title: The probability of parallel genetic evolution from standing genetic variation.
abstract: Parallel evolution is often assumed to result from repeated adaptation to novel, yet ecologically similar, environments. Here, we develop and analyse a mathematical model that predicts the probability of parallel genetic evolution from standing genetic variation as a function of the strength of phenotypic selection and constraints imposed by genetic architecture. Our results show that the probability of parallel genetic evolution increases with the strength of natural selection and effective population size and is particularly likely to occur for genes with large phenotypic effects. Building on these results, we develop a Bayesian framework for estimating the strength of parallel phenotypic selection from genetic data. Using extensive individual-based simulations, we show that our estimator is robust across a wide range of genetic and evolutionary scenarios and provides a useful tool for rigorously testing the hypothesis that parallel genetic evolution is the result of adaptive evolution. An important result that emerges from our analyses is that existing studies of parallel genetic evolution frequently rely on data that is insufficient for distinguishing between adaptive evolution and neutral evolution driven by random genetic drift. Overcoming this challenge will require sampling more populations and the inclusion of larger numbers of loci.
authors: Program of Bioinformatics and Computational Biology, University of Idaho, Moscow, ID, USA.|Department of Zoology, University of British Columbia, Vancouver, BC, Canada.
authors: A
authors: A
authors: MacPherson
authors: Program of Bioinformatics and Computational Biology, University of Idaho, Moscow, ID, USA.|Department of Biological Sciences, University of Idaho, Moscow, ID, USA.
authors: S L
authors: SL
authors: Nuismer
mesh_terms: D001499:Bayes Theorem; D019143:Evolution, Molecular; D040961:Genetic Drift; D014644:Genetic Variation; D011336:Probability; D012641:Selection, Genetic
keywords: 
  QTL
  ; Bayesian; adaptation; ecological genetics; ecological selection; genetic architecture