Discovery of host genetic factors through multi-locus GWAS against toxoplasmosis in sheep: addressing one health perspectives
Bibliographic record
Abstract
Toxoplasma gondii stands as one of the most successful pathogens, capable of infecting nearly all warm-blooded species. It is estimated that up to 50% of human population might harbor Toxoplasmosis infections. One of the primary transmission routes is the consumption of tissue cysts from infected farm animals used for food production. Thus, controlling Toxoplasmosis in farm animals is of vital importance for human health and food safety. Selective breeding in farm animals, where available, could complement classical control measures like biosecurity measures, vaccination, and test-and-cull methods. This multidisciplinary approach will make the eradication of Toxoplasmosis more effective. For this purpose, we conducted four multi-locus genome-wide association (GWA) approaches to identify the polygenic factors underlying innate resistance to Toxoplasma gondii in naturally infected sheep. Our findings indicate that 16 single nucleotide polymorphisms (SNPs), exhibiting varying degrees of statistical power, play a significant role in host immunity against T. gondii infection. We propose the genes containing these SNPs or located within 100 ± Kb of them (PLSCR5, EPHA3, DGKB, IL12B, CGA, WDR64, TMEM158, CLMP, and SIAE) as potential candidate genes. This study represents the first exploration of host genetic factors against Toxoplasmosis in livestock, utilizing the ovine paradigm as its foundation.
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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 | 0.001 | 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".