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

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 statistic...

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Autores principales:	Anna Hutchinson, Guillermo Reales, Thomas Willis, Chris Wallace
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/93eb8dbb93eb4685b6d8293ba0b38246
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spelling	oai:doaj.org-article:93eb8dbb93eb4685b6d8293ba0b382462021-12-02T20:03:30ZLeveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.1553-73901553-740410.1371/journal.pgen.1009853https://doaj.org/article/93eb8dbb93eb4685b6d8293ba0b382462021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009853https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Genome-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.Anna HutchinsonGuillermo RealesThomas WillisChris WallacePublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 10, p e1009853 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Anna Hutchinson Guillermo Reales Thomas Willis Chris Wallace Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
description	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.
format	article
author	Anna Hutchinson Guillermo Reales Thomas Willis Chris Wallace
author_facet	Anna Hutchinson Guillermo Reales Thomas Willis Chris Wallace
author_sort	Anna Hutchinson
title	Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
title_short	Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
title_full	Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
title_fullStr	Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
title_full_unstemmed	Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.
title_sort	leveraging auxiliary data from arbitrary distributions to boost gwas discovery with flexible cfdr.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/93eb8dbb93eb4685b6d8293ba0b38246
work_keys_str_mv	AT annahutchinson leveragingauxiliarydatafromarbitrarydistributionstoboostgwasdiscoverywithflexiblecfdr AT guillermoreales leveragingauxiliarydatafromarbitrarydistributionstoboostgwasdiscoverywithflexiblecfdr AT thomaswillis leveragingauxiliarydatafromarbitrarydistributionstoboostgwasdiscoverywithflexiblecfdr AT chriswallace leveragingauxiliarydatafromarbitrarydistributionstoboostgwasdiscoverywithflexiblecfdr
_version_	1718375673253330944

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

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