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

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  • Jian Li
  • Yan-fang Guo
  • Yufang Pei
  • Hong-Wen Deng

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.

Suggested Citation

  • Jian Li & Yan-fang Guo & Yufang Pei & Hong-Wen Deng, 2012. "The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    • Handle: RePEc:plo:pone00:0034486
    DOI: 10.1371/journal.pone.0034486
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    References listed on IDEAS

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    1. Evangelos Evangelou & Demetrius M Maraganore & John PA Ioannidis, 2007. "Meta-Analysis in Genome-Wide Association Datasets: Strategies and Application in Parkinson Disease," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-8, February.
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    Cited by:

    1. Emile R Chimusa & Michelle Daya & Marlo Möller & Raj Ramesar & Brenna M Henn & Paul D van Helden & Nicola J Mulder & Eileen G Hoal, 2013. "Determining Ancestry Proportions in Complex Admixture Scenarios in South Africa Using a Novel Proxy Ancestry Selection Method," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-14, September.
    2. Chizu Tanikawa & Yukinori Okada & Atsushi Takahashi & Katsutoshi Oda & Naoyuki Kamatani & Michiaki Kubo & Yusuke Nakamura & Koichi Matsuda, 2013. "Genome Wide Association Study of Age at Menarche in the Japanese Population," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.

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