PopGenome (version 2.7.2)

sweeps.stats-methods: Selective Sweeps

Description

This module calculates some statistics to detect selective sweeps.

Usage

# S4 method for GENOME
sweeps.stats(object,new.populations=FALSE,subsites=FALSE,
 freq.table=FALSE, FST=FALSE)
# S4 method for GENOME
get.sweeps(object)

Arguments

object

an object of class "GENOME"

new.populations

list of populations. default:FALSE

subsites

"transitions": SNPs that are transitions. "transversions": SNPs that are transversions. "syn": synonymous sites. "nonsyn": non-synonymous sites. "exon": SNPs in exon regions. "intron": SNPs in intron regions. "coding": SNPs in coding regions (CDS). "utr": SNPs in UTR regions. "gene": SNPs in genes. default:FALSE

freq.table

the frequency counts for the CLR test. "list"

FST

use FST values instead of the minor allele frequencies

Value

The return value is a modified object of class "GENOME" -------------------------------------------------------------- The following slots will be modified in the "GENOME" object --------------------------------------------------------------

CL

Composite Likelihood of SNPs

CLR

Nielsen's CLR test

Details

The freq.table contains the global sets of frequency counts. It can be produced with the module detail.stats. The values in the slot [e-mail]@minor.allele.frequencies can be used to create this global set. (use the R function table) freq.table is a list of length n.pops.

References

Cai JJ (2008) PGEToolbox: A Matlab toolbox for population genetics and evolution Journal of Heredity Jul-Aug;99(4):438-40.doi:10.1093/jhered/esm127 Nielson, R. (2005). Genomic scans for selective sweeps using SNP data Genome Res. 2005 15: 1566-1575

Examples

# NOT RUN {
# Reading one alignment stored in the folder Aln
# GENOME.class <- readData("\home\Aln")
#
# CL
# GENOME.class <- sweeps.stats(GENOME.class)
# GENOME.class@CL
#
# CLR
# create global set
# GENOME.class  <- detail.stats(GENOME.class)
# freq <- [e-mail]@minor.allele.freqs[[1]]
# freq.table <- list()
# freq.table[[1]] <- table(freq)
# define the region of interest
# GENOME.class.split <- splitting.data(GENOME.class, positions= ...)
# calculate CLR
# GENOME.class.split <- sweeps.stats(GENOME.class.split, freq.table=freq.table)
# GENOME.class@CLR
 

# }