Refined mapping of a QTL for somatic cell score on BTA27 in the German Holstein using combined linkage and linkage disequilibrium analysis
Bibliographic record
Abstract
Genetic selection for udder health is often based on the indicator trait somatic cell score (SCS), which is correlated with clinical mastitis and has a moderate heritability. We used combined linkage and linkage disequilibrium analysis to refine mapping of a previously reported quantitative trait locus (QTL) affecting SCS on Bos taurus autosome 27 (BTA27) in the German Holstein population. A granddaughter design of six grandsire families with 492 sons progeny tested for an average of 190 daughters per son was investigated. Nineteen microsatellite markers were genotyped along a segment of 26.2 cM proximally on BTA27. A chromosome-wide significant QTL was identified between DIK2879 and KIBS272 using combined analysis. The region of interest for future fine mapping experiments was narrowed to the marker interval KIBS272-DIK2191 with a confidence interval of 3.27 cM. The QTL was estimated to be responsible for 18% of the genetic variation in SCS. Application of a 2-QTL model did not result in higher test statistics. Animals likely to be heterozygous or homozygous at the QTL were identified. This study provides a basis for the selection of further markers in linkage disequilibrium with the QTL affecting SCS on BTA27. Key words: Fine-mapping, mastitis, BTA27, somatic cell score, Holstein
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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 | 0.000 |
| 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".