Exact Max CATT Test: MAX CATT test in case-control genetic association analysis

Description

Perform exact MAX3 test, MAX2 test and Cochran-Armitage trend test for one-locus genetic association analysis. MAX3 is the maximum of the Cochran-Armitage trend test statistic for dominant, recessive and additive models, and MAX2 is the maximum of dominant and recessive models. It also provides approximated p-value of a MAX test with the normal approximation method.

Usage

maxact.test(data, max3 = TRUE, exact=TRUE, alternative = "two.sided")
catt.test(data, theta, exact=TRUE, alternative = "two.sided")

Arguments

data

2x3 contingency table of data values, with rows representing case/control, and columns representing the three genotypes

max3

a logical indicating whether MAX3 or MAX2 test statistic should be used

theta

parameter of the Cochran-Armitage trend test

exact

a logical indicating whether an exact p-value should be computed

alternative

a character string describing the alternative hypothesis, which must be one of '"two.sided"', '"greater"' or '"less"'

Value

data.name: a character string giving the names of the data.
p.value: the p-value of the test.
method: a character string describing the method used.
statistic: the value of the test statistic.
alternative: a character string describing the alternative hypothesis.

References

Tian, J., Xu, C., Zhan, H., Yang, Y. (2009) "Exact MAX tests in case-control association analysis (Manuscript)."

Examples

Run this code

data.sladek <- matrix(c(129, 326, 229, 198, 325, 143), 2, 3, byrow=TRUE)

## MAX3 test (exact p-value)
maxact.test(data.sladek) 

## MAX2 test (exact p-value):
maxact.test(data.sladek, max3=FALSE, exact=TRUE)


## MAX test (p-value calculated from the normal approximation method), it
## is not recommends, see NOTE for more information
 maxact.test(data.sladek, max3=TRUE, exact=FALSE)
 maxact.test(data.sladek, max3=FALSE, exact=FALSE)

## Cochran-Armitage trend test, assuming we know the model 
 catt.test(data.sladek, theta=0)    #recessive model
 catt.test(data.sladek, theta=0.5)  #additive model
 catt.test(data.sladek, theta=1)    #dominant model

#the  alternative hypothesis is less
 catt.test(data.sladek, theta=0, alternative="less")