The goal of SPCompute is to compute power and sample size for replication GWAS study, while accommodates different kinds of covariate effects. The methodology used in the software is described in this paper by Ziang Zhang and Lei Sun. The detailed implementation guideline can be found in the vignette of this package.


You can install the development version from GitHub with:

# install.packages("devtools")


This is a basic example which shows you how to solve a common problem of computing power for genetic association testing with a binary trait:

## basic example code
parameters <- list(preva = 0.2, pG = 0.3, pE = 0.3, gammaG = 0.1, betaG = 0.1, betaE = 0.3)
Compute_Power(parameters, n = 8000, response = "binary", covariate = "none")
#> [1] 0.6404552