Open AccessSMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included
Author(s) -
Jianle Sun,
Ruiqi Lyu,
Luojia Deng,
Qianwen Li,
Yang Zhao,
Yue Zhang
Publication year - 2022
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009948
Subject(s) - computer science , genome wide association study , bayesian probability , markov chain monte carlo , meta analysis , data mining , computational biology , artificial intelligence , biology , medicine , genetics , gene , genotype , single nucleotide polymorphism
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.