Genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using a dense SNP map
Bibliographic record
Abstract
Quantitative trait loci (QTL) are chromosome regions which are significantly associated with the expression of a phenotypic trait in a particular population. Detection of a QTL is carried out using association with a genetic marker, such as a single nucleotide polymorphism (SNP), which is in linkage disequilibrium (LD) with the QTL. The two main categories of association studies are linkage analyses (LA), which consider LD within families and linkage disequilibrium methods, which make use of LD across an entire population. The recent reduction in genotyping costs has allowed for testing individuals for a large number of SNP. This substantial increase in genotypic data has lead to denser marker distributions on the bovine genome thus potentially increasing the power of QTL detection studies. The objective of this study was to scan the bovine genome to detect QTL for 305 day lactation milk yield (MY), 305 day lactation fat yield (FY), 305 day lactation protein yield (PY), herd life (HL), somatic cell score (SCS), interval from calving to first service in cows (CTFS) and age at first service in heifers (AFS). HL is a measure of longevity measured in the number of lactations a cow stays in the herd. SCS refers to the amount of somatic cells a cow has in her milk and is an important indicator trait for mastitis. CTFS is the period from parturition to first insemination in days and AFS is the age in days at which a heifer was artificially inseminated for the first time. Fertility traits, such as CTFS and AFS, are indicators of reproductive efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".