The genomic landscape of recombination rate variation in <i>Chlamydomonas reinhardtii</i> reveals a pronounced effect of linked selection
Bibliographic record
Abstract
Abstract Recombination confers a major evolutionary advantage by breaking up linkage disequilibrium (LD) between harmful and beneficial mutations and facilitating selection. Here, we use genome-wide patterns of LD to infer fine-scale recombination rate variation in the genome of the model green alga Chlamydomonas reinhardtii and estimate rates of LD decay across the entire genome. We observe recombination rate variation of up to two orders of magnitude, finding evidence of recombination hotspots playing a role in the genome. Recombination rate is highest just upstream of genic regions, suggesting the preferential targeting of recombination breakpoints in promoter regions. Furthermore, we observe a positive correlation between GC content and recombination rate, suggesting a role for GC-biased gene conversion or selection on base composition within the GC-rich genome of C. reinhardtii . We also find a positive relationship between nucleotide diversity and recombination, consistent with widespread influence of linked selection in the genome. Finally, we use estimates of the effective rate of recombination to calculate the rate of sex that occurs in natural populations of this important model microbe, estimating a sexual cycle roughly every 770 generations. We argue that the relatively infrequent rate of sex and large effective population size creates an population genetic environment that increases the influence of linked selection on the genome.
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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 | 0.001 |
| 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) | 0.000 | 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".