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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: Sep 20, 2025

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

Deconstructing the GWAS library: next-generation GWAS.

Weirui Zhang^1,2, Svenja Koslowski^3,4, Marouane Benzaki^3,4

¹Cardiovascular Research Institute, National University Health System, Singapore.

Physiological Reviews

|May 23, 2025

Summary

This summary is machine-generated.

Genome-wide association studies (GWAS) advance cardiovascular disease research by identifying genetic risk factors. Prioritizing these variants with new technologies accelerates the development of targeted therapies for heart conditions.

Keywords:

GWAS cardiac epigenetics genetics noncoding variants

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Area of Science:

Genetics
Cardiovascular Medicine
Bioinformatics

Background:

Genome-wide association studies (GWAS) have identified numerous genetic variants linked to cardiovascular traits and diseases.
These studies have significantly improved our understanding of cardiac disease genetics and identified patient risk factors.
However, the functional mechanisms of many identified variants remain unelucidated, limiting their therapeutic potential.

Purpose of the Study:

To review GWAS in heart disease and methods for prioritizing disease-relevant variants.
To highlight examples of variant identification and subsequent therapeutic development.
To discuss current challenges and future directions in the field.

Main Methods:

Overview of GWAS findings in cardiovascular diseases.
Description of bioinformatic and experimental approaches for variant prioritization.
Case studies illustrating the identification of specific variants and their role in therapy development.

Main Results:

GWAS have revealed a substantial genetic architecture for cardiovascular diseases.
Prioritization tools, including high-throughput screening and machine learning, aid in identifying pathogenic variants.
Several studies have successfully translated variant discoveries into novel therapeutic strategies.

Conclusions:

Despite progress, a significant number of GWAS-identified variants require functional validation.
Advanced computational and experimental methods are crucial for uncovering disease pathways.
Addressing current research challenges will accelerate the translation of genetic discoveries into effective cardiovascular therapies.