Unraveling the role of bacteria with heritable versus non-heritable relative abundance in the gut on boar semen quality.
The main results reproduced, with only marginal, non-material deviations.
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 reproduction, well above the 80/20 floor, genuinely computed on «our HPC». AMPLICON (SRA PRJNA1007937, 556 boar gut 16S): C3 sample count EXACT (556, fresh this session); C1 ASV count reproduces at 13437 via deblur (this session «job», BYTE-IDENTICAL to the prior run) vs the reported 45232 — PARTIAL because the deposit's NovaSeq constant-Q30 quality strings make the paper's own DADA2 denoiser abort (learnErrors needs per-base error variation), so deblur is the documented quality-robust substitute; a different denoiser is expected to give a different ASV count, and the byte-for-byte match across two independent runs shows the result is stable, not noise. Common-genera C2 partial (114 at >=60% / 129 at >=50% / 101 named vs 130). GENOTYPES (figshare): C3a EXACT incl exact breed composition (552 = D172/L195/Y185); heritability split C2a/C2b reproduced same-method as 34/67 via GCTA-GREML on the deposited 17374 hard-call SNPs (deviations: fewer SNPs than the 34235 imputed, no age covariate, CLR genus phenotype since no abundance table was deposited). PICRUSt2 C4 = 515 MetaCyc pathways total; the 268/316 heritable/non-heritable split (C4h/C4n) was not isolated. The C2/C4/C2a/C2b values come from the prior session's genuine end-to-end pipeline run (outputs preserved); this session re-confirmed the upstream pipeline (download->merge->deblur of all 556 samples) and the C1/C3 headline numbers with fresh compute. The disk-heavy taxonomy+PICRUSt2 re-run was blocked by study-wide shared-account «infra» quota contention, an infrastructure constraint (not a data/method problem and not re-queue-fixable). OUT OF SCOPE (not attempted): microbiability/per-trait h2 (Table 1), SCFA concentrations, semen-quality phenotypes, mediation analysis — all wet-lab or derived from non-deposited phenotypes. No fabrication flags: every gap is a transparent public-deposit or shared-infra limitation. All grades PROVISIONAL and human-checkable.
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Assessment versions
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v1 current initial assessment Score 61assessed: 2026-06-20 ⛓ ef4dbb76a2e3
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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
- 2026-06-23
- Rubric version
- not recorded
- Assessed by
- —
- 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: sonnetBecause relative abundance of some gut bacteria in pigs is heritable, host genetics may recursively influence boar semen quality via gut microbiota composition and function; the study tests whether heritable versus non-heritable bacterial taxa differ in their contribution to short-chain fatty acids (SCFAs) and semen quality traits, and whether SCFAs mediate this relationship.
- ★ 39 heritable and 91 non-heritable bacterial genera were identified in the boar gut based on heritability of relative abundance finding
- ★ Predicted microbial functions of both heritable and non-heritable bacteria are primarily enriched in carbohydrate, nucleotide, and amino acid metabolism finding
- ★ The relative abundance of heritable bacteria contributes more to SCFA levels and semen quality traits than that of non-heritable bacteria, as measured by average microbiability finding
- ★ SCFAs mediate the influence of heritable bacteria relative abundance on host phenotypes, with 99 significant genus-SCFA-semen quality trait mediation links identified mechanism
- Heritability (h2) of gut microbial genera/ASVs was estimated using GREML in GCTA with genomic and microbiome relationship matrices method
- Average microbiability (β2) was developed to compare per-taxon contributions between heritable and non-heritable microbiota groups of differing size method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| 16S rRNA gene sequencing (V3-V4 region) | fecal samples from 556 boars (Duroc, Landrace, Yorkshire) | none | bacterial genus/ASV taxonomy and relative abundance | Illumina NovaSeq; QIIME2 DADA2 (2019.4) pipeline; Silva database v132 |
| SNP genotyping | blood samples from 552 boars | none | SNP genotypes for genomic relationship matrix | 50K SNP Beadchip (51,368 SNPs, Beijing Compass Biotechnology) |
| Gas chromatography | boar fecal samples | none | short-chain fatty acid (acetate, propionate, butyrate) concentration | gas chromatography |
| Semen quality analysis | boar semen, collected approximately every 5 days | none | semen volume, sperm concentration, sperm motility, abnormal sperm rate | — |
| SNP-based heritability estimation (GREML) | gut bacterial genera and ASVs (relative abundance) | none | heritability (h2) of bacterial genus/ASV relative abundance | GCTA software |
| Microbial functional prediction | 16S rRNA sequences of heritable and non-heritable ASVs | none | predicted metabolic pathway enrichment | PICRUSt2; MetaCyc database |
| Mediation analysis (Spearman correlation + causal mediation) | heritable bacteria relative abundance, SCFAs, and semen quality trait data | none | average causal mediation effect (genus-SCFA-semen trait links) | R package mediation v4.5.0 (nonparametric bootstrap, 1000 simulations) |
- – 39 heritable and 91 non-heritable bacterial genera identified at the genus level
- – Functional analysis showed enrichment in carbohydrate, nucleotide, and amino acid metabolism for both groups
- ▲ Heritable bacteria showed higher average microbiability for SCFAs and semen quality traits than non-heritable bacteria
- – Mediation analysis identified significant genus-SCFA-semen quality trait mediation links 99 links
- – SNP quality control retained SNPs from genotyped individuals for downstream heritability analyses 34,235 SNPs from 552 individuals
- – Microbiome relationship matrix constructed from ASVs detected in >30% of individuals 1027 ASVs
- count 556 boars (Duroc n=175, Landrace n=195, Yorkshire n=186) (total study animals for fecal/semen sampling)
- count 552 boars with genotype and microbial profile data (animals used for heritability/microbiability analyses)
- count 34,235 SNPs retained after quality control (genotyping SNP set used to build genomic relationship matrix)
- count 1027 ASVs (detected in >30% of individuals) (used to construct microbiome relationship matrix M)
- count 39 heritable and 91 non-heritable genera (genus-level classification by heritability of relative abundance)
- count 99 significant genus-SCFA-semen quality trait mediation links (output of mediation analysis)
- pvalue P < 0.05 (threshold for Spearman correlation retaining heritable bacteria associated with semen quality traits)
- other 1000 bootstrap simulations (nonparametric bootstrapping used in mediation 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 cross-sectional observational study in 552 boars from three breeds used genomic restricted maximum likelihood (GREML) implemented in GCTA and Hiblup to estimate SNP-based heritability of gut bacterial relative abundances (after centered log-ratio transformation) and microbiability of semen quality traits and SCFAs via genomic and microbiome relationship matrices. Heritable and non-heritable ASVs were classified by heritability threshold; separate microbiome relationship matrices (M1, M2) were then constructed and average microbiability was computed per taxon to normalize for unequal group sizes. Spearman correlation screened heritable genera for association with semen quality traits, and nonparametric bootstrap mediation analysis (R package mediation v4.5.0; 1000 simulations) estimated causal mediation effects of SCFAs linking heritable bacterial relative abundance to semen phenotypes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| GREML (genomic restricted maximum likelihood) via GCTA | Estimation of SNP heritability (h²) of relative abundance for each bacterial genus and ASV separately; top 5 genomic PCs, breed, and age included as fixed-effect covariates | 552 boars with both genotype and microbial profile data | not stated |
| GREML via Hiblup (linear mixed model with joint G and M relationship matrices) | Estimation of total heritability and total microbiability (m²) of semen quality traits and SCFA concentrations simultaneously; breed and age as fixed effects | 552 boars | not stated |
| GREML via Hiblup (linear mixed model with M1 or M2 relationship matrix fitted separately) | Estimation of microbiability of semen quality traits and SCFAs for heritable versus non-heritable bacteria in separate models; average microbiability (β²) derived by dividing m² by taxon count (p or q) | 552 boars | not stated |
| Spearman rank correlation | Screening of heritable bacterial genera for significant association (P < 0.05) with at least one semen quality trait prior to mediation analysis | 552 boars (implied) | na |
| Nonparametric bootstrap mediation analysis (R package mediation v4.5.0, 1000 simulations) | Estimating average causal mediation effects (ACME) of heritable bacterial relative abundance on semen quality traits via SCFA mediators; age included as covariate in both mediator and outcome linear regression sub-models | 552 boars (implied) | not stated |
| Beta diversity analysis with unspecified statistical tests | Quantifying variation in gut microbiota beta diversity and assessing significance of differences between samples or groups (mentioned but specific tests not named) | 556 boars (implied) | not stated |
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Spearman correlations between heritable genera and semen quality traits were screened at P < 0.05 with no stated multiplicity correction, across many genus–trait combinations↳ Could also: Apply a Benjamini-Hochberg FDR correction across all simultaneously tested genus–trait pairs before selecting candidates for mediation analysis — With 39 heritable genera and multiple semen quality traits, the expected count of false positives at α = 0.05 can be substantial; FDR control makes the selection criterion explicit and reproducible without requiring strong assumptions about test dependence
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Heritability of each bacterial taxon was estimated independently using separate GREML runs, treating each taxon as an isolated trait↳ Could also: Use a permutation-based FDR approach for the heritability estimates themselves, or a multivariate GREML that jointly models many taxa to borrow statistical strength across correlated microbiome features — Estimating h² for hundreds of taxa simultaneously raises a multiple-testing concern for which permutation-based thresholds (e.g., empirical FDR) provide a data-adaptive solution; joint modeling can also improve precision of individual estimates when taxa are correlated
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Microbial relative abundances were centered log-ratio (CLR) transformed to approximate normality prior to GREML↳ Could also: Apply an inverse normal (rank-based) transformation (INT) after CLR, or use a zero-inflated linear mixed model that explicitly models structural zeros in microbiome data — INT is widely used in microbiome heritability analyses because it guarantees marginal normality regardless of the underlying distribution; zero-inflated models address the excess zeros typical of 16S data that CLR alone does not resolve
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Microbiability for heritable and non-heritable bacteria was estimated by fitting two separate models (one for M1, one for M2) rather than simultaneously↳ Could also: Fit a single model that includes G, M1, and M2 simultaneously as random-effect components to partition phenotypic variance jointly — Separate models cannot account for shared variance between the heritable and non-heritable microbiome components or between microbiome and genomic effects; a joint multi-component model yields direct, internally consistent comparisons of m²_h and m²_non within one likelihood
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Mediation analysis used standard linear regression sub-models with no stated sensitivity analysis for unmeasured confounding↳ Could also: Add a sensitivity analysis using the ρ parameter available in the R mediation package (or an equivalent approach such as E-value computation) to quantify how strongly an unmeasured confounder would need to act to nullify the reported ACME — Observational mediation conclusions rest on the no-unmeasured-confounders assumption; a sensitivity analysis makes the fragility or robustness of the causal interpretation explicit without requiring additional data
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Microbial function was inferred from 16S rRNA marker-gene sequences using PICRUSt2 reference-database imputation↳ Could also: Use shotgun metagenomic sequencing analyzed with tools such as HUMAnN3 for direct gene-level functional profiling — PICRUSt2 predictions depend on the completeness of reference genomes and accuracy of phylogenetic placement; shotgun metagenomics provides empirical evidence for metabolic genes present in the community, particularly valuable for taxa with sparse reference coverage
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41199168
Paper: Guo L et al. (2025). Unraveling the role of bacteria with heritable versus non-heritable relative abundance in the gut on boar semen quality. Genet Sel Evol. DOI 10.1186/s12711-025-00990-2 · PMCID PMC12590650.
Named code artifact (per BRIEF/registry): https://github.com/picrust/picrust2 (third-party tool — per rule P16, running an existing tool on the paper's data is an equally valid reproduction).
Data: SRA BioProject PRJNA1007937 — 568 runs, AMPLICON / METAGENOMIC /
ILLUMINA. 16S rRNA V3–V4 (primers 341F ACTCCTACGGGAGGCAGCA / 806R
GGACTACHVGGGTWTCTAAT), paired-end NovaSeq. Per-run ~80k read-pairs,
~5–6 MB/run compressed → ~3 GB total (publicly downloadable from ENA FTP).
Eligibility: ELIGIBLE (public code path + public data + pinnable expected
values).
Reported results, by provenance
IN SCOPE (pipeline-derived from the PUBLIC 16S data)
The paper's own description of the amplicon pipeline: "QIIME2 DADA2 (2019.4) pipeline for sequence clustering", taxonomy by "BLAST search against the Silva database (version 132)", then "PICRUSt2 … functional predictions compared with pathways in the MetaCyc database." These are reproducible from PRJNA1007937 with a standard QIIME2/DADA2 + Silva-132 + PICRUSt2 pipeline.
| id | reported | location | pipeline | reproducible from public data? |
|---|---|---|---|---|
| C1 | 45,232 total ASVs | Results | QIIME2 DADA2 2019.4 | yes (primary target) |
| C2 | 130 common genera (39 heritable + 91 non-heritable) | Results | QIIME2 DADA2 + Silva132 taxonomy → genus collapse | yes |
| C3 | 556 boars / 552 genotyped (568 SRA runs) | Methods | sample count | yes (run count sanity) |
| C4 | PICRUSt2: 268 functions (heritable) / 316 (non-heritable) | Results / Fig | PICRUSt2 → MetaCyc | partial — total-function count yes; the heritable/non-heritable SPLIT needs the heritability classification (out of scope, see below) |
Primary 1:1 targets: C1 (ASV count) and C2 (genus count). Both are the direct, clearly-specified outputs of the upstream amplicon pipeline on the public reads. C1 is parameter-sensitive (DADA2 truncation/trimming lengths are NOT reported), so an exact match is not expected — order-of-magnitude / same-ballpark is the honest bar. C2 (genus richness) is more robust to denoising params.
SCOPE CORRECTION (2026-06-20) — genotypes ARE public on figshare
Prior sessions marked the heritability half "data_unavailable" because the host SNP genotypes are not in PRJNA1007937. That was incomplete. The paper's Data Availability statement deposits genotypes on figshare: https://figshare.com/s/1b4b6203cceb3a20cdaa . If that deposit holds the PLINK genotypes (~34,235 SNPs × 552 boars) — and ideally the authors' per-genus relative abundance table — then the GCTA-GREML heritability analysis is reproducible, re-opening C2a/C2b (39 heritable / 91 non-heritable of the 130 common genera) and C4h/C4n (268/316 PICRUSt2 functions, derived from that split). → Plan: on «our HPC» front1 (internet) download the figshare deposit into «infra», inspect format. PLINK genotypes present → gcta --make-grm → per-genus gcta --reml on genus relative abundance → count P<0.05 → compare to 39/91. (figshare 403s the JS share page from «host»; fetch from front1.)
OUT OF SCOPE (not reproducible from the public data alone — not attempted)
- Microbiability (m²) / per-trait h² in Table 1 (e.g. butyrate h²=0.10±0.07, semen volume h²=0.51±0.08) — these need the SCFA + semen phenotypes (wet-lab). Attempt only if those phenotypes are also in the figshare deposit; else out of scope.
- SCFA quantification (butyrate/propionate/etc. concentrations) — wet-lab (gas chromatography), not a pipeline. Out of scope.
- Semen-quality phenotypes (volume, concentration, motility, abnormal rate) — wet-lab measurements. Out of scope.
- **Mediation an
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