Interaction between Selection and Biased Gene Conversion in Mammalian Protein-Coding Sequence Evolution Revealed by a Phylogenetic Covariance Analysis
Bibliographic record
Abstract
According to the nearly-neutral model, variation in long-term effective population size among species should result in correlated variation in the ratio of nonsynonymous over synonymous substitution rates (dN/dS). Previous empirical investigations in mammals have been consistent with this prediction, suggesting an important role for nearly-neutral effects on protein-coding sequence evolution. GC-biased gene conversion (gBGC), on the other hand, is increasingly recognized as a major evolutionary force shaping genome nucleotide composition. When sufficiently strong compared with random drift, gBGC may significantly interfere with a nearly-neutral regime and impact dN/dS in a complex manner. Here, we investigate the phylogenetic correlations between dN/dS, the equilibrium GC composition (GC*), and several life-history and karyotypic traits in placental mammals. We show that the equilibrium GC composition decreases with body mass and increases with the number of chromosomes, suggesting a modulation of the strength of biased gene conversion due to changes in effective population size and genome-wide recombination rate. The variation in dN/dS is complex and only partially fits the prediction of the nearly-neutral theory. However, specifically restricting estimation of the dN/dS ratio on GC-conservative transversions, which are immune from gBGC, results in correlations that are more compatible with a nearly-neutral interpretation. Our investigation indicates the presence of complex interactions between selection and biased gene conversion and suggests that further mechanistic development is warranted, to tease out mutation, selection, drift, and conversion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".