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gap

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

Note

Adapted from GENECOUNTING

Author(s)

Jing Hua Zhao

Examples

## Not run: 
data(hla)
gc.em(hla[,3:8],locus.label=c("DQR","DQA","DQB"),control=gc.control(assignment="t"))

## End(Not run)

gap

Genetic Analysis Package

v1.2.3-1

GPL (>= 2)

Authors

Jing Hua Zhao and colleagues with inputs from Kurt Hornik and Brian Ripley

Initial release

2021-4-21