Genome-wide associations of aortic distensibility suggest causality for aortic aneurysms and brain white matter hyperintensities.
Part of the results reproduced; minor but material deviations remained.
- ✓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). The paper is well described and was DESIGNED to be reproducible: authors deposited full GWAS+MTAG summary statistics openly (Imperial HPC repo DOI 10.14469/hpc/10653) and documented all tools (BOLT-LMM discovery, MTAG multi-trait, EPACTS-3.2.9 SHIP replication, LDSC heritability/rg, METAL meta-analysis). The intended 1:1 reproduction was the downstream LDSC pipeline on the deposited sumstats (SNP-heritability 0.10-0.41; genetic correlations; locus counts; lead-SNP lookups) - a valid third-party-tool-on-paper's-data reproduction. BLOCKER: as of 2026-06-19 the deposit is externally inaccessible because Imperial is migrating data.hpc.imperial.ac.uk -> Helix (until ~Nov 2026); the deposit is not yet on Helix and is only obtainable by email request. No public mirror (GWAS Catalog has curated lead SNPs only, fullPvalueSet=False; no Zenodo/figshare). Individual-level UKB (App 18545, N=32,590) and SHIP (N=2,787) are controlled-access. NOT attempted: primary GWAS (controlled UKB/SHIP), and the downstream LDSC reproduction (sumstats temporarily unobtainable). Re-attempt is straightforward once Helix migration completes or via «email». Annotation datasets GSE87112 (Hi-C) and GSE117715 (scRNA-seq) verified present on GEO but are out-of-scope (annotation only). Honest partial: nothing fabricated; the blocker is a transient repository migration, not missing/withdrawn data.
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: 2026-06-19 ⛓ 22ab7352a78c
✎ 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
- 2026-06-19
- 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: opusWhat is the genetic basis of aortic distensibility and area, and do these aortic traits play a causal role in aortic aneurysms and brain small vessel disease (white matter hyperintensities)?
- ★ Genome-wide association of six CMR-derived aortic traits in up to 32,590 UK Biobank participants identifies 102 loci (including 27 novel associations) for aortic distensibility and area. finding
- ★ Associated loci tag genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. finding
- ★ Functional analyses highlight four signalling pathways (TGF-β, IGF, VEGF and PDGF) associated with aortic distensibility. mechanism
- ★ Phenome-wide Mendelian randomization provides evidence for a causal role of aortic distensibility in development of aortic aneurysms. finding
- ★ Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, linking aortic traits to brain small vessel disease. finding
- Distinct sex-specific associations with aortic traits are identified. finding
- Multi-trait analysis (MTAG) across six correlated phenotypes increases loci discovery power beyond single-trait GWAS. method
- Convolutional neural networks enable automated aortic segmentation to derive quantitative aortic traits from cardiac MRI. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Genome-wide association study (single-trait, BOLT-LMM mixed model) | UK Biobank Caucasian participants | none | SNP associations with six aortic traits (AAdis, DAdis, AAmax, AAmin, DAmax, DAmin) | BOLT-LMM |
| Multi-trait genome-wide analysis (MTAG) | UK Biobank Caucasian participants | none | Combined SNP associations across six aortic traits | MTAG |
| Cardiac MRI with CNN-based automated aortic segmentation | UK Biobank participants free from known aortic disease | none | Ascending/descending aortic areas and distensibilities | Cardiac magnetic resonance imaging |
| SNP-based heritability estimation (LDSC) | UK Biobank GWAS summary statistics | none | h2SNP of aortic traits | LD score regression (LDSC) |
| eQTL/sQTL annotation | Human arterial tissue (GTEx v8) | none | cis-eQTL and sQTL effects of lead SNPs on nearby genes | GTEx v8 |
| Phenome-wide Mendelian randomization (MR-PheWAS) and multivariable MR | UK Biobank / GWAS summary data | none (genetic instruments) | Causal estimates of aortic distensibility on aneurysms and white matter hyperintensities | — |
| Co-expression network analysis | Aortic tissue/transcriptomic data | none | Co-expression modules associated with aortic traits | — |
- – 95 significant loci identified across six traits in stage 1 single-trait GWAS 95 loci
- ▲ MTAG increased ascending aortic distensibility significant loci from 10 to 26 and descending from 7 to 13; total across traits reached 102 10→26; 7→13; 102 total
- ▼ AAdis distensibility strongly negatively correlated with age r=-0.552
- ▼ DAdis distensibility strongly negatively correlated with age r=-0.539
- ▲ Maximum and minimum aortic areas strongly correlated (phenotypic and genotypic) r=0.99 (ascending); r=0.98 (descending)
- – 24 of 38 distensibility-associated loci had lead SNPs that were significant eQTLs/sQTLs in arterial tissue 24/38
- ▲ Strong lead SNP association at ELN locus for AAdis beta=0.077, p=4.78E-62
- ▲ Height and weight correlated more strongly with AAmax than BMI r=0.383 (height), 0.391 (weight) vs 0.210 (BMI)
- correlation r = -0.552 (AAdis vs age, p<0.001)
- correlation r = -0.539 (DAdis vs age, p<0.001)
- correlation r = 0.99 (phenotypic), rg = 0.99 (genotypic) (ascending aorta max vs min area)
- correlation rg = 0.45 (genotypic correlation ascending vs descending distensibility, p<9.25E-7)
- other h2SNP 0.10 to 0.41 (SNP heritability range (DAdis single trait to AAmax) via LDSC)
- other h2SNP = 0.21 (DAdis); 0.24 (AAdis) (MTAG multi-trait heritability of distensibility)
- pvalue 5 × 10^-8 (genome-wide significance threshold)
- count 32,590 (areas); 29,895 (distensibility) (GWAS sample size; 9,753,033 variants MAF>=0.01)
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 reports a genome-wide association study (GWAS) of six MRI-derived aortic traits (distensibility and areas) in up to 32,590 UK Biobank participants, using mixed-model association (BOLT-LMM) followed by a multi-trait meta-analytic boost (MTAG) combining all six phenotypes to increase locus discovery. SNP-based heritability was estimated with LD score regression (LDSC), and Pearson correlations were used to relate aortic traits to biometric variables and to each other (phenotypic and genetic correlations). Downstream, the authors describe eQTL/sQTL annotation (GTEx v8) and, per the abstract, phenome-wide and multivariable Mendelian randomization analyses linking aortic distensibility to aortic aneurysm and white matter hyperintensity outcomes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Mixed-model linear regression association (BOLT-LMM) | Single-trait GWAS of all six aortic traits (areas and distensibilities) | N = 32,590 for area traits; N = 29,895 for distensibility traits | not stated |
| Multi-trait analysis of GWAS (MTAG) | Combined analysis of all six aortic phenotypes to boost power for distensibility loci | same underlying GWAS samples (up to 32,590/29,895) | not stated |
| LD score regression (LDSC) for SNP-based heritability | Estimation of h2SNP for each of the six aortic traits | not stated beyond overall GWAS N | not stated |
| Pearson correlation (r) | Aortic traits vs biometric variables (age, height, weight, BMI) and phenotypic/genotypic correlations between aortic traits | full study cohort (up to 32,590) | not stated |
| Genetic correlation (rg) analysis | Between ascending and descending aortic traits, and between max/min areas | not stated | not stated |
| Mendelian randomization (MR-PheWAS and multivariable MR) | Causal inference between aortic distensibility and aortic aneurysm / cerebral white matter hyperintensities | not stated in provided excerpt | not stated |
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Genome-wide significance was defined using the conventional p < 5 x 10^-8 threshold as the multiple-testing correction for the GWAS.↳ Could also: A permutation-based or FDR-based approach (e.g., Benjamini-Hochberg) tailored to the actual number of independent tests in the specific dataset could also be used. — This can adapt the significance threshold to the exact LD structure and number of independent tests in a given sample, which may be more or less conservative than the fixed conventional threshold depending on the population studied.
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Trait-trait and trait-biometric relationships were assessed with Pearson correlation coefficients.↳ Could also: Spearman rank correlation or partial correlation adjusting for covariates (e.g., age, sex) could also be used. — Spearman correlation is often preferred when the linearity or normality of the relationship is uncertain, while partial correlation can isolate the association of interest from potential confounders such as age or body size.
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SNP-based heritability was estimated using LD score regression (LDSC).↳ Could also: Alternative heritability estimation methods such as GREML (implemented in GCTA) or SumHer could also be used. — These methods can provide complementary heritability estimates, and comparing across methods (which differ in their assumptions about LD and MAF-dependent architecture) is a common way to assess robustness of heritability estimates.
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The multi-trait analysis (MTAG) approach was used to boost power for distensibility loci by leveraging genetic correlation among the six aortic traits.↳ Could also: A standard multivariate GWAS method (e.g., MTAG's alternative, MOSTest, or a multi-trait mixed model) could also be applied. — Different multi-trait methods make different assumptions about genetic architecture and may identify a partially different but overlapping set of loci, which can be useful for cross-validating findings.
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Causal relationships were explored using Mendelian randomization (MR-PheWAS and multivariable MR), as noted in the abstract.↳ Could also: Sensitivity analyses such as MR-Egger, weighted median, or MR-PRESSO could also be reported alongside the primary MR estimates. — These complementary MR methods make different assumptions about pleiotropy and instrument validity, and reporting them together is a widely used way to assess the robustness of causal inference to violations of core MR assumptions.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-35922433
Paper: Francis et al. 2022, Nat Commun 13:4505. "Genome-wide associations of aortic distensibility suggest causality for aortic aneurysms and brain white matter hyperintensities." DOI 10.1038/s41467-022-32219-x · PMCID PMC9349177.
What kind of study
UK Biobank GWAS of six cardiac-MRI–derived aortic traits (ascending/descending aorta max area, min area, distensibility: AAmax, AAmin, DAmax, DAmin, AAdis, DAdis) in up to N=32,590 participants (distensibility traits N=29,895), with SHIP replication (N=2,787), multi-trait analysis (MTAG), and extensive downstream genomic analysis.
Pipelines / tools named (from Methods + Code availability)
| Tool | Role | Code link |
|---|---|---|
| BOLT-LMM v2.3.4 | discovery GWAS (UKB, mixed model) | alkesgroup.broadinstitute.org/BOLT-LMM |
| MTAG v1.0.8 | multi-trait GWAS | github.com/JonJala/mtag |
| EPACTS-3.2.9 | SHIP replication GWAS (per-cohort linear regression) | github.com/statgen/EPACTS (this is the brief's code link) |
| METAL v2011-03-25 | meta-analysis of SHIP cohorts | — |
| LDSC | SNP-heritability (h²SNP) + genetic correlations | (ldsc) |
| MAGMA / FUMA / DEPICT | gene-based & pathway annotation | — |
In scope (pipeline-derived, reproducible in principle)
Reproducible by running third-party tools on the deposited summary statistics (DOI 10.14469/hpc/10653 → Stage1_GWAS_results [10654] + MTAG results [10655]):
- SNP-heritability via LDSC — reported range 0.10 (DAdis single) → 0.41 (AAmax). (strongest 1:1 target)
- Locus counts — 102 loci across six traits (27 novel); countable by p<5e-8 + LD clumping.
- Lead-SNP effect sizes / p-values (Table 1) — direct lookup in sumstats.
- Genetic correlations (rg) between traits via LDSC.
This is a valid third-party-tool-on-paper's-data reproduction (Brief P16): the code need not be the authors' own.
Out of scope
- Primary discovery GWAS (BOLT-LMM) — needs UKB individual-level cardiac-MRI + genotype data → controlled access (UKB Application 18545).
- SHIP replication GWAS (EPACTS) — needs controlled SHIP individual data.
- Wet-lab / expression / 3D-chromatin annotation (GTEx eQTL, GSE87112 Hi-C, GSE117715 co-expression), DEPICT pathways, PheWAS — annotation/external.
- MR causal claims — need external outcome sumstats; partially feasible but secondary, and blocked by the same input gap.
Actual blocker (why this is a DROP)
The deposited summary statistics — the only in-scope input — are currently not obtainable:
data.hpc.imperial.ac.uk(DOI 10.14469/hpc/10653) 302-redirects every request to a static page: the service is being migrated to Helix (helix.imperial.ac.uk), "no longer accessible outside the Imperial network", migration until ~Nov 2026.- Deposit not yet on Helix (InvenioRDM API: absent).
- External access only by emailing «email» with the DOI.
- No public mirror: GWAS Catalog has only curated lead-SNP records
(GCST90137440–55,
fullPvalueSet=False, no FTP sumstats); no Zenodo/figshare.
→ drop_reason = data_restricted (on-request / temporarily restricted during
migration). Re-attemptable after Helix migration or via email request.
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.
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.