GENETIC AND GENOMIC FACTORS INFLUENCING GESTATIONAL LENGTH IN BEEF CATTLE
Bibliographic record
Abstract
Gestation length in beef cattle is typically measured as the length of time in days from breeding to calving and is a trait under moderate genetic influence. Currently, the research surrounding gestation length variation is primarily conducted in dairy breeds because of increased use of artificial insemination (AI) in dairy operations compared with the beef industry. Identifying genetic regions and variants associated with the trait would allow for greater understanding of the variability in gestation length. This study aimed to evaluate the selection success of breeding bulls with predicted short or long gestation lengths on their offspring’s gestation lengths, birth weights and weaning weights and calving ease, and to investigate the genetic basis of gestation length. Five Hereford bulls were bred to 153 first-calf heifers using AI, and the calf records were used to determine influence of sire on economically important traits. A genetic analysis of the sires was completed to determine if there were specific variants in the coding exon sequences of six candidate genes (SIGLEC5, SIGLEC14, CTU1, FOXD2, EXOC4, ZNF613) that could be associated with gestation length. Kompetitive Allele Specific PCR (KASP) was used to genotype dams, sires, and their offspring for the c.617T>G, p.H143Q mutation identified through sequence analysis. Significant (P<0.05) effects of sire, dam breed, and calf sex were observed on measured traits. The genotype of the sire for c.617T>G also had a significant effect on gestation length, with TT sired calves gestating for 5.5 days shorter on average than GG sired calves. This study provides evidence of a potential genetic marker that could be used for gestation length prediction in Canadian beef cattle operations.
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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 | 0.000 | 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".