Premium
RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS
Author(s) -
Perfeito L.,
Sousa A.,
Bataillon T.,
Gordo I.
Publication year - 2014
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/evo.12234
Subject(s) - epistasis , biology , fitness landscape , genetic fitness , approximate bayesian computation , mutation rate , mutation accumulation , population , adaptation (eye) , mutation , evolutionary biology , genetics , genetic drift , population size , range (aeronautics) , genetic variation , gene , demography , materials science , neuroscience , sociology , composite material
Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of −0.01, and an effective number of traits nine in mut S − E. coli . This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models.