Improved polygenic prediction by Bayesian multiple regression on summary statistics

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
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Science
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Acceso en línea:https://doaj.org/article/08ca063725f844dd8f1661fc58a72c97
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Sumario: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.

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