Heritable gene expression differences between lake and stream stickleback include both parallel and antiparallel components
Bibliographic record
Abstract
The repeated phenotypic patterns that characterize populations undergoing parallel evolution provide support for a deterministic role of adaptation by natural selection. Determining the level of parallelism also at the genetic level is thus central to our understanding of how natural selection works. Many studies have looked for repeated genomic patterns in natural populations, but work on gene expression is less common. The studies that have examined gene expression have found some support for parallelism, but those studies almost always used samples collected from the wild that potentially confounds the effects of plasticity with heritable differences. Here we use two independent pairs of lake and stream threespine stickleback (Gasterosteus aculeatus) raised in common garden conditions to assess both parallel and antiparallel (that is, similar versus different directions of lake-stream expression divergence in the two watersheds) heritable gene expression differences as measured by total RNA sequencing. We find that more genes than expected by chance show either parallel (22 genes, 0.18% of expressed genes) or antiparallel (24 genes, 0.20% of expressed genes) lake-stream expression differences. These results correspond well with previous genomic studies in stickleback ecotype pairs that found similar levels of parallelism. We suggest that parallelism might be similarly constrained at the genomic and transcriptomic levels.
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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".