gap (version 1.1-16)

hap: 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,

1. mb Maximum dynamic storage to be allocated, in Mb
2. pr Prior (ie population) probability threshold
3. po Posterior probability threshold
4. to Log-likelihood convergence tolerance
5. th Posterior probability threshold for output
6. maxit Maximum EM iteration
7. n Force numeric allele coding (1/2) on output (off)
8. ss Tab-delimited speadsheet file output (off)
9. rs Random starting points for each EM iteration (off)
10. rp Restart from random prior probabilities
11. ro Loci added in random order (off)
12. rv Loci added in reverse order (off)
13. sd Set seed for random number generator (use date+time)
14. mm Repeat final maximization multiple times
15. mi Create multiple imputed datasets. If set >0
16. mc Number of MCMC steps between samples
17. ds Starting value of Dirichlet prior parameter
18. de Finishing value of Dirichlet prior parameter
19. q Quiet operation (off)
20. hapfile a file for haplotype frequencies
21. assignfile a file for haplotype assignment

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

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.

See Also

genecounting

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)