| Literature DB >> 35222990 |
Yagoub Adam1, Chaimae Samtal2, Jean-Tristan Brandenburg3, Oluwadamilare Falola2, Ezekiel Adebiyi1,4,5,6.
Abstract
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. Copyright:Entities:
Keywords: GWAS; Meta-analysis; PostGWAS; pGWAS
Mesh:
Year: 2021 PMID: 35222990 PMCID: PMC8847724 DOI: 10.12688/f1000research.53962.1
Source DB: PubMed Journal: F1000Res ISSN: 2046-1402