WSS: Weighted Sum Statistic

Description

The WSS method has been proposed by Madsen and Browning (2009) as a pooling approach. In WSS, rare-variant counts within the same gene for each individual are accumulated rather than collapsing on them. Second, it introduces a weighting term to emphasize alleles with a low frequency in controls. Finally, the scores for all samples are ordered, and the WSS is computed as the sum of ranks for cases. The significance is determined by a permutation procedure.

Usage

1
  WSS(y, X, 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

perm

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

Details

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

Value

An object of class "assoctest", basically a list with the following elements:

wss.stat

wss statistic

perm.pval

permuted p-value

args

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

name

name of the statistic

Author(s)

Gaston Sanchez

References

Madsen BE, Browning SR (2009) A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLoS Genetics, 5(2): e1000384

See Also

CMC

Examples

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  ## 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(123)
  genotype = matrix(rbinom(total*10, 2, 0.05), nrow=total, ncol=10)

  # apply WSS with 500 permutations
  mywss = WSS(phenotype, genotype, perm=500)
  mywss
  
## End(Not run)