A comparative study of population genetic structure reveals patterns consistent with selection at functional microsatellites in common sunflower
Bibliographic record
Abstract
ABSTRACT Microsatellites have long been considered non-functional, neutrally evolving regions of the genome. Recent findings suggest that they can function as drivers of rapid adaptive evolution. Previous work on common sunflower identified 479 transcribed microsatellites where allele length significantly correlates with gene expression (eSTRs) in a stepwise manner. Here, a population genetic approach is used to test whether eSTR allele length variation is under selection. Genotypic variation among and within populations at 13 eSTRs was compared with that at 19 anonymous microsatellites in 672 individuals from 17 natural populations of sunflower from across a cline running from Saskatchewan to Oklahoma. Expected heterozygosity, allelic richness, and allelic diversity were significantly lower at eSTRs, a pattern consistent with higher relative rates of purifying selection. Further, an analysis of variation in microsatellite allele lengths ( lnRV ), and heterozygosities ( lnRH ), indicate recent selective sweeps at the eSTRs. Mean microsatellite allele lengths at four eSTRs within populations are significantly correlated with latitude consistent with the predictions of the tuning knob model which predicts stepwise relationships between microsatellite allele length and phenotypes. This finding suggests that shorter or longer alleles at eSTRs may be favored in climatic extremes. Collectively, our results imply that eSTRs are likely under selection and that they may be playing a role in facilitating local adaptation across a well-defined cline in the common sunflower.
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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) | 0.001 | 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".