RT Journal Article
SR Electronic
T1 Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 679878
DO 10.1101/679878
A1 Jamie R. Oaks
A1 Nadia L’Bahy
A1 Kerry A. Cobb
YR 2019
UL http://biorxiv.org/content/early/2019/07/04/679878.abstract
AB Factors that influence the distribution, abundance, and diversification of species can simultaneously affect multiple evolutionary lineages within or across communities. These include environmental changes and inter-specific ecological interactions that cause ranges of multiple, co-distributed species to contract, expand, or become fragmented. Such processes predict temporally clustered patterns of evolutionary events across species, such as synchronous population divergences and/or changes in population size. There have been a number of methods developed to infer shared divergences or changes in effective population size, but not both, and the latter has been limited to approximate Bayesian computation (ABC). We introduce a general, full-likelihood Bayesian method that can use genomic data to estimate temporal clustering of an arbitrary mix of population divergences and population-size changes across taxa. Applying this method to simulated data, we find that estimating the timing and sharing of demographic changes is much more challenging than divergences. Even under favorable simulation conditions, the ability to infer shared demographic events is quite limited and very sensitive to prior assumptions, which is in sharp contrast to accurate, precise, and robust estimates of shared divergence times. Our results also suggest that previous estimates of co-expansion among five Alaskan populations of threespine sticklebacks (Gasterosteus aculeatus) were likely spurious, and driven by a combination of misspecified prior assumptions and the lack of information about the timing of demographic changes when invariant characters are ignored. We conclude by discussing potential avenues to improve the estimation of synchronous demographic changes across populations.