Kuhner MK. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22: 768-770

Department of Genome Sciences Box 357730 University of Washington Seattle, 98195-7730, USA.
Bioinformatics (Impact Factor: 4.98). 04/2006; 22(6):768-70. DOI: 10.1093/bioinformatics/btk051
Source: PubMed


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

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Available from: Mary K Kuhner, Mar 11, 2015
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    • "Rosenberg 2004). We estimated coalescent-based migration rates (M ) and effective population size parameters (H) using a maximum-likelihood approach in LA- MARC (v.2.0; Kuhner 2006 "
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    • "Estimations were based on Bayesian model using the Markov Chain Monte Carlo ( MCMC ) approach implemented in Lamarc 2 . 1 . 9 software ( Kuhner , 2006 ) . Because of the high number of populations , to estimate growth rate , we constrained migration ( maintained migration constant ) , and migration was estimated in independent runs . "
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    • "Growth rate and timing of population dynamic changes were estimated from coalescent simulations implemented in LAMARC 2.1.9 (Kuhner 2006). The observed distribution of the number of pair base differences between sequences is indicated by the gray bars, while the expected distribution under a model of sudden demographic expansion is represented by a black line. "
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