Bayesian Analysis of Genetic Differentiation Between Populations
University of Helsinki · University of Oulu
Abstract
We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the…
Citation impact
- FWCI
- 16.54
- Percentile
- 100%
- References
- 42
Authors
3Topics & keywords
- Biology
- Approximate Bayesian computation
- Markov chain Monte Carlo
- Bayesian probability
- Population
- Substructure
- Posterior probability
- Evolutionary biology