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Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

View ORCID ProfileAnna Hutchinson, Guillermo Reales, Chris Wallace
doi: https://doi.org/10.1101/2020.12.04.411710
Anna Hutchinson
1MRC Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
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  • For correspondence: anna.hutchinson@mrc-bsu.cam.ac.uk
Guillermo Reales
2Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
3Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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Chris Wallace
1MRC Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK
2Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0AW, UK
3Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, CB2 2QQ, UK
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1. Abstract

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary data has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate (FDR) can be a more powerful approach as sample sizes increase and many associations are expected in each study. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions. We relax the distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary data from any continuous distribution (“Flexible cFDR”). Our method is iterative, whereby additional layers of auxiliary data can be leveraged in turn. Through simulations we show that flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional data to find additional genetic associations for asthma, which we validated in the larger UK Biobank data resource.

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-ND 4.0 International license.
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Posted December 04, 2020.
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Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR
Anna Hutchinson, Guillermo Reales, Chris Wallace
bioRxiv 2020.12.04.411710; doi: https://doi.org/10.1101/2020.12.04.411710
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Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR
Anna Hutchinson, Guillermo Reales, Chris Wallace
bioRxiv 2020.12.04.411710; doi: https://doi.org/10.1101/2020.12.04.411710

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