Open AccessEstimation of Population Growth or Decline in Genetically Monitored Populations
Author(s) -
Mark Beaumont
Publication year - 2003
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1093/genetics/164.3.1139
Subject(s) - markov chain monte carlo , sample size determination , statistics , inference , sampling (signal processing) , biology , population , bayesian probability , sample (material) , effective population size , allele frequency , population size , markov chain , bayesian inference , mathematics , econometrics , allele , genetics , computer science , genetic variation , demography , gene , artificial intelligence , chemistry , filter (signal processing) , chromatography , sociology , computer vision
This article introduces a new general method for genealogical inference that samples independent genealogical histories using importance sampling (IS) and then samples other parameters with Markov chain Monte Carlo (MCMC). It is then possible to more easily utilize the advantages of importance sampling in a fully Bayesian framework. The method is applied to the problem of estimating recent changes in effective population size from temporally spaced gene frequency data. The method gives the posterior distribution of effective population size at the time of the oldest sample and at the time of the most recent sample, assuming a model of exponential growth or decline during the interval. The effect of changes in number of alleles, number of loci, and sample size on the accuracy of the method is described using test simulations, and it is concluded that these have an approximately equivalent effect. The method is used on three example data sets and problems in interpreting the posterior densities are highlighted and discussed.