In this thesis,we study how a meta-analysis of genetic association studies is influenced by the degree of genotype imputation uncertainty in the studies combined and the size of meta-analysis. We consider the fixed effect meta-analysis model to evaluate the accuracy and efficiency of imputation-based meta-analysis results under different levels of imputation accuracy. We also examine the impact of genotype imputation on the between-study heterogeneity and type 1 error in the random effects meta-analysis model. Simulation results reaffirm 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. Genotype imputation affects a random effects meta-analysis in a non-obvious way as estimation of between-study heterogeneity and interpretation of association results depend heavily on the number of studies combined. We propose an adjusted fixed effect meta-analysis approach for adding imputation-based studies to a meta-analysis of existing typed studies in a controlled way to improve precision and reliability. The proposed method should help in designing an effective meta-analysis study.
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/23725 |
| Date | 28 July 2014 |
| Creators | Omondi, Emmanuel |
| Contributors | Acar, Elif (Statistics), Liu, Michelle ( Biochemistry and Medical Genetics) Muthukumarana, Saman (Statistics) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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