Geneland {Geneland}R Documentation

Simulation and inference of a spatial statistical model for landscape genetics.

Description

The main function mcmcFmodel takes geo-referenced individual multilocus genetic data and tries to detect population structure, i.e sub-populations making use of both spatial and genetic information.

Details

The overall population is viewed as a co-existence of sub-populations at Hardy-Weiberg equilibrium. The sub-populations are supposed to be spatially organised through the so-called colored Poisson-Voronoi tessellation. Allele frequencies are assumed to be drawn from the F-model as described in (Falush 2003) although the particular case of independent Dirichlet allele frequencies, as described by Pritchard (2000), is also handled. Individuals within populations are assumed to be randomly located and Hardy-Weiberg equilibrium and linkage equilibrium are assumed.

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 mcmcFmodel. Function PostProcessChain read some output files of mcmcFmodel and computes some statistics suitable to print maps of inferred populations.

See Storage format section in mcmcFmodel help page.

The following functions are provided by the package:

simFmodel: simulation from the prior of the spatial F-model

mcmcFmodel: 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 sub-domains

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 mcmcFmodel and PostProcessChain to print your own figures.

PlotDrift: Graphical display of drift factors along MCMC run

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

Plotnclass: Graphical display of number of populations along MCMC run

Plotntile: Graphical display of number of tiles along MCMC run

PosteriorMode: Computation and/or graphical display of mode in the posterior distribution of class membership at each pixel

Fstat: Computations of pairwise F statistics between inferred subpopulations

FormatGenotypes: Transform a file of genotypes into a format suitable for function mcmcFmodel

setplot: Internal function

rdiscr: Internal function

Author(s)

Gilles Guillot

http://www.inapg.inra.fr/ens_rech/mathinfo/personnel/guillot/welcome.html

References

On the implementation of mixture models in population genetics:

- J.K. Pritchard, M. Stephens and P. Donnelly, Inference of population structure using multilocus genotype data, Genetics, pp 945-959 vol. 155, 2000

- Falush D., M. Stephens and J.K. Pritchard, Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies, Genetics, pp 1567-1587, vol 164, 2003

On the implementation of variable dimension MCMC algorihtm in population genetics:

- Corander, J.C., Waldmann, P. and Sillanpaa, M.J., Bayesian analysis of genetic differentiation between populations, Genetics, 2003, 163, 367-374

- Corander, J.C., P. Waldmann, P. Martinen and M.J. Sillanpaa, , BAPS2: Enhanced possibilities for the analysis of genetic population structure, Bioinformatics, vol. 20,number 15, 2004

On the use of Voronoi tessellations in population genetics :

- Dupanloup, I., Schneider, S. and Excoffier, L., A simulated annealing approach to define genetic structure of populations, Molecular Ecology, 2002, 11, 2571-2581

On the model (and sub-models) implemented in Geneland

- Guillot G. A. Estoup, F. Mortier, J.F. Cosson, A spatial statistical model for landscape genetics, Genetics, 2005

- Guillot, G., Geneland : A program for landscape genetics. Molecular Ecology Notes, submited.


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