hap.score: 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.

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, n.miss.loci=NA, sexid=NA)

Arguments

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"

an added option which contains the individual IDs

handle.miss

flag to handle missing genotype data, 0=no, 1=yes

n.miss.loci

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

Value

List with the following components:
score.globalGlobal statistic to test association of trait with haplotypes that have frequencies >= skip.haplo.
dfDegrees of freedom for score.global.
score.global.pP-value of score.global based on chi-square distribution, with degrees of freedom equal to df.
score.global.p.simP-value of score.global based on simulations (set equal to NA when n.sim=0).
score.haploVector of score statistics for individual haplotypes that have frequencies >= skip.haplo.
score.haplo.pVector of p-values for score.haplo, based on a chi-square distribution with 1 df.
score.haplo.p.simVector of p-values for score.haplo, based on simulations (set equal to NA when n.sim=0).
score.max.p.simP-value of maximum score.haplo, based on simulations (set equal to NA when n.sim=0).
haplotypeMatrix 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.probVector of haplotype probabilies, corresponding to the haplotypes in the matrix haplotype.
locus.labelVector of labels for loci, of length K (same as input argument).
n.simNumber of simulations.
n.val.globalNumber of valid simulated global statistics.
n.val.haploNumber of valid simulated score statistics (score.haplo) for individual haplotypes.

References

Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for association of traits with haplotypes when linkage phase is ambiguous. Submitted to Amer J Hum Genet.

Details

This is a version which substitutes haplo.em

Examples

Run this code

data(hla)
y<-hla[,2]
geno<-hla[,3:8]
hap.score(y,geno,locus.label=c("DRB","DQA","DQB"))
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,n.miss.loci=1,method="hap")
# up to seven missing loci and with 10,000 permutations
hap.score(y,geno,handle.miss=1,n.miss.loci=7,method="hap",n.sim=10000)

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

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