Identification and examination of molecular genetic markers associated with somatic cell scores of Ontario Holstein cattle
Bibliographic record
Abstract
The main purpose of this research was to identify molecular genetic markers associated with somatic cell scores (SCS) for genetic improvement of mastitis resistance in Ontario Holsteins. Mastitis resistance is expected to be improved via selection based on the molecular genetic markers associated with SCS. The statistical power of detecting QTL-marker linkage via bulked segregant analysis is a function of the size of the segregating population, the proportion selected in the extreme tails of the phenotypic distribution, the size of gene effects, and the degree of dominance of the QTL. By increasing the size of the recorded population and decreasing the proportion selected, the number of DNA extractions to form the DNA bulks and genotyping reactions can be substantially decreased for a given statistical power. Bayesian segregation analysis using Gibbs sampling approach was applied for analyzing a set of field data for SCS and for comparison four sets of data simulated with different genetic parameters. The segregation analysis of field data for SCS suggested that occurrence of a major gene significantly affected the SCS of Ontario Holsteins. The estimated heritability of SCS was approximately 0.16. The major gene variance accounted for about 17% of the total genetic variance and the estimate of the frequency of the positive allele was 0.30. However, the precision of these estimates was questionable, based on the results of simulation, and the actual QTL effect seemed likely to be underestimated. Bulked segregant analysis was applied to detect amplified fragment length polymorphism (AFLP) associated with SCS. Seventy primer pair combinations from eight 'Eco'R I and nine 'Taq' I primers were used to screen the genomes of Ontario Holsteins. Only fragments smaller than 500bp were screened using the ABI 377 sequencer with the internal size standard GS-500 ROX. Two AFLP fragments (151 bp and 214 bp) were found to be much more frequent among cows with high estimated breeding value (EBV) for SCS and two other AFLP fragments (105 bp and 261 bp) were much more prevalent among cows with low EBV for SCS. Those markers can potentially be used to select for increased mastitis resistance.
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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 | 0.001 | 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) | 0.001 | 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".