Description
Compute Power or Sample Size for GWAS with Covariate Effect.
Description
Fast computation of the required sample size or the achieved power, for GWAS studies with different types of covariate effects and different types of covariate-gene dependency structure. For the detailed description of the methodology, see Zhang (2022) "Power and Sample Size Computation for Genetic Association Studies of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.
README.md
SPCompute
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.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("AgueroZZ/SPCompute")
Example
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:
library(SPCompute)
## 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