RT Journal Article
SR Electronic
T1 Bayesian Inference of Natural Selection from Allele Frequency Time Series
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 037200
DO 10.1101/037200
A1 Joshua G. Schraiber
A1 Steven N. Evans
A1 Montgomery Slatkin
YR 2016
UL http://biorxiv.org/content/early/2016/01/19/037200.abstract
AB 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 non-equilibrium 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.