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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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). |
format | Online Article Text |
id | pubmed-8491908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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