Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis

Abstract

The genetic study of diverging, closely related populations is required for basic questions on demography and speciation, as well as for biodiversity and conservation research. However, it is often unclear whether divergence is due simply to separation or whether populations have also experienced gene flow. These questions can be addressed with a full model of population separation with gene flow, by applying a Markov chain Monte Carlo method for estimating the posterior probability distribution of model parameters. We have generalized this method and made it applicable to data from multiple unlinked loci. These loci can vary in their modes of inheritance, and inheritance scalars can be implemented either as constants or as parameters to be estimated. By treating inheritance scalars as parameters it is also possible to address variation among loci in the impact via linkage of recurrent selective sweeps or background selection. These methods are applied to a large multilocus data set from Drosophila pseudoobscura and D. persimilis. The species are estimated to have diverged ‫000,005ف‬ years ago. Several loci have nonzero estimates of gene flow since the initial separation of the species, with considerable variation in gene flow estimates among loci, in both directions between the species.

MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity

“…These estimates are generally consistent with the reported divergence time estimates of the wrens (Lee and Edwards 2008; 270,000 y.b.p) and finches (Jennings and Edwards 2005; 432,000 y.b.p.) using the same rates and a similar coalescent-based isolation with migration model that used Markov chain Monte Carlo [49,60]. We additionally note that Lee and Edwards [61] estimated low levels of migration (< 1.0 migrants per generation) in the fairy wrens which is also consistent with the higher posterior probability for the low migration model that we found via ABC model selection.…”

“…The data can be formatted as IM files [49], or FASTA files. While the data configuration file now accommodates multiple locus data, MTML-msBayes can analyze single locus data sets thereby superceding the previous single-locus msBayes.…”

Neutral and functionally important genes shed light on phylogeography and the history of high‐altitude colonization in a widespread New World duck

“…Using a two‐population model (i.e., multiple pairwise comparisons), this analysis allowed us to examine the direction of gene exchange based on estimates of historical gene flow between the three populations (Hey, 2005, 2010; Hey & Nielsen, 2004; Nielsen & Wakeley, 2001). Because IMa2 assumes no intralocus recombination, we tested for evidence of recombination in our data using the four‐gamete test (Hudson & Kaplan, 1985) in DnaSP v.5.10 (Librado & Rozas, 2009).…”

Population demographic history of a temperate shrub, Rhododendron weyrichii (Ericaceae), on continental islands of Japan and South Korea

“…To estimate the IM history among four major regional population groups (Jeju, Kyushu, Shikoku, and Kii), the population demographic parameters (Figure 3) population split time ( t  =  T μ), which is a product of the absolute time in years ( T ) and the mutation rate (μ), population size (θ = 4 N μ, where N is effective population size), migration rate per mutation event ( m  =  M /μ, where M is migration rate), and population migration rate (2 NM  = 0.5θ m ) were estimated using IMa2 (Hey & Nielsen, 2004, 2007; see Appendix S1).…”

“…This is particularly problematic in phylogeographic analyses of continental island species, because here population divergence can be triggered by interglacial geographic isolation and/or glacial habitat isolation. In the last decade, demographic modeling of natural populations based on the coalescent approach has become commonplace (Hey & Nielsen, 2004; Nielsen & Beaumont, 2009). This approach can statistically estimate demographic parameters such as migration rate, divergence time, and changes in population size within a given time scale.…”