%0 Journal Article %1 Heled:2008ty %A Heled, J %A Drummond, AJ %D 2008 %J BMC Evol Biol %K from:alexei.drummond %N 1 %P 289 %R 10.1186/1471-2148-8-289 %T Bayesian inference of population size history from multiple loci. %U http://dx.doi.org/10.1186/1471-2148-8-289 %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{Heled:2008ty, 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 = {2009-05-14T22:10:56.000+0200}, author = {Heled, J and Drummond, AJ}, biburl = {https://www.bibsonomy.org/bibtex/241f5b111c50ce5cc6ef7fb1c9bd6f8c5/compevol}, da = {20081024}, date-modified = {2008-11-28 10:40:25 -0800}, dep = {20081023}, doi = {10.1186/1471-2148-8-289}, edat = {2008/10/25 09:00}, interhash = {14496c421925d3c4e13ecea1a2101711}, intrahash = {41f5b111c50ce5cc6ef7fb1c9bd6f8c5}, issn = {1471-2148 (Electronic)}, jid = {100966975}, journal = {BMC Evol Biol}, jt = {BMC evolutionary biology}, keywords = {from:alexei.drummond}, language = {ENG}, mhda = {2008/10/25 09:00}, month = Oct, number = 1, own = {NLM}, pages = 289, phst = {2008/02/22 {$[$}received{$]$}; 2008/10/23 {$[$}accepted{$]$}; 2008/10/23 {$[$}aheadofprint{$]$}}, pii = {1471-2148-8-289}, pmid = {18947398}, pst = {aheadofprint}, pt = {JOURNAL ARTICLE}, pubm = {Print-Electronic}, so = {BMC Evol Biol. 2008 Oct 23;8(1):289.}, stat = {Publisher}, timestamp = {2009-05-14T22:10:57.000+0200}, title = {Bayesian inference of population size history from multiple loci.}, url = {http://dx.doi.org/10.1186/1471-2148-8-289}, volume = 8, year = 2008 }