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G3 (Bethesda). 2016 Jul 7;6(7):1793-8. doi: 10.1534/g3.116.029439.

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

Author information

1
Department of Computer Science, University of California, Los Angeles, California 90095.
2
Department of Biomedical Informatics and.
3
Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505 Republic of Korea, 05.
4
Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea buhm.han@amc.seoul.kr eeskin@cs.ucla.edu.
5
Department of Computer Science, University of California, Los Angeles, California 90095 Department of Human Genetics, University of California, Los Angeles, California 90095 buhm.han@amc.seoul.kr eeskin@cs.ucla.edu.

Abstract

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.

KEYWORDS:

GWAS; genetic association studies; heterogeneity; meta-analysis

PMID:
27194809
PMCID:
PMC4938634
DOI:
10.1534/g3.116.029439
[Indexed for MEDLINE]
Free PMC Article

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