-access, not authors' defects: 6/7 discovery cohorts are controlled-access and external proxy correlations (R=0.75/0.81) need measured counts absent from public data, so the from-scratch interaction meta-analysis and Fig 3 were not re-run. q7 is graded limited because the exact tallies are an internal-consistency re-count of the authors' own output rather than an independent regeneration from genotypes.
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.