ABC inference of multi-population divergence with admixture from unphased population genomic data.

ABSTRACT: Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in nonmodel systems. Inferential tools for historical demography given these data sets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large data sets that are now attainable from nonmodel taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation, given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci or more

SUBMITTER: Robinson JD 

PROVIDER: S-EPMC4285295 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature