gap (version 0.4)

genecounting: Gene counting for haplotype analysis

Description

Gene counting for haplotype analysis with missing data

Usage

genecounting(data,weight=NULL,convll,handle.miss,eps,maxit,pl)

Arguments

data

genotype table

weight

a column of frequencies

convll

set convergence criteria according to log-likelihood, if its value set to 1

handle.miss

to handle missing data, if its value set to 1

eps

the actual convergence criteria, with default value 1e-5

maxit

maximum number of iterations, with default value 50

pl

criteria for trimming haplotypes according to posterior probabilities

Value

• The returned value is a list containing:
• h
  haplotype frequency estimates under linkage disequilibrium
• h0
  haplotype frequency estimates under linkage equilibrium
• prob
  genotype probability estimates
• lnl0
  log-likelihood under linkage equilibrium
• lnl1
  log-likelihood under linkage disequilibrium
• hapid
  unique haplotype identifier (defunct, see gc.em)
• npusr
  number of parameters according user-given alleles
• npdat
  number of parameters according to observed
• htrtable
  design matrix for haplotype trend regression (defunct, see gc.em)
• iter
  number of iterations used in gene counting
• converge
  a flag indicating convergence status of gene counting
• Dprime
  a multilocus version of Hedrick's D'

References

Zhao, J. H., Lissarrague, S., Essioux, L. and P. C. Sham (2002). Gene-counting for 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

gc.em, kbyl

Examples

# Now we use the HLA data for testing
data(hla)
hla.gc<-genecounting(hla[,3:8])
summary(hla.gc)
hla.gc$lnl0
hla.gc$lnl1

# Now we use ALDH2 data
data(aldh2)
aldh2.gc<-genecounting(aldh2[,3:6],handle.miss=1)
summary(aldh2.gc)
aldh2.gc$lnl0
aldh2.gc$lnl1