RPower: an R package to estimate power and determine the sample size for replication studies of genome-wide association studies
Replication study is a commonly used verification method to filter out false positives in genome-wide association studies (GWAS). If an association can be confirmed in a replication study, it will have a high confidence to be true positive. To design
a replication study, traditional approaches calculate power by treating replication study as another independent primary study. These approaches do not use the information given by primary study. Besides, they need to specify a minimum
detectable effect size, which may be subjective. One may think to replace the minimum effect size with the observed effect sizes in the power calculation. However, this approach will make the designed replication study underpowered
since we are only interested in the positive associations from the primary study and the problem of the "winner's curse" will occur.
|W. Jiang and W. Yu
"Power Estimation and Sample Size Determination for Replication Studies of Genome-Wide Association Studies",
accepted in the Fourteenth Asia Pacific Bioinformatics Conference (APBC 2016).
|Where to download RPower
It can be directly installed in the R environment with following command:
Use the following command to load the package in the R environment:
|How to use it?
The principal components of RPower are repPowerEB, repSampleSize and repSampleSize2. Also there is a simple function SEest in the package.