Candidate gene association analyses for ketosis resistance in Holsteins
Bibliographic record
Abstract
High-yielding dairy cattle are susceptible to ketosis, a metabolic disease that negatively affects the health, fertility, and milk production of the cow. Interest in breeding for more robust dairy cattle with improved resistance to disease is global; however, genetic evaluations for ketosis would benefit from the additional information provided by genetic markers. Candidate genes that are proposed to have a biological role in the pathogenesis of ketosis were investigated in silico and a custom panel of 998 putative single nucleotide polymorphism (SNP) markers was developed. The objective of this study was to test the associations of these new markers with deregressed estimated breeding values (EBV) for ketosis. A sample of 653 Canadian Holstein cows that had been previously genotyped with a medium-density SNP chip were regenotyped with the custom panel. The EBV for ketosis in first and later lactations were obtained for each animal and deregressed for use as pseudo-phenotypes for association analyses. Results of the mixed inheritance model for single SNP association analyses suggested 15 markers in 6 unique candidate genes were associated with the studied trait. Genes encoding proteins involved in metabolic processes, including the synthesis and degradation of fatty acids and ketone bodies, gluconeogenesis, lipid mobilization, and the citric acid cycle, were identified to contain SNP associated with ketosis resistance. This work confirmed the presence of previously described quantitative trait loci for dairy cattle, suggested novel markers for ketosis-resistance, and provided insight into the underlying biology of this disease.
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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 | 0.001 |
| 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) | 0.002 | 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".