Improved polygenic prediction by Bayesian multiple regression on summary statistics

Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based met...

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Autores principales:	Luke R. Lloyd-Jones, Jian Zeng, Julia Sidorenko, Loïc Yengo, Gerhard Moser, Kathryn E. Kemper, Huanwei Wang, Zhili Zheng, Reedik Magi, Tõnu Esko, Andres Metspalu, Naomi R. Wray, Michael E. Goddard, Jian Yang, Peter M. Visscher
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/08ca063725f844dd8f1661fc58a72c97
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spelling	oai:doaj.org-article:08ca063725f844dd8f1661fc58a72c972021-12-02T15:36:19ZImproved polygenic prediction by Bayesian multiple regression on summary statistics10.1038/s41467-019-12653-02041-1723https://doaj.org/article/08ca063725f844dd8f1661fc58a72c972019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12653-0https://doaj.org/toc/2041-1723Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.Luke R. Lloyd-JonesJian ZengJulia SidorenkoLoïc YengoGerhard MoserKathryn E. KemperHuanwei WangZhili ZhengReedik MagiTõnu EskoAndres MetspaluNaomi R. WrayMichael E. GoddardJian YangPeter M. VisscherNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Luke R. Lloyd-Jones Jian Zeng Julia Sidorenko Loïc Yengo Gerhard Moser Kathryn E. Kemper Huanwei Wang Zhili Zheng Reedik Magi Tõnu Esko Andres Metspalu Naomi R. Wray Michael E. Goddard Jian Yang Peter M. Visscher Improved polygenic prediction by Bayesian multiple regression on summary statistics
description	Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.
format	article
author	Luke R. Lloyd-Jones Jian Zeng Julia Sidorenko Loïc Yengo Gerhard Moser Kathryn E. Kemper Huanwei Wang Zhili Zheng Reedik Magi Tõnu Esko Andres Metspalu Naomi R. Wray Michael E. Goddard Jian Yang Peter M. Visscher
author_facet	Luke R. Lloyd-Jones Jian Zeng Julia Sidorenko Loïc Yengo Gerhard Moser Kathryn E. Kemper Huanwei Wang Zhili Zheng Reedik Magi Tõnu Esko Andres Metspalu Naomi R. Wray Michael E. Goddard Jian Yang Peter M. Visscher
author_sort	Luke R. Lloyd-Jones
title	Improved polygenic prediction by Bayesian multiple regression on summary statistics
title_short	Improved polygenic prediction by Bayesian multiple regression on summary statistics
title_full	Improved polygenic prediction by Bayesian multiple regression on summary statistics
title_fullStr	Improved polygenic prediction by Bayesian multiple regression on summary statistics
title_full_unstemmed	Improved polygenic prediction by Bayesian multiple regression on summary statistics
title_sort	improved polygenic prediction by bayesian multiple regression on summary statistics
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/08ca063725f844dd8f1661fc58a72c97
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Improved polygenic prediction by Bayesian multiple regression on summary statistics

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