The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Bayesian inference of population size history from multiple loci
Open accessAbstract
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. CONCLUSION: 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.
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Cites
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Metadata
- Authors
- Heled, J., & Drummond, A. J.
- Venue
- BMC Evolutionary Biology
- Published
- 2008
- Volume
- 8
- Issue
- 1
- Pages
- 289
- ISSN
- 1471-2148 (ISSN-L)
- OpenAlex
- W2061929543
- Cited by
- 738
- References
- 5
- FWCI
- 10.90
- Type
- journal article
- Language
- en
- Open access
- gold
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