Description Usage Arguments Details Value References Examples
The method is based on the work from Beaumont and Balding (2004) where they introduce a F_{ST} based hierarchical Bayesian model to detect loci that are subject to selection. In this Bayesian approach they use a logistic regression model to distinguish between locusspecific effects like selection and populationspecific effects which are shared by all loci (e.g effects caused by migration rates) (Riebler, 2008). Foll and Gaggiotti (2008) extended this work using a reversible jump MCMC (Green, 1995) which enables testing the hypothesis that a locus is subject to selection; a very similar approach was developed in parallel by Riebler & Stefan (2008). The method is implemented in a software named BayeScan (http://cmpg.unibe.ch/software/BayeScan/). The new method introduced here is a modification of BayeScan (see details).
1 
input 
textfile or an Robject returned from the function 
GROUP 
SNP groups 
nb 
number of MCMC runs 
runtime 
length of MCMC runs 
BlockFeST considers all SNPs separately but generates exactly one regionspecific alpha
for each region (or group of SNPs). Example files can be found in the subdirectory "exdata".
returned value is an object of class "BAYESRETURN"
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Following Slots will be filled
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alpha 
alpha (α) effects 
beta 
beta (β) effects 
var_alpha 
variance of alphas 
fst 
Fe_{ST} values 
region.names 
names of region 
[1] Foll M and OE Gaggiotti (2008). A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180: 977993
[2] Beaumont M, Balding D. 2004. Identifying adaptive genetic divergence among populations from genome scans.Molecular Ecology. 13:969980.
[3] Riebler A, Held L, Stephan W. 2008. Bayesian variable selection for detecting adaptive genomic differences among populations. Genetics 178: 18171829
[4] Green PJ. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82: 711732.
1 2 3 4  snps < system.file("extdata", "snps.txt", package="BlockFeST")
groups < system.file("extdata", "groups.txt", package="BlockFeST")
BlockFeST.result < BlockFeST(input=snps, GROUP=groups, nb=3, runtime=10)
P < calcPval(BlockFeST.result)

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