Genome size and repeat content contribute to a complex architecture of flowering time in <i>Amaranthus tuberculatus</i>
Bibliographic record
Abstract
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. K-mer based genome size estimates vary by up to 20% across individuals, with transposable elements, unknown repeats, and rDNAs being the primary contributors to this variability. 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. Compared to newly-characterized gene copy number and polygenic nucleotide changes underlying variation in flowering time, we show that genome size remains a modest but significant contributor to the genetic basis of flowering time. Differences in flowering time across sexes and habitats are not mirrored by genome size variation, but rather polygenic variation and a gene copy number variant within the ATP synthesis pathway. Repeat content nonetheless shows non-neutral distributions across the genome, and across latitudinal and environmental gradients, reflecting numerous governing processes that in turn influence quantitative genetic variation for phenotypes key to plant adaptation. Author Summary The remarkable and seemingly inconsequential variation in genome size across species has long been an enigma in evolutionary biology. Calling this viewpoint into question, correlations between genome size variation and traits linked to fitness are increasingly uncovered. While this suggests that DNA content itself may be a source of adaptive genetic variation, repeat elements that propagate at the cost of the host are known to largely mediate this variation and may thus limit adaptive potential. Here we look to disentangle these multi-level dynamics, characterizing repeat dynamics across the genome and among individuals across diverse collections of a widespread agricultural weed, linking repeat content to genome size variation, and characterizing the relative importance of its phenotypic consequences. In Amaranthus tuberculatus , we find non-neutral repeat distributions across individuals across the range, and while this repeat variation underlies both variation in genome size and flowering time, we show that it makes a relatively minor contribution to variation in a fitness-related trait across the landscape relative to monogenic and polygenic features. Together, this work broadens our perspective on the complex selective dynamics that govern intraspecific variation in genome size and traits key to fitness in plants.
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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 | 0.000 | 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) | 0.001 | 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".