The influence of population structure on gene expression and flowering time variation in the ubiquitous weed<i><scp>C</scp>apsella bursa‐pastoris</i>(Brassicaceae)
Bibliographic record
Abstract
Population structure is a potential problem when testing for adaptive phenotypic differences among populations. The observed phenotypic differences among populations can simply be due to genetic drift, and if the genetic distance between them is not considered, the differentiation may be falsely interpreted as adaptive. Conversely, adaptive and demographic processes might have been tightly associated and correcting for the population structure may lead to false negatives. Here, we evaluated this problem in the cosmopolitan weed Capsella bursa-pastoris. We used RNA-Seq to analyse gene expression differences among 24 accessions, which belonged to a much larger group that had been previously characterized for flowering time and circadian rhythm and were genotyped using genotyping-by-sequencing (GBS) technique. We found that clustering of accessions for gene expression retrieved the same three clusters that were obtained with GBS data previously, namely Europe, the Middle East and Asia. Moreover, the three groups were also differentiated for both flowering time and circadian rhythm variation. Correction for population genetic structure when analysing differential gene expression analysis removed all differences among the three groups. This may suggest that most differences are neutral and simply reflect population history. However, geographical variation in flowering time and circadian rhythm indicated that the distribution of adaptive traits might be confounded by population structure. To bypass this confounding effect, we compared gene expression differentiation between flowering ecotypes within the genetic groups. Among the differentially expressed genes, FLOWERING LOCUS C was the strongest candidate for local adaptation in regulation of flowering time.
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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 | 0.000 |
| 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".