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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".