Nearly neutral theory of molecular evolution

According to the neutral theory of molecular evolution, the rate at which molecular changes accumulate between species should be equal to the rate of neutral mutations and hence relatively constant across species. However, this is a per-generation rate. Since larger organisms have longer generation times, the neutral theory predicts that their rate of molecular evolution should be slower. However, molecular evolutionists found that rates of protein evolution were fairly independent of generation time. Noting that population size is generally inversely proportional to generation time, Tomoko Ohta proposed that if most amino acid substitutions are slightly deleterious, this would increase the rate of effectively neutral mutation rate in small populations, which could offset the effect of long generation times. However, because noncoding DNA substitutions tend to be more neutral, independent of population size, their rate of evolution is correctly predicted to depend on population size / generation time, unlike the rate of non-synonymous changes. In this case, the faster rate of neutral evolution in proteins expected in small populations (due to a more lenient threshold for purging deleterious mutations) is offset by longer generation times (and vice versa), but in large populations with short generation times, noncoding DNA evolves faster while protein evolution is retarded by selection (which is more significant than drift for large populations) ==Theory==

The rate of substitution, \rho is : \rho = ugN_e \bar P_{fix}, where u is the mutation rate, g is the generation time, and N_e is the effective population size. The last term is the probability that a new mutation will become fixed. Early models assumed that u is constant between species, and that g increases with N_e . Kimura’s equation for the probability of fixation in a haploid population gives: : P_{fix} = \frac{1-e^{- s}}{1-e^{- s N_e}} , where s is the selection coefficient of a mutation. When |s| \ll \frac{1}{N_e} (completely neutral), P_{fix}= \frac{1}{N_e} , and when - s \gg \frac{1}{N_e} (extremely deleterious), P_{fix} decreases almost exponentially with N_e . Mutations with -s \simeq \frac{1}{N_e} are called nearly neutral mutations. These mutations can fix in small- N_e populations through genetic drift. In large- N_e populations, these mutations are purged by selection. If nearly neutral mutations are common, then the proportion for which P_{fix} \ll \frac{1}{N_e} is dependent on N_e The effect of nearly neutral mutations can depend on fluctuations in s . Early work used a “shift model” in which s can vary between generations but the mean fitness of the population is reset to zero after fixation. This basically assumes the distribution of s is constant (in this sense, the argument in the previous paragraphs can be regarded as based on the “shift model”). This assumption can lead to indefinite improvement or deterioration of protein function. Alternatively, the later “fixed model” fixes the distribution of mutations’ effect on protein function, but allows the mean fitness of population to evolve. This allows the distribution of s to change with the mean fitness of population. The “fixed model” provides a slightly different explanation for the rate of protein evolution. In large N_e populations, advantageous mutations are quickly picked up by selection, increasing the mean fitness of the population. In response, the mutation rate of nearly neutral mutations is reduced because these mutations are restricted to the tail of the distribution of selection coefficients. The “fixed model” expands the nearly neutral theory. Tachida classified evolution under the “fixed model” based on the product of N_e and the variance in the distribution of s : a large product corresponds to adaptive evolution, an intermediate product corresponds to nearly neutral evolution, and a small product corresponds to almost neutral evolution. According to this classification, slightly advantageous mutations can contribute to nearly neutral evolution. ==The "drift barrier" theory==

Michael Lynch has proposed that variation in the ability to purge slightly deleterious mutations (i.e. variation in N_e) can explain variation in genomic architecture among species, e.g. the size of the genome, or the mutation rate. Specifically, larger populations will have lower mutation rates, more streamlined genomic architectures, and generally more finely tuned adaptations. However, if robustness to the consequences of each possible error in processes such as transcription and translation substantially reduces the cost of making such errors, larger populations might evolve lower rates of global proofreading, and hence have higher rates of error. This may explain why Escherichia coli has higher rates of transcription error than Saccharomyces cerevisiae. This is supported by the fact that transcriptional error rates in E. coli depend on protein abundance (which is responsible for modulating the locus-specific strength of selection), but do so only for high-error-rate C to U deamination errors in S. cerevisiae. == See also ==