Hidden genetic variation evolves with ecological specialization: the genetic basis of phenotypic plasticity in Arctic charr ecomorphs
Bibliographic record
Abstract
The genetic variance that determines phenotypic variation can change across environments through developmental plasticity and in turn play a strong role in evolution. Induced changes in genotype-phenotype relationships should strongly influence adaptation by exposing different sets of heritable variation to selection under some conditions, while also hiding variation. Therefore, the heritable variation exposed or hidden from selection is likely to differ among habitats. We used ecomorphs from two divergent populations of Arctic charr (Salvelinus alpinus) to test the prediction that genotype-phenotype relationships would change in relation to environment. If present over several generations this should lead to divergence in genotype-phenotype relationships under common conditions, and to changes in the amount and type of hidden genetic variance that can evolve. We performed a common garden experiment whereby two ecomorphs from each of two Icelandic lakes were reared under conditions that mimicked benthic and limnetic prey to induce responses in craniofacial traits. Using microsatellite based genetic maps, we subsequently detected QTL related to these craniofacial traits. We found substantial changes in the number and type of QTL between diet treatments and evidence that novel diet treatments can in some cases provide a higher number of QTL. These findings suggest that selection on phenotypic variation, which is both genetically and environmentally determined, has shaped the genetic architecture of adaptive divergence in Arctic charr. However, while adaptive changes are occurring in the genome there also appears to be an accumulation of hidden genetic variation for loci not expressed in the contemporary environment.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".