BlockFeST {BlockFeST} | R Documentation |
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 locus-specific effects like selection and population-specific 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).
BlockFeST(input,GROUP=FALSE,nb=20,runtime=500)
input |
textfile or an R-object 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 region-specific 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: 977-993
[2] Beaumont M, Balding D. 2004. Identifying adaptive genetic divergence among populations from genome scans.Molecular Ecology. 13:969-980.
[3] Riebler A, Held L, Stephan W. 2008. Bayesian variable selection for detecting adaptive genomic differences among populations. Genetics 178: 1817-1829
[4] Green PJ. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82: 711-732.
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)