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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.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jan 16, 2026

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

Published on: January 25, 2018

Genome-Wide Association Study: A Soybean Example.

Shameela Mohamedikbal^1,2, Robyn Anderson^1,2, Cassandria Tay Fernandez^1,2

¹Centre for Applied Bioinformatics, University of Western Australia, Perth, WA, Australia.

Methods in Molecular Biology (Clifton, N.J.)

|October 1, 2025

Summary

This summary is machine-generated.

Genome-wide association studies (GWAS) identify genetic variants linked to traits. This guide demonstrates using the R-package rMVP for efficient SNP data analysis and filtering in GWAS.

Keywords:

Genome Wide Association Study R Soybean bcftools rMVP vcf

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Last Updated: Jan 16, 2026

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with specific phenotypes.
Numerous R-packages and command-line tools exist for conducting GWAS.

Purpose of the Study:

To provide a practical example of performing GWAS using the R-package rMVP.
To illustrate the process of downloading and filtering single-nucleotide polymorphism (SNP) data for GWAS.

Main Methods:

Data acquisition and filtering of SNP data.
Utilizing the rMVP R-package for GWAS analysis.
Statistical analysis of genetic associations.

Main Results:

Successful demonstration of SNP data processing and filtering.
Execution of GWAS analysis using rMVP.
Identification of potential SNPs associated with the phenotype of interest.

Conclusions:

The R-package rMVP offers a user-friendly approach for GWAS analysis.
Effective data filtering is essential for accurate GWAS results.
This workflow facilitates the identification of genetic associations.