RT Journal Article
SR Electronic
T1 fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.10.21.465274
DO 10.1101/2021.10.21.465274
A1 Hutchinson, Anna
A1 Liley, James
A1 Wallace, Chris
YR 2021
UL http://biorxiv.org/content/early/2021/10/22/2021.10.21.465274.1.abstract
AB 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.htmlCompeting Interest StatementThe authors have declared no competing interest.