trans-PCO

trans-PCO detects trans effects of noncoding GWAS-associated variants on gene co-expression modules by mapping trans-eQTLs and linking variants to biological pathways and regulatory networks.

Key Features:

• Principal Component Analysis-Based Multivariate Association Test: Trans-PCO employs a principal component (PC)-based multivariate association test that analyzes gene expression at the network level to detect trans effects.
• Simulation Superiority: Rigorous simulations demonstrated that trans-PCO substantially outperforms existing methods for mapping trans-eQTLs.
• Application on Large Datasets: The method was applied to whole-blood gene expression datasets Dunedin Genomics (DGN, 913 samples) and eQTLGen (31,684 samples).
• Identification of High-Quality Trans-eSNP-Module Pairs: Trans-PCO identified 14,985 high-quality trans-expression single nucleotide polymorphism (eSNP)-module pairs associated with 197 co-expression gene modules and diverse biological processes.
• Colocalization Analyses: The approach facilitates colocalization analyses between GWAS loci of complex traits and trans-eQTLs to elucidate variant effects on regulatory networks.

Scientific Applications:

• Mapping trans-eQTLs: Enables detection and mapping of trans-expression quantitative trait loci that affect gene networks.
• Linking GWAS Variants to Pathways: Connects noncoding trait-associated GWAS variants to co-expression modules and biological pathways.
• Investigating Molecular Mechanisms of Complex Traits: Supports interpretation of how trait-associated variants influence gene regulation and pathways relevant to complex disease genetics.

Methodology:

Computational methods explicitly include a principal component-based multivariate association test, simulation-based performance evaluation, application to Dunedin Genomics (DGN) and eQTLGen whole-blood gene expression datasets, and colocalization analyses between GWAS loci and trans-eQTLs.