AssotesteR (version 0.1-10)

SEQSUM: SEQSUM: Sequential Sum Score Test

Description

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).

Usage

SEQSUM(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)

Value

"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

Details

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

References

Basu S, Pan W (2011) Comparison of Statistical Tests for Disease Association with Rare Variants. Genetic Epidemiology, 35: 606-619

See Also

SCORE, SUM

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(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)