Jlfdr: Controlling the Joint Local False Discovery Rate in Joint Analysis of Summary Statistics from Multiple GWASs

About Jlfdr

In genome-wide association studies (GWASs) of common diseases/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is diffcult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze data sets from multiple GWASs.

Here we implement a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). This method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate (Fdr) at a certain level. Details about the method can be seen in our reference paper below.

Related Publication
W. Jiang and W. Yu
"Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies",

Where to download Jlfdr

The R-package is available at :
Windows:  Jlfdr_1.0.zip
Linux:        Jlfdr_1.0.tar.gz

The manual is available at: Jlfdr-manual.pdf

Environment configuration

It can be directly installed in the R environment with following command:

Windows:   install.packages("Jlfdr_1.0.zip",repos=NULL)
Linux:          install.packages("Jlfdr_1.0.tar.gz",repos=NULL)

Use the following command to load the package in the R environment:


How to use it?

The principal component of Jlfdr package is FdrControl2. Also we implement a Fdr controlling method BayesFdr for single-study analysis.

1. To jointly analyze summary statistics from multiple GWASs, we need to obtain the z-values of each genotyped SNPs in all studies. We have put example summary statistics data of two studies (SmryStats1 and SmryStats2) in our package. You can use data(SmryStats1) and data(SmryStats2) to load the example data. You can also obtain the ground-truth parameters (allele frequencies, odds ratios) of the example data using data(Param1) and data(Param2)

2. You can use FdrControl2 to jointly analyze summary statistics from two stuides.

FdrControl2(z1, z2, K=2, q=0.05, beta0=length(z1)/5, plot=T, output=T, dir='output')

Details about the function can be seen using help(FdrControl2)}.

3. To analyze summary statistics from single study with controlling Fdr, BayesFdr can be used.

BayesFdr(z, q, K = 2, beta0 = length(z)/5)

Details about the function can be seen using help(BayesFdr)