Identification of genetic markers associated with hyperketonemia patterns in early lactation Holstein cows
Bibliographic record
Abstract
Ketosis, evidenced by hyperketonemia with elevated blood β-hydroxybutyrate (BHB) levels, is a significant metabolic disorder of dairy cattle, typically diagnosed within the first 6 weeks post-calving when high energy levels are essential to milk production. Our study aimed to identify genetic markers linked to hyperketonemia (HYK) patterns in Holstein cows during early lactation and compare these to HYK-negative cows. We screened 964 cows for HYK using a threshold of BHB ≥1.2 mmol/L during the first 2 weeks postpartum (screening period, SP). Cows that tested negative initially were retested the following week. Cows were deemed HYK-negative (CON group) if BHB levels were below 1.2 mmol/L in both tests, while those with BHB levels exceeding this threshold at any test were treated and classified as HYK-positive (HYK+). Post-treatment, HYK+ cows were monitored for two-week follow-up period (FP) and classified based on their recovery: cured (CUR; consistently low BHB), recurrent (REC; fluctuating BHB levels), severe (SEV; high initial BHB that decreased), or chronic (CHR; persistently high BHB). Using 489 cows that were genotyped, a GWAS was conducted using GCTA software, revealing significant associations of several SNPs across different HYK patterns when compared to the CON group. These SNPs were primarily linked to genes affecting milk traits and were enriched in biological pathways relevant to protein glycosylation, inflammatory response, glucose homeostasis, and fatty acid synthesis. Our findings highlight genomic regions, potential candidate genes, and biological pathways related to ketosis, underscoring potential targets for improving health management in dairy cattle. These insights could lead to better strategies for managing ketosis through genetic selection, ultimately enhancing dairy cattle welfare and productivity. Further research with a larger number of cows is recommended to validate these findings and help confirm the implicated SNPs and genes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".