A genome-wide association study on rumination time in first-lactation dairy cattle
Bibliographic record
Abstract
Rumination time (RT) in dairy cattle is a crucial indicator of health, production, reproduction, and greenhouse gas emissions. With moderate heritability estimates for RT, there is potential for further analyses regarding the genetic architecture of the trait. To identify genomic regions associated with RT, we conducted a GWAS on SNPs in a cohort of 452 mid-first-lactation Canadian Holstein cows, followed by the annotation of genes and enrichment analyses of QTL. Animals were genotyped using a medium-density SNP panel (50K). Quality control measures were used to remove markers residing on nonautosomal chromosomes or with minor allele frequencies <5%, and SNP or animals with call rates lower than 90%. The SNP effects were estimated using single-step GBLUP. Significant markers were identified using a chromosome-wise modified Bonferroni correction, based on the expected number of independent chromosome segments. We identified 35 SNPs significantly associated with RT, mapping 34 genes within a 50-kbp interval up and downstream from these SNPs. Additionally, 19 QTL were found enriched in these genomic regions. Notably, genes such as ATP2B4, LDB3, WARS2, and PTPRO were identified, suggesting potential links to muscle fiber activity and milk solids percentage. The enriched QTL were associated with traits related to fat and protein synthesis and deposition in both milk and muscle tissues. Gene Ontology analysis highlighted terms related to muscle contraction and neuronal communication, consistent with the physiological processes underlying RT. Our findings offer new insights into the genetic architecture of RT, advancing the understanding of the physiological mechanisms governing this complex trait.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".