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Last data update: 2014.03.03

R: Haplotype reconstruction

hap	R Documentation

Haplotype reconstruction

Description

Haplotype reconstruction using sorting and trimming algorithms

Usage

hap(id,data,nloci,loci=rep(2,nloci),names=paste("loci",1:nloci,sep=""),
              control=hap.control())

Arguments

`id`	a column of subject id
`data`	genotype table
`nloci`	number of loci
`loci`	number of alleles at all loci
`names`	locus names
`control`	is a function with the following arguments, mb Maximum dynamic storage to be allocated, in Mb pr Prior (ie population) probability threshold po Posterior probability threshold to Log-likelihood convergence tolerance th Posterior probability threshold for output maxit Maximum EM iteration n Force numeric allele coding (1/2) on output (off) ss Tab-delimited speadsheet file output (off) rs Random starting points for each EM iteration (off) rp Restart from random prior probabilities ro Loci added in random order (off) rv Loci added in reverse order (off) sd Set seed for random number generator (use date+time) mm Repeat final maximization multiple times mi Create multiple imputed datasets. If set >0 mc Number of MCMC steps between samples ds Starting value of Dirichlet prior parameter de Finishing value of Dirichlet prior parameter q Quiet operation (off) hapfile a file for haplotype frequencies assignfile a file for haplotype assignment

Details

The package can hanlde much larger number of multiallelic loci. For large sample size with relatively small number of multiallelic loci, genecounting should be used.

Value

The returned value is a list containing:

`l1`	log-likelihood assuming linkage disequilibrium
`converge`	convergence status, 0=failed, 1=succeeded
`niter`	number of iterations

References

Clayton DG (2001) SNPHAP. http://www-gene.cimr.cam.ac.uk/clayton/software

Zhao JH and W Qian (2003) Association analysis of unrelated individuals using polymorphic genetic markers. RSS 2003, Hassalt, Belgium

Zhao JH (2004). 2LD, GENECOUNTING and HAP: Computer programs for linkage disequilibrium analysis. Bioinformatics 20: 1325-1326

Note

adapted from hap

Examples

## Not run: 
# 4 SNP example, to generate hap.out and assign.out alone
data(fsnps)
hap(id=fsnps[,1],data=fsnps[,3:10],nloci=4)
dir()
file.show("hap.out")
file.show("assign.out")

# to generate results of imputations
control <- hap.control(ss=1,mi=5,hapfile="h",assignfile="a")
hap(id=fsnps[,1],data=fsnps[,3:10],nloci=4,control=control)
dir()

## End(Not run)