Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
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Publication Date
2021-10Journal Title
PLoS Genet
ISSN
1553-7390
Publisher
Public Library of Science (PLoS)
Volume
17
Issue
10
Language
eng
Type
Article
This Version
VoR
Metadata
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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) https://doi.org/10.1371/journal.pgen.1009853
Description
Funder: GlaxoSmithKline
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 covariates 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 can be a more powerful approach. 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, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. 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 genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
Sponsorship
Wellcome Trust (107881/Z/15/Z)
Engineering and Physical Sciences Research Council (EP/R511870/1)
National Institute for Health Research (IS-BRC-1215-20014)
Medical Research Council (MC_UU_00002/4)
Identifiers
PMC8559959, 34669738
External DOI: https://doi.org/10.1371/journal.pgen.1009853
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331089
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