powerGG: Power for GxG interactions in genetic association studies
Description
This routine carries out (analytical, approximate) power calculations for identifying Gene-Gene interactions in Genome Wide Association Studies
Usage
powerGG(n, power, model, caco, alpha, alpha1)
Arguments
n
Sample size: combined number of cases and controls. Note: exactly one of n and power should be specified.
power
Power: targeted power. Note: exactly one of n and power should be specified.
model
List specifying the genetic model. This list contains the following objects:
prev Prevalence of the outcome in the population. Note that for case-only and empirical Bayes estimators to be valid,
the prevalence needs to be low.
pGene1 Probability that the first binary SNP is 1 (i.e. not the minor allele frequency for a three level SNP).
pGene2 Probability that the first binary SNP is 1 (i.e. not the minor allele frequency for a three level SNP).
beta.LOR Vector of length three with the odds ratios of the first genetic, second genetic, and GxG interaction effect, respectively.
nSNP Number of SNPs (genes) being tested.
caco
Fraction of the sample that are cases (default = 0.5).
alpha
Overall (family-wise) Type 1 error (default = 0.05).
alpha1
Significance level at which testing during the first stage (screening) takes place. If alpha1 = 1, there is no screening.
Value
n is specified or the required combined sample size
for the case-control and case-only approaches if power is specified.
Details
The routine computes power calculations for a two-stage procedure with marginal screening followed by either case-control or case-only testing.
References
Kooperberg C, LeBlanc M (2008). Increasing the power of identifying gene x gene interactions in genome-wide association studies.
Genetic Epidemiology, 32, 255-263.