GWAS
Definition
Genome-Wide Association Studies (GWAS) are large-scale statistical analyses that scan genomes across thousands of individuals to identify genetic variants associated with specific traits or diseases. By comparing single nucleotide polymorphisms (SNPs) between cases and controls, GWAS reveals correlations between genetic loci and phenotypes. These studies have identified thousands of disease-associated variants, providing insights into genetic architecture, heritability, and biological mechanisms. GWAS typically employs stringent statistical thresholds (p < 5×10⁻⁸) to account for multiple testing across millions of variants. While GWAS identifies associations, it doesn't prove causation, requiring functional validation and integration with other omics data to understand biological mechanisms underlying complex diseases.
Visualize GWAS in Nodes Bio
Researchers can visualize GWAS results as networks connecting SNPs to genes, phenotypes, and biological pathways. Nodes Bio enables integration of GWAS hits with protein-protein interactions, gene regulatory networks, and pathway databases to identify functional clusters and prioritize candidate genes. Network analysis reveals how disease-associated variants converge on shared biological processes, helping researchers move from statistical associations to mechanistic hypotheses.
Visualization Ideas:
- SNP-to-gene-to-pathway networks showing functional enrichment of GWAS hits
- Multi-trait GWAS networks revealing shared genetic architecture across diseases
- Integration networks combining GWAS signals with protein interactions and gene expression data
Example Use Case
A research team investigating type 2 diabetes performs GWAS on 50,000 patients and identifies 127 significant SNPs. To understand biological mechanisms, they map these variants to nearby genes and visualize the resulting gene network in Nodes Bio. The analysis reveals that many GWAS hits cluster around insulin signaling and beta-cell function pathways. By overlaying gene expression data from pancreatic tissue, they identify TCF7L2 and KCNJ11 as central hub genes, prioritizing them for functional validation studies and potential therapeutic targeting.