Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Department of Biostatistics, Nanjing Medical University School of Public Health, Nanjing, Jiangsu, China.
PLoS Comput Biol. 2022 Mar 14;18(3):e1009948. doi: 10.1371/journal.pcbi.1009948. eCollection 2022 Mar.
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.
贝叶斯方法广泛应用于 GWAS 荟萃分析。但是，在计算时间和内存空间方面的大量消耗给大规模荟萃分析带来了巨大的挑战。在这项研究中，我们提出了一种名为 SMetABF 的算法，用于在 GWAS 荟萃分析中快速获得最优 ABF，其中引入了散弹式随机搜索（SSS）来改进贝叶斯 GWAS 荟萃分析框架 MetABF。模拟研究证实，与穷举法和 MCMC 相比，SMetABF 在速度和准确性方面表现良好。SMetABF 应用于真实的 GWAS 数据集，以发现与帕金森病（PD）相关的几个重要基因座，结果支持 PD 与其他自身免疫性疾病之间的潜在关系。作为一个 R 包和一个网络工具开发，SMetABF 将成为整合不同研究和识别与复杂性状相关的更多变体的有用工具。
