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

Jianle Sun | Zendy; Ruiqi Lyu | Zendy; Luojia Deng | Zendy; Qianwen Li | Zendy; Yang Zhao | Zendy; Yue Zhang | Zendy

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SMetABF: 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

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) - genome wide association study , computer science , bayesian probability , markov chain monte carlo , meta analysis , data mining , artificial intelligence , medicine , biology , biochemistry , 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.

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