PCGA

PCGA integrates GWAS summary statistics with bulk and single-cell RNA sequencing expression profiles to identify susceptibility genes and their associated tissues and cell types for mapping phenotype–cell–gene associations.

Key Features:

• Integration of GWAS Summary Statistics: PCGA leverages GWAS summary statistics to jointly estimate susceptibility genes and the tissues/cell types associated with complex phenotypes.
• DESE Methodology: The platform implements the published DESE (Disease-Expression Susceptibility Estimation) method to integrate GWAS summary statistics with transcriptome data for estimating driver tissues and genes.
• Extensive Expression Profiles: PCGA incorporates bulk and single-cell RNA-seq expression profiles spanning 54 human tissues, 2,214 human cell types, and 4,384 mouse cell types.
• Hierarchical Framework: The approach employs a curated hierarchical framework that sequentially estimates associated tissues and cell types for a phenotype.
• Phenotype-Cell-Gene Association Landscape: PCGA constructs a landscape of associated tissues/cell types and genes by estimating associations across 1,871 public GWASs, reported to be consistent with existing biological knowledge.

Scientific Applications:

• Dissection of disease etiology: Joint estimation of genes and tissues/cell types links non-coding GWAS loci to putative regulated genes and implicated biological contexts.
• Prioritization for functional follow-up: The results can be used to prioritize susceptibility genes and driver tissues/cell types for downstream experimental validation and therapeutic investigation.
• Cross-species expression comparison: Inclusion of human and mouse cell-type profiles enables comparative analyses for translational inference.
• Integrative genomics for precision medicine: Integration of GWAS and transcriptomic data supports interpretation of genotype-to-phenotype mechanisms relevant to genomics and personalized medicine.

Methodology:

PCGA applies the DESE method to integrate GWAS summary statistics with transcriptome (bulk and single-cell RNA-seq) data and uses a curated hierarchical framework to sequentially estimate associated tissues, cell types, and susceptibility genes, including analyses across 1,871 public GWASs.