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ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we descri...

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Detalles Bibliográficos
Autores principales: Kang, Eun Yong, Park, Yurang, Li, Xiao, Segrè, Ayellet V., Han, Buhm, Eskin, Eleazar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938634/
https://www.ncbi.nlm.nih.gov/pubmed/27194809
http://dx.doi.org/10.1534/g3.116.029439
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author Kang, Eun Yong
Park, Yurang
Li, Xiao
Segrè, Ayellet V.
Han, Buhm
Eskin, Eleazar
author_facet Kang, Eun Yong
Park, Yurang
Li, Xiao
Segrè, Ayellet V.
Han, Buhm
Eskin, Eleazar
author_sort Kang, Eun Yong
collection PubMed
description Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study.
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spelling pubmed-49386342016-07-19 ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis Kang, Eun Yong Park, Yurang Li, Xiao Segrè, Ayellet V. Han, Buhm Eskin, Eleazar G3 (Bethesda) Investigations Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study. Genetics Society of America 2016-05-18 /pmc/articles/PMC4938634/ /pubmed/27194809 http://dx.doi.org/10.1534/g3.116.029439 Text en Copyright © 2016 Kang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Investigations
Kang, Eun Yong
Park, Yurang
Li, Xiao
Segrè, Ayellet V.
Han, Buhm
Eskin, Eleazar
ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title_full ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title_fullStr ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title_full_unstemmed ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title_short ForestPMPlot: A Flexible Tool for Visualizing Heterogeneity Between Studies in Meta-analysis
title_sort forestpmplot: a flexible tool for visualizing heterogeneity between studies in meta-analysis
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4938634/
https://www.ncbi.nlm.nih.gov/pubmed/27194809
http://dx.doi.org/10.1534/g3.116.029439
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