RESEARCH ARTICLE Transcriptional profiling of two Atlantic salmon strains: implications for reintroduction into Lake Ontario
Bibliographic record
Abstract
Abstract One of the major challenges facing conservation biology is characterizing the genetic variation underlying adaptation to different environments. Gene expression is the process whereby genomic information is converted into phenotype and quantitative variation in gene expression is linked to phenotypic variation. Identifying gene transcription profiles that provide fitness benefits in specific environments would promote more effective species reintroduction and conservation practices. In this study, we developed a custom oligonucleotide microarray for Atlantic salmon (Salmo sa-lar) and used this microarray to measure gene transcription in gill tissue for two Atlantic salmon strains currently being reintroduced into Lake Ontario: LaHave (anadromous) and Sebago (landlocked). We measured gene transcription in juvenile salmon from each strain that had been reared under the same conditions and identified genes differentially expressed between the two strains. We used the normalized transcription data and microsatellite genotype data to parti-tion the variance into effects of selection versus genetic drift. We found that although there was little genetic differentia-tion (FST = 0.038) between the two strains, 21 genes were significantly differentially expressed between the two strains, and in all cases the difference was consistent with divergence by selection. We use this analysis to predict the Sebago strain will be more likely to be successfully rein-troduced, highlighting how the combination of population genetics with gene expression can help to guide reintro-duction efforts.
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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".