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 and Yue Zhang

PLOS Computational Biology, 2022, vol. 18, issue 3, 1-18

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.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.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009948 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 09948&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009948

DOI: 10.1371/journal.pcbi.1009948

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().