© American Dairy Science Association, 2008. A Whole-Genome Scan to Map Quantitative Trait Loci for Conformation and Functional Traits in Canadian Holstein Bulls
Bibliographic record
Abstract
Genetic improvement of livestock populations can be achieved through detection and mapping of genetic markers linked to quantitative trait loci (QTL). With the completion of the bovine genome sequence assembly, single nucleotide polymorphism (SNP) assays spanning the whole bovine genome and research work on large-scale identification, validation, and analysis of genotypic variation in cattle has become possible. A total of 462 Canadian Holstein Bulls were used to test the association between SNP and QTL. Single locus linkage disequilibrium regression model was implemented to perform a whole genome scan to identify and map QTL affecting conformation and functional traits. One thousand five hundred thirty-six SNP markers from introns and exons of potential QTL regions for economically important traits across the bovine genome were selected for association analysis. A total of 45 and 151 SNP were found to be associated with 17 conformation and functional traits at a genome- and chromosome-wise significance level, respectively. Among the 196 significant SNP, 169 of them are newly detected in this study, whereas 27 of them have been reported in previous literature and 161 of these were located in genes and are worth further investigating to potentially identify the causative mutations underlying the QTL. The single locus linkage disequilibrium regression method using SNP marker genotypes has proven to be a successful methodology for detecting and mapping QTL in dairy cattle populations. Key words: genome scan, single nucleotide polymorphism, quantitative trait loci detection, linkage disequilibrium mapping
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".