twas_sim

twas_sim performs simulation and statistical power analysis for transcriptome-wide association studies (TWAS) by integrating expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) findings to evaluate methods for identifying genes associated with non-coding genetic variants in complex diseases.

Key Features:

• Simulation Capability: Provides a framework to simulate TWAS scenarios and evaluate the performance of TWAS methodologies across varied genetic architectures and study designs.
• Power Analysis: Conducts statistical power analyses to assess the ability of TWAS methods to detect associations between genetic variants and gene expression.
• Scalability: Supports computational scalability to handle large genomic datasets typical in GWAS and eQTL studies.
• Extensibility: Architecture supports extension to incorporate new TWAS methods or adapt existing approaches.

Scientific Applications:

• Feasibility Studies: Enables ad hoc simulations to demonstrate the feasibility of different TWAS approaches for methodological development.
• Method Comparison: Allows comparative evaluation of multiple TWAS methods by simulating their performance under controlled scenarios.
• Power Assessment: Supports design and planning of studies by estimating required sample sizes and power to detect meaningful gene-expression associations.

Methodology:

Integrates eQTL data with GWAS findings to simulate TWAS scenarios and performs power analyses focused on detection of associations involving non-coding genetic variants.