Genomic Regions Associated with Resistance to Gastrointestinal Nematode Parasites in Sheep—A Review
Bibliographic record
Abstract
Gastrointestinal nematodes (GINs) can be a major constraint and global challenge to the sheep industry. These nematodes infect the small intestine and abomasum of grazing sheep, causing symptoms such as weight loss, diarrhea, hypoproteinemia, and anemia, which can lead to death. The use of anthelmintics to treat infected animals has led to GIN resistance, and excessive use of these drugs has resulted in residue traced in food and the environment. Resistance to GINs can be measured using multiple traits, including fecal egg count (FEC), Faffa Malan Chart scores, hematocrit, packed cell volume, eosinophilia, immunoglobulin (Ig), and dagginess scores. Genetic variation among animals exists, and understanding these differences can help identify genomic regions associated with resistance to GINs in sheep. Genes playing important roles in the immune system were identified in several studies in this review, such as the CFI and MUC15 genes. Results from several studies showed overlapping quantitative trait loci (QTLs) associated with multiple traits measuring resistance to GINs, mainly FEC. The discovery of genomic regions, positional candidate genes, and QTLs associated with resistance to GINs can help increase and accelerate genetic gains in sheep breeding programs and reveal the genetic basis and biological mechanisms underlying this trait.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".