AssotesteR (version 0.1-10)

CMAT: CMAT: Cumulative Minor Allele Test

Description

CMAT is a pooling method proposed by Zawistowski et al (2010). CMAT works by comparing weighted minor-allele counts (for cases and controls) against the weighted major-allele counts (for cases and controls). Although the CMAT test statistic is based on a chi-square statistic, it does not follow a known distribution and its significance has to be determined by a permutation procedure.

Usage

CMAT(y, X, maf = NULL, weights = NULL, perm = 100)

Arguments

y

numeric vector with phenotype status: 0=controls, 1=cases. No missing data allowed

X

numeric matrix or data frame with genotype data coded as 0, 1, 2. Missing data is allowed

maf

optional numeric value to specify a threshold for the minor allele frequency of rare variants (NULL by default)

weights

optional vector of weights for the variants (NULL by default)

perm

positive integer indicating the number of permutations (100 by default)

Value

"assoctest", basically a list with the following elements:

cmat.stat

cmat statistic

perm.pval

permuted p-value

args

descriptive information with number of controls, cases, variants, rare variants, maf, and permutations

name

name of the statistic

Details

By default, argument maf=NULL meaning that no rare variants are selected

By default, argument weights=NULL but different values for the variants can be provided

Statistical significance is determined by permutation procedure

There is no imputation for the missing data. Missing values are simply ignored in the computations.

References

Zawistowski M, Gopalahrishnan S, Ding J, Li Y, Grimm S, Zollner S (2010) The American Journal of Human Genetics, 87: 604-617

See Also

CMC, WSS

Examples

  ## Not run: 
#   
#   # number of cases
#   cases = 500
# 
#   # number of controls
#   controls = 500
# 
#   # total (cases + controls)
#   total = cases + controls
# 
#   # phenotype vector
#   phenotype = c(rep(1,cases), rep(0,controls))
# 
#   # genotype matrix with 10 variants (random data)
#   set.seed(1234)
#   genotype = matrix(rbinom(total*10, 2, 0.051), nrow=total, ncol=10)
# 
#   # apply CMAT with 500 permutations
#   mycmat1 = CMAT(phenotype, genotype, perm=500)
#   mycmat1
# 
#   # apply CMAT with maf=0.05 and 500 permutations
#   mycmat2 = CMAT(phenotype, genotype, maf=0.05, perm=500)
#   mycmat2
#   ## End(Not run)