A more accurate method for colocalisation analysis allowing for multiple causal variants.

In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes th...

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Autor principal:	Chris Wallace
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/75c41204d8c44516b1af45f23a8a4998
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id	oai:doaj.org-article:75c41204d8c44516b1af45f23a8a4998
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spelling	oai:doaj.org-article:75c41204d8c44516b1af45f23a8a49982021-12-02T20:03:02ZA more accurate method for colocalisation analysis allowing for multiple causal variants.1553-73901553-740410.1371/journal.pgen.1009440https://doaj.org/article/75c41204d8c44516b1af45f23a8a49982021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009440https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.Chris WallacePublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 9, p e1009440 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Chris Wallace A more accurate method for colocalisation analysis allowing for multiple causal variants.
description	In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.
format	article
author	Chris Wallace
author_facet	Chris Wallace
author_sort	Chris Wallace
title	A more accurate method for colocalisation analysis allowing for multiple causal variants.
title_short	A more accurate method for colocalisation analysis allowing for multiple causal variants.
title_full	A more accurate method for colocalisation analysis allowing for multiple causal variants.
title_fullStr	A more accurate method for colocalisation analysis allowing for multiple causal variants.
title_full_unstemmed	A more accurate method for colocalisation analysis allowing for multiple causal variants.
title_sort	more accurate method for colocalisation analysis allowing for multiple causal variants.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/75c41204d8c44516b1af45f23a8a4998
work_keys_str_mv	AT chriswallace amoreaccuratemethodforcolocalisationanalysisallowingformultiplecausalvariants AT chriswallace moreaccuratemethodforcolocalisationanalysisallowingformultiplecausalvariants
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A more accurate method for colocalisation analysis allowing for multiple causal variants.

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