Nonetheless, the Neutral Theory and its “nearly neutral” corollary make straightforward predictions that can be expressed verbally or with a minimum of mathematics.
Many biologists tend to avoid population genetics because of its mathematical complexities. My purpose here is to review the concept of “near-neutrality” in relation to the Neutral Theory and to review some recent evidence relating to the importance of slightly deleterious mutations in evolution. The so-called “nearly-neutral” theory is a corollary of Kimura’s theory that has attracted considerable attention recently.
Motoo Kimura’s Neutral Theory of Molecular Evolution provides the central organizing concepts for modern evolutionary biology. Slightly deleterious variants are a transient feature of evolution in the long term, but they have had substantial impact on contemporary species, including our own. When properly analyzed, many (probably most) claimed cases of positive selection will turn out to involve the fixation of slightly deleterious mutations by genetic drift in bottlenecked populations. The use of conceptually flawed tests for positive selection has become widespread in recent years, seriously harming the quest for an understanding of genome evolution. Numerous data support this prediction, although they have often been wrongly interpreted as evidence for positive Darwinian selection. Because bottlenecks often occur in speciation and slightly deleterious mutations in coding regions will usually be nonsynonymous, we should expect that the ratio of nonsynonymous to synonymous fixed differences between species should often exceed the ratio of nonsynonymous to synonymous polymorphisms within species. Recent evidence has strongly supported several of these predictions, including the prediction that slightly deleterious variants will accumulate in a species that has undergone a severe bottleneck or in cases where recombination is reduced or absent. The nearly-neutral theory represents a development of Kimura’s Neutral Theory of Molecular Evolution that makes testable predictions that go beyond a mere null model.
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