  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 data sets from
multiple GWASs. 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 data sets 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 data sets of four phenotypes. The
R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html.
