SEQSUM: SEQSUM: Sequential Sum Score Test
In AssotesteR: Statistical Tests for Genetic Association Studies

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

SCORE, SUM

  ## 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)