Genomic signals found using RNA sequencing show signatures of selection and subtle population differentiation in walleye ( <i>Sander vitreus</i> ) in a large freshwater ecosystem
Bibliographic record
Abstract
Abstract RNA sequencing is an effective approach for studying aquatic species yielding both physiological and genomic data. However, its population genetic applications are not well‐characterized. We investigate this possible role for RNA sequencing for population genomics in Lake Winnipeg, Manitoba, Canada, walleye ( Sander vitreus ). Lake Winnipeg walleye represent the largest component of the second‐largest freshwater fishery in Canada. In the present study, large female walleye were sampled via nonlethal gill biopsy over two years at three spawning sites representing a latitudinal gradient in the lake. Genetic variation from sequenced mRNA was analyzed for neutral and adaptive markers to investigate population structure and possible adaptive variation. We find low population divergence ( F ST = 0.0095), possible northward gene flow, and outlier loci that vary latitudinally in transcripts associated with cell membrane proteins and cytoskeletal function. These results indicate that Lake Winnipeg walleye may be effectively managed as a single demographically connected metapopulation with contributing subpopulations and suggest genomic differences possibly underlying observed phenotypic differences. Despite its high cost relative to other genotyping methods, RNA sequencing data can yield physiological in addition to genetic information discussed here. We therefore argue that it is useful for addressing diverse molecular questions in the conservation of freshwater species.
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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 | 0.001 | 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".