Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria.
Laboratory of Biotechnology, Environment, Agri-food and Health, Sidi Mohammed Ben Abdellah University, Fez, Fez-Meknes, 30000, Morocco.
F1000Res. 2021 Oct 4;10:1002. doi: 10.12688/f1000research.53962.1. eCollection 2021.
Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.
全基因组关联研究(GWAS)提供了大量与人类复杂特征和疾病相关的统计学意义上显著的单核苷酸多态性(SNP)的信息。通过进行 GWAS 研究,科学家们已经成功地将数十万到数百万个 SNP 与单个表型联系起来。此外,一些与罕见疾病相关的 SNP 也已经得到了深入的测试。然而,经典的 GWAS 研究尚未为表型或疾病机制背后的功能和生物学机制提供可靠的、有见识的见解。因此,已经推荐了几种后 GWAS(pGWAS)方法。目前,还没有简单的科学文献为进行 pGWAS 分析提供快速指南。pGWAS 是更好地理解 SNP 之外的生物学机制的关键步骤。在这里,我们提供了进行 pGWAS 分析的概述,并展示了每种方法背后的挑战。此外,我们为每个 pGWAS 方法的关键文章提供了指导,并介绍了 pGWAS 分析中的总体问题。最后,我们包括一个定制的 pGWAS 管道,以指导新用户在进行研究时使用。