title: Bayesian Inference of Natural Selection from Allele Frequency Time Series.
abstract: The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in nonequilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, Washington 98195 schraib@uw.edu.
authors.forename: Joshua G
authors.initials: JG
authors.lastname: Schraiber
authors.affiliation: Department of Statistics, University of California, Berkeley, California Department of Mathematics, University of California, Berkeley, California.
authors.forename: Steven N
authors.initials: SN
authors.lastname: Evans
authors.affiliation: Department of Integrative Biology, University of California, Berkeley, California 94720.
authors.forename: Montgomery
authors.initials: M
authors.lastname: Slatkin
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D004171:Diploidy; D005787:Gene Frequency; D006736:Horses / Q000235:genetics; D008957:Models, Genetic; D012641:Selection, Genetic; D012880:Skin Pigmentation / Q000235:genetics; D012984:Software
keywords: Bayesian inference; diffusion theory; natural selection; path augmentation