The Nonstationary Dynamics of Fitness Distributions: Asexual Model with Epistasis and Standing Variation. Issue 4 (1st December 2016)

• Record Type: Journal Article
• Authors: Martin, Guillaume; Roques, Lionel
• Is Part Of: Genetics. Volume 204:Issue 4(2016)
• Page Start: 1541
• Page End: 1558
• Publication Date: 2016-12-01
• Subjects: asexual evolution -- clonal interference -- transient dynamics -- mutation‐selection balance -- Fisher's geometric model; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.116.187385
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Various models describe asexual evolution by mutation, selection, and drift. Some focus directly on fitness, typically modeling drift but ignoring or simplifying both epistasis and the distribution of mutation effects (traveling wave models). Others follow the dynamics of quantitative traits determining fitness (Fisher's geometric model), imposing a complex but fixed form of mutation effects and epistasis, and often ignoring drift. In all cases, predictions are typically obtained in high or low mutation rate limits and for long-term stationary regimes, thus losing information on transient behaviors and the effect of initial conditions. Here, we connect fitness-based and trait-based models into a single framework, and seek explicit solutions even away from stationarity. The expected fitness distribution is followed over time via its cumulant generating function, using a deterministic approximation that neglects drift. In several cases, explicit trajectories for the full fitness distribution are obtained for arbitrary mutation rates and standing variance. For nonepistatic mutations, especially with beneficial mutations, this approximation fails over the long term but captures the early dynamics, thus complementing stationary stochastic predictions. The approximation also handles several diminishing returns epistasis models ( e.g., with an optimal genotype); it can be applied at and away from equilibrium. General results arise at equilibrium, where fitness distributions display a "phase transition" with mutation rate. Beyond this phase transition, in Fisher's geometric model, the full trajectory of fitness and trait distributions takes a simple form; robust to the details of the mutant phenotype distribution. Analytical arguments are explored regarding why and when the deterministic approximation applies.