genomic control
The Bayesian genomic control statistics with the following parameters,
| 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.
gcontrol(data,zeta,kappa,tau2,epsilon,ngib,burn,idum)
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 |
The returned value is a list containing:
deltot |
the probability of being an outlier |
x2 |
the chi-squared statistic |
A |
the A vector |
Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997-1004
Adapted from gcontrol by Bobby Jones and Kathryn Roeder, use -Dexecutable for standalone program, function getnum in the original code needs %*s to skip id string
Bobby Jones, Jing Hua Zhao
## Not run:
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.