4.4 Article

From Drift to Draft: How Much Do Beneficial Mutations Actually Contribute to Predictions of Ohta's Slightly Deleterious Model of Molecular Evolution?

Journal

GENETICS
Volume 214, Issue 4, Pages 1005-1018

Publisher

GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.119.302869

Keywords

nearly neutral theory; distribution of fitness effects; beneficial mutations; linked selection

Funding

  1. Swedish Research Council
  2. Swedish Foundation for Strategic Research

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Since its inception in 1973, the slightly deleterious model of molecular evolution, also known as the nearly neutral theory of molecular evolution, remains a central model to explain the main patterns of DNA polymorphism in natural populations. This is not to say that the quantitative fit to data are perfect. A recent study used polymorphism data from Drosophila melanogaster to test whether, as predicted by the nearly neutral theory, the proportion of effectively neutral mutations depends on the effective population size (N-e). It showed that a nearly neutral model simply scaling with N-e variation across the genome could not alone explain the data, but that consideration of linked positive selection improves the fit between observations and predictions. In the present article, we extended the work in two main directions. First, we confirmed the observed pattern on a set of 59 species, including high-quality genomic data from 11 animal and plant species with different mating systems and effective population sizes, hence a priori different levels of linked selection. Second, for the 11 species with high-quality genomic data we also estimated the full distribution of fitness effects (DFE) of mutations, and not solely the DFE of deleterious mutations. Both N-e and beneficial mutations contributed to the relationship between the proportion of effectively neutral mutations and local N-e across the genome. In conclusion, the predictions of the slightly deleterious model of molecular evolution hold well for species with small N-e, but for species with large N-e, the fit is improved by incorporating linked positive selection to the model.

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