Haplotype disease risk is calculated resolving haplotype ambiguity and adjusting for covariates and population stratification.
haplotypeOddsRatio(formula, gtypevar, data, stratvar=NULL, nsim=100, tol=1e-8)
# S3 method for haploOR
print(x, ...)It is a list of class haploOR
The function call that produced this output.
Table with estimated coefficients, standard error, Z-statistic and p-value.
Covariance matrix of the estimated log odds-ratiios.
Average of the simulated deviances. Its theoretical properties are unknown.
Average of the simulated aic.
Deviance of null model.
Degrees of freedom of null model.
Degrees of freedom of full model.
The "print" method formats the results into a user-friendly table.
The formula for logistic regression without the haplotype variable.
The variable names in the data frame corresponding to the loci of interest. Each variables counts the number of mutant genotypes a subject has at that locus. Legal values are 0, 1, 2 & NA.
The name of the dataframe being analyzed. It should have all the variables in the formula as well as those in genotype and stratvar.
Name of the stratification variable. This is used to account for population stratification. The haplotype frequencies are estimated within each stratum.
Variance should be inflated to account for inferred ambiguous haplotypes. The estimates are recalculated by simulating the disease haplotype copy number and variance added to average.
Tolerance limit for the EM algorithm convergence.
Object of class haploOR.
Other print options.
Venkatraman E. Seshan
This implements the method in the reference below.
Venkatraman ES, Mitra N, Begg CB. (2004) A method for evaluating the impact of individual haplotypes on disease incidence in molecular epidemiology studies. Stat Appl Genet Mol Biol. v3:Article27.
# simulated data with 2 loci haplotypes 1=00, 2=01, 3=10, 4=11
# control haplotype probabilities p[i] i=1,2,3,4
# haplotype pairs (i<=j) i=j: probs = p[i]^2 ; iRun the code above in your browser using DataLab