PSVII-27 Genetic analysis of disease resilience of nursery-to-finish pigs under a natural disease challenge model using reaction norms
Bibliographic record
Abstract
Abstract Disease resilience is the ability of an animal to maintain performance across environments with different disease challenge loads (CL) and can be quantified using random regression reaction norm models that describe phenotype as a function of CL. Objectives of this study were to: 1) develop measures of CL using growth rate and clinical disease phenotypes under a natural disease challenge; 2) evaluate genetic variation in disease resilience. Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, and subjective health scores, collected on 50 batches of 60/75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. All pigs were genotyped using a 650K SNP panel. Different CL were derived from estimates of contemporary group effects and used as environmental covariates in reaction norm analyses of average daily gain (ADG) and treatment rate (TRT). The CL were compared based on model loglikelihoods and estimates of genetic variance, using both linear and cubic spline reaction norm models. Linear reaction norm models fitted the data significantly better than the standard genetic model and the cubic spline models fitted the data significantly better than the linear reaction norm model for most traits. CL based on early finisher ADG provided the best fit for nursery ADG, while CL based on clinical disease phenotypes was best for finisher ADG and TRT. With increasing CL, estimates of heritability for ADG initially decreased and then increased, while estimates of heritability for TRT generally increased with CL. Genetic correlations were low between ADG or TRT at high versus low CL but high for close CLs. Results can be used to select more resilient pigs across different CL levels, or high-performance animals at a given CL level, or a combination of these. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGenCanada.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".