rehh (version 2.0.4)

ihh2ihs: Compute iHS (standardized iHH)

Description

Compute iHS (standardized iHH).

Usage

ihh2ihs(res_ihh, freqbin = 0.025, minmaf = 0.05)

Arguments

res_ihh

A dataframe with nsnps rows and seven columns as obtained from the scan_hh function applied to the population of interest.

freqbin

Size of the bin to standardize log(iHHA/iHHD) according to the underlying "core" allele frequency at the focal SNP. Allele frequency bins are built from minmaf to 1-minmaf in steps of size freqbin. If freqbin is set to 0, standardization is performed considering each observed frequency as a discrete frequency class (useful in case of a large number of SNPs and few different haplotypes).

If freqbin is set to an integer of 1 or greater, a corresponding number of equally sized bins are used.

minmaf

SNPs with a MAF (Minor Allele Frequency) lower than minmaf are discarded from the analysis

Value

The returned value is a list containing two elements

res.ihs

a dataframe with nsnps rows and four columns (Chromosome name, position of the SNP, iHS and Pvalue in a log10 scale)

summary.class

matrix with nclasses rows and three columns (Number of SNPs belonging to this class,position of the SNP, mean iHH in this class, standard deviation of iHH in this class)

Details

iHS is calculated as described in Voight et al. (2006)

References

Gautier, M. and Naves, M. (2011). Footprints of selection in the ancestral admixture of a New World Creole cattle breed. Molecular Ecology, 20, 3128--3143.

Voight, B.F. and Kudaravalli, S. and Wen, X. and Pritchard, J.K. (2006). A map of recent positive selection in the human genome. Plos Biology, 4, e72.

See Also

calc_ehh,scan_hh,distribplot,ihsplot

Examples

# NOT RUN {
data(wgscan.cgu)
## results from a genome scan (44,057 SNPs)
##see ?wgscan.eut and ?wgscan.cgu for details
res.ihs<-ihh2ihs(wgscan.cgu)
# }