Abstract
Summary GWAS discovery is limited in power to detect associations that exceed the stringent genome-wide significance threshold, but this limitation can be alleviated by leveraging relevant auxiliary data. Frameworks utilising the conditional false discovery rate (cFDR) can be used to leverage continuous auxiliary data (including GWAS and functional genomic data) with GWAS test statistics and have been shown to increase power for GWAS discovery whilst controlling the FDR. Here, we describe an extension to the cFDR framework for binary auxiliary data (such as whether SNPs reside in regions of the genome with specific activity states) and introduce an all-encompassing R package to implement the cFDR approach, fcfdr, demonstrating its utility in an application to type 1 diabetes.
Availability and implementation The fcfdr R package is freely available at: https://github.com/annahutch/fcfdr. Scripts and data to reproduce the analysis in this paper are freely available at: https://annahutch.github.io/fcfdr/articles/t1d_app.html
Competing Interest Statement
The authors have declared no competing interest.
