WSS {AssotesteR} | R Documentation |
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.
WSS(y, X, perm = 100)
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) |
There is no imputation for the missing data. Missing values are simply ignored in the computations.
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 |
Gaston Sanchez
Madsen BE, Browning SR (2009) A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic. PLoS Genetics, 5(2): e1000384
## 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)