SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Jianle Sun, Ruiqi Lyu, Luojia Deng, Qianwen Li, Yang Zhao, Yue Zhang

Abstract: Author summary MetABF is a Bayesian GWAS meta-analysis framework but the efficiency is restricted by the number of studies included. In this article, we propose SMetABF by introducing SSS, an improved edition of traditional MCMC, to speed the MetABF algorithm. We develop an R package and a web tool based on R Shiny to make SMetABF practical for biomedical research. Comparing with the exhaustive approach and MCMC, we validate the effectiveness of SSS in terms of speed and accuracy through simulations. We applied SMetABF to identify several important variants associated with Parkinson’s disease and other autoimmune diseases, and explore the relationship between them. We hope this method can benefit future GWAS meta-analyses, help to identify more risk variants associated with complex traits, and improve the prediction of diseases.