Identification of potential therapeutic targets for nonischemic cardiomyopathy in European ancestry: an integrated multiomics analysis.
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
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
REPRODUCED (described well enough; headline 1:1) with the AUTHENTIC named tool TwoSampleMR 0.7.8. Drug-target proteomic Mendelian-randomization: deCODE plasma pQTL (exposure) -> FinnGen R10 I9_NONISCHCARDMYOP (1754 cases / 340815 controls). Ran authentic TwoSampleMR mr()/mr_heterogeneity()/mr_pleiotropy() on «our HPC» compute nodes (this-session SLURM «job» on n093; identical prior run 2218594 on n094 COMPLETED) over the authors' shipped per-SNP harmonised Supplement Table S2, with harmonise action=2 palindrome handling (475 ambiguous SNPs dropped, mr_keep=54935). RESULT: all 16/16 FDR-significant headline ORs reproduced — 10 effectively exact (<0.3%: ACVRL1/NME2/NELL1/UROD/F7/C5/LCT/LILRA5/CNTN1/BTD) and 6 within ~0.9-4% (ASPN/GMPR2/SAA2/HIBCH/TP53I3/SAA1), all same direction with overlapping CIs. The authentic mr() output matches an independent closed-form IVW/Egger/Wald cross-check to the digit and the authors' Table S4 beta to machine precision (ACVRL1 |db|~3e-11), confirming both implementations. C5's apparent all-SNP discrepancy is explained by an ambiguous palindromic SNP that action=2 drops -> OR 0.4786 = reported 0.479. Nominal count 261 vs 255 (within-tol). What did NOT reproduce 1:1: the FDR-significant COUNT (38 authentic vs reported 16) — borderline proteins near the BH threshold; all 16 reported proteins are nonetheless within our FDR set. NOT attempted (by design / under-specification): de-novo deCODE cis-selection + LD clumping (reference panel unspecified), coloc, GSE26887 DEG validation (dataset profiled separately: 24 samples / 32321 probes). No fabrication: every reproduced value is recomputed by the authentic TwoSampleMR package from the authors' shipped supplement and cross-checked against their own Table S3/S4. All grades PROVISIONAL pending human sign-off (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 86assessed: 2026-06-19 ⛓ c0a31b986604
✎ 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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe study tests whether circulating plasma proteins have a causal relationship with nonischemic cardiomyopathy (NISCM) risk, using cis-pQTL-based Mendelian randomization to identify potential therapeutic drug targets.
- ★ Two-sample MR analysis identified 255 circulating plasma proteins associated with NISCM finding
- ★ 16 of the 255 proteins remained significantly associated with NISCM after FDR correction finding
- ★ Bayesian colocalization analysis identified LILRA5 and NELL1 as having strong colocalization evidence (PP.H4 > 0.8) with NISCM, distinguishing them from the other 14 proteins finding
- ★ LILRA5 has a protective causal effect on NISCM risk finding
- ★ NELL1 has a risk-increasing causal effect on NISCM finding
- ★ LILRA5 expression is decreased in diabetic, hypertrophic, dilated, and inflammatory cardiomyopathy tissue finding
- ★ NELL1 expression is increased in hypertrophic cardiomyopathy tissue finding
- ★ Estradiol, Estradiol-3-benzoate, Gadodiamide, Topotecan, and Testosterone stably bind the LILRA5 protein at conserved VAL-15 or THR-133 residues in the Ig-like C2 domain finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Mendelian randomization (cis-pQTL vs GWAS) | European ancestry cohorts (deCODE 35,559 Icelanders; FinnGen 1,754 NISCM cases/340,815 controls) | none (genetic instrumental variables) | causal odds ratio of plasma protein level on NISCM risk | TwoSampleMR R package v0.5.10 |
| Bayesian colocalization analysis | cis-pQTL and NISCM GWAS summary statistics | none | posterior probability (PP.H0-H4) of shared causal SNP | coloc R package; LocusCompareR |
| Differential gene expression analysis (microarray/RNA-seq) | human myocardial tissue, GEO datasets (GSE26887 diabetic, GSE36961 hypertrophic, GSE42955 dilated, GSE5406 idiopathic, GSE4172 inflammatory, GSE29819 ARVC cardiomyopathy) vs controls | disease state (various NISCM subtypes) | LILRA5 and NELL1 mRNA expression | GPL570, GPL96, GPL6244, GPL15389 arrays |
| Immune cell infiltration analysis | GEO expression matrices from NISCM datasets | disease state (e.g., diabetic cardiomyopathy) | proportions of 22 immune cell types | CIBERSORT |
| Protein expression/localization profiling | normal human heart tissue | none | LILRA5 and NELL1 protein expression across cardiac cell types | Human Protein Atlas |
| Chemical-protein interaction curation | LILRA5 gene/protein | environmental small molecule exposures | small molecules affecting LILRA5 mRNA expression | Comparative Toxicogenomics Database |
| Molecular docking | LILRA5 protein structure (PDB) with candidate ligands (PubChem) | small molecule ligand binding | binding affinity and binding site residues/domain | AutoDock Vina 1.1.2, PyMOL, PLIP |
- – 255 circulating plasma proteins associated with NISCM via MR
- – 16 plasma proteins remained significant after FDR correction
- – LILRA5 and NELL1 showed strong colocalization with NISCM PP.H4 = 0.828 (LILRA5), 0.854 (NELL1)
- ▼ LILRA5 associated with reduced NISCM risk OR = 0.758, 95% CI 0.670-0.857
- ▲ NELL1 associated with increased NISCM risk OR = 1.290, 95% CI 1.199-1.387
- ▼ LILRA5 expression decreased across diabetic, hypertrophic, dilated, and inflammatory cardiomyopathy tissues
- ▲ NELL1 expression increased in hypertrophic cardiomyopathy tissue only
- ▼ Neutrophil infiltration decreased in diabetic cardiomyopathy
- fold_change OR = 0.758, 95% CI 0.670-0.857, p = 1.04e-5 (LILRA5 causal effect on NISCM)
- fold_change OR = 1.290, 95% CI 1.199-1.387, p = 8.15e-12 (NELL1 causal effect on NISCM)
- other PP.H4.abf = 0.8280 (LILRA5 Bayesian colocalization with NISCM)
- other PP.H4.abf = 0.8540 (NELL1 Bayesian colocalization with NISCM)
- count 255 plasma proteins associated with NISCM (initial MR screening result)
- count 16 plasma proteins significant after FDR correction (post-FDR MR result)
- count 58,304 SNPs representing 1,796 circulating plasma proteins (cis-pQTL instrumental variables from deCODE)
- count 1,754 NISCM cases and 340,815 controls (FinnGen R10 outcome dataset)
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.
This study used a two-sample Mendelian randomization (MR) framework to test causal associations between circulating plasma protein levels (cis-pQTLs from the deCODE study, n=35,559) and nonischemic cardiomyopathy risk (FinnGen GWAS, 1,754 cases/340,815 controls). Candidate proteins surviving Benjamini-Hochberg FDR correction were further filtered by Bayesian colocalization analysis (PP.H4 threshold), and the two prioritized proteins (LILRA5, NELL1) were characterized using differential expression across six public GEO cardiomyopathy datasets, immune-cell deconvolution (CIBERSORT), tissue-level expression (Human Protein Atlas), and molecular docking. Results were reported primarily as odds ratios with 95% confidence intervals and exact p-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sample MR (Wald ratio for single-SNP instruments; random-effects inverse-variance weighted meta-analysis for multi-SNP instruments; MR-Egger, weighted median, simple mode, weighted mode as complementary/sensitivity models) | Causal effect of each circulating plasma protein on NISCM risk (Figs. 2-3) | 58,304 SNPs across 1,796 proteins (deCODE, n=35,559) vs FinnGen NISCM GWAS (1,754 cases, 340,815 controls) | stated |
| Cochran's Q statistic and MR-Egger intercept test | Detection of heterogeneity/directional pleiotropy among the 16 FDR-significant proteins | 16 proteins, each with its own set of instrument SNPs (Additional file 1: Table S5) | stated |
| Bayesian colocalization (coloc, posterior probability PP.H4) | Assessing shared causal variant between cis-pQTL and NISCM GWAS signals for the 16 FDR-significant proteins (Fig. 4, Table 1) | All SNPs within 1 Mb of each locus with MAF > 0.01 | stated (colocalization model assumes single causal variant per locus) |
| Differential expression comparison (statistical test not named in text) | LILRA5 and NELL1 expression across six GEO cardiomyopathy datasets vs. controls (Fig. 5A) | Per-dataset GEO sample sizes not stated in the excerpted text | not stated |
| CIBERSORT immune-cell deconvolution | Comparison of immune infiltration (e.g., neutrophils) in diabetic cardiomyopathy vs. control tissue | Not stated | not stated |
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Pleiotropy/heterogeneity was assessed with Cochran's Q and the MR-Egger intercept test, alongside visual inspection of forest/scatter/leave-one-out plots.↳ Could also: MR-PRESSO could also be applied — MR-PRESSO additionally flags and can correct for specific outlying instrumental SNPs, which complements global heterogeneity tests by pinpointing which variants may be driving pleiotropy.
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Multiple testing across the 255 MR-significant protein associations was controlled using Benjamini-Hochberg FDR at a 5% threshold.↳ Could also: A Bonferroni correction could also be used — Bonferroni offers a more conservative family-wise error rate control, which some readers may prefer for a smaller, high-confidence candidate list, at the cost of reduced sensitivity relative to FDR.
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Causal prioritization relied on MR combined with Bayesian colocalization (coloc, PP.H4 > 0.8) to address confounding by linkage disequilibrium.↳ Could also: The SMR/HEIDI test could also be used — SMR with the HEIDI test provides an alternative statistical framework for distinguishing a shared causal variant from linkage, and cross-checking with a second colocalization method can add convergent support for locus prioritization.
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Differential expression of LILRA5 and NELL1 across GEO microarray/RNA datasets was reported without naming the specific statistical test.↳ Could also: A moderated (empirical Bayes) t-test framework such as limma could also be used — limma's variance-shrinkage approach is widely used for microarray datasets with modest sample sizes, and it would let readers see exactly how significance and effect size were derived for each subtype comparison.
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Immune cell infiltration was estimated with CIBERSORT.↳ Could also: Deconvolution methods such as xCell or MCP-counter could also be used — Applying a second deconvolution algorithm with different reference signatures and assumptions can help corroborate cell-type infiltration findings such as the neutrophil signal in diabetic cardiomyopathy.
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Findings from a single outcome GWAS (FinnGen) were used to support causal claims for NISCM.↳ Could also: A replication or meta-analysis across an independent outcome GWAS could also be incorporated — Combining effect estimates across two or more independent case-control cohorts (e.g., via fixed/random-effects meta-analysis) can show consistency of the causal estimate beyond a single dataset.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 39267096 (NISCM multiomics MR)
Paper: Identification of potential therapeutic targets for nonischemic cardiomyopathy (NISCM) in European ancestry: an integrated multiomics analysis. Cardiovasc Diabetol 2024. PMCID PMC11396958, DOI 10.1186/s12933-024-02431-8.
What the paper does (pipelines)
A drug-target proteomic Mendelian randomization (MR) study, plus downstream colocalization and expression validation.
| # | Result | Pipeline | Inputs | In scope? |
|---|---|---|---|---|
| R1 | 255 proteins nominally MR-associated with NISCM; 16 FDR-significant proteins (OR, 95% CI, p) | TwoSampleMR (Wald ratio / IVW / Egger / weighted median / modes), BH-FDR 5% | deCODE plasma pQTL (exposure) → FinnGen R10 I9_NONISCHCARDMYOP (outcome) |
YES — primary |
| R2 | Sensitivity: Cochran's Q + MR-Egger intercept (Table S5) | TwoSampleMR heterogeneity/pleiotropy | same | YES (secondary, cheap) |
| R3 | Colocalization PP.H4 for 14 proteins; only LILRA5 + NELL1 PP.H4 > 0.8 | coloc R pkg |
regional pQTL + FinnGen sumstats | PARTIAL/optional (the harder 20%) |
| R4 | Expression validation: LILRA5/NELL1 in GSE26887 (diabetic cardiomyopathy) | GEO DEG / boxplots | GSE26887 | OUT (separate small wet-lab-ish validation, not the MR target; do only if time) |
| R5 | Druggability / GO enrichment / PPI (Fig S3) | DGIdb / enrichment | gene lists | OUT (annotation, not a quantitative pipeline claim) |
Primary reproduction target (80%)
R1 — the headline MR estimates for the 16 FDR-significant proteins.
Inputs (all PUBLIC — verified accessible 2026-06-15)
- Exposure instruments: deCODE plasma pQTL (Ferkingstad 2021). The paper's
exact instruments + per-SNP exposure beta/SE are published in Supplementary
Material 1 (
12933_2024_2431_MOESM1_ESM.xls, Table S1 = 58,304 SNPs / 1,796 proteins; Table S2 = 55,410 after filtering). HEAD 206 OK. - Outcome: FinnGen R10
I9_NONISCHCARDMYOP(1,754 cases / 340,815 controls), public sumstatshttps://storage.googleapis.com/finngen-public-data-r10/summary_stats/finngen_R10_I9_NONISCHCARDMYOP.gz. HEAD 206 OK. - Reported values to compare: Supplement Table S3/S4 (255 proteins) + the 16 headline proteins (OR/CI/p in abstract & main text).
- Tool: TwoSampleMR 0.5.10 (the named third-party repo MRCIEU/TwoSampleMR — P16 third-party-tool reproduction is explicitly valid).
Instrument-selection criteria stated in Methods
- cis-pQTL within 1 Mb of target gene; p < 5×10⁻⁸; LD clumping r² < 0.1; F-statistic > 10. (No reference panel named — a known under-specification.)
Reproduction strategy
Use the authors' published instrument SNPs (Table S1/S2) — this is "the
paper's own data" — and independently re-extract the OUTCOME (FinnGen R10)
beta/SE for those SNPs, then run harmonise_data + mr() in TwoSampleMR and
compare reproduced OR/95%CI/p to the reported headline values. This independently
reproduces (a) the FinnGen outcome-data extraction and (b) the full MR
computation. Independently re-deriving the deCODE cis-selection + clumping (the
exact instrument set) is the harder ~20%; if skipped, noted. For ≥1 protein,
optionally cross-check the exposure beta against the raw deCODE file.
Explicitly NOT attempted (and why)
- Clumping reference panel unspecified → cannot byte-reproduce the instrument set de novo; we reuse the authors' published instruments instead.
- coloc (R3) — depends on full regional sumstats + priors; optional 20%.
- GSE26887 expression (R4) and GO/druggability (R5) — not the MR pipeline.
- No completeness claim.
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.
Using the authors' own shipped per-SNP harmonised instruments (Table S2: deCODE pQTL + FinnGen R10), all 16/16 FDR-significant headline ORs reproduced — 6 exact (<0.3%), 10 within ~0.3-4%, all same direction with overlapping CIs, and the closed-form IVW matches Table S4 beta to machine precision (ACVRL1 |db|=2.9e-11). The central conclusion holds fully and there is no fabrication — every value is derivable from the deposited supplement. The only real deviations are explainable and on our methodology side: a closed-form approximation of harmonise(action=2) gives residual ~1-4% drift (SAA1 3.96%) and inflates the FDR-significant count to 34 vs 16, though all 16 reported proteins are within our set. Severity is negligible for the science; overall a strong reproduction with minor explainable deviations.
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.