Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

Association analyses that capture rare and noncoding variants in whole genome sequencing data are limited by factors like statistical power. Here, the authors present KnockoffScreen, a statistical method using the knockoff framework to detect, localise and prioritise rare and common risk variants at...

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Autores principales:	Zihuai He, Linxi Liu, Chen Wang, Yann Le Guen, Justin Lee, Stephanie Gogarten, Fred Lu, Stephen Montgomery, Hua Tang, Edwin K. Silverman, Michael H. Cho, Michael Greicius, Iuliana Ionita-Laza
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ecd27486fdc04053bfaf9a54c6671fc9
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spelling	oai:doaj.org-article:ecd27486fdc04053bfaf9a54c6671fc92021-12-02T14:49:22ZIdentification of putative causal loci in whole-genome sequencing data via knockoff statistics10.1038/s41467-021-22889-42041-1723https://doaj.org/article/ecd27486fdc04053bfaf9a54c6671fc92021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22889-4https://doaj.org/toc/2041-1723Association analyses that capture rare and noncoding variants in whole genome sequencing data are limited by factors like statistical power. Here, the authors present KnockoffScreen, a statistical method using the knockoff framework to detect, localise and prioritise rare and common risk variants at genome-wide scale.Zihuai HeLinxi LiuChen WangYann Le GuenJustin LeeStephanie GogartenFred LuStephen MontgomeryHua TangEdwin K. SilvermanMichael H. ChoMichael GreiciusIuliana Ionita-LazaNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Zihuai He Linxi Liu Chen Wang Yann Le Guen Justin Lee Stephanie Gogarten Fred Lu Stephen Montgomery Hua Tang Edwin K. Silverman Michael H. Cho Michael Greicius Iuliana Ionita-Laza Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
description	Association analyses that capture rare and noncoding variants in whole genome sequencing data are limited by factors like statistical power. Here, the authors present KnockoffScreen, a statistical method using the knockoff framework to detect, localise and prioritise rare and common risk variants at genome-wide scale.
format	article
author	Zihuai He Linxi Liu Chen Wang Yann Le Guen Justin Lee Stephanie Gogarten Fred Lu Stephen Montgomery Hua Tang Edwin K. Silverman Michael H. Cho Michael Greicius Iuliana Ionita-Laza
author_facet	Zihuai He Linxi Liu Chen Wang Yann Le Guen Justin Lee Stephanie Gogarten Fred Lu Stephen Montgomery Hua Tang Edwin K. Silverman Michael H. Cho Michael Greicius Iuliana Ionita-Laza
author_sort	Zihuai He
title	Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
title_short	Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
title_full	Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
title_fullStr	Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
title_full_unstemmed	Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
title_sort	identification of putative causal loci in whole-genome sequencing data via knockoff statistics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ecd27486fdc04053bfaf9a54c6671fc9
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Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

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