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

摘要

In genome-wide association studies (GWASs) of common diseases/traits, weoften analyze multiple GWASs with the same phenotype together to discoverassociated genetic variants with higher power. Since it is difficult to accessdata with detailed individual measurements, summary-statistics-basedmeta-analysis methods have become popular to jointly analyze data sets frommultiple GWASs. In this paper, we propose a novel summary-statistics-basedjoint analysis method based on controlling the joint local false discovery rate(Jlfdr). We prove that our method is the most powerful summary-statistics-basedjoint analysis method when controlling the false discovery rate at a certainlevel. In particular, the Jlfdr-based method achieves higher power thancommonly used meta-analysis methods when analyzing heterogeneous data sets frommultiple GWASs. Simulation experiments demonstrate the superior power of ourmethod over meta-analysis methods. Also, our method discovers more associationsthan meta-analysis methods from empirical data sets of four phenotypes. TheR-package is available at: http://bioinformatics.ust.hk/Jlfdr.html.