Identifying selection signatures for immune response and resilience to Aleutian disease in mink using genotype data
Bibliographic record
Abstract
Aleutian disease (AD) brings tremendous financial losses to the mink industry. Selecting AD-resilient mink has been conducted to control AD. Such selections could have altered the patterns of genetic variation responding to selection pressures. This study aimed to identify selection signatures for immune response (IRE) and resilience to AD. A total of 1,411 mink from an AD-positive facility were used. For IRE, 264 animals were categorized according to the combined results of enzyme-linked immunosorbent assay (ELISA) and counterimmunoelectrophoresis (CIEP). For resilience, two grouping methods were used: 1) general resilience performance (GRP, n = 30) was evaluated based on the feed conversion ratio, Kleiber ratio, and pelt quality; and 2) female reproductive performance (FRP, n = 36) was measured based on the number of kits alive 24 h after birth. Detection methods were the pairwise fixation index, nucleotide diversity, and cross-population extended haplotype homozygosity. A total of 619, 569, and 526 SNPs were identified as candidates for IRE, GRP, and FRP, respectively. The annotated genes were involved in immune system process, growth, reproduction, and pigmentation. Two olfactory-related Gene Ontology (GO) terms were significant (q < 0.05) for all traits, suggesting the impact of AD on the sense of smell of infected mink. Differences in detected genes and GO terms among different color types for IRE indicated variations in immune response to AD among color types. The mitogen-activated protein kinase (MAPK) signaling pathway was significant (q < 0.05) for FRP, suggesting that AD may disrupt MAPK signaling and affect FRP. The findings of this research contribute to our knowledge of the genomic architecture and biological mechanisms underlying AD resilience in mink.
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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) | 0.001 | 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".