An evaluation of the corticotropin-releasing hormone and leptin gene SNPs relative to cattle behaviour
Bibliographic record
Abstract
Temperament in cattle, defined as an animal’s response to handling by humans, had been associated with production traits such as average daily gain and meat quality, and can also be considered a welfare issue. Temperament is a stress response trait, and therefore the hypothalamic-pituitary adrenal (HPA) axis likely plays a role in determining individual animal’s responses. The purpose of this study was to examine whether there are associations between single nucleotide polymorphisms in two genes involved in both the HPA axis and growth, Corticotropin-releasing hormone (CRH) and Leptin (LEP), and various measurements of temperament in beef cattle. In this study, 400 crossbred beef steers were evaluated over three sessions using a traditional subjective score and three objective measurements of response to handling: Strain Gauge (absolute strain force, ASF), Movement Measurement Device and Exit Time (ET) as well as habituation for all measurements (session 3 values – session 1 values). Backgrounding growth and final carcass data were also collected. The steers were genotyped at three previously reported SNPs: CRH 22C>G, CRH 240C>G and LEP 73C>T by PCR-RFLP. Subsequently, the genotypes and two-way interactions between LEP and each CRH SNP were analyzed as effects on the various temperament, growth and carcass measurements. There was a significant interaction between LEP and CRH 240C>G for ASF 1, ET 3 and ET 3-1, with the LEP CC/CRH 240C>G CC genotype appearing favorable. Additionally, the LEP CC/CRH 22C>G GG genotype appears to be favorable for ASF 1. These results indicate that it may be possible for cattle producers to select for favorable temperament using these SNPs, however these results should first be validated in additional populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".