Using genomics to enhance selection of novel traits in North American dairy cattle,
Bibliographic record
Abstract
The objectives of this paper were to briefly review progress in the genetic evaluation of novel traits in Canada and the United States, assess methods to predict selection accuracy based on cow reference populations, and illustrate how the use of indicator traits could increase genomic selection accuracy. Traits reviewed are grouped into the following categories: udder health, hoof health, other health traits, feed efficiency and methane emissions, and other novel traits. The status of activities expected to lead to national genetic evaluations is indicated for each group of traits. For traits that are more difficult to measure or expensive to collect, such as individual feed intake or immune response, the development of a cow reference population is the most effective approach. Several deterministic methods can be used to predict the reliability of genomic evaluations based on cow reference population size, trait heritability, and other population parameters. To provide an empirical validation of those methods, predicted accuracies were compared with observed accuracies for several cow reference populations and traits. Reference populations of 2,000 to 20,000 cows were created through random sampling of genotyped Holstein cows in Canada and the United States. The effects of single nucleotide polymorphisms (SNP) were estimated from those cow records, after excluding the dams of validation bulls. Bulls that were first progeny tested in 2013 and 2014 were then used to carry out a validation and estimate the observed accuracy of genomic selection based on those SNP effects. Over the various cow population sizes and traits considered in the study, even the best prediction methods were found, on average, to either under-evaluate observed accuracy by 0.20 or over-evaluate it by 0.22, depending on the approach used to estimate the number of independently segregating chromosome segments. In some instances, differences between observed and predicted accuracies were as large as 0.47. Indicator traits can be very useful for the selection of novel traits. To illustrate this, protein yield, body weight, and mid-infrared data were used as indicator traits for feed efficiency. Using those traits in conjunction with 5,000 cow records for dry matter intake increased the reliability of genomic predictions for young animals from 0.20 to 0.50.
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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 | 0.004 | 0.003 |
| 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 | 0.001 | 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".