Genomic regions and signaling pathways associated with indicator traits for feed efficiency in juvenile Atlantic salmon (Salmo salar).
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.
- Nothing in this column.
- 🔴Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
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
PARTIAL (honest mix; some claims reproduce 1:1, KEGG mismatches, the GWAS half is a data-restricted drop). The paper has two computational tracks. Track A 'Genomic regions/QTL' (Figs 1-4, Table 1; GCTA mlm-loco GWAS) is NOT reproducible: the Axiom SNP genotypes are owned by AquaGen AS and 'not publicly available', phenotypes only 'on request' -> data_restricted, not attempted. Track B 'signaling pathways' (RNAseq, Fig 5, Table 2): raw fastq ARE public (E-MTAB-8305, 184 liver samples = matches paper exactly) and the pipeline tool is public (bcbio-nextgen, third-party), BUT the EdgeR trait-association regression that produces every reported number needs per-fish phenotypes (ALC, ALN, growth FW/IW, family) that are restricted -- the E-MTAB-8305 SDRF carries only placeholder metadata (body weight '+/- 20'). So a from-raw-fastq rerun on «our HPC» would yield a count matrix that cannot be tied to any reported value; per the 80/20 rule I did NOT burn compute on an ungradeable result (no SLURM jobs). Instead I reproduced the paper's FINAL, clearly-specified steps directly from the authors' OWN deposited supplementary tables: applying q<0.05 + sign-of-slope to the deposited per-gene table (Table S1, 31235 genes) reproduces the TAG counts essentially exactly (ALC 799/741 exact, ALN 900->899 off-by-one at the rounding boundary, shared 317/281 exact), and all 7 transcription factors in Table 2 match the deposited slope+q values exactly to 2 dp -- no fabrication detected in the headline RNAseq numbers. The KEGG pathway counts are the one discrepancy: counting p<0.05 rows in the deposited Table S2 gives 50/68/15/14 vs the reported 59/88/24/35 (systematically fewer) -- flagged for human review (likely analysis-versioning/filtered table, not assertable as fabrication; the qualitative claims -- Proteasome most enriched, more positive than negative pathways -- hold). NOT attempted: the GWAS/QTL track (restricted genotypes), the full bcbio-nextgen alignment/counting rerun (ungradeable without restricted phenotypes), SalMotifDB TF DB query, and KEGG kegga rerun. This is a deposited-output / internal-consistency verification, NOT a from-raw-data pipeline reproduction, and is labelled as such throughout.
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Assessment versions
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v1 current initial assessment Score 85assessed: 2026-06-15 ⛓ 5aac8a46e0dc
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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
- 2026-06-15
- 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: opusCan putative QTL and liver gene-expression signatures be identified for indicator phenotypes of growth, nitrogen/carbon metabolism, and feed efficiency in juvenile Atlantic salmon, in order to establish the genetic basis of feed-efficiency indicator traits?
- ★ A QTL for pre-smolt growth was identified on chromosome 9. finding
- ★ A QTL for carbon metabolism in the liver (ALC) was identified on chromosome 12, closely related to tank-level feed conversion ratio. finding
- ★ Isotope-derived feed-efficiency indicator traits (from 15N and 13C of muscle and growth) showed no convincing QTL, suggesting they are polygenic. finding
- ★ Fish with high carbon and nitrogen metabolism in the liver convert feed protein more efficiently, primarily due to higher expression of proteasome, lipid, and carbon metabolic pathways in liver. mechanism
- ★ Seven transcription factors associated with carbon and nitrogen metabolism were located within the identified QTL regions. finding
- Stable-isotope-based indicator traits (IFCR/IFER) enable individual-level assessment of feed efficiency usable in GWAS and marker-assisted selection. method
- Including nitrogen and carbon metabolism traits substantially improved prediction of tank-FCR (73% of variance explained vs 53-63% by growth and sampling day alone). finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Stable isotope analysis (element analysis isotope ratio mass spectrometry, atom % 13C and 15N) | Atlantic salmon (Salmo salar) juveniles, muscle/liver/adipose tissue; 2249-2280 fish per trait | diet labeled with 15N (2%) and 13C (1%) during 12-day feed conversion test | atom % 13C/15N in muscle (AMC, AMN), liver (ALC, ALN), adipose (AAC); IFCR/IFER indicators | isotope ratio mass spectrometry (Institute for Energy Technology, Kjeller, Norway) |
| SNP genotyping array / GWAS | Atlantic salmon, fin-clip DNA from 2300 fish, 23 full-sib families | none | SNP associations with growth, metabolism, and feed-efficiency indicator traits (54,200 SNPs after filtering) | AquaGen custom Axiom SNP array (Thermo Fisher/Affymetrix, 56,177 SNPs) |
| RNAseq (transcriptomic analysis) | Atlantic salmon liver, 184 fish from all families | none | gene expression associated with carbon and nitrogen metabolism in liver | — |
| Growth phenotyping (weight) | Atlantic salmon, 2281 fish in family tanks | 12-day feed conversion test, fishmeal-based diet | weight gain (WG), relative weight gain (RG), initial/final weight | — |
| DNA extraction and quantification | Atlantic salmon fin clips (20 mg) | none | DNA concentration | Sbeadex livestock kit (LGC Genomics); Nanodrop 8000 (Thermo Fisher Scientific) |
- – QTL for pre-smolt growth detected on chromosome 9
- – QTL for liver carbon metabolism (ALC) detected on chromosome 12, related to tank-FCR
- – No convincing QTL for isotope-derived muscle feed-efficiency indicator traits, indicating polygenic architecture
- ▲ Higher liver expression of proteasome, lipid, and carbon metabolic pathways in more protein-efficient fish
- – Seven transcription factors associated with carbon/nitrogen metabolism located in QTL regions
- – Growth, isotope-based indicators, and sampling day jointly explained 73% of variance in tank-FCR 73%
- – Genetic correlation of tank-FCR with muscle nitrogen/carbon metabolism indicators ~1.0 and with liver carbon metabolism (ALC) ~0.9 (prior study) rg~1.0; rg~0.9
- count 2281 (total fish in 12-day feed conversion test)
- count 54,200 SNPs (SNPs included after filtering)
- count 56,177 SNPs (SNPs on AquaGen custom Axiom array)
- pvalue 9.23×10^-7 (−log10 p = 6.03) (Bonferroni 5% genome-wide significance threshold (0.05/54,200))
- correlation rg ~1.0 (genetic correlation of tank-FCR with muscle 15N/13C metabolism indicators)
- correlation rg ~0.9 (genetic correlation of tank-FCR with liver carbon metabolism (ALC))
- other 73% vs 53-63% (variance in tank-FCR explained with vs without isotope metabolism traits)
- count 184 fish (number of fish used in RNAseq liver analysis)
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 GWAS study in Atlantic salmon (n = 2249–2280 per trait, from 2281 fish across 23 full-sib families) tested associations between 54,200 SNPs and 11 indicator traits for feed efficiency, growth, and nitrogen/carbon metabolism using a linear mixed model with a leave-one-chromosome-out genomic relationship matrix (GCTA --mlm-loco), with significance assessed via a built-in likelihood-ratio test. Genome-wide significance was set using a Bonferroni threshold (α = 0.05 / 54,200 SNPs = 9.23 × 10⁻⁷; −log10(p) = 6.03), with chromosome-wide thresholds also calculated. A separate RNA-seq analysis on 184 fish was conducted to identify genes associated with carbon and nitrogen metabolism in liver, though the RNA-seq pipeline and differential expression method are not described in the provided text excerpt. Results were visualized using Manhattan plots and QQ-plots; no allelic effect sizes or confidence intervals were reported.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Linear mixed model with likelihood-ratio test (GCTA --mlm-loco GWAS) | SNP association testing for 11 traits: WG, RG, AMC, AMN, ALC, ALN, AAC, IFCR_AMC, IFCR_AMN, IFER_AMC, IFER_AMN | 2249–2280 fish per trait (from 2281 total genotyped individuals) | not stated |
| RNA-seq differential expression analysis (method and software not specified in provided text) | Identifying genes associated with carbon and nitrogen metabolism in liver tissue | 184 fish | not stated |
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Genome-wide significance was assessed with a Bonferroni threshold, and the authors themselves note it is conservative for LD-correlated SNPs↳ Could also: A permutation-based genome-wide threshold (e.g., 1,000 phenotype permutations) or a Benjamini-Hochberg FDR correction could also be used — Permutation-based thresholds directly account for the LD correlation structure among SNPs, potentially providing better-calibrated genome-wide error rates and recovering associations that a strict Bonferroni correction would miss
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Eleven traits were analyzed in separate single-trait GWASs without any multiplicity correction across traits↳ Could also: A multi-trait GWAS (e.g., using GEMMA's multivariate LMM, MultiPhen, or mvBIMBAM) or a Bonferroni/FDR adjustment across traits could also be applied — Because several traits showed near-unity genetic correlations, a multi-trait framework jointly models correlated phenotypes, which can increase power for pleiotropic loci while naturally accounting for the cross-trait testing burden
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The GWAS model included a polygenic random effect via a GRM (--mlm-loco) but did not include tank as an explicit random effect, even though 50 fish per tank shared a common 12-day tank environment↳ Could also: Tank could be included as an additional random effect in the linear mixed model (Yi = a + bx + tankj + gi⁻ + εi) — Fish sharing the same tank may exhibit correlated residuals due to shared feeding conditions and social effects; explicitly modeling tank variance partitions this source of environmental covariance from the polygenic component and may improve precision of SNP effect estimates
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Allelic substitution effect estimates and the proportion of phenotypic or genetic variance explained by identified QTL are not reported↳ Could also: The b coefficients from the fitted model, along with the QTL variance explained (e.g., as % of total genetic variance or heritability), could also be reported for each lead SNP — Effect size metrics allow assessment of biological magnitude independently of sample size, facilitate cross-study comparison, and are directly relevant to the marker-assisted selection application the paper proposes
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The RNA-seq analysis method, normalization strategy, and differential expression tool are not described in the provided text excerpt↳ Could also: Standard RNA-seq DE workflows such as DESeq2 (negative binomial Wald test with BH FDR), edgeR (quasi-likelihood F-test), or limma-voom (moderated t-test) are widely used for this data type — Specifying the pipeline, normalization method, and FDR threshold enables reproducibility assessment and direct comparison with other transcriptomic studies of feed efficiency in salmonids
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Family structure was handled by including a genome-wide GRM as a random effect; the experimental design used only 23 families with divergent EBVs↳ Could also: A pedigree-based mixed model (using a numerator relationship matrix A rather than the genomic G) or a within-family regression approach could also control for family structure in this full-sib design — With only 23 families and strong divergent selection of parents, a within-family design or haplotype-based association test may better separate QTL effects from between-family confounding that could be incompletely captured by the GRM in a small, structured population
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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A QTL for liver carbon metabolism (atom % 13C) on chromosome 12 was associated with tank feed conversion ratio in Atlantic salmon.other atlantic-salmon-liver 2020×1papers★ This paper is the founder (earliest)
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No significant QTL were detected for isotope-derived muscle feed efficiency indicators in Atlantic salmon, indicating polygenic genetic architecture.other atlantic-salmon-muscle none 2020×1papers★ This paper is the founder (earliest)
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A QTL for pre-smolt body weight gain was detected on chromosome 9 in Atlantic salmon.other atlantic-salmon 2020×1papers★ This paper is the founder (earliest)
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Growth, isotope-based feed efficiency indicators, and sampling day jointly explained 73% of variance in tank feed conversion ratio in Atlantic salmon juveniles.other atlantic-salmon 2020×1papers★ This paper is the founder (earliest)
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Tank feed conversion ratio showed near-unity genetic correlation with muscle isotope-based feed efficiency indicators (~1.0) and high genetic correlation with liver carbon metabolism (~0.9) in Atlantic salmon.other atlantic-salmon 2020×1papers★ This paper is the founder (earliest)
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Seven transcription factors associated with carbon and nitrogen metabolism were identified within QTL regions for feed efficiency traits in Atlantic salmon.other atlantic-salmon 2020×1papers★ This paper is the founder (earliest)
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Proteasome, lipid metabolism, and carbon metabolic pathways were upregulated in liver of more protein-efficient Atlantic salmon juveniles.RNA-seq atlantic-salmon-liver up 2020×1papers★ 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.
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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-33158415
Dvergedal et al. 2020, Genet Sel Evol 52:66. "Genomic regions and signaling pathways associated with indicator traits for feed efficiency in juvenile Atlantic salmon (Salmo salar)." DOI 10.1186/s12711-020-00587-x.
The paper has two distinct computational tracks.
Track A — "Genomic regions" / QTL (Figs 1–4, Table 1) → OUT OF SCOPE (data_restricted)
- Pipeline: GCTA
--mlm-locolinear mixed-model GWAS over 54,200 Axiom SNPs vs 12 phenotypes; Bonferroni −log10(p)=6.03 threshold; QTL on Ssa9 (growth) and Ssa12 (carbon metabolism). - Inputs are NOT public. Genotypes are "owned by AquaGen AS and used under license … not publicly available" (Availability statement). Phenotypes only "on request." No accession. → cannot be reproduced from public data.
- Not attempted. drop_reason for this track:
data_restricted.
Track B — "Signaling pathways" / RNAseq (Fig 5, Table 2) → PARTIALLY IN SCOPE
- Pipeline as described: bcbio-nextgen (trim, align to ICSASG_v2, count) on
E-MTAB-8305 → EdgeR per-gene regression of expression on the trait (ALC or
ALN), with growth (FW−IW)/FW as a covariate and family intercepts → TAG at
FDR q<0.05 → KEGG over-representation via limma
kegga→ TF annotation via SalMotifDB. - Raw fastq are public (E-MTAB-8305, 184 liver samples — matches the paper's "184 fish" exactly). The pipeline code is a public third-party tool (bcbio).
- BUT the regression's covariates — per-fish ALC, ALN, growth (FW,IW), family — are NOT public (same "on request" phenotype restriction). The E-MTAB-8305 SDRF carries only placeholder metadata (body weight = "+/- 20", genotype = "wild type", sex = "not available"); no isotope/trait/growth/family fields. → A from-raw-fastq reproduction of the reported TAG/KEGG numbers is not possible from public data (the heavy bcbio run would yield a count matrix that cannot be tied to any reported value).
What IS reproducible in Track B (no compute)
The authors deposited their per-gene regression output (Additional file 1 / Table S1: geneID, baseMean, ALC.slope, ALC.padj, ALN.slope, ALN.padj for 31,235 genes) and the KEGG pathway p-values (Additional file 2 / Table S2, 155 pathways). The paper's final, clearly-specified steps can therefore be reproduced directly from the deposited data:
- Apply q<0.05 + sign-of-slope to Table S1 → the reported TAG counts.
- Apply p<0.05 to Table S2 → the reported KEGG pathway counts.
- Cross-check the 7 transcription factors in Table 2 against Table S1.
This is an internal-consistency / deposited-output verification (and a fabrication check), not a from-raw-data pipeline reproduction. Graded and clearly labelled as such.
80/20 decision
The clearly-specified low-hanging outputs (TAG counts, Table-2 TFs, KEGG counts) are checkable from deposited data with zero compute and are done here. The hard last 20% — re-running bcbio-nextgen on 184 fastq to regenerate the count matrix — was deliberately NOT attempted: it cannot reach any reported number without the restricted phenotypes, so it would consume «our HPC» compute for an ungradeable result. No SLURM jobs were submitted.
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.
The RNAseq track reproduces well from the authors' own deposited tables: TAG counts (799/741/978, shared 317/281) and all 7 Table-2 transcription factors match Table S1 essentially 1:1, with only a single rounding off-by-one (ALN 900→899) — no fabrication evident in the headline numbers. The one real deviation is the KEGG pathway counts (59/88/24/35 reported vs 50/68/15/14 derivable from deposited Table S2), a moderate, systematic gap most likely from table versioning/filtering, with the qualitative enrichment conclusions still holding. The GWAS/QTL half is data-restricted (proprietary genotypes, on-request phenotypes) and therefore untested rather than refuted, so this is a partial but honest reproduction whose deviations sit on the data-availability and deposited-table-version side, not on a demonstrable authors' computational defect.
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.