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 fo

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:
convergeIndicator of convergence of the EM algorithm (1=converged, 0 = failed).
niterNumber of iterations completed in the EM alogrithm.
locus.infoA 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.labelVector of labels for loci, of length K (see definition of input values).
haplotypeMatrix of unique haplotypes. Each row represents a unique haplotype, and the number of columns is the number of loci.
hap.probVector of mle's of haplotype probabilities. The ith element of hap.prob corresponds to the ith row of haplotype.
hap.prob.noLDSimilar to hap.prob, but assuming no linkage disequilibrium.
lnlikeValue of lnlike at last EM iteration (maximum lnlike if converged).
lrLikelihood ratio statistic to test no linkage disequilibrium among all loci.
indx.subjVector 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.
nrepsVector for the count of haplotype pairs that map to each subject's marker genotypes.
hap1codeVector 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.
hap2codeSimilar to hap1code, but for each subject's second haplotype.
postVector of posterior probabilities of pairs of haplotypes for a person, given thier marker phenotypes.
htrtableA 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

Examples

Run this code

data(hla)
gc.em(hla[,3:8],locus.label=c("DQR","DQA","DQB"),control=gc.control(assignment="t"))