Abstract
BackgroundMany data summary statistics have been developed to detect departures from neutral expectations of evolutionary models. However questions about the neutrality of the evolution of genetic loci within natural populations remain difficult to assess. One critical cause of this difficulty is that most methods for testing neutrality make simplifying assumptions simultaneously about the mutational model and the population size model. Consequentially, rejecting the null hypothesis of neutrality under these methods could result from violations of either or both assumptions, making interpretation troublesome.ResultsHere we harness posterior predictive simulation to exploit summary statistics of both the data and model parameters to test the goodness-of-fit of standard models of evolution. We apply the method to test the selective neutrality of molecular evolution in non-recombining gene genealogies and we demonstrate the utility of our method on four real data sets, identifying significant departures of neutrality in human influenza A virus, even after controlling for variation in population size.ConclusionImportantly, by employing a full model-based Bayesian analysis, our method separates the effects of demography from the effects of selection. The method also allows multiple summary statistics to be used in concert, thus potentially increasing sensitivity. Furthermore, our method remains useful in situations where analytical expectations and variances of summary statistics are not available. This aspect has great potential for the analysis of temporally spaced data, an expanding area previously ignored for limited availability of theory and methods.
Highlights
Many data summary statistics have been developed to detect departures from neutral expectations of evolutionary models
We employed a suite of summary statistics to test the assumption of neutrality on four example data sets
Because selection is expected to change both the distribution of mutations on the tree and the shape of the sample genealogy [30], statistics that measure both of these departures were included in the analysis
Summary
Many data summary statistics have been developed to detect departures from neutral expectations of evolutionary models. One critical cause of this difficulty is that most methods for testing neutrality make simplifying assumptions simultaneously about the mutational model and the population size model. The field of population genetics has a long history in the development of tests of selective neutrality. This is both because of the difficulty of developing a tractable alternative to the neutral theory and because of the ongoing debate about how well the neutral theory can explain real data. Most tests require accurate knowledge of the number of mutations that have occurred or the branch lengths in the gene tree, and do not adequately take into account the uncertainty in these quantities (i.e. most tests implicitly assume an infinite-sites model of evolution). Tree-based summary statistics are often based on one estimate of the genealogy, despite the fact that the true genealogy and branch lengths are seldom known
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
