Literature DB >> 35222990

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

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:
© 2021 Adam Y et al.

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


  95 in total

1.  Genomic control for association studies.

Authors:  B Devlin; K Roeder
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

2.  Meta-analysis of genome-wide association studies with overlapping subjects.

Authors:  Dan-Yu Lin; Patrick F Sullivan
Journal:  Am J Hum Genet       Date:  2009-12       Impact factor: 11.025

Review 3.  Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

Authors:  Rita M Cantor; Kenneth Lange; Janet S Sinsheimer
Journal:  Am J Hum Genet       Date:  2010-01       Impact factor: 11.025

4.  Gene and pathway-based second-wave analysis of genome-wide association studies.

Authors:  Gang Peng; Li Luo; Hoicheong Siu; Yun Zhu; Pengfei Hu; Shengjun Hong; Jinying Zhao; Xiaodong Zhou; John D Reveille; Li Jin; Christopher I Amos; Momiao Xiong
Journal:  Eur J Hum Genet       Date:  2010-01       Impact factor: 4.246

5.  Gowinda: unbiased analysis of gene set enrichment for genome-wide association studies.

Authors:  Robert Kofler; Christian Schlötterer
Journal:  Bioinformatics       Date:  2012-05-26       Impact factor: 6.937

6.  Imputation-based analysis of association studies: candidate regions and quantitative traits.

Authors:  Bertrand Servin; Matthew Stephens
Journal:  PLoS Genet       Date:  2007-05-30       Impact factor: 5.917

7.  MAGMA: generalized gene-set analysis of GWAS data.

Authors:  Christiaan A de Leeuw; Joris M Mooij; Tom Heskes; Danielle Posthuma
Journal:  PLoS Comput Biol       Date:  2015-04-17       Impact factor: 4.475

8.  Functional mapping and annotation of genetic associations with FUMA.

Authors:  Kyoko Watanabe; Erdogan Taskesen; Arjen van Bochoven; Danielle Posthuma
Journal:  Nat Commun       Date:  2017-11-28       Impact factor: 14.919

9.  Genome-Wide Gene-Based Multi-Trait Analysis.

Authors:  Yamin Deng; Tao He; Ruiling Fang; Shaoyu Li; Hongyan Cao; Yuehua Cui
Journal:  Front Genet       Date:  2020-05-19       Impact factor: 4.599

10.  Common Methods for Performing Mendelian Randomization.

Authors:  Alexander Teumer
Journal:  Front Cardiovasc Med       Date:  2018-05-28
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