sffdr package
Introduction
The sffdr package implements the surrogate functional false discovery
rate (sfFDR) procedure. This methodology integrates GWAS summary
statistics from related traits (i.e., pleiotropy) to increase
statistical power within the functional FDR framework. The inputs into
sffdr are a set of p-values from a GWAS of interest and a set of
p-values from one or many informative GWAS.
The significance quantities estimated by sffdr can be used for a
variety of analyses:
- Functional p-value (fp): Controls the type I error rate and can be used for standard significance analyses.
- Functional q-value (fq): A measure of significance in terms of the positive FDR (closely related to FDR).
- Functional local FDR (flfdr): A posterior error probability that is useful for functional fine-mapping and assessing significance of a SNP.
Citing this package
The methods implemented in this package are described in:
Bass AJ, Wallace C. Exploiting pleiotropy to enhance variant discovery with functional false discovery rates. Nature Computational Science; 2025.
Note that this work is an extension of the functional FDR methodology
and the software builds on some of the functions in the fFDR package
found at https://github.com/StoreyLab/fFDR.
Getting help
To report any bugs or issues related to usage please report it on GitHub at https://github.com/ajbass/sffdr.
Installation
You can install the development version of sffdr from GitHub:
# install development version of package install.packages("devtools") devtools::install_github("ajbass/sffdr")
Quick start guide
To demonstrate the package, we will use a sample dataset containing 10,000 SNPs for body mass index (BMI) as the primary trait of interest, with body fat percentage (BFP), cholesterol, and triglycerides as informative traits.
library(sffdr) data(bmi) # Define primary p-values and informative p-values p <- sumstats$bmi z <- as.matrix(sumstats[,-1]) head(sumstats)