SNP‐array reveals genome‐wide patterns of geographical and potential adaptive divergence across the natural range of <scp>A</scp>tlantic salmon (<i><scp>S</scp>almo salar</i>)
Bibliographic record
Abstract
Atlantic salmon (Salmo salar) is one of the most extensively studied fish species in the world due to its significance in aquaculture, fisheries and ongoing conservation efforts to protect declining populations. Yet, limited genomic resources have hampered our understanding of genetic architecture in the species and the genetic basis of adaptation to the wide range of natural and artificial environments it occupies. In this study, we describe the development of a medium-density Atlantic salmon single nucleotide polymorphism (SNP) array based on expressed sequence tags (ESTs) and genomic sequencing. The array was used in the most extensive assessment of population genetic structure performed to date in this species. A total of 6176 informative SNPs were successfully genotyped in 38 anadromous and freshwater wild populations distributed across the species natural range. Principal component analysis clearly differentiated European and North American populations, and within Europe, three major regional genetic groups were identified for the first time in a single analysis. We assessed the potential for the array to disentangle neutral and putative adaptive divergence of SNP allele frequencies across populations and among regional groups. In Europe, secondary contact zones were identified between major clusters where endogenous and exogenous barriers could be associated, rendering the interpretation of environmental influence on potentially adaptive divergence equivocal. A small number of markers highly divergent in allele frequencies (outliers) were observed between (multiple) freshwater and anadromous populations, between northern and southern latitudes, and when comparing Baltic populations to all others. We also discuss the potential future applications of the SNP array for conservation, management and aquaculture.
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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 | 0.000 | 0.000 |
| 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".