AssotesteR (version 0.1-10)

ORWSS: ORWSS: Odds Ratio Weighted Sum Statistic

Description

The ORWSS method has been proposed by Feng et al (2011) and it is based on a weighted sum statistic like the WSS method of Madsen and Browning (2009). ORWSS uses the logarithm of the odds ratio of a genetic variant as the weight for that variant, rather than the variance estimated in controls.

Usage

ORWSS(y, X, c.param = NULL, 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

c.param

optional value to specify the c parameter. See reference Feng et al, 2011

perm

positive integer indicating the number of permutations (100 by default)

Value

"assoctest", basically a list with the following elements:

orwss.stat

orwss statistic

perm.pval

permuted p-value

args

descriptive information with number of controls, cases, variants, and permutations

name

name of the statistic

Details

When c.param=NULL, the weights of the sum statistic are simply the logarithm of the amended Odds Ratio of each variant (as in Dai et al 2012). Alternative values like c.param=1.64 or c.param=1.28 are suggested in Feng et al (2011).

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

References

Feng T, Elston RC, Zhu X (2011) Detecting Rare and Common Variants for Complex Traits: Sibpair and Odds Ratio Weighted Sum Statistics (SPWSS, ORWSS). Genetic Epidemiology, 35: 398-409

Dai Y, Jiang R, Dong J (2012) Weighted selective collapsing strategy for detecting rare and common variants in genetic association study. BMC Genetics, 13:7

See Also

WSS

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 ORWSS with c.param=NULL and 500 permutations
#   myorwss1 = ORWSS(phenotype, genotype, c.param=NULL, perm=100)
#   myorwss1
# 
#   # apply ORWSS with c.param=1.64 (see Feng et al 2011)
#   myorwss2 = ORWSS(phenotype, genotype, c.param=1.64, perm=100)
#   myorwss2
#   ## End(Not run)