Bayesian methods have been widely used in GWAS meta-analysis, but as the number of included studies grows, their time-consuming iteration procedures can pose great challenges to computation resources. In this research, we propose an algorithm named SMetABF to rapidly gain the optimal ABF in GWAS meta-analysis, where an improvement of MCMC named shotgun stochastic search (SSS) is introduced to improve Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in terms of computational speed and accuracy. SMetABF is applied to real GWAS data to find several essential loci related to Parkinson's disease (PD) and support the underlying relationship between PD and other autoimmune disease. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and draw a pooled conclusion.
 

GWAS Meta-analysis,Bayesian,Shotgun