Invited review: Management of genetic defects in dairy cattle populations
Bibliographic record
Abstract
When related animals are mated to one another, genetic defects may become apparent if recessive mutations are inherited from both sides of the pedigree. The widespread availability of high-density DNA genotypes for millions of animals has made it possible to identify and track known defects as well as to identify and track previously unknown defects that cause early embryonic losses. Although the number of known defects has increased over time, the availability of carrier information has been used to dramatically reduce the frequency of many disorders. The economic impact of known genetic defects in the US dairy cattle population has decreased by ∼2/3 since 2016, due largely to the avoidance of carrier-to-carrier matings. Effective population management requires robust systems for reporting new defects, identification of causal mechanisms, and development of commercially available tests. The United States and Canada depend on informal cooperation among many groups, including farmers, purebred cattle associations, genetics companies, and researchers, to identify emerging and causal defects. The structure of a collaborative system including all key sectors of the dairy cattle industry to support long-term population management is described. This review provides a comprehensive overview of the landscape surrounding genetic defects in dairy cattle. Topics covered include current defects of relevance to commercial dairy producers, trends in carrier frequencies over time, how best to manage these defects, strategies for detecting emerging diseases, and marketing and trade considerations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".