Potential of eight mutations for marker-assisted breeding in Chinese Lulai black pigs
Bibliographic record
Abstract
Molecular marker-assisted selection (MAS) provides an efficient tool for pig breeding. In this study, according to the literature, we selected eight effective or causal mutations from eight functional genes, including five causal mutations in PHKG1 ( rs330928088), MUC13 ( rs319699771), IGF2 ( g.3072 G> A), VRTN ( g.20311_20312ins291), and MYH3 ( XM_013981330.2:g.-1805_-1810del) genes, and three effective mutations in LIPE ( rs328830166), LEPR ( rs45435518), and MC4R ( rs81219178) genes, to investigate their potential breeding effect in 418 Lulai pigs. The linear model was used to analyse the association between mutations and intramuscular fat (IMF) content, average backfat thickness, and muscle moisture %. The results revealed that amongst the three effective mutations, only the mutation in the LEPR gene, which affects IMF deposition, was significantly associated with IMF content. However, the other molecular markers were not significantly associated with the affected traits reported in previous studies, and these mutations are ineffective for MAS in the Lulai black pig population. Therefore, causal mutations in PHKG1, IGF2, and VRTN genes, and an effective mutation in LEPR gene could be used as effective breeding makers for MAS in Lulai pigs. These results can provide helpful information for further breeding in Lulai black pigs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".