4.0 Article

A universal scaling law determines time reversibility and steady state of substitutions under selection

期刊

THEORETICAL POPULATION BIOLOGY
卷 82, 期 1, 页码 66-76

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.tpb.2012.03.007

关键词

Substitutions; Reversibility; Universality; Fitness landscape

资金

  1. National Institutes of Health [R01 HG004708]
  2. Alfred P. Sloan Research Fellowship

向作者/读者索取更多资源

Monomorphic loci evolve through a series of substitutions on a fitness landscape. Understanding how mutation, selection, and genetic drift drive this process, and uncovering the structure of the fitness landscape from genomic data are two major goals of evolutionary theory. Population genetics models of the substitution process have traditionally focused on the weak-selection regime, which is accurately described by diffusion theory. Predictions in this regime can be considered universal in the sense that many population models exhibit equivalent behavior in the diffusion limit. However, a growing number of experimental studies suggest that strong selection plays a key role in some systems, and thus there is a need to understand universal properties of models without a priori assumptions about selection strength. Here we study time reversibility in a general substitution model of a monomorphic haploid population. We show that for any time-reversible population model, such as the Moran process, substitution rates obey an exact scaling law. For several other irreversible models, such as the simple Wright-Fisher process and its extensions, the scaling law is accurate up to selection strengths that are well outside the diffusion regime. Time reversibility gives rise to a power-law expression for the steady-state distribution of populations on an arbitrary fitness landscape. The steady-state behavior is dominated by weak selection and is thus adequately described by the diffusion approximation, which guarantees universality of the steady-state formula and its applicability to the problem of reconstructing fitness landscapes from DNA or protein sequence data. (C) 2012 Elsevier Inc. All rights reserved.

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