Genetic Variants in STAT3 Promoter Regions and Their Application in Molecular Breeding for Body Size Traits in Qinchuan Cattle
Bibliographic record
Abstract
Signal transducer and activator of transcription 3 (STAT3) plays a critical role in leptin-mediated regulation of energy metabolism. This study investigated genetic variation in STAT3 promoter regions and verified their contribution to bovine body size traits. We first estimated the degree of conservation in STAT3, followed by measurements of its mRNA expression during fetal and adult stages of Qinchuan cattle. We then sequenced the STAT3 promoter region to determine genetic variants and evaluate their association with body size traits. From fetus to adult, STAT3 expression increased significantly in muscle, fat, heart, liver, and spleen tissues (p < 0.01), but decreased in the intestine, lung, and rumen (p < 0.01). We identified and named five single nucleotide polymorphisms (SNPs): SNP1-304A>C, SNP2-285G>A, SNP3-209A>C, SNP4-203A>G, and SNP5-188T>C. These five mutations fell significantly outside the Hardy–Weinberg equilibrium (HWE) (Chi-squared test, p < 0.05) and significantly associated with body size traits (p < 0.05). Individuals with haplotype H3H3 (CC-GG-CC-GG-CC) were larger in body size than other haplotypes. Therefore, variations in the STAT3 gene promoter regions, most notably haplotype H3H3, may benefit marker-assisted breeding of Qinchuan cattle.
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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 | 0.001 | 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".