Demystifying phenotypes: The comparative genomics of evo-devo
Bibliographic record
Abstract
Developmental geneticists have spearheaded the synthesis of evolutionary and developmental biology, a.k.a 'evo-devo', leading to a wealth of recent insights about how morphological diversity has evolved. However, there exists a gap between these disciplines, and evo-devo has not benefited from an integration of the principles derived from population genetics and molecular evolution. In order to contribute to the remediation of this deficiency, we recently performed a study investigating how genes diverge among closely related species of Drosophila as a function of when they are expressed during development. We found that patterns of genetic divergence parallels morphology: interspecific divergence accumulates as development progresses. We also sought to test whether this positive gradient of divergence over ontogeny is best explained by purifying selection constraining the divergence of early-expressed genes or positive selection driving the evolution of those expressed later. Interestingly, we found evidence that both processes occur simultaneously. We argue that comparative genomics approaches, by juxtaposing gene- and phenotypelevel divergence, particularly among closely related species, have much to contribute to the ongoing evo-devo synthesis, complementing traditional genetics-based techniques with largescale screening analyses uncovering the mechanisms underlying developmental change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".