PSXII-24 Identification of selection signatures for response of American mink to Aleutian mink disease virus infection
Bibliographic record
Abstract
Abstract Aleutian disease (AD) is one of the most important health problems in the mink industry worldwide, leading to economic losses. We used a set of single nucleotide polymorphisms (SNPs) to detect the genomic regions potentially under selection for response to Aleutian mink disease virus (AMDV) infection in black American mink. A total of 191 mink which were inoculated with a local strain of AMDV and survived until pelting were genotyped using genotyping-by-sequencing technique. The presence of viral DNA in the spleen samples were tested by polymerase chain reaction. After filtering, 47,800 SNPs at 171 individuals were used for further analyses. Signatures of selection for response to AMDV infection were detected using fixation index (FST) and nucleotide diversity (θπ) statistics measured between negative and positive groups. The overlap of top 1% SNPs obtained from both FST and θπ scores were considered as potential selection signs. This measurement identified a total of 21 candidate regions containing 11 genes which were likely subjected to selection for viral clearance. Several identified genes were those that modulate immune system (TCF4), reproductive process (CATSPERB, MAS1 and IGF2R), response to stimulus (WNT11 and MAS1), and functions of heart (TENM4 and WNT11) and liver (IGF2R). In addition, gene ontology showed that 63.6% of detected genes (seven) were involved in binding activities (GO:0005488). These genes can be used in molecular assessment of viral clearance in American mink. The results indicated that selection for viral clearance and thus AD tolerant animals can be a feasible strategy to deal with the global AMDV infection on mink farms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".