Polygenic selection drives the evolution of convergent transcriptomic landscapes across continents within a Nearctic sister species complex
Bibliographic record
Abstract
In contrast to the plethora of studies focusing on the genomic basis of adaptive phenotypic divergence, the role of gene expression during speciation has been much less investigated and consequently less understood. Yet, the convergence of differential gene expression patterns between closely related species-pairs might reflect the role of natural selection during the process of ecological speciation. Here, we test for intercontinental convergence in differential transcriptional signatures between limnetic and benthic sympatric species-pairs of Lake Whitefish (Coregonus clupeaformis) and its sister lineage, the European Whitefish (Coregonus lavaretus), using six replicated sympatric species-pairs (two in North America, two in Norway and two in Switzerland). We characterized both sequence variation in transcribed regions and differential gene expression between sympatric limnetic and benthic species across regions and continents. Our first finding was that differentially expressed genes (DEG) between limnetic and benthic whitefish tend to be enriched in shared polymorphism among sister lineages. We then used both genotypes and covariation in expression in order to infer polygenic selection at the gene level. We identified parallel outliers and DEG involving genes primarily overexpressed in limnetic species relative to the benthic species. Our analysis finally revealed the existence of shared genomic bases underlying parallel differential expression across replicated species-pairs from both continents, such as a cis-eQTL affecting the pyruvate kinase expression level involved in glycolysis. Our results are consistent with a long-standing role of natural selection in maintaining trans-continental diversity at phenotypic traits involved in ecological speciation between limnetic and benthic whitefishes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".