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Identifying causal variants by fine mapping across multiple studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations a...

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Autores principales: LaPierre, Nathan, Taraszka, Kodi, Huang, Helen, He, Rosemary, Hormozdiari, Farhad, Eskin, Eleazar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491908/
https://www.ncbi.nlm.nih.gov/pubmed/34543273
http://dx.doi.org/10.1371/journal.pgen.1009733
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author LaPierre, Nathan
Taraszka, Kodi
Huang, Helen
He, Rosemary
Hormozdiari, Farhad
Eskin, Eleazar
author_facet LaPierre, Nathan
Taraszka, Kodi
Huang, Helen
He, Rosemary
Hormozdiari, Farhad
Eskin, Eleazar
author_sort LaPierre, Nathan
collection PubMed
description Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).
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spelling pubmed-84919082021-10-06 Identifying causal variants by fine mapping across multiple studies LaPierre, Nathan Taraszka, Kodi Huang, Helen He, Rosemary Hormozdiari, Farhad Eskin, Eleazar PLoS Genet Research Article Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL). Public Library of Science 2021-09-20 /pmc/articles/PMC8491908/ /pubmed/34543273 http://dx.doi.org/10.1371/journal.pgen.1009733 Text en © 2021 LaPierre et al 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
LaPierre, Nathan
Taraszka, Kodi
Huang, Helen
He, Rosemary
Hormozdiari, Farhad
Eskin, Eleazar
Identifying causal variants by fine mapping across multiple studies
title Identifying causal variants by fine mapping across multiple studies
title_full Identifying causal variants by fine mapping across multiple studies
title_fullStr Identifying causal variants by fine mapping across multiple studies
title_full_unstemmed Identifying causal variants by fine mapping across multiple studies
title_short Identifying causal variants by fine mapping across multiple studies
title_sort identifying causal variants by fine mapping across multiple studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491908/
https://www.ncbi.nlm.nih.gov/pubmed/34543273
http://dx.doi.org/10.1371/journal.pgen.1009733
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