gc.em {gap}

Gene counting for haplotype analysis

Description

Gene counting for haplotype analysis

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.

Details

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

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.

Note

Adapted from GENECOUNTING.

Author(s)

Jing Hua Zhao

References

Zhao JH, Lissarrague S, Essioux L, Sham PC (2002). “GENECOUNTING: haplotype analysis with missing genotypes.” Bioinformatics, 18(12), 1694-5. ISSN 1367-4803 (Print) 1367-4803, doi:10.1093/bioinformatics/18.12.1694.

Zhao JH, Sham PC (2003). “Generic number systems and haplotype analysis.” Comput Methods Programs Biomed, 70(1), 1-9. ISSN 0169-2607 (Print) 0169-2607, doi:10.1016/s0169-2607(01)00193-6.

See Also

genecounting, LDkl

Examples

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

## End(Not run)