56 Awardee Talk: Genetic Improvement of Disease Resilience
Bibliographic record
Abstract
Abstract Infectious disease represents one of the largest cost components to the swine industry, incurring veterinary costs, loss of pigs due to mortality, reduced performance, and reduced animal welfare. Strategies to reduce the incidence and impact of infectious disease include biosecurity, vaccination, veterinary treatment, and selection for genetic resistance. However, biosecurity protocols that keep most infectious pathogens out are not feasible at the commercial level in hog-dense regions, effective vaccines are only available and/or efficacious for some pathogens, and complete genetic resistance is also limited to only a few pathogens (e.g. F18 E.coli) or is only possible to achieve by gene editing (e.g. the PRRS-resistant pig created by editing the CD163 gene). Given these limitations, commercial pigs will continue to be exposed to and infected by pathogens for the foreseeable future. In such a scenario, the ability of an animal to clear the infection while maintaining performance is an important characteristic, which is referred to as disease resilience and is a useful target for inclusion in breeding programs. However, collection of data on disease resilience for genetic improvement requires animals to be exposed to disease, which is not possible in the nucleus herds of breeding programs that most selection is practiced in. To study the genetic basis of disease resilience and develop phenotypes, genetic tests, or indicator traits that could be used to select for disease resilience, a polymicrobial natural disease challenge model was established in grow-finish pigs at the Center de Développement du Porc du Québec, in collaboration with PigGen Canada. The purpose of this presentation is to present results on phenotypes that are relevant to disease resilience, including estimates of genetic parameters, and on potential indicator traits for disease resilience that could be collected in nucleus herds. Funding by Genome Canada, Genome Alberta, PigGen Canada, and USDA NIFA grant #2017-67007-26144.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.016 |
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".