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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: Jun 13, 2026

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

Genome-wide gene and pathway analysis.

Li Luo¹, Gang Peng, Yun Zhu

¹Human Genetics Center, School of Public Health, The University of Texas, Houston, TX 77225, USA.

European Journal of Human Genetics : EJHG

|May 6, 2010

Summary

Gene and pathway-based Genome-Wide Association Studies (GWAS) offer a powerful approach to uncover complex disease genetics. This method complements single nucleotide polymorphism (SNP) analysis by considering joint effects within genes and pathways.

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Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

Current Genome-Wide Association Studies (GWAS) predominantly focus on single nucleotide polymorphisms (SNPs), which has limited utility for complex diseases.
Dissecting the complex genetic architecture of common diseases requires methods beyond single SNP analysis.

Purpose of the Study:

To propose and develop gene and pathway-based GWAS as a complementary approach to single SNP-based GWAS.
To introduce novel statistical methods that account for correlations among SNPs within genes and among genes within pathways.

Main Methods:

Development of three new statistics: linear combination test, quadratic test, and decorrelation test.
Examination of the null distribution for the proposed statistics.
Application of the methods to GWAS data for rheumatoid arthritis from large consortia.

Main Results:

Gene and pathway-based GWAS can identify genes with large genetic effects and novel genes where small individual SNP effects collectively contribute to disease risk.
This approach facilitates the definition of disease pathways and elucidation of functional bases for association findings.
Replication of findings at the gene or pathway level is more robust than at the individual SNP level.

Conclusions:

Gene and pathway-based GWAS provide a valuable framework for understanding the genetic underpinnings of complex diseases.
These methods enhance the power to detect genetic associations and improve the interpretability and replicability of GWAS findings.
The developed statistics effectively address the challenges of genetic correlations in association studies.