Description Usage Arguments Details Value Author(s) References See Also Examples
The RWAS method has been proposed by Sul et al (2011) as a pooling method that groups variants and computes a weighted sum of differences between case and control mutation counts where weights are estimated from data. Under the null hypothesis the RWAS statistic has an asymptotic standard normal distribution, but a permutation procedure can also be applied to assess statistical significance
1 |
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 |
numeric value indicating the minor allele frequency threshold for rare variants ( |
perm |
positive integer indicating the number of permutations ( |
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:
rwas.stat |
rwas statistic |
asym.pval |
asymptotic p-value |
perm.pval |
permuted p-value, only when |
args |
descriptive information with number of controls, cases, variants, rare variants, maf and permutations |
name |
name of the statistic |
Gaston Sanchez
Sul JH, Han B, He D, Eskin E (2011) An Optimal Weighted Aggregated Association Test for Identification of Rare Variants Involved in Common Diseases. Genetics, 188: 181-188
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## 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 RWAS with maf=0.05 and 500 permutations
myrwas = RWAS(phenotype, genotype, maf=0.05, perm=500)
myrwas
## End(Not run)
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