PARIS

PARIS aggregates GWAS variants into biological pathways and uses empirical genomic randomization to assess pathway-level enrichment while correcting for SNP coverage and density, linkage disequilibrium, gene size, and pathway size to detect enriched genetic signals in association studies.

Key Features:

• Pathway-level aggregation: Aggregates variants (e.g., SNPs) from GWAS results into predefined biological pathways for combined analysis.
• Empirical genomic randomization: Estimates significance levels by randomized resampling of the genome to control for type I error and multiple testing.
• Bias correction: Corrects for SNP coverage and density, gene size, and pathway size when evaluating pathway enrichment.
• Linkage disequilibrium accounting: Directly incorporates linkage disequilibrium effects into significance estimation.
• Association-method independence: Operates independently of the underlying GWAS association analysis method used to produce input results.

Scientific Applications:

• Pathway enrichment detection in GWAS: Identifies pathways significantly enriched with positive association results that may be missed by single-SNP analysis.
• Application to KEGG and AGRE data: Has been applied using the KEGG database to analyze the Autism Genetic Resource Exchange (AGRE) GWAS dataset to reveal significantly enriched pathways.

Methodology:

Aggregates GWAS variants into pathways, leverages GWAS association results, and applies empirical genomic randomization to estimate significance while correcting for SNP coverage/density, linkage disequilibrium, gene size, and pathway size.