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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Related Experiment Video

Updated: Oct 2, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

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

Yagoub Adam¹, Chaimae Samtal², Jean-Tristan Brandenburg³

¹Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun, 112233, Nigeria.

|February 28, 2022

Summary

This summary is machine-generated.

Genome-wide association studies (GWAS) identify genetic variants linked to traits. Post-GWAS (pGWAS) analysis is crucial for understanding biological mechanisms, and this guide offers a pipeline for pGWAS research.

Keywords:

GWAS Meta-analysis PostGWAS pGWAS

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Last Updated: Oct 2, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genetics
Genomics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify millions of single-nucleotide polymorphisms (SNPs) associated with human complex traits and diseases.
Classic GWAS lack insight into the functional and biological mechanisms underlying identified SNP associations.
Post-GWAS (pGWAS) methods are essential for deeper biological understanding beyond statistical associations.

Purpose of the Study:

To provide a comprehensive overview of pGWAS analysis methods.
To highlight the challenges and key literature for each pGWAS approach.
To present a custom pGWAS pipeline for researchers.

Main Methods:

Literature review of existing pGWAS methodologies.
Analysis of challenges and limitations associated with different pGWAS techniques.
Development and demonstration of a practical pGWAS pipeline.

Main Results:

An overview of various pGWAS methods is presented.
Challenges inherent in pGWAS analyses are discussed.
A user-friendly pGWAS pipeline is provided for practical application.

Conclusions:

pGWAS analysis is vital for elucidating the biological basis of GWAS findings.
This work serves as a guide for researchers new to pGWAS.
The provided pipeline facilitates the understanding of SNP functions in complex traits and diseases.