Forces driving transposable element load variation during Arabidopsis range expansion
Bibliographic record
Abstract
Genetic load refers to the accumulated and potentially life-threatening deleterious mutations in populations. Understanding the mechanisms underlying genetic load variation of transposable element (TE) insertion, a major large-effect mutation, during range expansion is an intriguing question in biology. Here, we used 1,115 global natural accessions of Arabidopsis (Arabidopsis thaliana) to study the driving forces of TE load variation during its range expansion. TE load increased with range expansion, especially in the recently established Yangtze River basin population. Effective population size, which explains 62.0% of the variance in TE load, high transposition rate, and selective sweeps contributed to TE accumulation in the expanded populations. We genetically mapped and identified multiple candidate causal genes and TEs, and revealed the genetic architecture of TE load variation. Overall, this study reveals the variation in TE genetic load during Arabidopsis expansion and highlights the causes of TE load variation from the perspectives of both population genetics and quantitative genetics.
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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".