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fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

View ORCID ProfileAnna Hutchinson, James Liley, View ORCID ProfileChris Wallace
doi: https://doi.org/10.1101/2021.10.21.465274
Anna Hutchinson
1MRC Biostatistics Unit, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0SR, UK
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  • For correspondence: anna.hutchinson@mrc-bsu.cam.ac.uk
James Liley
2MRC Human Genetics Unit, IGMM, University of Edinburgh, Crewe Rd S, Edinburgh, UK
3The Alan Turing Institute, 96 Euston Rd, Somers Town, London, NW1 2DB, UK
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Chris Wallace
1MRC Biostatistics Unit, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0SR, UK
4Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
5Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 22, 2021.
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fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS
Anna Hutchinson, James Liley, Chris Wallace
bioRxiv 2021.10.21.465274; doi: https://doi.org/10.1101/2021.10.21.465274
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fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS
Anna Hutchinson, James Liley, Chris Wallace
bioRxiv 2021.10.21.465274; doi: https://doi.org/10.1101/2021.10.21.465274

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