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. 2013;9(12):e1003995.
doi: 10.1371/journal.pgen.1003995. Epub 2013 Dec 5.

Contributions of protein-coding and regulatory change to adaptive molecular evolution in murid rodents

Affiliations

Contributions of protein-coding and regulatory change to adaptive molecular evolution in murid rodents

Daniel L Halligan et al. PLoS Genet. 2013.

Abstract

The contribution of regulatory versus protein change to adaptive evolution has long been controversial. In principle, the rate and strength of adaptation within functional genetic elements can be quantified on the basis of an excess of nucleotide substitutions between species compared to the neutral expectation or from effects of recent substitutions on nucleotide diversity at linked sites. Here, we infer the nature of selective forces acting in proteins, their UTRs and conserved noncoding elements (CNEs) using genome-wide patterns of diversity in wild house mice and divergence to related species. By applying an extension of the McDonald-Kreitman test, we infer that adaptive substitutions are widespread in protein-coding genes, UTRs and CNEs, and we estimate that there are at least four times as many adaptive substitutions in CNEs and UTRs as in proteins. We observe pronounced reductions in mean diversity around nonsynonymous sites (whether or not they have experienced a recent substitution). This can be explained by selection on multiple, linked CNEs and exons. We also observe substantial dips in mean diversity (after controlling for divergence) around protein-coding exons and CNEs, which can also be explained by the combined effects of many linked exons and CNEs. A model of background selection (BGS) can adequately explain the reduction in mean diversity observed around CNEs. However, BGS fails to explain the wide reductions in mean diversity surrounding exons (encompassing ~100 Kb, on average), implying that there is a substantial role for adaptation within exons or closely linked sites. The wide dips in diversity around exons, which are hard to explain by BGS, suggest that the fitness effects of adaptive amino acid substitutions could be substantially larger than substitutions in CNEs. We conclude that although there appear to be many more adaptive noncoding changes, substitutions in proteins may dominate phenotypic evolution.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Estimates of mean nucleotide diversity (π) in house mice, divergence to rat (drat) and their ratio (π/d) plotted against the distance from the nearest protein-coding exon (panel A) or CNE (panel B).
Mean estimates of π/d can be approximated well by a negative exponential function (red line), obtained by fitting the function f(x) = A(1-B(exp(-x/d))) to mean π/d by nonlinear least squares (see Materials and Methods for details). The bottom panel shows the number of sites (in Mb) on a log scale that contribute to each bin.
Figure 2
Figure 2. Estimates of diversity (π), divergence to rat (drat) and Tajima's D for all categories of sites analysed for non-CpG-prone sites (A).
Estimates of α and ωa for each category of selected site, obtained from an analysis of the site frequency spectrum using non-CpG-prone sites and divergence to rat (B). Inferred numbers of adaptive substitutions separating mouse and rat for the mutually-exclusive selected sequence categories, assuming there are on average 0.18 substitutions per neutral site between mouse and rat, estimated from four-fold degenerate sites (C).
Figure 3
Figure 3. Patterns of nucleotide diversity (π), divergence to rat (d) and π/d, in the flanks of zero-fold degenerate and four-fold degenerate protein-coding sites identified as either having a fixed substitution between M. m. castaneus and M. famulus or no substitution.
Figure 4
Figure 4. Mean predicted and observed π/d in genomic windows binned by absolute distance from exon and CNE boundaries.
Mean observed π/d are shown as black dots. Mean predicted values from a model assuming that reductions in diversity for each linked selected site are exponentially distributed (model C) are shown as blue lines. Mean predictions from the best fitting model of background selection (Model D) are shown as red lines.

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