Genetic parameters and QTL mapping for novel metabolic traits in early-lactation Holsteins
Bibliographic record
Abstract
Early lactation in dairy cows is characterized by negative energy balance and compromised immune function that could lead to metabolic or inflammatory diseases. Circulating biochemical blood variables are increasingly used as indicators of metabolic and inflammatory diseases in many species. This study aimed to estimate genetic parameters and identify candidate genes and quantitative trait loci (QTL) associated with serum proteins (total protein, albumin, globulin, and albumin-to-globulin ratio), liver enzymes (gamma-glutamyltransferase [GGT], aspartate-amino-transferase [AST], glutamate dehydrogenase [GLDH]) and other serum variables (glucose, urea, nonesterified fatty acids [NEFA], β-hydroxybutyric acid [BHBA], and cholesterol). The study population consisted of genotyped lactating Holsteins (938 cows with 80,709 single nucleotide polymorphisms [SNPs]) and serum concentrations of biochemical variables sampled at 2-14 days in milk (DIM) from 11 commercial farms in Alberta. The heritability of the serum variables ranged from 0.04 to 0.35, with cholesterol the most heritable (0.35 ± 0.07), while both glucose and urea were the least heritable (0.04 ± 0.05). Strong genetic correlations were observed between NEFA and GGT (0.78 ± 0.34), AST (0.74 ± 0.29), and BHBA (0.70 ± 0.26). Genome-wide association studies (GWAS) identified 45 and 7 SNPs associated with GGT and cholesterol concentrations, respectively. Candidate genes and QTLs within 100 kb up- and downstream of significant SNPs were detected for GGT and cholesterol. Multiple candidate genes and QTLs in the identified regions are implicated in pathways influencing metabolic disorders, production, and fertility in lactating dairy cows. Overall, the results showed low-to-moderate heritability and identified candidate genes and QTL regions associated with serum GGT and cholesterol. These results have potential utility in efforts to enhance the resilience of dairy cows through genetic selection.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".