powerGG: Power for GxG interactions in genetic association studies

In powerGWASinteraction: Power Calculations for GxE and GxG Interactions for GWAS

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)

Example output

Loading required package: mvtnorm
Loading required package: pwr
      case.control case.only
power    0.8525731 0.9180744
            case.control case.only
sample.size         9434      8186

powerGWASinteraction documentation built on May 1, 2019, 8:48 p.m.