The creation-mutation-selection model: mutation rates and effective population sizes
Irlam, G.
Show abstract
For sexual species it has been hypothesized that, over macroevolutionary timescales, species-level processes such as differential extinction may bias the germline mutation rates of surviving species toward values that maximize long-term population mean fitness. If this hypothesis is correct, then the mutation rate is expected to lie near a population-optimal value that depends on several evolutionary and demographic parameters. Using previously published data, these parameters were estimated for several well-studied species pairs, enabling a quantitative evaluation of this prediction. Across the species examined, empirical estimates of mutation rates fall within the range that is predicted by the underlying model to yield appreciable population mean fitness, given substantial uncertainty in several parameter values. The underlying model is consistent with a previously reported inverse relationship between effective population sizes and mutation rates within broad clades. Although not intended to provide precise predictions for individual species, these results are compatible with the hypothesis that macroevolutionary processes contribute to shaping germline mutation rates toward population-optimal values.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Reversion is most likely under high mutation supply, when compensatory mutations don't fully restore fitness costs 92%
- Establishment of a new sex-determining allele driven by sexually antagonistic selection 91%
- Molecular population genetics of Sex-lethal (Sxl) in the D. melanogaster species group - a locus that genetically interacts with Wolbachia pipientis in Drosophila melanogaster 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.