Selective Sweeps Reveal Candidate Genes for Adaptation to Drought and Salt Tolerance in Common Sunflower, <i>Helianthus annuus</i>
Bibliographic record
Abstract
Here we report the results of an analysis of variation at 128 EST-based microsatellites in wild Helianthus annuus, using populations from the species' typical plains habitat in Kansas and Colorado, as well as two arid desert and two distinct brackish marsh areas in Utah. The test statistics lnRV and lnRH were used to find regions of the genome that were significantly less variable in one population relative to the others and thus are likely to contain genes under selection. A small but detectable percentage (1.5-6%) of genes showed evidence for selection from both statistics in any particular environment, and a total of 17 loci showed evidence of selection in at least one environment. Distance-based measures provided additional evidence of selection for 15 of the 17 loci. Global F(ST)-values were significantly higher for candidate loci, as expected under divergent selection. However, pairwise F(ST)-values were lower for populations that shared a selective sweep. Moreover, while spatially separated populations undergoing similar selective pressures showed evidence of divergence at some loci, they evolved in concert at other loci. Thus, this study illustrates how selective sweeps might contribute both to the integration of conspecific populations and to the differentiation of races or species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".