Interpreting Meta-Analyses of Genome-Wide Association Studies
Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many fa...
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Public Library of Science
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3291559/ |
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pubmed-3291559 |
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pubmed-32915592012-03-06 Interpreting Meta-Analyses of Genome-Wide Association Studies Han, Buhm Eskin, Eleazar Research Article Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect. Public Library of Science 2012-03-01 /pmc/articles/PMC3291559/ /pubmed/22396665 http://dx.doi.org/10.1371/journal.pgen.1002555 Text en Han, Eskin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Han, Buhm Eskin, Eleazar |
| spellingShingle |
Han, Buhm Eskin, Eleazar Interpreting Meta-Analyses of Genome-Wide Association Studies |
| author_facet |
Han, Buhm Eskin, Eleazar |
| author_sort |
Han, Buhm |
| title |
Interpreting Meta-Analyses of Genome-Wide Association Studies |
| title_short |
Interpreting Meta-Analyses of Genome-Wide Association Studies |
| title_full |
Interpreting Meta-Analyses of Genome-Wide Association Studies |
| title_fullStr |
Interpreting Meta-Analyses of Genome-Wide Association Studies |
| title_full_unstemmed |
Interpreting Meta-Analyses of Genome-Wide Association Studies |
| title_sort |
interpreting meta-analyses of genome-wide association studies |
| description |
Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect. |
| publisher |
Public Library of Science |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3291559/ |
| _version_ |
1611509770220470272 |