Identification of QTLs for low somatic cell count in Murrah buffaloes
Bibliographic record
Abstract
Mastitis, the most frequent and costly disease in buffalo, is the major cause of morbidity. The somatic cell count, an indirect indicator of susceptibility/resistance to mastitis, is a low heritable trait and thus a perfect candidate for marker assisted selection. Half sib families (12) were created and the somatic cell count was recorded at 3 stages of lactation during the first lactation of the 2,422 daughters belonging to 12 sires. Partial genome scan was carried out using interval mapping with different algorithms. The QTLs obtained for each half sib family were further subjected to meta analysis to identify chromosomal regions associated with somatic cell count on 8 chromosomes of buffalo. Four metaQTL regions were identified on chromosomes BBU1q, BBU8, and BBU10; 3 metaQTL regions on BBU2q, BBU9 and BBU15; 2 metaQTL regions on BBU6 and 1 on BBU7 of buffalo. Comparative genomics was used for finding out genes underlying the metaQTL regions; 1,065 genes were underlying the metaQTL regions in buffaloes assuming buffalo–cattle–human synteny. Genes (78) mapped to immune response. These genes are supposedly important candidate genes for further analysis. Gene ontology and network analysis was carried out on these genes. The genes identified belonged to immune response and defense mechanism. The QTL markers identified in the present analysis can be used in the breeding programs of buffalo to select the bulls, which are less susceptible to mastitis.
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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 | 0.000 |
| 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".