Effect of host genetics and gut microbiome on fat deposition traits in pigs
Bibliographic record
Abstract
Fat deposition affects meat quality, flavor, and production in pigs. Fat deposition is influenced by both genetics and environment. Symbiotic microbe with the host is an important environmental factor to influence fat deposition. In this study, the fat deposition traits were measured in 239 individuals obtained from Tongcheng pigs × Large White pigs resource population. The interactions between genetics and gut microbiome in fat deposition traits were investigated through whole-genome sequencing and cecum microbial 16S ribosomal RNA sequencing. The results showed that the percentage of leaf fat (PL) and intramuscular fat content (IMF) were significantly influenced by host genetics–gut microbiome interaction. The effects of interactions between host genetics and gut microbiome on PL and IMF were 0.13 and 0.29, respectively. The heritability of PL and IMF was estimated as 0.71 and 0.89, respectively. The microbiability of PL and IMF was 0.20 and 0.26, respectively. Microbiome-wide association analysis (MWAS) revealed Anaeroplasma, Paraprevotella, Pasteurella, and Streptococcus were significantly associated with PL, and Sharpea and Helicobacter exhibited significant association with IMF (p < 0.05). Furthermore, Paraprevotella was also identified as a critical microbe affecting PL based on the divergent Wilcoxon rank-sum test. Overall, this study reveals the effect of host genetics and gut microbiome on pig fat deposition traits and provides a new perspective on the genetic improvement of pig fat deposition traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".