bayescan: Identifying candidate loci under natural selection with...

In AFLPsim: Hybrid Simulation and Genome Scan for Dominant Markers

Description

This function calls Bayescan program from within R to identifying candidate loci under natural selection from genetic data.

Usage

bayescan(mat, filename, nbp = 20, pilot = 5000, burn = 50000, exec=NULL)

Arguments

mat	A matrix with genotypic data to test in hybridsim format
filename	a character string giving the name of the output file (without extension)
nbp	Number of pilot runs (default is 2)
pilot	Length of pilot runs (default is 50)
burn	Burnin length (default is 5000)
exec	a character string giving the path to BAYESCAN. By default it tries to guess it depending on the operating system (see details).

Details

bayescan tries to guess the name of the executable program depending on the operating system. Specifically, the followings are used: "bayescan_2.1" under Linux and Mac, or "C:/Program Files/BayeScan2.1/binaries/BayeScan2.1_win32bits_cmd_line.exe" under Windows.

Value

Several files with the results and a data.frame with the following variables:

prob	The posterior probability for the model including selection
log10.PO.	The logarithm of Posterior Odds to base 10
qval	q-values for each locus for the model including selection
alpha	The estimated alpha coefficient indicating the strength and direction of selection. See Bayescan 2.1 manual
fst	The Fst coefficient averaged over populations

Author(s)

F. Balao [e-mail], J.L. García-Castaño

References

Foll, M. & O. Gaggiotti. 2008. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180: 977-993.

See Also

gscan sim2bayescan

Examples

## Not run
hybrids<-hybridsim(Nmarker=100, Na=30, Nb=30, Nf1=30, type="selection", Nsel=25, hybrid="F1", S=100)
outbayes<-bayescan(hybrids, filename="bayescanresults", nbp=10, pilot=50, burn=100)
outbayes

Example output

sh: 1: bayescan_2.1: not found
[1] "Bayescan_2.1 not found"
NULL

AFLPsim documentation built on May 29, 2017, 11:31 a.m.