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 pop...

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Autores principales:	Nathan LaPierre, Kodi Taraszka, Helen Huang, Rosemary He, Farhad Hormozdiari, Eleazar Eskin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/f03d0a348c2948b3b20956613bd556eb
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spelling	oai:doaj.org-article:f03d0a348c2948b3b20956613bd556eb2021-12-02T20:03:02ZIdentifying causal variants by fine mapping across multiple studies.1553-73901553-740410.1371/journal.pgen.1009733https://doaj.org/article/f03d0a348c2948b3b20956613bd556eb2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009733https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404Increasingly 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).Nathan LaPierreKodi TaraszkaHelen HuangRosemary HeFarhad HormozdiariEleazar EskinPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 9, p e1009733 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Nathan LaPierre Kodi Taraszka Helen Huang Rosemary He Farhad Hormozdiari Eleazar Eskin Identifying causal variants by fine mapping across multiple studies.
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).
format	article
author	Nathan LaPierre Kodi Taraszka Helen Huang Rosemary He Farhad Hormozdiari Eleazar Eskin
author_facet	Nathan LaPierre Kodi Taraszka Helen Huang Rosemary He Farhad Hormozdiari Eleazar Eskin
author_sort	Nathan LaPierre
title	Identifying causal variants by fine mapping across multiple studies.
title_short	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_sort	identifying causal variants by fine mapping across multiple studies.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f03d0a348c2948b3b20956613bd556eb
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Identifying causal variants by fine mapping across multiple studies.

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