14 Genome-wide Association Studies of Disease Resilience Traits from a Natural Polymicrobial Disease Challenge in Pigs
Bibliographic record
Abstract
Abstract Infectious diseases cause tremendous financial loss in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. The objective of this study was to identify quantitative trait loci (QTL) for disease resilience based on both univariate and bivariate genome-wide association studies (GWAS). Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, subjective health scores, feed and water intake traits and carcass traits, collected on 50 batches of 60 or 75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. Multiple QTL were detected for all traits. The major histocompatibility complex (MHC) region (22–25 Mb on chromosome 7) was found to be associated with multiple traits, including late nursery and finisher growth rates, average daily feed intake and intake rate, average daily water dispensed, water intake duration, and number of visits to the drinker. The MHC region explained ~13% of genetic variance for late nursery growth rate. Further fine mapping identified four QTL in the MHC region for late nursery growth rate that spanned the class I, II, and III regions. Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity QTL, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, MHC and other QTL identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGen Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".