title: A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments.
abstract: The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)--that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type--showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher's geometric model.
authors.affiliation: School of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
authors.forename: Claudia
authors.initials: C
authors.lastname: Bank
authors.forename: Ryan T
authors.initials: RT
authors.lastname: Hietpas
authors.forename: Alex
authors.initials: A
authors.lastname: Wong
authors.forename: Daniel N
authors.initials: DN
authors.lastname: Bolon
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Jensen
mesh_terms: D000222:Adaptation, Physiological; D001499:Bayes Theorem; D019143:Evolution, Molecular; D056084:Genetic Fitness; D018841:HSP90 Heat-Shock Proteins / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D009154:Mutation; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*
chemical_list: C074510:HSP82 protein, S cerevisiae; D018841:HSP90 Heat-Shock Proteins; D029701:Saccharomyces cerevisiae Proteins
keywords: Fisher’s geometric model (FGM); adaptation; adaptive walk; distribution of fitness effects; experimental evolution