Trans-ethnic association study of blood pressure determinants in over 750,000 individuals.
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓No authors-side cause for any deviation
- 🔴Could not use the authors’ exact input data
- 🔴Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
This paper has a computational component, but its primary data is legally or ethically access-restricted — identifiable patient cohorts, rare-disease genomes, or controlled-access biobanks that cannot be openly shared. The reproduction therefore could not be attempted. That is a neutral verdict: it does not mean the result is wrong or that the authors fell short — only that, for legitimate privacy reasons, it cannot be independently checked from public data. We deliberately do NOT assign a 0–100 score here, because a low number would wrongly read as a failed reproduction.
▸Reproduction agent’s raw note
DROP (data_restricted). Paper: 'Trans-ethnic association study of blood pressure determinants in over 750,000 individuals' (PMID 30578418, doi 10.1038/s41588-018-0303-9) is a multi-cohort trans-ethnic GWAS meta-analysis of systolic/diastolic/pulse blood pressure. The pipeline-derived results (per-variant association statistics, trans-ethnic meta-analysis, novel-locus discovery) require INDIVIDUAL-LEVEL genotype + phenotype data from controlled/application-only cohorts — primarily UK Biobank (application-only, MTA) and the Million Veteran Program (dbGaP controlled-access) plus consortium cohorts. That input is not publicly obtainable, so the end-to-end association pipeline cannot be re-run for reproduction. The link-mined code artifact (github.com/gabraham/flashpca) is only a sub-component (fast PCA for ancestry/population-structure covariates), not the GWAS/meta-analysis pipeline, and the link-mined data accession (GEO GSE107585, a kidney single-nucleus RNA-seq dataset) is a text-mining/enrichment false positive — it is not the GWAS input and cannot pin any reported BP-association value. NOT ATTEMPTED: no «our HPC» compute submitted, because no public input data exists to feed a faithful reproduction; fabricating a result to avoid the drop is explicitly disallowed by the brief. Published GWAS summary statistics may exist downstream, but re-deriving them is the restricted-data step. Verdict provisional for human audit.
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 assessmentassessed: 2026-06-16 ⛓ 8cb8426af00b
✎ 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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusCan a trans-ethnic, multi-omic analysis of blood pressure in over 750,000 individuals reinterpret the genetic architecture of blood pressure to identify novel genes, tissues, phenome, and medication contexts underlying blood pressure homeostasis?
- ★ Discovery and replication GWAS of SBP, DBP and pulse pressure in up to 776,078 individuals identified 208 novel common blood pressure SNPs and 53 rare variants. finding
- ★ A transcriptome-wide association study detected 4,043 blood pressure associations with genetically-predicted expression of 840 genes across 45 tissues. finding
- ★ Murine renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells. finding
- ★ 505 independent loci (201 novel, 304 previously reported) were associated with one or more blood pressure traits. finding
- ★ Ten rare missense variants from seven genes (PDE3A, SLC9A3R2, RRAS, PHC3, DBH, COL21A1, NOX4) were associated with blood pressure traits. finding
- ★ Trans-ethnic meta-analysis combined with TWAS, PheWAS of genetic risk scores, pathway/tissue enrichment and gene-drug analyses constitutes the multi-omic framework for blood pressure homeostasis. method
- Effect-size correlations between white, black, and Hispanic groups in MVP were weaker than previously reported, though directions of effect were largely consistent. finding
- Most blood pressure SNPs are noncoding and reside in regulatory elements, motivating gene-based imputed-expression association tests. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| GWAS common variant meta-analysis (single variant, MAF >1%) | Human; MVP + UK Biobank discovery, ICBP + BioVU replication | none | SBP, DBP and pulse pressure association (mmHg per allele) | — |
| GWAS rare exonic variant analysis (MAF <1%) | Human; MVP discovery, BioVU + BP-ICE exome chip replication | none | blood pressure trait association of missense variants | exome chip |
| Genome-wide rare variant analysis | Human; MVP discovery, UK Biobank replication | none | rare variant blood pressure association | — |
| Transcriptome-wide association study (TWAS / genetically-predicted gene expression) | Human; 45 tissues | none | association of genetically-predicted gene expression with blood pressure (840 genes) | — |
| Phenome-wide association study (PheWAS) | Human; MVP | blood pressure genetic risk scores | clinical phenome associations | — |
| Single-cell RNA sequencing | Mouse (murine) kidney | none | cell-type-specific expression of blood pressure genes (kidney tubule cells) | — |
| Pathway and tissue gene set enrichment analysis | Human | none | enriched pathways and tissues | — |
| Conditional analysis of rare variants on sentinel common variants | Human; MVP whites discovery sample | none | effect-size change after conditioning | — |
- – 208 novel common blood pressure SNPs and 53 rare variants discovered
- – 4,043 blood pressure associations with genetically-predicted expression of 840 genes across 45 tissues
- ▲ Blood pressure genes upregulated in murine kidney tubule cells
- – 505 independent loci identified (201 novel, 304 previously reported); replicated novel loci included 124 SBP, 4 DBP, 123 pulse pressure
- – Novel loci had smaller per-allele effects (0.24, 0.14, 0.18 mmHg for SBP, DBP, PP) than known loci (0.32, 0.27, 0.27 mmHg) 0.24/0.14/0.18 vs 0.32/0.27/0.27 mmHg per allele
- – Sentinel SNPs at all loci explained 3.56%, 1.06%, 3.72% of variance for SBP, DBP, pulse pressure; novel variants contributed 0.80%, 0.24%, 0.72% 3.56%/1.06%/3.72%
- – Ten missense variants from seven genes associated with blood pressure; mean absolute effects 1.52, 0.63, 1.50 mmHg per allele for SBP, DBP, PP 1.52/0.63/1.50 mmHg per allele
- ▼ rs139491786 (SLC9A3R2) showed >50% effect reduction after conditioning on common variant rs140869992 >50% reduction; r2=0.35
- count 776,078 participants (up to) (total GWAS sample for SBP, DBP, pulse pressure)
- count 459,777 meta-analyzed in discovery (318,891 MVP + 140,886 UKB) (discovery sample)
- count 316,301 replication participants (ICBP + BioVU) (common variant replication)
- count 445,360 UKB replication for genome-wide rare variants; BP-ICE Nmax 420,704 (rare variant replication)
- pvalue meta-analysis P < 5 × 10−8 (genome-wide significance threshold)
- fold_change 0.32, 0.27, 0.27 mmHg per allele (known loci) (mean effect of trait-increasing alleles SBP/DBP/PP)
- other r2 = 0.35 (LD between rs139491786 and rs140869992 in SLC9A3R2)
- other 13% of annual deaths worldwide attributable to elevated blood pressure (background burden estimate)
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.
This is a large trans-ethnic genome-wide association meta-analysis of systolic, diastolic and pulse pressure in up to ~776,078 participants, using a discovery (MVP + UK Biobank, N=459,777) and independent replication (ICBP, BioVU, BP-ICE) design for common and rare variants. Single-variant association testing was combined via fixed-effect-style meta-analysis, with locus novelty and replication defined by pre-specified P-value, distance, and linkage-disequilibrium thresholds; conditional analyses identified independent secondary signals, and downstream transcriptome-wide association, PheWAS, pathway/tissue enrichment, and single-cell expression analyses were performed. Results were reported primarily as per-allele effect estimates (mmHg) and P-values, with variance-explained summaries for the identified loci.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Single-variant genome-wide association test (per-trait, genome-wide significance threshold P < 5 × 10⁻⁸) | Discovery and meta-analysis of common variants for SBP, DBP and pulse pressure | up to 459,777 discovery; up to 776,078 combined as stated | not stated |
| Meta-analysis combining discovery and replication association statistics | Common-variant replication (ICBP/BioVU) and rare-variant replication (UKB, BP-ICE) | 316,301 common-variant replication; up to 420,704 (BP-ICE) and 445,360 (UKB) rare-variant replication | not stated |
| Conditional (single-variant conditional) association analysis | Identification of conditionally independent secondary signals and conditioning rare exonic variants on sentinel common variants in MVP whites | 318,891 (MVP discovery) as stated | not stated |
| Correlation of per-allele effect estimates across ancestry groups | Trans-ancestry comparison of known and novel loci across white, black, and Hispanic MVP samples | — | not stated |
| Transcriptome-wide / gene-based association of genetically predicted gene expression with blood pressure | TWAS detecting 4,043 associations for 840 genes across 45 tissues | — | not stated |
| Phenome-wide association study (PheWAS) of blood pressure genetic risk scores; pathway and tissue gene-set enrichment analyses | Blood pressure clinical phenome in MVP and downstream enrichment analyses | — | not stated |
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Replication and significance were defined using fixed P-value thresholds (e.g., discovery P < 1 × 10⁻⁶, replication P < 0.05, combined P < 5 × 10⁻⁸).↳ Could also: Reporting effect estimates with 95% confidence intervals alongside the threshold-based decisions. — Confidence intervals convey the precision and plausible range of each per-allele effect, which complements threshold-based significance and aids cross-study comparison.
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Cross-ancestry consistency was assessed by correlating per-allele effect estimates across white, black, and Hispanic groups.↳ Could also: A formal trans-ethnic meta-analysis framework (e.g., MANTRA or a random-effects/heterogeneity model such as Cochran's Q or I²). — Such methods explicitly model and quantify between-ancestry effect heterogeneity, adding a formal test statistic to accompany the descriptive effect-size correlations.
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Variant-level multiplicity was handled via the conventional genome-wide threshold of 5 × 10⁻⁸.↳ Could also: Reporting a study-specific false discovery rate (e.g., Benjamini-Hochberg) for the families of downstream tests (TWAS, PheWAS, enrichment). — An FDR summary across the many downstream comparisons would provide an additional, explicitly stated control on the expected proportion of false positives within each analysis family.
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Independent secondary signals were identified using single-variant conditional analysis.↳ Could also: Approximate conditional and joint analysis (e.g., GCTA-COJO) or fine-mapping approaches that yield credible sets. — These approaches can jointly model multiple signals and provide posterior probabilities or credible intervals for likely causal variants, adding resolution to the independent-signal characterization.
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Descriptive cohort characteristics were summarized as mean (standard deviation).↳ Could also: Also presenting medians with interquartile ranges for skewed measures. — Median/IQR can convey the distribution shape for variables that may be non-normal, complementing the mean and SD already 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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Sentinel SNPs explained 3.56%/1.06%/3.72% of variance in SBP/DBP/PP.other human 2018×1papers★ This paper is the founder (earliest)
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505 independent blood pressure loci identified (201 novel, 304 known) via trans-ethnic GWAS.other human 2018×1papers★ This paper is the founder (earliest)
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Genetically-predicted expression of 840 genes across 45 tissues associated with blood pressure (TWAS).other human 2018×1papers★ This paper is the founder (earliest)
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Ten missense variants in seven genes associated with blood pressure (mean effects 1.52/0.63/1.50 mmHg for SBP/DBP/PP).other human 2018×1papers★ This paper is the founder (earliest)
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SLC9A3R2 rare variant rs139491786 effect reduced >50% after conditioning on common variant rs140869992.other human down 2018×1papers★ This paper is the founder (earliest)
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Blood pressure genes upregulated in murine kidney tubule cells.scRNA-seq mouse kidney up 2018×1papers★ This paper is the founder (earliest)
Citation network
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Data lineage
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What was reproduced
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Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a data_restricted drop: the trans-ethnic BP GWAS meta-analysis (PMID 30578418) depends on individual-level genotype/phenotype data from controlled-access cohorts (UK Biobank, Million Veteran Program) that are not publicly obtainable, so no reported association value could be put against a reproduced output. The limitation is on the data-availability side and is no-fault — controlled access is structural, not an authors' defect — so q5/q7 are graded yellow (undetermined) rather than red (fabrication-suspect). The link-mined code (flashpca, only a PCA sub-component) and data accession (GEO GSE107585, a kidney snRNA-seq false positive) are mismatched artifacts, not the real pipeline. No reproduction was attempted and none could be, so the central claim remains untested rather than refuted.
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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