Discriminating Selection on Lateral Plate Phenotype and Its Underlying Gene, <i>Ectodysplasin</i> , in Threespine Stickleback
Bibliographic record
Abstract
When a signature of natural selection is discovered on a gene that is pleiotropic or in tight linkage with other genes, it is challenging to determine which of the affected phenotypes is under selection. One way to make progress is to employ methods for analyzing natural selection on correlated traits, including both genotype and phenotype. We used this approach in threespine stickleback to estimate selection on a rapidly evolving trait, lateral armor plates, while controlling for variation at its major underlying gene, Ectodysplasin (Eda), and vice versa. This allowed for independent estimates of selection on lateral plates and on Eda via other traits. Previously, we demonstrated allele frequency changes at Eda in a pond experiment. Here we show that this resulted from selection on both plates and on Eda, implying additional selection on other phenotypic traits affected by the same gene. This represents the first evidence for direct selection on lateral plates independent of selection on the Eda locus and highlights the value of measuring selection on both phenotypes and genotypes in studies of adaptation.
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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 | 0.000 |
| 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".