Biology subjects

Irlam, G.

Publications and source records attributed to Irlam, G..

3 recordsLinked to original sources

bioRxiv2023

The creation-mutation-selection model: simulation and the advantage of sex

The creation-mutation-selection model makes predictions regarding the fitness of asexual and sexual populations in an environment that includes both positive and negative selection. However, for the asexual case, the model predictions depend upon the variance or skewness of the population log fitness distribution, something which the model does not provide. This makes the analytical comparison of the fitness of asexual and sexual populations appear to be intractable. Instead, simulation is required. The fitness of asexual and sexual populations is simulated over a range of plausible parameter values. The fitness is found to agree with the predictions of the creation-mutation-selection model. The cost of asexuality is found to exceed the cost of sex by a very large margin for eukaryotic species today, and probably for the first eukaryotes. Prokaryotes and the mitochondrion are viewed as asexual species for which asexuality incurs little cost.

evolutionary biology↗

bioRxiv2023

The creation-mutation-selection model: mutation rates and effective population sizes

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.

evolutionary biology↗

bioRxiv2023

The creation-mutation-selection model: the model and mathematical analysis

The rarity of positively selected sites may lead to the expectation that they have only a minor effect on total genetic load. A framework for the genetic load associated with positively selected sites in sexual populations is presented and analyzed. This framework defines the latent load as the genetic load associated with positively selected sites that are destined to fix, but for which beneficial alleles have not yet become established. A formula for the latent load is derived that incorporates various real-world complicating factors. In humans, the latent load is estimated to be larger than the mutational load, and much larger than the substitutional load. The germline spontaneous mutation rate that maximizes population mean fitness is predicted within this framework to occur when the mean latent load is approximately equal to the mutational load. This population-optimal mutation rate differs from the minimal mutation rate predicted by purely microevolutionary considerations that focus on individual-level selection. It is hypothesized that, over macroevolutionary timescales, species-level processes such as differential extinction may bias the mutation rates of surviving species toward the population-optimal value. Overall, this framework highlights the importance of positively selected sites to the total genetic load, and suggests a potential role for the latent load in shaping the long-term evolution of the mutation rate.

evolutionary biology↗