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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: Wallace, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504726/
https://www.ncbi.nlm.nih.gov/pubmed/34587156
http://dx.doi.org/10.1371/journal.pgen.1009440
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author Wallace, Chris
author_facet Wallace, Chris
author_sort Wallace, Chris
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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.
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spelling pubmed-85047262021-10-12 A more accurate method for colocalisation analysis allowing for multiple causal variants Wallace, Chris PLoS Genet Research Article 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. Public Library of Science 2021-09-29 /pmc/articles/PMC8504726/ /pubmed/34587156 http://dx.doi.org/10.1371/journal.pgen.1009440 Text en © 2021 Chris Wallace https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wallace, Chris
A more accurate method for colocalisation analysis allowing for multiple causal variants
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504726/
https://www.ncbi.nlm.nih.gov/pubmed/34587156
http://dx.doi.org/10.1371/journal.pgen.1009440
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