ArticlePLoS computational biology2022
SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.Bioinformatics (Oxford, England) · 2026Article
- ti-scMR: trajectory-inference-based dynamic single-cell Mendelian randomization identifies causal genes underlying phenotypic differences.NAR genomics and bioinformatics · 2025Article
- Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.Human genetics · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- STS-BN: An efficient Bayesian network method for detecting causal SNPs.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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.
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Registered trials
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