powerGG {powerGWASinteraction}

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.

Details

The routine computes power calculations for a two-stage procedure with marginal screening followed by either case-control or case-only testing.

Value

A data frame consisting of two numbers: the power for the case-control and case-only approaches if n is specified or the required combined sample size for the case-control and case-only approaches if power is specified.

Author(s)

Charles Kooperberg, [e-mail]

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.

See Also

powerGG

Examples

mod1 <- list(prev=0.05, pGene1=0.3, pGene2=0.3, beta.LOR=c(0,0,.6),nSNP=500000)
powerGG(n=10000,mod=mod1,caco=0.5,alpha=.05,alpha1=.001)
powerGG(power=0.8,mod=mod1,caco=0.5,alpha=.05,alpha1=.001)