Phylogenetic Patterns of GC-Biased Gene Conversion in Placental Mammals and the Evolutionary Dynamics of Recombination Landscapes
Bibliographic record
Abstract
GC-biased gene conversion (gBGC) is a major evolutionary force shaping genomic nucleotide landscapes, distorting the estimation of the strength of selection, and having potentially deleterious effects on genome-wide fitness. Yet, a global quantitative picture, at large evolutionary scale, of the relative strength of gBGC compared with selection and random drift is still lacking. Furthermore, owing to its dependence on the local recombination rate, gBGC results in modulations of the substitution patterns along genomes and across time which, if correctly interpreted, may yield quantitative insights into the long-term evolutionary dynamics of recombination landscapes. Deriving a model of the substitution process at putatively neutral nucleotide positions from population-genetics arguments, and accounting for among-lineage and among-gene effects, we propose a reconstruction of the variation in gBGC intensity at the scale of placental mammals, and of its scaling with body-size and karyotypic traits. Our results are compatible with a simple population genetics model relating gBGC to effective population size and recombination rate. In addition, among-gene variation and phylogenetic patterns of exon-specific levels of gBGC reveal the presence of rugged recombination landscapes, and suggest that short-lived recombination hot-spots are a general feature of placentals. Across placental mammals, variation in gBGC strength spans two orders of magnitude, at its lowest in apes, strongest in lagomorphs, microbats or tenrecs, and near or above the nearly neutral threshold in most other lineages. Combined with among-gene variation, such high levels of biased gene conversion are likely to significantly impact midly selected positions, and to represent a substantial mutation load. Altogether, our analysis suggests a more important role of gBGC in placental genome evolution, compared with what could have been anticipated from studies conducted in anthropoid primates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".