Data from: Shared selective pressure and local genomic landscape lead to repeatable patterns of genomic divergence in sunflowers
Bibliographic record
Abstract
AbstractThe repeated evolution of traits in organisms facing similar environmental conditions is considered to be fundamental evidence for the role of natural selection in moulding phenotypes. Yet, aside from case studies of parallel evolution and its genetic basis, the repeatability of evolution at the level of the whole genome remains poorly characterized. Here, through the use of transcriptome sequencing, we examined genomic divergence for three pairs of sister species of sunflowers. Two of the pairs (Helianthus petiolaris – H. debilis and H. annuus – H. argophyllus) have diverged along a similar latitudinal gradient and presumably experienced similar selective pressure. In contrast, a third species pair (H. exilis – H. bolanderi) diverged along a longitudinal gradient. Analyses of divergence, as measured in terms of FST, indicated little repeatability across the three pairs of species for individual genetic markers (SNPs), modest repeatability at the level of individual genes and the highest repeatability when large regions of the genome were compared. As expected, higher repeatability was observed for the two species pairs that have diverged along a similar latitudinal gradient, with genes involved in flowering time among the most divergent genes. Genes showing extreme low or high differentiation were more similar than genes showing medium levels of divergence, implying that both purifying and divergent selection contributed to repeatable patterns of divergence. The location of a gene along the chromosome also predicted divergence levels, presumably because of shared heterogeneity in both recombination and mutation rates. In conclusion, repeated genome evolution appeared to result from both similar selective pressures and shared local genomic landscapes.
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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 | 0.001 |
| 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) | 0.002 | 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".