title: A continuous method for gene flow.
abstract: Most modern population genetics inference methods are based on the coalescence framework. Methods that allow estimating parameters of structured populations commonly insert migration events into the genealogies. For these methods the calculation of the coalescence probability density of a genealogy requires a product over all time periods between events. Data sets that contain populations with high rates of gene flow among them require an enormous number of calculations. A new method, transition probability-structured coalescence (TPSC), replaces the discrete migration events with probability statements. Because the speed of calculation is independent of the amount of gene flow, this method allows calculating the coalescence densities efficiently. The current implementation of TPSC uses an approximation simplifying the interaction among lineages. Simulations and coverage comparisons of TPSC vs. MIGRATE show that TPSC allows estimation of high migration rates more precisely, but because of the approximation the estimation of low migration rates is biased. The implementation of TPSC into programs that calculate quantities on phylogenetic tree structures is straightforward, so the TPSC approach will facilitate more general inferences in many computer programs.
authors.affiliation: Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306, USA. mp05e@my.fsu.edu
authors.forename: Michal
authors.initials: M
authors.lastname: Palczewski
authors.forename: Peter
authors.initials: P
authors.lastname: Beerli
mesh_terms: D003198:Computer Simulation; D051456:Gene Flow; D005789:Genealogy and Heraldry; D005828:Genetics, Population / Q000379:methods*; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010802:Phylogeny; D011153:Population / Q000235:genetics*; D011336:Probability
keywords: Markov chain Monte Carlo (MCMC); coalescent; continuous Markov model; gene flow; migration; nonhomogeneous Poisson process