A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits

Statistical colocalisation is a method to identify causal genes and shared genetic aetiology across traits. Here, the authors describe HyPrColoc, an efficient Bayesian divisive clustering algorithm which integrates summary statistics from genome-wide association studies to detect clusters of colocal...

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Main Authors:	Christopher N. Foley, James R. Staley, Philip G. Breen, Benjamin B. Sun, Paul D. W. Kirk, Stephen Burgess, Joanna M. M. Howson
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-02-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-20885-8

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spelling	doaj-3c6045c88c554f35b4d4238ae36ad69a2021-02-07T12:13:42ZengNature Publishing GroupNature Communications2041-17232021-02-0112111810.1038/s41467-020-20885-8A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traitsChristopher N. Foley0James R. Staley1Philip G. Breen2Benjamin B. Sun3Paul D. W. Kirk4Stephen Burgess5Joanna M. M. Howson6MRC Biostatistics Unit, Cambridge Institute of Public Health, University of CambridgeCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of CambridgeSchool of Mathematics, University of Edinburgh, Kings BuildingsCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of CambridgeMRC Biostatistics Unit, Cambridge Institute of Public Health, University of CambridgeMRC Biostatistics Unit, Cambridge Institute of Public Health, University of CambridgeCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of CambridgeStatistical colocalisation is a method to identify causal genes and shared genetic aetiology across traits. Here, the authors describe HyPrColoc, an efficient Bayesian divisive clustering algorithm which integrates summary statistics from genome-wide association studies to detect clusters of colocalised traits from large numbers of traits.https://doi.org/10.1038/s41467-020-20885-8
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Christopher N. Foley James R. Staley Philip G. Breen Benjamin B. Sun Paul D. W. Kirk Stephen Burgess Joanna M. M. Howson
spellingShingle	Christopher N. Foley James R. Staley Philip G. Breen Benjamin B. Sun Paul D. W. Kirk Stephen Burgess Joanna M. M. Howson A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits Nature Communications
author_facet	Christopher N. Foley James R. Staley Philip G. Breen Benjamin B. Sun Paul D. W. Kirk Stephen Burgess Joanna M. M. Howson
author_sort	Christopher N. Foley
title	A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
title_short	A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
title_full	A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
title_fullStr	A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
title_full_unstemmed	A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
title_sort	fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-02-01
description	Statistical colocalisation is a method to identify causal genes and shared genetic aetiology across traits. Here, the authors describe HyPrColoc, an efficient Bayesian divisive clustering algorithm which integrates summary statistics from genome-wide association studies to detect clusters of colocalised traits from large numbers of traits.
url	https://doi.org/10.1038/s41467-020-20885-8
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A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits

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