Outlier SNPs show more genetic structure between two Bay of Fundy metapopulations of Atlantic salmon than do neutral SNPs
Bibliographic record
Abstract
Atlantic salmon of Eastern Canada were once of considerable importance to aboriginal, recreational, and commercial fisheries, yet many populations are now in decline, particularly those of the inner Bay of Fundy (iBoF), which were recently listed as endangered. We investigated whether nonneutral SNPs could be used to assign individual Atlantic salmon accurately to either the iBoF or the outer Bay of Fundy (oBoF) metapopulations because this has been difficult with existing neutral markers. We first searched for markers under diversifying selection by genotyping eight captively bred Bay of Fundy (BoF) populations for 320 SNP loci with the Sequenom MassARRAY™ system and then analysed the data set with four different F(ST) outlier detection programs. Three outlier loci were identified by both BayesFST and BayeScan whereas seven outlier loci, including the three previously mentioned, were identified by both Fdist and Arlequin. A subset of 14 nonneutral SNPs was more accurate (85% accuracy) than a subset of 67 neutral SNPs (75% accuracy) at assigning individual salmon back to their metapopulation. We then chose a subset of nine outlier SNP markers and used them to inexpensively genotype archived DNA samples from seven wild BoF populations using Invader™ chemistry. Hierarchical AMOVA of these independent wild samples corroborated our previous findings of significant genetic differentiation between iBoF and oBoF salmon metapopulations. Our research shows that identifying and using outlier loci is an important step towards achieving the goal of consistently and accurately distinguishing iBoF from oBoF Atlantic salmon, which will aid in their conservation.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".