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. 2021 Oct 4:10:1002.
doi: 10.12688/f1000research.53962.1. eCollection 2021.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Yagoub Adam  1 , Chaimae Samtal  2 , Jean-Tristan Brandenburg  3 , Oluwadamilare Falola  2 , Ezekiel Adebiyi  1   4   5   6
Affiliations

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Yagoub Adam et al. F1000Res. .

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.

Keywords: GWAS; Meta-analysis; PostGWAS; pGWAS.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. A general pGWAS pipeline consi sts of three main steps: Step 1 aims to perform data preprocessing, Step 2 for data visualization, and Step 3 for downstream pGWAS analysis.
See this image and copyright information in PMC

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