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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: Mar 14, 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

Tissue-specific pathway association analysis using genome-wide association study summaries.

Wenyu Wang¹, Jingcan Hao¹, Shuyu Zheng²

¹Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center.

Bioinformatics (Oxford, England)

|September 22, 2016

Summary

This summary is machine-generated.

A new tissue-specific pathway interaction enrichment analysis algorithm (TPIEA) improves the identification of genetic pathways linked to complex diseases like osteoporosis. This method enhances disease-relevant pathway recognition compared to traditional approaches.

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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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Published on: January 9, 2020

Area of Science:

Genetics
Bioinformatics
Systems Biology

Background:

Pathway association analysis is crucial for understanding complex disease genetics.
Current methods lack tissue-specificity, limiting their accuracy.
Identifying tissue-specific genetic contributions is vital for disease research.

Purpose of the Study:

To develop a novel algorithm, tissue-specific pathway interaction enrichment analysis (TPIEA), that incorporates tissue specificity.
To apply TPIEA to genome-wide association study (GWAS) data for bone mineral density (BMD).
To evaluate TPIEA's performance against classical pathway enrichment methods.

Main Methods:

Developed the TPIEA algorithm for tissue-specific pathway enrichment analysis.
Applied TPIEA to large Caucasian and Chinese GWAS summary datasets for BMD.
Compared TPIEA's results with traditional pathway enrichment analyses.

Main Results:

TPIEA identified significant pathways for BMD, including KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, at a false discovery rate (FDR) < 0.05.
These identified pathways have known associations with osteoporosis development.
TPIEA demonstrated superior performance in recognizing disease-relevant pathways compared to classical methods.

Conclusions:

TPIEA effectively identifies tissue-specific pathways relevant to complex diseases.
The algorithm addresses the limitations of current pathway association analyses by incorporating tissue specificity.
TPIEA offers a valuable tool for advancing genetic research in complex diseases.