BACKGROUND:Effective population size (Ne) is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci.RESULTS:Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus Drosophila ananassae data set representing 16 different populations.CONCLUSIONS:The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.
Description
Abstract | Bayesian inference of population size history from multiple loci
%0 Journal Article
%1 18947398
%A Heled, Joseph
%A Drummond, Alexei
%D 2008
%J BMC Evolutionary Biology
%K imported myown
%N 1
%P 289
%R 10.1186/1471-2148-8-289
%T Bayesian inference of population size history from multiple loci
%U http://www.biomedcentral.com/1471-2148/8/289
%V 8
%X BACKGROUND:Effective population size (Ne) is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci.RESULTS:Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus Drosophila ananassae data set representing 16 different populations.CONCLUSIONS:The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.
@article{18947398,
abstract = {BACKGROUND:Effective population size (Ne) is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci.RESULTS:Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus Drosophila ananassae data set representing 16 different populations.CONCLUSIONS:The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.},
added-at = {2009-01-20T08:42:55.000+0100},
author = {Heled, Joseph and Drummond, Alexei},
biburl = {https://www.bibsonomy.org/bibtex/29e381f8b678fe138c1d116bb3aab8f00/alexei.drummond},
description = {Abstract | Bayesian inference of population size history from multiple loci},
doi = {10.1186/1471-2148-8-289},
interhash = {14496c421925d3c4e13ecea1a2101711},
intrahash = {9e381f8b678fe138c1d116bb3aab8f00},
issn = {1471-2148},
journal = {BMC Evolutionary Biology},
keywords = {imported myown},
number = 1,
pages = 289,
pubmedid = {18947398},
timestamp = {2011-02-01T22:57:38.000+0100},
title = {Bayesian inference of population size history from multiple loci},
url = {http://www.biomedcentral.com/1471-2148/8/289},
volume = 8,
year = 2008
}