Genetic architecture of divergence: the selfing syndrome in <i>Ipomoea lacunosa</i>
Bibliographic record
Abstract
PREMISE: Highly selfing plant species frequently display a distinctive suite of traits termed the selfing syndrome. Here we tested the hypothesis that these traits are grouped into correlated evolutionary modules and determined the degree of independence between such modules. METHODS: We evaluated phenotypic correlations and QTL overlaps in F2 offspring of a cross between the morning glories Ipomoea lacunosa and I. cordatotriloba and investigated how traits clustered into modules at both the phenotypic and genetic level. We then compared our findings to other QTL studies of the selfing syndrome. RESULTS: In the I. lacunosa selfing syndrome, traits grouped into modules that displayed correlated evolution within but not between modules. QTL overlap predicted phenotypic correlations, and QTLs affecting the same trait module were significantly physically clustered in the genome. The genetic architecture of the selfing syndrome varied across systems, but the pattern of stronger within- than between-module correlation was widespread. CONCLUSIONS: The genetic architecture we observe in the selfing syndrome is consistent with a growing understanding of floral morphological integration achieved via pleiotropy in clustered traits. This view of floral evolution is consistent with resource limitation or predation driving the evolution of the selfing syndrome, but invites further research into both the selective causes of the selfing syndrome and how genetic architecture itself evolves in response to changes in mating system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".