R: Bayesian calculation of region-specific Fixation Index (FST)...

BlockFeST {BlockFeST} | R Documentation

## Bayesian calculation of region-specific Fixation Index (FST) to detect local adaptation

### Description

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).

### Usage

    BlockFeST(input,GROUP=FALSE,nb=20,runtime=500)

### Arguments

`input` | textfile or an R-object returned from the function `getBayes()` provided by the R-package PopGenome
`GROUP` | SNP groups
`nb` | number of MCMC runs
`runtime` | length of MCMC runs

### Details

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".

### Value

returned value is an object of class `"BAYESRETURN"`
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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

### References

[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.

### Examples

    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)

[Package BlockFeST version 2.0 Index]