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Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations
BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950790/ https://www.ncbi.nlm.nih.gov/pubmed/17786212 http://dx.doi.org/10.1371/journal.pone.0000841 |
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author | Ioannidis, John P.A. Patsopoulos, Nikolaos A. Evangelou, Evangelos |
author_facet | Ioannidis, John P.A. Patsopoulos, Nikolaos A. Evangelou, Evangelos |
author_sort | Ioannidis, John P.A. |
collection | PubMed |
description | BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. METHODOLOGY/PRINCIPAL FINDINGS: To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I(2) inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I(2) = 32–77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies. SIGNIFICANCE: Between-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations. |
format | Text |
id | pubmed-1950790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-19507902007-09-05 Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations Ioannidis, John P.A. Patsopoulos, Nikolaos A. Evangelou, Evangelos PLoS One Research Article BACKGROUND: Meta-analysis is the systematic and quantitative synthesis of effect sizes and the exploration of their diversity across different studies. Meta-analyses are increasingly applied to synthesize data from genome-wide association (GWA) studies and from other teams that try to replicate the genetic variants that emerge from such investigations. Between-study heterogeneity is important to document and may point to interesting leads. METHODOLOGY/PRINCIPAL FINDINGS: To exemplify these issues, we used data from three GWA studies on type 2 diabetes and their replication efforts where meta-analyses of all data using fixed effects methods (not incorporating between-study heterogeneity) have already been published. We considered 11 polymorphisms that at least one of the three teams has suggested as susceptibility loci for type 2 diabetes. The I(2) inconsistency metric (measuring the amount of heterogeneity not due to chance) was different from 0 (no detectable heterogeneity) for 6 of the 11 genetic variants; inconsistency was moderate to very large (I(2) = 32–77%) for 5 of them. For these 5 polymorphisms, random effects calculations incorporating between-study heterogeneity revealed more conservative p-values for the summary effects compared with the fixed effects calculations. These 5 associations were perused in detail to highlight potential explanations for between-study heterogeneity. These include identification of a marker for a correlated phenotype (e.g. FTO rs8050136 being associated with type 2 diabetes through its effect on obesity); differential linkage disequilibrium across studies of the identified genetic markers with the respective culprit polymorphisms (e.g., possibly the case for CDKAL1 polymorphisms or for rs9300039 and markers in linkage disequilibrium, as shown by additional studies); and potential bias. Results were largely similar, when we treated the discovery and replication data from each GWA investigation as separate studies. SIGNIFICANCE: Between-study heterogeneity is useful to document in the synthesis of data from GWA investigations and can offer valuable insights for further clarification of gene-disease associations. Public Library of Science 2007-09-05 /pmc/articles/PMC1950790/ /pubmed/17786212 http://dx.doi.org/10.1371/journal.pone.0000841 Text en Ioannidis et al. 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. |
spellingShingle | Research Article Ioannidis, John P.A. Patsopoulos, Nikolaos A. Evangelou, Evangelos Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title | Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title_full | Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title_fullStr | Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title_full_unstemmed | Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title_short | Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations |
title_sort | heterogeneity in meta-analyses of genome-wide association investigations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950790/ https://www.ncbi.nlm.nih.gov/pubmed/17786212 http://dx.doi.org/10.1371/journal.pone.0000841 |
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