Whole-genome sequencing association analysis reveals the genetic architecture of meat quality traits in Chinese Qingyu pigs
Bibliographic record
Abstract
The Chinese Qingyu pig breed is an invaluable indigenous genetic resource. However, few studies have investigated the genetic architecture of meat quality traits in Qingyu pigs. Here, 30 purebred Qingyu pigs were subjected to whole-genome sequencing. After quality control, 18 436 759 SNPs were retained. Genome-wide association studies (GWAS) were then performed for meat pH and color at three postmortem time points (45 min, 24 h, and 48 h) using single-marker regression analysis. In total, 11 and 69 SNPs were associated with meat pH and color of the longissimus thoracis muscle (LTM), respectively, while 54 and 29 SNPs were associated with meat pH and color of the semimembranosus muscle (SM), respectively. Seven SNPs associated with pork pH were shared by all three postmortem time points. Several candidate genes for meat traits were identified, including four genes (CXXC5, RYR3, BNIP3, and MYCT1) related to skeletal muscle development, regulation of Ca2+ release in the muscle, and anaerobic respiration, which are promising candidates for selecting superior meat quality traits in Qingyu pigs. To our knowledge, this is the first study investigating the postmortem genetic architecture of pork pH and color in Qingyu pigs. Our findings further the current understanding of the genetic factors influencing meat quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".