Detect population structure (i.e sub-populations), making use of genetic (and optionally geographic) information.
The main purpose of the program is to perform Bayesian inference of all the parameters involved
through Markov Chain Monte-Carlo simulation.
This is achievied by the function
PostProcessChain read some output files of
and computes some statistics suitable to print maps of inferred
Storage format section in
MCMC help page.
The following functions are provided by the package:
simFmodel: simulation from the prior of the spatial
simdata: Simulation of georeferenced genotypes under an IBD + barrier
show.simdata: Graphical display of data simulated by simdata
MCMC: Full Bayesian Markov Chain Monte Carlo
inference of parameters in the spatial F-model
PostProcessChain: Post-procesing of MCMC output
for maps of posterior probability of populations subdomains
PlotTessellation: Graphical display of inferred
The following functions are very basic and are only intended to be an
aid for those not familiar with R. Most probably you may want to use
directly the output files of
PostProcessChain to print your own figures.
PlotDrift: Graphical display of drift factors along MCMC
PlotFreqA: Graphical display of allele frequencies in the
ancestral population along MCMC run
PlotFreq: Graphical display of allele frequencies in the
present time population along MCMC run
Plotnpop: Graphical display of number of populations
along MCMC run
Arnaud Estoup, Gilles Guillot, Filipe Santos
G. Guillot, Estoup, A., Mortier, F. Cosson, J.F. A spatial statistical model for landscape genetics. Genetics, 170, 1261-1280, 2005.
G. Guillot, Mortier, F., Estoup, A. Geneland : A program for landscape genetics. Molecular Ecology Notes, 5, 712-715, 2005.
Gilles Guillot, Filipe Santos and Arnaud Estoup, Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface Bioinformatics 2008 24(11):1406-1407.
G. Guillot. Inference of structure in subdivided populations at low levels of genetic differentiation. The correlated allele frequencies model revisited. Bioinformatics, 24:2222-2228, 2008
G. Guillot and F. Santos A computer program to simulate multilocus genotype data with spatially auto-correlated allele frequencies. Molecular Ecology Resources, 2009
G. Guillot, R. Leblois, A. Coulon, A. Frantz Statistical methods in spatial genetics, Molecular Ecology, 2009.
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