Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for...

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Autores principales:	Verena Zuber, Johanna Maria Colijn, Caroline Klaver, Stephen Burgess
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/25ac00d0332e4bc4a929fc5680c24f8b
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spelling	oai:doaj.org-article:25ac00d0332e4bc4a929fc5680c24f8b2021-12-02T16:56:33ZSelecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization10.1038/s41467-019-13870-32041-1723https://doaj.org/article/25ac00d0332e4bc4a929fc5680c24f8b2020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13870-3https://doaj.org/toc/2041-1723Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets.Verena ZuberJohanna Maria ColijnCaroline KlaverStephen BurgessNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Verena Zuber Johanna Maria Colijn Caroline Klaver Stephen Burgess Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
description	Multivariable Mendelian randomization (MR) extends the standard MR framework to consider multiple risk factors in a single model. Here, Zuber et al. propose MR-BMA, a Bayesian variable selection approach to identify the likely causal determinants of a disease from many candidate risk factors as for example high-throughput data sets.
format	article
author	Verena Zuber Johanna Maria Colijn Caroline Klaver Stephen Burgess
author_facet	Verena Zuber Johanna Maria Colijn Caroline Klaver Stephen Burgess
author_sort	Verena Zuber
title	Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
title_short	Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
title_full	Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
title_fullStr	Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
title_full_unstemmed	Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization
title_sort	selecting likely causal risk factors from high-throughput experiments using multivariable mendelian randomization
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/25ac00d0332e4bc4a929fc5680c24f8b
work_keys_str_mv	AT verenazuber selectinglikelycausalriskfactorsfromhighthroughputexperimentsusingmultivariablemendelianrandomization AT johannamariacolijn selectinglikelycausalriskfactorsfromhighthroughputexperimentsusingmultivariablemendelianrandomization AT carolineklaver selectinglikelycausalriskfactorsfromhighthroughputexperimentsusingmultivariablemendelianrandomization AT stephenburgess selectinglikelycausalriskfactorsfromhighthroughputexperimentsusingmultivariablemendelianrandomization
_version_	1718382787261628416

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

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