title: 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.
abstract: 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.
  
  
  
  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.
  
  
  
  The R-package is available at: http://bioinformatics.ust.hk/Jlfdr.html .
  
  
  
  eeyu@ust.hk.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Jiang
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
mesh_terms: D003198:Computer Simulation; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015201:Meta-Analysis as Topic; D010641:Phenotype; D012984:Software