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The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies

Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation aff...

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Detalles Bibliográficos
Autores principales: Li, Jian, Guo, Yan-fang, Pei, Yufang, Deng, Hong-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320624/
https://www.ncbi.nlm.nih.gov/pubmed/22496814
http://dx.doi.org/10.1371/journal.pone.0034486
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author Li, Jian
Guo, Yan-fang
Pei, Yufang
Deng, Hong-Wen
author_facet Li, Jian
Guo, Yan-fang
Pei, Yufang
Deng, Hong-Wen
author_sort Li, Jian
collection PubMed
description Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary.
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spelling pubmed-33206242012-04-11 The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies Li, Jian Guo, Yan-fang Pei, Yufang Deng, Hong-Wen PLoS One Research Article Genotype imputation is often used in the meta-analysis of genome-wide association studies (GWAS), for combining data from different studies and/or genotyping platforms, in order to improve the ability for detecting disease variants with small to moderate effects. However, how genotype imputation affects the performance of the meta-analysis of GWAS is largely unknown. In this study, we investigated the effects of genotype imputation on the performance of meta-analysis through simulations based on empirical data from the Framingham Heart Study. We found that when fix-effects models were used, considerable between-study heterogeneity was detected when causal variants were typed in only some but not all individual studies, resulting in up to ∼25% reduction of detection power. For certain situations, the power of the meta-analysis can be even less than that of individual studies. Additional analyses showed that the detection power was slightly improved when between-study heterogeneity was partially controlled through the random-effects model, relative to that of the fixed-effects model. Our study may aid in the planning, data analysis, and interpretation of GWAS meta-analysis results when genotype imputation is necessary. Public Library of Science 2012-04-05 /pmc/articles/PMC3320624/ /pubmed/22496814 http://dx.doi.org/10.1371/journal.pone.0034486 Text en Li 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
Li, Jian
Guo, Yan-fang
Pei, Yufang
Deng, Hong-Wen
The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title_full The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title_fullStr The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title_full_unstemmed The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title_short The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies
title_sort impact of imputation on meta-analysis of genome-wide association studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3320624/
https://www.ncbi.nlm.nih.gov/pubmed/22496814
http://dx.doi.org/10.1371/journal.pone.0034486
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