Genomewide single nucleotide polymorphism discovery in Atlantic salmon (<i>Salmo salar</i>): validation in wild and farmed American and European populations
Bibliographic record
Abstract
A considerable number of single nucleotide polymorphisms (SNPs) are required to elucidate genotype-phenotype associations and determine the molecular basis of important traits. In this work, we carried out de novo SNP discovery accounting for both genome duplication and genetic variation from American and European salmon populations. A total of 9 736 473 nonredundant SNPs were identified across a set of 20 fish by whole-genome sequencing. After applying six bioinformatic filtering steps, 200 K SNPs were selected to develop an Affymetrix Axiom(®) myDesign Custom Array. This array was used to genotype 480 fish representing wild and farmed salmon from Europe, North America and Chile. A total of 159 099 (79.6%) SNPs were validated as high quality based on clustering properties. A total of 151 509 validated SNPs showed a unique position in the genome. When comparing these SNPs against 238 572 markers currently available in two other Atlantic salmon arrays, only 4.6% of the SNP overlapped with the panel developed in this study. This novel high-density SNP panel will be very useful for the dissection of economically and ecologically relevant traits, enhancing breeding programmes through genomic selection as well as supporting genetic studies in both wild and farmed populations of Atlantic salmon using high-resolution genomewide information.
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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 | 0.002 |
| 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".