fcfdr is an all-encompassing R package to implement the cFDR approach for a variety of auxiliary covariates. As input it requires GWAS p-values for a trait of interest and auxiliary data values. It then outputs “v-values” which can be interpreted as GWAS p-values that have been adjusted using the auxiliary data values.

If you have any questions please do not hesitate to contact me: annahutchinson1995@gmail.com

GitHub repository: https://github.com/annahutch/fcfdr

Manuscripts

Flexible cFDR:

Hutchinson A, Reales G, Willis T, Wallace C (2021) Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR. PLoS Genet 17(10): e1009853. https://doi.org/10.1371/journal.pgen.1009853

Binary cFDR:

Hutchinson A, Liley J, Wallace C (2021) fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS. bioRxiv 2021.10.21.465274. https://doi.org/10.1101/2021.10.21.465274

Installation

You can install fcfdr from GitHub with:

# install.packages("devtools")
devtools::install_github("annahutch/fcfdr")

Examples

See the vignettes for examples of usage.

Brief history of cFDR

The cFDR framework was first introduced by Andreassen and colleagues in 2013. The group also applied the approach to uncover new genetic associations for blood pressure and multiple sclerosis by leveraging GWAS test statistics for related traits.

In 2015, Liley and Wallace showed that the conventional cFDR approach does not control the frequentist FDR and described an extension of the approach to ensure that FDR is controlled, however their approach is rather conservative. In 2021, Liley and Wallace described an elegant cFDR framework that takes as input GWAS p-values for related traits and returns “v-values” which can be interpreted as GWAS p-values that have been adjusted using the auxiliary related trait data, and thus can be used directly in any error-rate controlling procedure (e.g. Benjamini-Hochberg).

Up until this point, the cFDR framework was designed for a very specific setting, that is to increase GWAS discovery (in the “principal trait”) by leveraging GWAS test statistics from a genetically related (“conditional”) trait. In 2021 we described an extension of the cFDR framework called Flexible cFDR that allows for auxiliary covariates sampled from arbitrary continuous distributions to be leveraged with GWAS test statistics - thus enabling broader applicability. We also describe an extension for binary covariates, called Binary cFDR. In practice, Flexible cFDR and Binary cFDR can be used to leverage functional genomic data with GWAS results to boost power for GWAS discovery whilst controlling the FDR.