hap.score {gap} | R Documentation

## Score statistics for association of traits with haplotypes

### Description

Compute score statistics to evaluate the association of a trait with haplotypes, when linkage phase is unknown and diploid marker phenotypes are observed among unrelated subjects. For now, only autosomal loci are considered. This package haplo.score which this function is based is greatly acknowledged.

### Usage

    hap.score(y, geno, trait.type="gaussian", offset=NA, x.adj=NA,
              skip.haplo=0.005, locus.label=NA, miss.val=0, n.sim=0,
              method="gc", id=NA, handle.miss=0, mloci=NA, sexid=NA)

### Arguments

`y` | Vector of trait values. For trait.type = "binomial", y must have values of 1 for event, 0 for no event.
`geno` | 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(geno) = 2*K. Rows represent alleles for each subject.
`trait.type` | Character string defining type of trait, with values of "gaussian", "binomial", "poisson", "ordinal".
`offset` | Vector of offset when trait.type = "poisson"
`x.adj` | Matrix of non-genetic covariates used to adjust the score statistics. Note that intercept should not be included, as it will be added in this function.
`skip.haplo` | Skip score statistics for haplotypes with frequencies < skip.haplo
`locus.label` | Vector of labels for loci, of length K (see definition of geno matrix).
`miss.val` | Vector of codes for missing values of alleles.
`n.sim` | Number of simulations for empirical p-values. If n.sim=0, no empirical p-values are computed.
`method` | method of haplotype frequency estimation, "gc" or "hap"
`id` | an added option which contains the individual IDs
`handle.miss` | flag to handle missing genotype data, 0=no, 1=yes
`mloci` | maximum number of loci/sites with missing data to be allowed in the analysis
`sexid` | flag to indicator sex for data from X chromosome, i=male, 2=female

### Details

This is a version which substitutes haplo.em

### Value

List with the following components:

`score.global` | Global statistic to test association of trait with haplotypes that have frequencies >= skip.haplo.
`df` | Degrees of freedom for score.global.
`score.global.p` | P-value of score.global based on chi-square distribution, with degrees of freedom equal to df.
`score.global.p.sim` | P-value of score.global based on simulations (set equal to NA when n.sim=0).
`score.haplo` | Vector of score statistics for individual haplotypes that have frequencies >= skip.haplo.
`score.haplo.p` | Vector of p-values for score.haplo, based on a chi-square distribution with 1 df.
`score.haplo.p.sim` | Vector of p-values for score.haplo, based on simulations (set equal to NA when n.sim=0).
`score.max.p.sim` | P-value of maximum score.haplo, based on simulations (set equal to NA when n.sim=0).
`haplotype` | Matrix of hapoltypes analyzed. The ith row of haplotype corresponds to the ith item of score.haplo, score.haplo.p, and score.haplo.p.sim.
`hap.prob` | Vector of haplotype probabilies, corresponding to the haplotypes in the matrix haplotype.
`locus.label` | Vector of labels for loci, of length K (same as input argument).
`n.sim` | Number of simulations.
`n.val.global` | Number of valid simulated global statistics.
`n.val.haplo` | Number of valid simulated score statistics (score.haplo) for individual haplotypes.

### References

Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA (2002) Score tests for association of traits with haplotypes when linkage phase is ambiguous. Amer J Hum Genet 70:425-34

### Examples

    ## Not run:
    data(hla)
    y<-hla[,2]
    geno<-hla[,3:8]
    # complete data
    hap.score(y,geno,locus.label=c("DRB","DQA","DQB"))
    # incomplete genotype data
    hap.score(y,geno,locus.label=c("DRB","DQA","DQB"),handle.miss=1,mloci=1)
    unlink("assign.dat")

    ### note the differences in p values in the following runs
    data(aldh2)
    # to subset the data since hap doesn't handle one allele missing
    deleted<-c(40,239,256)
    aldh2[deleted,]
    aldh2<-aldh2[-deleted,]
    y<-aldh2[,2]
    geno<-aldh2[,3:18]
    # only one missing locus
    hap.score(y,geno,handle.miss=1,mloci=1,method="hap")
    # up to seven missing loci and with 10,000 permutations
    hap.score(y,geno,handle.miss=1,mloci=7,method="hap",n.sim=10000)

    # hap.score takes considerably longer time and does not handle missing data
    hap.score(y,geno,n.sim=10000)

    ## End(Not run)

[Package gap version 1.2.1 Index]