SSEA

SSEA performs pathway-based enrichment analysis of genome-wide association studies (GWAS) by selecting representative single nucleotide polymorphisms (SNPs) per gene and testing whether pathways are enriched for trait-associated SNPs.

Key Features:

• Adaptive Truncated Product Statistic: Identifies representative SNPs for each gene using an adaptive truncated product statistic and allows selection of potentially more than one SNP per gene.
• Pathway-level SNP re-selection: Calculates the average number of representative SNPs across all genes in a pathway and re-selects SNPs based on this pathway-wide metric to balance representation across genes.
• SNP ranking by association significance: Ranks the selected SNPs according to their statistical association with the trait of interest.
• Weighted Kolmogorov-Smirnov test: Tests whether SNPs from a given pathway are significantly enriched for high-ranking associations using a weighted Kolmogorov-Smirnov test.

Scientific Applications:

• Schizophrenia GWAS pathway discovery: Applied to large GWAS datasets for schizophrenia, identifying 22 pathways in European-American and 11 pathways in African-American samples with nominal P-values ≤ 0.001 and false discovery rates (FDR) < 5%, including eight overlapping pathways across populations.

Methodology:

For each gene within a pathway, representative SNPs are identified using an adaptive truncated product statistic; the average number of representative SNPs per pathway is computed and SNPs are re-selected accordingly; selected SNPs are ranked by association significance and pathway enrichment is assessed with a weighted Kolmogorov-Smirnov test.