haplo.cc {haplo.stats} | R Documentation |
Combine results from haplo.score, haplo.group, and haplo.glm for case-control study designs. Analyze the association between the binary (case-control) trait and the haplotypes relevant to the unrelated individuals' genotypes.
haplo.cc(y, geno, haplo.min.count=5, locus.label=NA, ci.prob=0.95, miss.val=c(0,NA), simulate=FALSE, sim.control=score.sim.control(), control=haplo.glm.control())
y |
Vector of trait values, must be 1 for cases and 0 for controls. |
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. |
haplo.min.count |
The minimum number of expected counts for a haplotype in the sample based on estimated frequencies. |
ci.prob |
Probability level for confidence interval on the Odds Ratios of each haplotype to span the true value. |
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 |
simulate |
Logical: if [F]alse, no empirical p-values are computed; if [T]rue, simulations are performed within haplo.score. Specific simulation parameters can be controlled in the sim.control parameter list. |
sim.control |
A list of control parameters to determine how simulations are performed for simulated p-values. The list is created by the function score.sim.control and the default values of this function can be changed as desired. See score.sim.control for details. |
control |
A list of control parameters for managing the execution of haplo.cc. The list is created by the function haplo.glm.control, which also manages control parameters for the execution of haplo.em. |
All function calls within haplo.cc are for the analysis of association between haplotypes and the case-control status (binomial trait). No additional covariates may be modeled with this function. Odd Ratios are in reference to the baseline haplotype. Odds Ratios will change if a different baseline is chosen using haplo.glm.control.
A list including the haplo.score object (score.lst), vector of subject counts by case and control group (group.count), haplo.glm object (fit.lst), confidence interval probability (ci.prob), and a data frame (cc.df) with the following components:
haplotypes |
The first K columns contain the haplotypes used in the analysis. |
Hap-Score |
Score statistic for association of haplotype with the binary trait. |
p-val |
P-value for the haplotype score statistic, based on a chi-square distribution with 1 degree of freedom. |
sim.p.val |
Vector of p-values for score.haplo, based on simulations in haplo.score (omitted when simulations not performed). P-value of score.global based on simulations (set equal to NA when simulate=F). |
pool.hf |
Estimated haplotype frequency for cases and controls pooled together. |
control.hf |
Estimated haplotype frequency for control group subjects. |
case.hf |
Estimated haplotype frequency for case group subjects. |
glm.eff |
The haplo.glm function modeled the haplotype effects as: baseline (Base), additive haplotype effect (Eff), or rare haplotypes pooled into a single group (R). |
OR.lower |
Lower limit of the Odds Ratio Confidence Interval. |
OR |
Odds Ratio based on haplo.glm model estimated coefficient for the haplotype. |
OR.upper |
Upper limit of the Odds Ratio Confidence Interval. |
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.
Lake S, LH, Silverman E, Weiss S, Laird N, Schaid DJ. "Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous. Human Heredity. 55 (2003): 56-65
haplo.em
,
haplo.score
,
haplo.group
,
haplo.score.merge
,
haplo.glm
print.haplo.cc
# For a genotype matrix geno.test, case/control vector y.test # The function call will be like this # cc.test <- haplo.cc(y.test, geno.test, locus.label=locus.label, haplo.min.count=3, ci.prob=0.95) #