Description Usage Arguments Details Value Author(s) References See Also Examples
SEQSUM has been proposed by Basu and Pan (2011) as a modification of the Sum test based on a model selection approach, following a similar philosophy as the CARV and RARECOVER methods. Assuming that there are M variants, the main idea behind the Sequential Sum test is to associate a sign to each variant indicating whether it has a positive effect or a negative effect. In other words, the purpose is to give signs to the variants so they reflect their effect (positive or negative).
1 | SEQSUM(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:
seqsum.stat |
seqsum statistic |
perm.pval |
permuted p-value |
signs |
a numeric vector with signs for the variants (1=positive, -1=negative) |
args |
descriptive information with number of controls, cases, variants, and permutations |
name |
name of the statistic |
Gaston Sanchez
Basu S, Pan W (2011) Comparison of Statistical Tests for Disease Association with Rare Variants. Genetic Epidemiology, 35: 606-619
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(123)
genotype = matrix(rbinom(total*10, 2, 0.05), nrow=total, ncol=10)
# apply SEQSUM with 500 permutations
myseq = SEQSUM(phenotype, genotype, perm=500)
myseq
## End(Not run)
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