The Rate of Molecular Evolution When Mutation May Not Be Weak
Bibliographic record
Abstract
Abstract One of the most fundamental rules of molecular evolution is that the rate of neutral evolution equals the mutation rate and is independent of effective population size. This result lies at the heart of the Neutral Theory, and is the basis for numerous analytic approaches that are widely applied to infer the action of natural selection across the genome and through time, and for dating divergence events using the molecular clock. However, this result was derived under the assumption that evolution is strongly mutation-limited, and it has not been known whether it generalizes across the range of mutation pressures or the spectrum of mutation types observed in natural populations. Validated by both simulations and exact computational analyses, we present a direct and transparent theoretical analysis of the Wright-Fisher model of population genetics, which shows that some of the most important rules of molecular evolution are fundamentally changed by considering recurrent mutation’s full effect. Surprisingly, the rate of the neutral molecular clock is found to have population-size dependence and to not equal the mutation rate in general. This is because, for increasing values of the population mutation rate parameter ( θ ), the time spent waiting for mutations quickly becomes smaller than the cumulative time mutants spend segregating before a substitution, resulting in a net deceleration compared to classical theory that depends on the population mutation rate. Furthermore, selection exacerbates this effect such that more adaptive alleles experience a greater deceleration than less adaptive alleles, introducing systematic bias in a wide variety of methods for inferring the strength and direction of natural selection from across-species sequence comparisons. Critically, the classical weak mutation approximation performs well only when θ< 0.1, a threshold that many biological populations seem to exceed.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".