Genetic architecture of collagen characteristics in beef
Bibliographic record
Abstract
The primary protein in intramuscular connective tissue is collagen, which requires prolonged cooking times to be solubilized. The objectives of this preliminary study were to estimate genetic parameters and identify genomic regions associated with unaged, aged, and total collagen, Ehrlich chromogen, and pyridinoline in beef cattle. Bayesian analyses with Gibbs sampling were implemented using BLUPF90 programs. Heritability ranged from 0.14 ± 0.12 to 0.59 ± 0.21 for unaged and aged collagen, respectively. A total of 28, 21, 22, 23, and 29 SNP windows explained more than 0.5% of the additive genetic variance for unaged, aged, and total collagen, and Ehrlich chromogen and pyridinoline, respectively. Functional analysis was performed using Ingenuity Pathway Analysis software. Genes within SNP windows were related to cell proliferation and growth, especially of tumour cells. The MTUS1 gene was found within an SNP window associated with pyridinoline, which has a direct path to the synthesis of type I collagen, the main collagen type found in meat. The results of this preliminary study may contribute to a better understanding of the genetic architecture of collagen traits in beef cattle. However, a validation study with a larger population size is essential to confirm our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".