Detection and Characterization of Amplified Fragment Length Polymorphism Markers for Clinical Mastitis in Canadian Holsteins
Bibliographic record
Abstract
Mastitis is the most frequent, complex, and costly disease in dairy cattle. Genetic improvement of milk production traits has accompanied an increased susceptibility to mastitis. To determine genome-wide quantitative trait locus-linked markers for mastitis resistance, a total of 200 cows, comprising 100 top clinical mastitis- (CM) resistant and 100 top CM-susceptible cows, were screened by selective DNA pooling and amplified fragment length polymorphism (AFLP) technique. The AFLP analysis on resistant and susceptible pools using 89 selective primer combinations revealed 27 significant AFLP markers at a false discovery rate (FDR) of < 5%. The most promising AFLP marker was then selected for further characterization. Individual AFLP genotyping of the marker on all selected animals confirmed a significant difference. Sequence analysis detected a single nucleotide polymorphism (A<-->G) responsible for the AFLP polymorphism, which was named CGIL4. The PCR-RFLP analysis indicated that the frequency of allele A was significantly higher in the resistant group. The logistic regression analysis demonstrated that the marker was significantly associated with somatic cell score, CM residual values, and production traits. Radiation hybrid mapping assigned the marker to Bos taurus autosome 22. The present study provides promising markers for marker-assisted selection for CM resistance. Our results also demonstrated the capability of AFLP on selective DNA pools as a method for detection of genome regions containing quantitative trait loci.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".