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

Jian Li, Yan-fang Guo, Yufang Pei and Hong-Wen Deng

PLOS ONE, 2012, vol. 7, issue 4, 1-7

Abstract: 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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0034486

DOI: 10.1371/journal.pone.0034486

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