Coevolution between transposable elements and recombination
Bibliographic record
Abstract
One of the most striking patterns of genome structure is the tight, typically negative, association between transposable elements (TEs) and meiotic recombination rates. While this is a highly recurring feature of eukaryotic genomes, the mechanisms driving correlations between TEs and recombination remain poorly understood, and distinguishing cause versus effect is challenging. Here, we review the evidence for a relation between TEs and recombination, and discuss the underlying evolutionary forces. Evidence to date suggests that overall TE densities correlate negatively with recombination, but the strength of this correlation varies across element types, and the pattern can be reversed. Results suggest that heterogeneity in the strength of selection against ectopic recombination and gene disruption can drive TE accumulation in regions of low recombination, but there is also strong evidence that the regulation of TEs can influence local recombination rates. We hypothesize that TE insertion polymorphism may be important in driving within-species variation in recombination rates in surrounding genomic regions. Furthermore, the interaction between TEs and recombination may create positive feedback, whereby TE accumulation in non-recombining regions contributes to the spread of recombination suppression. Further investigation of the coevolution between recombination and TEs has important implications for our understanding of the evolution of recombination rates and genome structure.This article is part of the themed issue 'Evolutionary causes and consequences of recombination rate variation in sexual organisms'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".