CALPHA: CALPHA: C-alpha Score Test

In AssotesteR: Statistical Tests for Genetic Association Studies

Description

The C-alpha score-test of Neyman and Scott (1966) has been proposed to be used in association astudies by Neale et al (2011) in order to test the observed distributino of rare variants in cases versis controls. Under the null hypothesis of no association between the variants and the phenotype, C-alpha assumes that the distribution of counts (copies of an observed variant) should follow a binomial distribution. The C-alpha test statistic contrasts the variance of each observed count with the expected variance, assuming the binomial distribution. Under the null hypothesis, the test statistic follows a standard normal distribution

Usage

  CALPHA(y, X, perm = NULL)

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 (NULL by default)

Details

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

Value

An object of class "assoctest", basically a list with the following elements:

calpha.stat	c-alpha statistic
asym.pval	asymptotic p-value
perm.pval	permuted p-value; only when perm is used
args	descriptive information with number of controls, cases, variants, and permutations
name	name of the statistic

Author(s)

Gaston Sanchez

References

Neyman J, Scott E (1966) On the use of c-alpha optimal tests of composite hypothesis. Bulletin of the International Statistical Institute, 41: 477-497

Neale BM, Rivas MA, Voight BF, Altshuler D, Devlin B, Orho-Melander M, Kathiresan S, Purcell SM, Roeder K, Daly MJ (2011) Testing for an unusual distribution of rare variants. PLoS Genetics, 7(3): e1001322

Basu S, Pan W (2011) Comparison of Statistical Tests for Disease Association With Rare Variants. Genetic Epidemiology, 35(7): 606-619

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, 0.10), nrow=total, ncol=10)

  # apply CALPHA with 500 permutations
  mycalpha = CALPHA(phenotype, genotype, perm=500)

  # this is what we get
  mycalpha
  
## End(Not run)

Example output

Loading required package: mvtnorm

 	 CALPHA: c-alpha Test 

Info: 
   cases  controls  variants   n.perms  
     500       500        10       500  

calpha.stat     asym.pval     perm.pval   
-348.500000      0.093765      0.036000   

AssotesteR documentation built on May 2, 2019, 3:55 a.m.