Parallel Evolution and Inheritance of Quantitative Traits
Bibliographic record
Abstract
Parallel phenotypic evolution, the independent evolution of the same trait in closely related lineages, is interesting because it tells us about the contribution of natural selection to phenotypic evolution. Haldane and others have proposed that parallel evolution also results from a second process, the similarly biased production of genetic variation in close relatives, an idea that has received few tests. We suggest that influence of shared genetic biases should be detectable by the disproportionate use of the same genes in independent instances of parallel phenotypic evolution. We show how progress in testing this prediction can be made through simple tests of parallel inheritance of genetic differences: similar additive, dominance, and epistasis components in analysis of line means and similar effective numbers of loci. We demonstrate parallel inheritance in two traits, lateral plate number and body shape, in two lineages of threespine stickleback that have adapted independently to freshwater streams on opposite sides of the Pacific Ocean. Notably, reduction of plate number in freshwater involves a substitution at the same major locus in both lineages. Our results represent only a first step in the study of the genetics of parallel phenotypic evolution in sticklebacks. Nevertheless, we have shown how such studies can be employed to test the genetic hypothesis of parallel evolution and how study of parallel evolution might yield insights into the roles of both selection and genetic constraint in phenotypic evolution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".