Identification of quantitative trait loci for fat percentage in buffaloes
Bibliographic record
Abstract
The milk fat percentage records of 2174 daughters belonging to 12 half sib families were analyzed for the identification of QTLs on 8 chromosomes in buffaloes using chromosome scans. The single marker analysis revealed 49 markers to be associated with milk fat percentage in 10 sire families. The interval mapping using R/qtl identified 43 QTLs on 8 chromosomes of buffalo. The meta-QTL analysis was carried out to define consensus QTLs in buffaloes and total 28 meta-QTL regions could be identified for milk fat percentage. Most of the QTLs identified in the experiments have been reported for cattle; however, few new chromosomal locations were also identified to be associated with fat percentage in buffaloes. The additional QTLs identified in buffalo may be due to high level of heterozygosity in buffalo compared to Holstein Friesian and other exotic milk breeds for which QTLs have beenreported. Assuming buffalo-cattle synteny, a total of 1118 genes were identified underlying the QTL regions, out of these 45 genes were identified to be associated with lipid metabolism. The interaction among the genes and gene ontology analysis confirmed their association with lipid metabolism. These 45 genes have potential to be candidate genes for milk fat percentage in buffaloes and underlie the QTL regions identified in buffaloes in the present study.
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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 | 0.001 |
| 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) | 0.002 | 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".