LFDREmpiricalBayes provides an empirical Bayes framework for estimating local false discovery rates (LFDR) and identifying genetic associations while incorporating prior biological knowledge about variants. The package implements the methodology proposed in:
Karimnezhad, A. and Bickel, D. R. (2016). Incorporating prior knowledge about genetic variants into the analysis of genetic association data: An empirical Bayes approach. Working paper. Retrieved from http://hdl.handle.net/10393/34889
This approach allows users to integrate prior information—such as functional annotations or biological relevance—into the LFDR estimation process, improving sensitivity and interpretability in large-scale genetic association studies.
From CRAN:
install.packages("LFDREmpiricalBayes")
From GitHub (development version):
# install.packages("remotes")
# remotes::install_github("empiricalbayes/LFDREmpiricalBayes")
install.packages("LFDREmpiricalBayes", repos = c('https://empiricalbayes.r-universe.dev', 'https://cloud.r-project.org'))
library(LFDREmpiricalBayes)
# Example: Simulated z-scores for genome-wide tests
set.seed(123)
z <- c(rnorm(900, 0, 1), rnorm(100, 3, 1))
# Fit the empirical Bayes LFDR model
fit <- lfdr_empirical_bayes(z) # replace with actual main function name
head(fit$lfdr)
Karimnezhad, A. and Bickel, D. R. (2016).
Incorporating prior knowledge about genetic variants into the analysis of genetic association data: An empirical Bayes approach.
Working paper. Retrieved from http://hdl.handle.net/10393/34889
If you use this package, please cite both the paper and this software:
Karimnezhad, A. and Bickel, D. R. (2016). LFDREmpiricalBayes: Empirical Bayes Method for Local False Discovery Rate Estimation (R package). GitHub: empiricalbayes/LFDREmpiricalBayes.
MIT (see LICENSE).
Questions or issues: open a GitHub issue or contact Ali Karimnezhad.