PASCAL

PASCAL performs gene-based analysis of GWAS summary statistics to identify pathway- and network-level genetic associations relevant to neurodevelopmental disorders such as ADHD.

Key Features:

• GWAS summary statistics input: Performs gene-based analysis (GBA) using GWAS summary statistics as primary input data.
• Linkage disequilibrium consideration: Accounts for linkage disequilibrium (LD) across genomic regions and contrasts its LD handling with methods like MAGMA and VEGAS.
• Pathway scoring algorithm: Applies a pathway scoring algorithm to evaluate the collective impact of genes within pathways or networks.
• Network and enrichment integration: Integrates gene network analyses and enrichment assessments for KEGG and GO terms.
• GENE2FUNC integration: Employs GENE2FUNC to generate gene expression heatmaps and perform differentially expressed gene (DEG) analysis using GTEX v7 and BrainSpan.
• LD-block gene highlighting: Identifies and highlights genes within the same LD block as known susceptibility genes.

Scientific Applications:

• Novel locus discovery: Identifies novel loci associated with ADHD, including FEZF1 (p = 2.2 × 10^-7) and FEZF1-AS1 (p = 4.58 × 10^-7).
• Enhanced genetic insight: Reveals additional candidate genes by evaluating LD blocks around known ADHD susceptibility genes.
• Gene interaction networks: Uncovers gene interactors associated with ADHD genes to map molecular interaction partners.
• Expression cluster analysis: Uses GENE2FUNC to demonstrate differential expression clusters across brain regions and developmental stages using GTEX v7 and BrainSpan.

Methodology:

Processes GWAS summary statistics to perform gene-based analysis that incorporates linkage disequilibrium, applies pathway scoring and network/enrichment analyses (KEGG, GO), and integrates GENE2FUNC for gene expression heatmaps and DEG analysis using GTEX v7 and BrainSpan.