title: A sequentially Markov conditional sampling distribution for structured populations with migration and recombination.
abstract: Conditional sampling distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.
authors.affiliation: Department of Statistics, University of California, Berkeley, CA 94720, USA. steinrue@stat.berkeley.edu
authors.forename: Matthias
authors.initials: M
authors.lastname: Steinrücken
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Paul
authors.forename: Yun S
authors.initials: YS
authors.lastname: Song
mesh_terms: D008390:Markov Chains; D011336:Probability; D011995:Recombination, Genetic