hap.score {gap} | R Documentation |
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.
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)
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 |
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 |
This is a version which substitutes haplo.em
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. |
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.
## Not run: 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) ## End(Not run)