PTWAS

PTWAS performs probabilistic transcriptome-wide association analysis to identify tissue- and cell-type-specific causal effects of gene expression on complex traits by integrating probabilistic eQTL annotations from multi-variant Bayesian fine-mapping with GWAS summary statistics.

Key Features:

• Probabilistic eQTL Annotations: Employs probabilistic annotations derived from multi-variant Bayesian fine-mapping to model eQTL uncertainty and increase power in TWAS associations.
• Instrumental Variables (IV) Analysis: Incorporates principles from instrumental variables analysis to support causal inference between gene expression and complex traits.
• Causal Inference Capabilities: Estimates tissue- or cell-type-specific causal effects of gene expression on complex traits and provides evaluation of causal assumptions.
• Tissue-Relevance and Heterogeneity Analysis: Utilizes eQTL data across 49 tissues from GTEx v8 to assess heterogeneity and tissue relevance of gene expression effects on complex traits.

Scientific Applications:

• Molecular mechanism dissection: Identifies genes with strong evidence of eQTL-mediated causal effects to dissect molecular mechanisms underlying complex traits.
• Large-scale trait analysis: Applied to analyze 114 complex traits using GWAS summary statistics from projects such as the UK Biobank to reveal context-dependent gene expression impacts across tissues.

Methodology:

Integrates multi-variant Bayesian fine-mapping to construct probabilistic eQTL annotations, combines those annotations with GWAS summary statistics to perform TWAS, and applies instrumental variables (IV) analysis principles for causal inference using GTEx v8 eQTL data across 49 tissues.