Literature DB >> 23666937

A continuous method for gene flow.

Michal Palczewski1, Peter Beerli.   

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.

Keywords:  Markov chain Monte Carlo (MCMC); coalescent; continuous Markov model; gene flow; migration; nonhomogeneous Poisson process

Mesh:

Year:  2013        PMID: 23666937      PMCID: PMC3697973          DOI: 10.1534/genetics.113.150904

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  19 in total

1.  Estimation of population parameters and recombination rates from single nucleotide polymorphisms.

Authors:  R Nielsen
Journal:  Genetics       Date:  2000-02       Impact factor: 4.562

2.  MrBayes 3: Bayesian phylogenetic inference under mixed models.

Authors:  Fredrik Ronquist; John P Huelsenbeck
Journal:  Bioinformatics       Date:  2003-08-12       Impact factor: 6.937

3.  Comparison of Bayesian and maximum-likelihood inference of population genetic parameters.

Authors:  Peter Beerli
Journal:  Bioinformatics       Date:  2005-11-29       Impact factor: 6.937

4.  Average number of nucleotide differences in a sample from a single subpopulation: a test for population subdivision.

Authors:  C Strobeck
Journal:  Genetics       Date:  1987-09       Impact factor: 4.562

5.  Isolation with migration models for more than two populations.

Authors:  Jody Hey
Journal:  Mol Biol Evol       Date:  2009-12-02       Impact factor: 16.240

6.  On computing the coalescence time density in an isolation-with-migration model with few samples.

Authors:  Asger Hobolth; Lars Nørvang Andersen; Thomas Mailund
Journal:  Genetics       Date:  2011-02-14       Impact factor: 4.562

7.  Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

Authors:  Peter Beerli; Michal Palczewski
Journal:  Genetics       Date:  2010-02-22       Impact factor: 4.562

8.  Estimating effective population size and mutation rate from sequence data using Metropolis-Hastings sampling.

Authors:  M K Kuhner; J Yamato; J Felsenstein
Journal:  Genetics       Date:  1995-08       Impact factor: 4.562

9.  A Hidden Markov Model approach to variation among sites in rate of evolution.

Authors:  J Felsenstein; G A Churchill
Journal:  Mol Biol Evol       Date:  1996-01       Impact factor: 16.240

10.  Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

Authors:  M Hasegawa; H Kishino; T Yano
Journal:  J Mol Evol       Date:  1985       Impact factor: 2.395

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  1 in total

1.  The Structured Coalescent and Its Approximations.

Authors:  Nicola F Müller; David A Rasmussen; Tanja Stadler
Journal:  Mol Biol Evol       Date:  2017-11-01       Impact factor: 16.240

  1 in total

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