gap (version 1.6)

hap.score: Score statistics for association of traits with haplotypes

Description

Score statistics for association of traits with haplotypes

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
)

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.

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

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.

This is a version which substitutes haplo.em.

References

schaid02gap

Examples

if (FALSE) {
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)
}