Abstract:
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Genotype imputation is a technique extensively used in genome-wide association studies (GWAS), as well as in their meta-analysis. The impact of imputation on testing genetic associations in a single GWAS has been addressed in the recent literature and a number of imputation-based test procedures have been proposed. However, the consequences of including imputation-based GWAS results in a meta-analysis remain largely unexplored.
In this work, we consider both fixed and random effects models to evaluate the accuracy and efficiency of imputation-based meta-analysis results under different levels of genotype imputation accuracy. Simulation results reaffirms that meta-analysis boosts the power of detecting genetic associations compared to individual study results. However, the power deteriorates with increasing uncertainty in imputed genotypes. To control the contribution of imputation-based studies, we propose a reweighing scheme based on genotype imputation accuracy. Our approach achieves a better detection power relative to the traditional approaches, and improve the validity and reliability of imputation-based meta-analysis results.
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