PSIII-10 Effect of WUR genotype on resilience to a polymicrobial natural disease challenge in pigs
Bibliographic record
Abstract
Abstract Porcine Reproductive and Respiratory Syndrome (PRRS) is caused by a virus that poses a threat to the global swine industry, costing the U.S. industry over 664 million dollars annually. Vaccination has limited effectiveness due to the virus’ antigenic and genetic shifts. Leveraging genetics to develop more resilient swine can, however, mitigate the effects of PRRS. Previous studies identified a Single Nucleotide Polymorphism (SNP) near the GBP5 gene (WUR) that was associated with resistance and resilience to PRRS, with the G allele being favorable over A. The objective of this study was to determine whether the WUR SNP is also associated with resilience to a polymicrobial natural disease challenge. Using a continuous flow system, a new batch of 60-75 naïve Yorkshire x Landrace nursery piglets was introduced every three weeks into a natural challenge facility that was initially seeded with multiple diseases, including PRRS. Traits recorded were growth rate, feed intake, backfat, loin depth, veterinary treatments, and mortality. Pigs were genotyped using a 600K SNP chip. Data from 2133 pigs were analyzed using a univariate linear mixed model that included, pen, litter, and animal genetics as random effects and WUR genotype as a fixed effect. Frequencies were 0.85, 0.14, and 0.01 for AA, AG, and GG. The G allele was favorable for most traits, with the contrast of AA vs AG significant for average daily gain in the nursery (0.339 vs 0.365 kg/d, p = 0.013) and number of treatments (2.48 vs 2.16 over 180 days, p = 0.072). Mortality rate was 26.0% for AA and 23.8% for AG (not significantly different). In conclusion, the G allele at the WUR SNP is potentially also associated with resilience to multi-factorial disease. Funded by Genome Canada, Genome Alberta, Genome Prairie, PigGen Canada, and USDA-NIFA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".