A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits
Bibliographic record
Abstract
Comparative genome scans can be used to identify chromosome regions, but not traits, that are putatively under selection. Identification of targeted traits may be more likely in recently domesticated populations under strong artificial selection for increased production. We used a North American Atlantic salmon 6K SNP dataset to locate genome regions of an aquaculture strain (Saint John River) that were highly diverged from that of its putative wild founder population (Tobique River). First, admixed individuals with partial European ancestry were detected using STRUCTURE and removed from the dataset. Outlier loci were then identified as those showing extreme differentiation between the aquaculture population and the founder population. All Arlequin methods identified an overlapping subset of 17 outlier loci, three of which were also identified by BayeScan. Many outlier loci were near candidate genes and some were near published quantitative trait loci (QTLs) for growth, appetite, maturity, or disease resistance. Parallel comparisons using a wild, nonfounder population (Stewiacke River) yielded only one overlapping outlier locus as well as a known maturity QTL. We conclude that genome scans comparing a recently domesticated strain with its wild founder population can facilitate identification of candidate genes for traits known to have been under strong artificial selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".