VSEAMS

VSEAMS performs non-parametric enrichment analysis of GWAS P-values within predefined gene or genomic intervals to identify functional loci associated with complex traits and disease susceptibility.

Key Features:

• Non-Parametric Enrichment Method: Extends a non-parametric SNP set enrichment method to assess overrepresentation of significant GWAS signals within functionally defined loci when only P-values are available.
• Functional Genomic Integration: Leverages functional genomic datasets to associate genetic variants with specific traits or diseases in a biological context.
• Summary-Statistic-Based Analysis: Operates on GWAS meta-analysis summary statistics (P-values) without requiring individual-level genotype data.
• Application to Transcription Factor Targets: Has been applied to detect enrichment of type 1 diabetes (T1D) GWAS associations near genes targeted by IKZF3, BATF, and ESRRA.
• Validation and Cross-Disease Insight: Validates known T1D susceptibility loci and identifies overlaps with other immune disease susceptibility regions.

Scientific Applications:

• GWAS–Functional Data Integration: Integrates GWAS summary statistics with functional genomic annotations to implicate biologically relevant loci.
• Candidate Gene and Pathway Prioritization: Prioritizes candidate genes and pathways for follow-up experimental validation in complex traits and diseases.
• Disease-Specific Enrichment Studies: Applied to type 1 diabetes to reveal enrichment patterns around transcription factor target genes and to explore shared immune-disease susceptibility regions.

Methodology:

Implements a non-parametric test for enrichment of GWAS signals within predefined genomic interval sets using GWAS meta-analysis P-values and without requiring individual-level genotype data.