Assessing footprints of selection in commercial <scp>A</scp>tlantic salmon populations using microsatellite data
Bibliographic record
Abstract
Relatively large rates of response to traits of economic importance have been observed in different selection experiments in salmon. Several QTL have been mapped in the salmon genome, explaining unprecedented levels of phenotypic variation. Owing to the relatively large selection intensity, individual loci may be indirectly selected, leaving molecular footprints of selection, together with increased inbreeding, as its likely relatives will share the selected loci. We used population differentiation and levels of linkage disequilibrium in chromosomes known to be harbouring QTL for body weight, infectious pancreatic necrosis resistance and infectious salmon anaemia resistance to assess the recent selection history at the genomic level in Atlantic salmon. The results clearly suggest that the marker SSA0343BSFU on chromosome 3 (body weight QTL) showed strong evidence of directional selection. It is intriguing that this marker is physically mapped to a region near the coding sequence of DVL2 , making it an ideal candidate gene to explain the rapid evolutionary response of this chromosome to selection for growth in Salmo salar. Weak evidence of diversifying selection was observed in the QTL associated with infectious pancreatic necrosis and infectious salmon anaemia resistance. Overall, this study showed that artificial selection has produced important changes in the Atlantic salmon genome, validating QTL in commercial salmon populations used for production purposes according to the recent selection history.
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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 | 0.000 |
| 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".