Journal Article

The Role of the Effective Population Size in Compensatory Evolution

Robert Piskol and Wolfgang Stephan

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 3, issue , pages 528-538
Published in print January 2011 |
Published online June 2011 | e-ISSN: 1759-6653 | DOI: https://dx.doi.org/10.1093/gbe/evr057

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The impact of the effective population size (Ne) on the efficacy of selection has been the focus of many theoretical and empirical studies over the recent years. Yet, the effect of Ne on evolution under epistatic fitness interactions is not well understood. In this study, we compare selective constraints at independently evolving (unpaired) and coevolving (paired) sites in orthologous transfer RNAs (tRNA molecules for vertebrate and drosophilid species pairs of different Ne. We show that patterns of nucleotide variation for the two classes of sites are explained well by Kimura's one- and two-locus models of sequence evolution under mutational pressure. We find that constraints in orthologous tRNAs increase with increasing Ne of the investigated species pair. Thereby, the effect of Ne on the efficacy of selection is stronger at unpaired sites than at paired sites. Furthermore, we identify a “core” set of tRNAs with high structural similarity to tRNAs from all major kingdoms of life and a “peripheral” set with lower similarity. We observe that tRNAs in the former set are subject to higher constraints and less prone to the effect of Ne, whereas constraints in tRNAs of the latter set show a large influence of Ne. Finally, we are able to demonstrate that constraints are relaxed in X-linked drosophilid tRNAs compared with autosomal tRNAs and suggest that Ne is responsible for this difference. The observed effects of Ne are consistent with the hypothesis that evolution of most tRNAs is governed by slightly to moderately deleterious mutations (i.e., |Nes| ≤ 5).

Keywords: effective population size; tRNA; compensatory evolution; selective constraints

Journal Article.  7830 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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