GIGSEA

GIGSEA interrogates gene set enrichment for trait-associated SNPs using GWAS-and-eQTL-imputed trait-associated differential gene expression.

Key Features:

• GWAS-and-eQTL imputation: Uses genotype-imputed differential gene expression from GWAS summary statistics combined with eQTL data to enable gene set enrichment analysis without requiring individual-level data.
• eQTL integration: Incorporates eQTL from large gene expression studies such as GTEx to address challenges including gene size, gene boundary, SNP distal regulation, and multiple-marker regulation.
• Weighted linear regression: Performs enrichment testing using a weighted linear regression model with weights based on imputation accuracy and model completeness to adjust for bias from gene set redundancy.
• Permutation test with matrix optimization: Evaluates enrichment significance via permutation test with computational efficiency improved through large matrix operations.

Scientific Applications:

• SNP enrichment analysis: Enables gene set enrichment analysis for trait-associated SNPs by leveraging imputed transcriptome-wide association study results.
• Complex trait genetics: Identifies enriched biological pathways and gene sets underlying complex traits using summary-level genetic data.

Methodology:

GIGSEA uses a weighted linear regression model with weights for imputation accuracy and model completeness to perform enrichment testing, and evaluates significance through permutation tests optimized via large matrix operations.