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Controlling the Joint Local False Discovery Rate is more Powerful than Meta-analysis Methods in Joint Analysis of Summary Statistics from Multiple Genome-wide Association Studies

Authors Jiang, Wei HKUST affiliated (currently or previously)
Yu, Weichuan View this author's profile
Issue Date 2017
Source Bioinformatics , v. 33, (4), February 2017, p. 500-507
Summary Motivation: In genome-wide association studies (GWASs) of common diseases/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is difficult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze datasets from multiple GWASs. Results: In this paper, we propose a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). We prove that our method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate at a certain level. In particular, the Jlfdr-based method achieves higher power than commonly used meta-analysis methods when analyzing heterogeneous datasets from multiple GWASs. Simulation experiments demonstrate the superior power of our method over meta-analysis methods. Also, our method discovers more associations than meta-analysis methods from empirical datasets of four phenotypes.
ISSN 1460-2059
Language English
Format Article
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