Distinct genomic architectures but the same gene underlie the convergent evolution of a plant supergene
Bibliographic record
Abstract
Evolution reflects a balance between innovation and constraint, often repurposing existing components in new contexts. Convergent evolution exemplifies this interplay, with similar traits evolving independently in different species, yet the genomic mechanisms enabling such repeatability remain poorly understood. Here, by analyzing ten chromosome-scale genome assemblies, including seven newly generated, we discovered that the S-locus supergene (a cluster of tightly linked genes controlling a floral dimorphism called distyly) arose independently multiple times within the primrose family, Darwin's iconic system for studying distyly. In each case, the same gene was independently duplicated and co-opted, yet the resulting genomic architectures differed, ranging from hemizygous (present on one chromosome copy) to heterozygous (on both copies). These diverse architectures shaped supergene evolution differently, with genetic degeneration occurring only in the heterozygous case. By uncovering multiple mechanisms for supergene origins, our work shows how convergent evolution can produce similar phenotypes by reusing the same genetic building blocks while exploring distinct genomic configurations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".