genomic control
Usage
gcontrol(
data,
zeta = 1000,
kappa = 4,
tau2 = 1,
epsilon = 0.01,
ngib = 500,
burn = 50,
idum = 2348
)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.
Details
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.
Note
Adapted from gcontrol by Bobby Jones and Kathryn Roeder, use -Dexecutable for standalone program, function getnum in the original code needs \
References
Devlin B, Roeder K (1999). “Genomic control for association studies.” Biometrics, 55(4), 997-1004. doi:10.1111/j.0006-341x.1999.00997.x .