Identification of single-nucleotide variants associated with susceptibility to Salmonella in pigs using a genome-wide association approach
Bibliographic record
Abstract
BACKGROUND: Salmonella enterica serovars are a major cause of foodborne illness and have a substantial impact on global human health. In Canada, Salmonella is commonly found on swine farms and the increasing concern about drug use and antimicrobial resistance associated with Salmonella has promoted research into alternative control methods, including selecting for pig genotypes associated with resistance to Salmonella. The objective of this study was to identify single-nucleotide variants in the pig genome associated with Salmonella susceptibility using a genome-wide association approach. Repeated blood and fecal samples were collected from 809 pigs in 14 groups on farms and tonsils and lymph nodes were collected at slaughter. Sera were analyzed for Salmonella IgG antibodies by ELISA and feces and tissues were cultured for Salmonella. Pig DNA was genotyped using a custom 54 K single-nucleotide variant oligo array and logistic mixed-models used to identify SNVs associated with IgG seropositivity, shedding, and tissue colonization. RESULTS: Variants in/near PTPRJ (p = 0.0000066), ST6GALNAC3 (p = 0.0000099), and DCDC2C (n = 3, p < 0.0000086) were associated with susceptibility to Salmonella, while variants near AKAP12 (n = 3, p < 0.0000358) and in RALGAPA2 (p = 0.0000760) may be associated with susceptibility. CONCLUSIONS: Further study of the variants and genes identified may improve our understanding of neutrophil recruitment, intracellular killing of bacteria, and/or susceptibility to Salmonella and may help future efforts to reduce Salmonella on-farm through genetic approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".