gap (version 1.1-2)

hwe.cc: A likelihood ratio test of population Hardy-Weinberg equilibrium for case-control studies

Description

A likelihood ratio test of population Hardy-Weinberg equilibrium for case-control studies

Usage

hwe.cc(model, case, ctrl, k0, initial1, initial2)

Arguments

model

model specification, dominant, recessive

case

a vector of genotype counts in cases

ctrl

a vector of genotype counts in controls

k0

prevalence of disease in the population

initial1

initial values

initial2

initial values

Value

• The returned value is a list with the following components.
• Cox
  statistics under a general model
• t2par
  under the null hypothesis
• t3par
  under the alternative hypothesis
• lrt.stat
  the log-likelihood ratio statistic
• pval
  the corresponding p value

References

Yu C, Zhang S, Zhou C, Sile S. A likelihood ratio test of population Hardy-Weinberg equilibrium for case-control studies. Genetic Epidemiology 33:275-280, 2009

Details

This is a collection of utility functions. The null hypothesis declares that the proportions of genotypes are according to Hardy-Weinberg law, while under the alternative hypothesis, the expected genotype counts are according to the probabilities that particular genotypes are obtained conditional on the prevalence of disease in the population. In so doing, Hardy-Weinberg equilibrium is considered using both case and control samples but pending on the disease model such that 2-parameter multiplicative model is built on baseline genotype $\alpha$, $\alpha\beta$ and $\alpha\gamma$.

See Also

hwe

Examples

### Saba Sile, email of Jan 26, 2007, data always in order of GG AG AA, p=Pr(G), q=1-p=Pr(A)

case=c(155,27,4)
ctrl=c(408,55,15)
k0<-.20
initial1 <- c(1.2,0.7,1-.910)
initial2 <- c(logit(1-0.107), log(0.85))               # logit(p), log(gamma)
hwe.cc("recessive",case,ctrl,k0,initial1,initial2)

### John Phillips III, TGFb1 data codon 10: TT CT CC, CC is abnormal and increasing TGFb1 activity
case=c(29,78,13)
ctrl=c(17,28,6)
k0 <- 1e-5
initial1 <- c(1.6,1.3,1-.608)
initial2 <- c(logit(1-0.392),log(1.07))                # logit(p), log(gamma)
hwe.cc("dominant",case,ctrl,k0,initial1,initial2)