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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...

Type II Diabetes II: Pathophysiology

Type II Diabetes II: Pathophysiology

PathophysiologyType 2 diabetes mellitus (T2DM ) is a chronic metabolic disorder characterized by insulin resistance and progressive pancreatic β-cell dysfunction, leading to impaired glucose homeostasis. It results from interactions among genetic predisposition, environmental factors, and metabolic stressors, such as overnutrition and a sedentary lifestyle.Insulin Resistance and Glucose DysregulationEarly T2DM involves insulin resistance in skeletal muscle, adipose tissue, and the liver.

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Type II Diabetes I: Introduction

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Related Experiment Video

Updated: Jun 25, 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

Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach.

John R B Perry¹, Mark I McCarthy, Andrew T Hattersley

¹Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter, UK

|March 3, 2009

Summary

This study investigated biological pathways associated with type 2 diabetes using genome-wide association data. No significant pathways were identified, suggesting complex genetic influences on type 2 diabetes risk.

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

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Genetics
Molecular Biology
Endocrinology

Background:

Genome-wide association studies (GWAS) have identified numerous genetic loci for type 2 diabetes.
A pathway-based approach complements single-marker studies to uncover additional risk factors.
Understanding genetic pathways is crucial for elucidating the complex etiology of type 2 diabetes.

Purpose of the Study:

To identify biological pathways associated with type 2 diabetes risk.
To complement existing GWAS findings by examining gene sets.
To potentially discover novel genetic loci contributing to type 2 diabetes.

Main Methods:

Utilized individual-level genotype data from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study (1,924 cases, 2,938 controls).
Incorporated summary-level data from the Diabetes Genetics Initiative (DGI) and Finland-United States Investigation of NIDDM Genetics (FUSION) studies.
Applied a modified Gene Set Enrichment Algorithm (GSEA) to analyze 439 pathways from KEGG, Gene Ontology, and BioCarta databases.

Main Results:

No statistically significant pathways were associated with type 2 diabetes after correcting for multiple testing (top P(adj) = 0.31).
The WNT-signaling pathway showed nominal association (P = 0.0007, excluding TCF7L2), with promising single gene associations like CCND2, SMAD3, and PRICKLE1.
These candidate genes are expressed in the pancreas, suggesting potential functional relevance.

Conclusions:

Common variants influencing type 2 diabetes risk likely reside within or near genes in multiple biological pathways.
Pathway-based analysis of GWAS data may yield varying success depending on the specific complex trait and its underlying pathophysiology.
Further research is needed to fully understand the role of pathways in type 2 diabetes genetics.