Quantifying the role of genome size and repeat content in adaptive variation and the architecture of flowering time in Amaranthus tuberculatus
Bibliographic record
Abstract
Genome size variation, largely driven by repeat content, is poorly understood within and among populations, limiting our understanding of its significance for adaptation. Here we characterize intraspecific variation in genome size and repeat content across 186 individuals of Amaranthus tuberculatus, a ubiquitous native weed that shows flowering time adaptation to climate across its range and in response to agriculture. Sequence-based genome size estimates vary by up to 20% across individuals, consistent with the considerable variability in the abundance of transposable elements, unknown repeats, and rDNAs across individuals. The additive effect of this variation has important phenotypic consequences-individuals with more repeats, and thus larger genomes, show slower flowering times and growth rates. However, compared to newly-characterized gene copy number and polygenic nucleotide changes underlying variation in flowering time, we show that genome size is a marginal contributor. Differences in flowering time are reflected by genome size variation across sexes and marginally, habitats, while polygenic variation and a gene copy number variant within the ATP synthesis pathway show consistently stronger environmental clines than genome size. Repeat content nonetheless shows non-neutral distributions across the genome, and across latitudinal and environmental gradients, demonstrating the numerous governing processes that in turn influence quantitative genetic variation for phenotypes key to plant adaptation.
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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) | 0.000 | 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".