haplotypeOddsRatio {genepi} | R Documentation |
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 class 'haploOR' print(x, ...)
formula |
The formula for logistic regression without the haplotype variable. |
gtypevar |
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. |
data |
The name of the dataframe being analyzed. It should have all the variables in the formula as well as those in genotype and stratvar. |
stratvar |
Name of the stratification variable. This is used to account for population stratification. The haplotype frequencies are estimated within each stratum. |
nsim |
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. |
tol |
Tolerance limit for the EM algorithm convergence. |
x |
Object of class haploOR. |
... |
Other print options. |
This implements the method in the reference below.
It is a list of class haploOR
call |
The function call that produced this output. |
coef |
Table with estimated coefficients, standard error, Z-statistic and p-value. |
var |
Covariance matrix of the estimated log odds-ratiios. |
deviance |
Average of the simulated deviances. Its theoretical properties are unknown. |
aic |
Average of the simulated aic. |
null.deviance |
Deviance of null model. |
df.null |
Degrees of freedom of null model. |
df.residual |
Degrees of freedom of full model. |
The "print" method formats the results into a user-friendly table.
Venkatraman E. Seshan
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 ; i<j: p[i]*p[j] p <- c(0.25, 0.2, 0.2, 0.35) p0 <- rep(0, 10) l <- 0 for(i in 1:4) {for(j in i:4) {l <- l+1; p0[l] <- 2*p[i]*p[j]/(1+1*(i==j))}} controls <- as.numeric(cut(runif(1000), cumsum(c(0,p0)), labels=1:10)) # case probabilities disease haplotype is 11 or <- c(2, 5) p1 <- p0*c(1,1,1,2,1,1,2,1,2,8); p1 <- p1/sum(p1) cases <- as.numeric(cut(runif(1000), cumsum(c(0,p1)), labels=1:10)) # now pool them together and set up the data frame dat <- data.frame(status=rep(0:1, c(1000, 1000))) # number of copies of mutant variant for locus 1 dat$gtype1 <- c(0,0,1,1,0,1,1,2,2,2)[c(controls, cases)] # number of copies of mutant variant for locus 2 dat$gtype2 <- c(0,1,0,1,2,1,2,0,1,2)[c(controls, cases)] # true number of copies of disease haplotype dat$hcn <- c(0,0,0,1,0,0,1,0,1,2)[c(controls, cases)] # model with genotypes only haplotypeOddsRatio(status ~ 1, c("gtype1","gtype2"), dat) # model from the logistic fit using the number of copies of disease haplotype glm(status ~ factor(hcn), dat, family=binomial)