A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments
Student Authors
Ryan T. HietpasUMass Chan Affiliations
Department of Biochemistry and Molecular PharmacologyDocument Type
Journal ArticlePublication Date
2014-03-01Keywords
*Adaptation, Physiological; Bayes Theorem; Evolution, Molecular; *Genetic Fitness; HSP90 Heat-Shock Proteins; High-Throughput Nucleotide Sequencing; Markov Chains; Models, Genetic; Monte Carlo Method; *Mutation; Saccharomyces cerevisiae; Saccharomyces cerevisiae ProteinsFisher’s geometric model (FGM)
adaptation
adaptive walk
distribution of fitness effects
experimental evolution
Ecology and Evolutionary Biology
Genetics and Genomics
Genomics
Metadata
Show full item recordAbstract
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.Source
Genetics. 2014 Mar;196(3):841-52. doi: 10.1534/genetics.113.156190. Epub 2014 Jan 7. Link to article on publisher's siteDOI
10.1534/genetics.113.156190Permanent Link to this Item
http://hdl.handle.net/20.500.14038/33371PubMed ID
24398421Related Resources
Link to Article in PubMedRights
Copyright © 2014 by the Genetics Society of America. Available freely online through the author-supported open access option.ae974a485f413a2113503eed53cd6c53
10.1534/genetics.113.156190