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Genome-wide Association Studies-GWAS

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Updated: May 5, 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

Network analysis of GWAS data.

Mark D M Leiserson¹, Jonathan V Eldridge, Sohini Ramachandran

¹Department of Computer Science, Brown University, Providence, RI 02912, United States; Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States.

Current Opinion in Genetics & Development

|November 30, 2013

Summary

This summary is machine-generated.

Genome-wide association studies (GWAS) help find genetic variants linked to traits. This review explores network-based methods to pinpoint causal variants for single or complex polygenic traits.

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Last Updated: May 5, 2026

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Published on: July 1, 2020

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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
Systems Biology

Background:

Genome-wide association studies (GWAS) identify genetic associations with specific traits.
Key challenges include pinpointing causal variants within associated haplotypes and identifying variants for polygenic traits.
Polygenic traits involve multiple genes within a biological pathway.

Purpose of the Study:

To review methods that leverage biological networks to address GWAS challenges.
To enhance the identification of causal variants in genetic association studies.
To explore the utility of protein-protein and protein-DNA interaction networks in GWAS.

Main Methods:

Review of recent computational and statistical methods.
Integration of protein-protein interaction (PPI) network data.
Integration of protein-DNA interaction (PDI) network data.

Main Results:

Network information aids in resolving causal variants from associated haplotypes.
Biological networks facilitate the identification of causal variants for polygenic traits.
These methods improve the biological interpretability of GWAS findings.

Conclusions:

Protein interaction networks are valuable resources for overcoming GWAS limitations.
Network-based approaches offer powerful strategies for fine-mapping and polygenic trait analysis.
Future research can further integrate multi-omics and network data for enhanced genetic discovery.