Identification of SNP<scp>s</scp>in Interferon Gamma, Interleukin-22, and Their Receptors and Associations with Health and Production-Related Traits in Canadian Holstein Bulls
Bibliographic record
Abstract
Genetic variants in a number of immunoregulatory genes have been previously associated with health and production traits in dairy cattle. Therefore, in the following study, the genes coding interferon gamma (IFNG), IFNG receptor 1 and 2 domains, interleukin-22 (IL22), and IL22 receptor alpha 1, were investigated for single nucleotide polymorphisms (SNPs) in Holstein bulls. These SNPs, along with SNPs previously identified in IL10, IL10 receptor, and transforming growth factor beta 1 (TGFB1) genes, were evaluated for statistical associations to estimated breeding values for milk somatic cell score (SCS), a trait highly correlated to mastitis incidence, and various production-related traits, including milk yield, protein yield, fat yield, and lactation persistency. While no significant associations were found between these SNPs and SCS, SNPs in IL10 receptor beta subunit showed a significant effect on protein yield and lactation persistency. While there is evidence that IL10 plays an important role during lactation, it is also likely that the effects of SNPs in IL10 receptor beta subunit on protein yield and lactation persistency are due to linkage disequilibrium with a neighboring QTL.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".