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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC3291559?pdf=render |
| _version_ | 1811273281937866752 |
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| author | Buhm Han Eleazar Eskin |
| author_facet | Buhm Han Eleazar Eskin |
| author_sort | Buhm Han |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T22:56:18Z |
| format | Article |
| id | doaj.art-e95c6f45a3a24ac596892523b375c241 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-12T22:56:18Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-e95c6f45a3a24ac596892523b375c2412022-12-22T03:13:12ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042012-01-0183e100255510.1371/journal.pgen.1002555Interpreting meta-analyses of genome-wide association studies.Buhm HanEleazar EskinMeta-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.http://europepmc.org/articles/PMC3291559?pdf=render |
| spellingShingle | Buhm Han Eleazar Eskin Interpreting meta-analyses of genome-wide association studies. PLoS Genetics |
| title | 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_short | Interpreting meta-analyses of genome-wide association studies. |
| title_sort | interpreting meta analyses of genome wide association studies |
| url | http://europepmc.org/articles/PMC3291559?pdf=render |
| work_keys_str_mv | AT buhmhan interpretingmetaanalysesofgenomewideassociationstudies AT eleazareskin interpretingmetaanalysesofgenomewideassociationstudies |