74 Genome-wide association study for gestation length in Canadian beef cattle.
Bibliographic record
Abstract
Abstract Gestation length (GL) is an important economic trait in beef cattle due to its impact on birth weight, calving ease, and cow longevity. Both genetic and environmental factors influence GL. Identifying the genetic components of GL will aid in predicting the GL of a calf. Previous genome-wide association studies (GWAS), primarily in dairy cattle, have commonly associated GL with Bos taurus autosome (BTA) 18, though significant associations have also been identified on BTA 2-5, 7-14, 19, 21, 22, 26, and 28. The present study aimed to compare the genomes of Canadian beef sires with GL data of offspring to identify regions associated with GL. In collaboration with Canadian purebred beef producers and beef breed associations, calving records of 80,903 purebred calves (46,005 Breed A, 533 Breed B, 33,401 Breed C, and 964 Breed D) from 2,924 purebred or fullblood bulls (2,218 Breed A, 11 Breed B, 656 Breed C, and 39 Breed D), were analyzed. GLs and birth weights were adjusted using linear and exponential regression models for calf sex, dam age, and birth year. Calf GLs were significantly different (P<0.001) between the four breeds, with Breed A at a mean of 304 ± 2 days, Breed B at 285 ± 1 days, Breed C at 283 ± 1 days, and Breed D at 282 ± 1 days. Birth weights were also significantly different (P<0.001) with means of 42 ± 2 kg for Breed C, 41 ± 2 for Breed A, 40 ± 2 for Breed D, and 39 ± 2 for Breed B. The breeds with larger birth weights did not have the longest GLs. Additionally, Breeds A, B, and D showed an inverse relationship between birth weight and GL in a ten-year period. Breeds A and B decreased birth weights while slightly increasing GL. Breed D decreased GL while increasing birth weight. For the GWAS, 74 unrelated bulls (3 Breed B, 56 Breed C, and 15 Breed D) were genotyped using single nucleotide polymorphism (SNP) panels ranging from 50K to 777K SNPs and all data were imputed to 777K SNPs. 491,357 SNPs remained after imputation and quality filtering. The bulls’ phenotypes for the GWAS were their average offspring GLs, which were adjusted with a linear regression model for breed, calf sex, dam age, and birth year. Using the BLINK and MLMM models in GAPIT 3.5.0, one SNP on BTA11 was significantly associated with GL (Benjamini-Hochberg adjusted P<0.01). Contrary to prior studies, no significant markers were found on BTA 18. These results aid in further mapping of a reproductive trait of interest and may aid in selection decisions for beef cattle producers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".