PascalX

PascalX performs gene and pathway enrichment analysis from genome-wide association study (GWAS) summary statistics by mapping SNP-wise data to genes and annotated gene sets and supporting analyses of individual GWAS and comparative analyses between pairs of GWAS studies.

Key Features:

• Implementation: Python library for processing GWAS summary statistics.
• SNP-to-gene mapping: Maps SNP-wise GWAS data to genes and annotated gene sets to identify enrichment signals.
• Gene scoring with SNP correlation: Generates gene scores by accounting for SNP correlation patterns.
• Statistical computation: Derives scores from the cumulative density function (CDF) of a linear combination of chi-squared (χ^2) distributed random variables, with options for approximate or exact high-precision evaluation.
• Analysis modes: Supports both individual GWAS analyses and comparative analyses between pairs of GWAS studies.
• Computational acceleration: Supports multithreading and GPU utilization for large-scale data analyses.

Scientific Applications:

• Gene and pathway enrichment from GWAS: Identify genes and annotated gene sets exhibiting significant enrichment signals using GWAS summary statistics.
• Comparative GWAS enrichment analysis: Compare enrichment signals between pairs of GWAS studies.

Methodology:

Generates gene scores by accounting for SNP correlation patterns and computing the CDF of a linear combination of chi-squared (χ^2) distributed random variables, evaluated either approximately or exactly with high precision; computation can be accelerated via multithreading and GPU utilization.