Assessing the Impact of Genotype Imputation on Genome-Wide Meta-Analysis

• Activity Number: 236
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: SSC
• Author(s): Elif Acar* and Emmanuel Omondi
• Companies: University of Manitoba and University of Basel
• Keywords: Between-study heterogeneity ; Cochran's Q statistic ; Imputation ; Inverse-variance weighting

Abstract

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.