Abstract
Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.
Publication types
-
Research Support, Non-U.S. Gov't
MeSH terms
-
Algorithms*
-
Bayes Theorem
-
Computer Simulation
-
Genome-Wide Association Study* / methods
-
Meta-Analysis as Topic
-
Polymorphism, Single Nucleotide
Grants and funding
YUZ was supported by the National Natural Science Foundation of China (11901387), the National Competition of Health and Longevity of China (JC2021CL029), Three-year Action Program of Shanghai Municipality for Strengthening the Construction of Public Health System Big Data and Artificial Intelligence Application (GWV-10.1-XK05), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-JKCS-028), and Shanghai Jiao Tong University "Jiaotong Star" Plan Medical Engineering Cross Research Project (No:YG2021QN07). YAZ was supported by the National Natural Science Foundation of China (82173620). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.