Characterization of two Pro-opiomelanocortin gene variants and their effects on carcass traits in beef cattle
Bibliographic record
Abstract
BACKGROUND: Carcass quantity (lean meat yield) and quality (degree of marbling) in beef cattle determines much of their economic value. Consequently, it is important to study genes that are part of the appetite pathway and that may ultimately affect carcass composition. Pro-opiomelanocortin is a prohormone that codes for many different peptides, several of which are involved in the appetite pathway. A single nucleotide polymorphism (SNP) c.288C>T in pro-opiomelanocortin (POMC) has previously been associated with hot carcass weight (HCW) and shipping weight (Ship wt) in beef cattle. RESULTS: While developing a commercial real time PCR test for POMC c.288C>T a 12 bp deletion (POMC c.293_304delTTGGGGGCGCGG) was identified. The deletion results in the removal of four amino acids (a valine, two glycines, and an alanine). Both the POMC c.288C>T and the deletion were genotyped in 386 crossbred steers and evaluated for associations with carcass traits. The animals with one copy of the deletion had a significantly smaller carcass rib-eye area (7.91 cm²; P = 0.02) in comparison to homozygous normal animals. Significant associations were observed between POMC c.288C>T with start-of-finishing weight (SOF WT; P = 0.04), hot carcass weight (HCW; P = 0.02), average fat and grade fat (both P = 0.05), carcass rib-eye area (REA; P = 0.03) and marbling (P = 0.02). CONCLUSIONS: These results suggest that it could be beneficial for beef producers to know both the deletion and POMC c.288C>T genotypes when making marketing and culling decisions.
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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 | 0.000 |
| 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".