haplo.stats (version 1.7.9)

haplo.score.slide: Score Statistics for Association of Traits with Haplotypes

Description

Used to identify sub-haplotypes from a group of loci. Run haplo.score on all contiguous subsets of size n.slide from the loci in a genotype matrix (geno). From each call to haplo.score, report the global score statistic p-value. Can also report global and maximum score statistics simulated p-values.

Usage

haplo.score.slide(y, geno, trait.type="gaussian", n.slide=2,
                  offset = NA, x.adj = NA, min.count=5,
                  skip.haplo=min.count/(2*nrow(geno)),
                  locus.label=NA, miss.val=c(0,NA),
                  haplo.effect="additive", eps.svd=1e-5,
                  simulate=FALSE, sim.control=score.sim.control(),
                  em.control=haplo.em.control())

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".

n.slide

Number of loci in each contiguous subset. The first subset is the ordered loci numbered 1 to n.slide, the second subset is 2 through n.slide+1 and so on. If the total number of loci in geno is n.loci, then there are n.loci - n.slide + 1 total subsets.

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.

min.count

The minimum number of counts for a haplotype to be included in the model. First, the haplotypes selected to score are chosen by minimum frequency greater than skip.haplo (based on min.count, by default). It is also used when haplo.effect is either dominant or recessive. This is explained best in the recessive instance, where only subjects who are homozygous for a haplotype will contribute information to the score for that haplotype. If fewer than min.count subjects are estimated to be affected by that haplotype, it is not scored. A warning is issued if no haplotypes can be scored.

skip.haplo

For haplotypes with frequencies < skip.haplo, categorize them into a common group of rare haplotypes.

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.

haplo.effect

The "effect" pattern of haplotypes on the response. This parameter determines the coding for scoring the haplotypes. Valid coding options for heterozygous and homozygous carriers of a haplotype are "additive" (1, 2, respectively), "dominant" (1,1, respectively), and "recessive" (0, 1, respectively).

eps.svd

epsilon value for singular value cutoff; to be used in the generalized inverse calculation on the variance matrix of the score vector.

simulate

Logical, if [F]alse (default) no empirical p-values are computed. If [T]rue simulations are performed. Specific simulation parameters can be controlled in the sim.control parameter list.

sim.control

A list of control parameters used to perform simulations for simulated p-values in haplo.score. The list is created by the function score.sim.control and the default values of this function can be changed as desired.

em.control

A list of control parameters used to perform the em algorithm for estimating haplotype frequencies when phase is unknown. The list is created by the function haplo.em.control and the default values of this function can be changed as desired.

Value

List with the following components:

df

Data frame with start locus, global p-value, simulated global p-value, and simulated maximum-score p-value.

n.loci

Number of loci given in the genotype matrix.

simulate

Same as parameter description above.

haplo.effect

The haplotype effect model parameter that was selected for haplo.score.

n.slide

Same as parameter description above.

locus.label

Same as parameter description above.

n.val.haplo

Vector containing the number of valid simulations used in the maximum-score statistic p-value simulation. The number of valid simulations can be less than the number of simulations requested (by sim.control) if simulated data sets produce unstable variables of the score statistics.

n.val.global

Vector containing the number of valid simulations used in the global score statistic p-value simulation.

Details

Haplo.score.slide is useful for a series of loci where little is known of the association between a trait and haplotypes. Using a range of n.slide values, the region with the strongest association will consistently have low p-values for locus subsets containing the associated haplotypes. The global p-value measures significance of the entire set of haplotypes for the locus subset. Simulated maximum score statistic p-values indicate when one or a few haplotypes are associated with the trait.

References

Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. "Score tests for association of traits with haplotypes when linkage phase is ambiguous." Amer J Hum Genet. 70 (2002): 425-434.

See Also

haplo.score, plot.haplo.score.slide, score.sim.control

Examples

# NOT RUN {
  data(hla.demo)

# Continuous trait slide by 2 loci on all 11 loci, uncomment to run it.
# Takes > 20 minutes to run
#  geno.11 <- hla.demo[,-c(1:4)]
#  label.11 <- c("DPB","DPA","DMA","DMB","TAP1","TAP2","DQB","DQA","DRB","B","A")
#  slide.gaus <- haplo.score.slide(hla.demo$resp, geno.11, trait.type = "gaussian",
#                                  locus.label=label.11, n.slide=2)

#  print(slide.gaus)
#  plot(slide.gaus)

# Run shortened example on 9 loci 
# For an ordinal trait, slide by 3 loci, and simulate p-values:
#  geno.9 <- hla.demo[,-c(1:6,15,16)]
#  label.9 <- c("DPA","DMA","DMB","TAP1","DQB","DQA","DRB","B","A")

#  y.ord <- as.numeric(hla.demo$resp.cat)

# data is set up, to run, run these lines of code on the data that was
# set up in this example. It takes > 15 minutes to run
#  slide.ord.sim <-  haplo.score.slide(y.ord, geno.9, trait.type = "ordinal",
#                      n.slide=3, locus.label=label.9, simulate=TRUE,
#                      sim.control=score.sim.control(min.sim=200, max.sim=500))

  # note, results will vary due to simulations
#  print(slide.ord.sim)
#  plot(slide.ord.sim)
#  plot(slide.ord.sim, pval="global.sim")
#  plot(slide.ord.sim, pval="max.sim")
# }