Identification of single nucleotide polymorphisms associated with serum acute phase proteins during the transition period in dairy cows
Bibliographic record
Abstract
The transition period is one of the most challenging phases for dairy cows. High incidences of metabolic and inflammatory disorders occur during this period due to negative energy balance and reduced immune functions. Acute phase proteins (APP), such as haptoglobin (Hp), serum amyloid A (SAA), and C-reactive protein (CRP), are important components of the innate immune response and serve as sensitive biomarkers of systemic inflammation. The current study aims to estimate heritability and identify candidate genes associated with serum levels of APP. Serum samples were collected from 700 Holstein cows during the transition period (2 wk prepartum and 2 wk postpartum). Heritability of serum APP ranged from 0.07 ± 0.07 to 0.44 ± 0.08 prepartum and 0.02 ± 0.06 to 0.30 ± 0.09 postpartum. C-reactive protein was the most heritable in both prepartum (0.44 ± 0.08) and postpartum (0.30 ± 0.09) periods. Low heritability was observed for haptoglobin at both time periods. Serum amyloid A was moderately heritable in both periods (prepartum 0.26 ± 0.08 and postpartum 0.15 ± 0.07). A GWAS was used to identify genetic variants significantly associated with serum levels of SAA, CRP, and Hp. For SAA, multiple candidate genes (72 pre- and 92 postpartum) and previously identified QTL (128 pre- and 143 postpartum) were mapped on BTA 29 within 100 kb up- and downstream of significant SNPs. For CRP, 6 significant SNPs and 16 candidate genes were identified on BTA 3, 11, and 15 at prepartum. However, no significant SNP was detected at postpartum. For Hp, 2 significant SNPs and 1 candidate gene were associated with prepartum Hp, whereas 1 significant SNP and 2 candidate genes were identified for Hp at postpartum. This study provides insight into the heritability of APP and molecular mechanisms of serum APP response, and the results could potentially be used to improve dairy cattle health through genetic selection and breeding.
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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 | 0.000 | 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".