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gap (version 1.2.3-1)

gc.em: Gene counting for haplotype analysis

Description

Gene counting for haplotype analysis with missing data, adapted for hap.score

Usage

gc.em(data, locus.label=NA, converge.eps=1e-06, maxiter=500, 
      handle.miss=0, miss.val=0, control=gc.control())

Arguments

data

Matrix of alleles, such that each locus has a pair of adjacent columns of alleles, and the order of columns corresponds to the order of loci on a chromosome. If there are K loci, then ncol(data) = 2*K. Rows represent alleles for each subject.

locus.label

Vector of labels for loci, of length K (see definition of data matrix).

converge.eps

Convergence criterion, based on absolute change in log likelihood (lnlike).

maxiter

Maximum number of iterations of EM.

handle.miss

a flag for handling missing genotype data, 0=no, 1=yes

miss.val

missing value

control

a function, see genecounting

Value

List with components:

converge

Indicator of convergence of the EM algorithm (1=converged, 0 = failed).

niter

Number of iterations completed in the EM alogrithm.

locus.info

A list with a component for each locus. Each component is also a list, and the items of a locus- specific list are the locus name and a vector for the unique alleles for the locus.

locus.label

Vector of labels for loci, of length K (see definition of input values).

haplotype

Matrix of unique haplotypes. Each row represents a unique haplotype, and the number of columns is the number of loci.

hap.prob

Vector of mle's of haplotype probabilities. The ith element of hap.prob corresponds to the ith row of haplotype.

hap.prob.noLD

Similar to hap.prob, but assuming no linkage disequilibrium.

lnlike

Value of lnlike at last EM iteration (maximum lnlike if converged).

lr

Likelihood ratio statistic to test no linkage disequilibrium among all loci.

indx.subj

Vector for index of subjects, after expanding to all possible pairs of haplotypes for each person. If indx=i, then i is the ith row of input matrix data. If the ith subject has n possible pairs of haplotypes that correspond to their marker phenotype, then i is repeated n times.

nreps

Vector for the count of haplotype pairs that map to each subject's marker genotypes.

hap1code

Vector of codes for each subject's first haplotype. The values in hap1code are the row numbers of the unique haplotypes in the returned matrix haplotype.

hap2code

Similar to hap1code, but for each subject's second haplotype.

post

Vector of posterior probabilities of pairs of haplotypes for a person, given thier marker phenotypes.

htrtable

A table which can be used in haplotype trend regression

References

Zhao, J. H., Lissarrague, S., Essioux, L. and P. C. Sham (2002). GENECOUNTING: haplotype analysis with missing genotypes. Bioinformatics 18(12):1694-1695

Zhao, J. H. and P. C. Sham (2003). Generic number systems and haplotype analysis. Comp Meth Prog Biomed 70: 1-9

See Also

genecounting, LDkl

Examples

Run this code
# NOT RUN {
data(hla)
gc.em(hla[,3:8],locus.label=c("DQR","DQA","DQB"),control=gc.control(assignment="t"))
# }

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