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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: Dec 31, 2025

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

Method for Genome-Wide Association Study: A Soybean Example.

Robyn Anderson¹, Cassandria Tay Fernandez¹, Yuxuan Yuan¹

¹School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia.

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

|January 2, 2020

Summary

This summary is machine-generated.

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

Keywords:

Genome Wide Association Study R Soybean bcftools rMVP vcf

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Last Updated: Dec 31, 2025

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

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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 analysis.
To illustrate the use of the R-package rMVP for genetic data analysis.

Main Methods:

Downloading and filtering single nucleotide polymorphism (SNP) data.
Conducting GWAS analysis using the rMVP R-package.

Main Results:

Demonstrated a workflow for SNP data preparation.
Successfully applied rMVP for GWAS analysis, enabling the identification of phenotype-associated SNPs.

Conclusions:

The R-package rMVP offers a user-friendly approach for GWAS.
This workflow facilitates the identification of genetic markers associated with traits of interest.