gap (version 1.1-1)

gcontrol: genomic control

Description

The Bayesian genomic control statistics with the following parameters,

ll{ n number of loci under consideration lambdahat median(of the n trend statistics)/0.46 Prior for noncentrality parameter Ai is Normal(sqrt(lambdahat)kappa,lambdahat*tau2) kappa multiplier in prior above, set at 1.6 * sqrt(log(n)) tau2 multiplier in prior above epsilon prior probability a marker is associated, set at 10/n ngib number of cycles for the Gibbs sampler after burn in burn number of cycles for the Gibbs sampler to burn in }

Armitage's trend test along with the posterior probability that each marker is associated with the disorder is given. The latter is not a p-value but any value greater than 0.5 (pout) suggests association.

Usage

gcontrol(data,zeta,kappa,tau2,epsilon,ngib,burn,idum)

Arguments

data

the data matrix

zeta

program constant with default value 1000

kappa

multiplier in prior for mean with default value 4

tau2

multiplier in prior for variance with default value 1

epsilon

prior probability of marker association with default value 0.01

ngib

number of Gibbs steps, with default value 500

burn

number of burn-ins with default value 50

idum

seed for pseudorandom number sequence

Value

• The returned value is a list containing:
• deltot
  the probability of being an outlier
• x2
  the $\chi^2$ statistic
• A
  the A vector

source

http://www.stat.cmu.edu

References

Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997-1004

Examples

test<-c(1,2,3,4,5,6,  1,2,1,23,1,2, 100,1,2,12,1,1, 
        1,2,3,4,5,61, 1,2,11,23,1,2, 10,11,2,12,1,11)
test<-matrix(test,nrow=6,byrow=T)
gcontrol(test)