Abstract
Abstract Summary: We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Θ = 4Neμ, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayesian analysis, and accomodates nucleotide sequence, SNP, microsatellite or elecrophoretic data, with resolved or unresolved haplotypes. It is available as portable source code and executables for all three major platforms. Availability: LAMARC 2.0 is freely available at Contact: lamarc@gs.washington.edu
Keywords
Coalescent theoryMarkov chain Monte CarloBayesian probabilityExecutablePopulationMarkov chainMaximum likelihoodHaplotypeStatisticsMicrosatelliteHaplotype estimationExponential growthBiologyComputer scienceMathematicsGeneticsPhylogenetic treeGeneAllele
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Publication Info
- Year
- 2006
- Type
- article
- Volume
- 22
- Issue
- 6
- Pages
- 768-770
- Citations
- 646
- Access
- Closed
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Cite This
Mary K. Kuhner
(2006).
LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters.
Bioinformatics
, 22
(6)
, 768-770.
https://doi.org/10.1093/bioinformatics/btk051
Identifiers
- DOI
- 10.1093/bioinformatics/btk051