Investigating Genomic Methods to Improve the Productivity of Canadian Dairy Goats
Bibliographic record
Abstract
The Canadian dairy goat sector has rapidly expanded in the last decade to meet increasing market demand for goat milk products. However, the productivity of Canadian dairy goats varies and increasing production efficiency is critical to the competitiveness of both individual farming operations and the sector. Genetic selection is one proven method of increasing the production efficiency of livestock populations that has been underutilized in the Canadian dairy goat sector. This thesis explored the application of quantitative genomic methodologies to improve genetic selection for milk production and conformation traits in the Canadian Alpine and Saanen dairy goat breeds. Specifically, these studies investigated the potential benefits of incorporating genomic information in the Canadian Dairy Goat Genetic Improvement Program and provided insight into the underlying genetic architecture of these traits in Canadian dairy goat populations. The results indicate that the implementation of single-step genomic evaluations could increase theoretical accuracy of genetic evaluations for selection candidates, does without records and bucks without daughter records, by an average of 35 to 54% for milk production traits and 50 to 82% for conformation traits. Results from single- and multiple-breed analyses were similar for the milk production traits, but the use of multiple-breed analyses was beneficial for the less heritable conformation traits, especially for the Saanen breed for which fewer phenotypic records were available. Furthermore, significant single nucleotide polymorphisms (SNP) were identified, corresponding to both previously reported (e.g., CSN1S1, DGAT1, ZNF16) and novel (e.g., DCK, MOB1B, and RPL8) positional and functional candidate genes, providing greater insight into the genetic architecture of milk production and conformation traits in these populations. Overall, these results suggest that genomic methodologies can be used to improve the production efficiency of Canadian dairy goat 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".