Twas_SIM, a Python-Based Tool for Simulation and Power Analysis of Transcriptome-Wide Association Analysis

Authors

Xinran Wang
Zeyun Lu
Arjun Bhattacharya
Bogdan Pasaniuc
Nicholas Mancuso

Publication Date

5-4-2023

Journal

Bioinformatics

Abstract

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex disease risk; however, most of these associations are non-coding, complicating identifying their proximal target gene. Transcriptome-wide association studies (TWASs) have been proposed to mitigate this gap by integrating expression quantitative trait loci (eQTL) data with GWAS data. Numerous methodological advancements have been made for TWAS, yet each approach requires ad hoc simulations to demonstrate feasibility. Here, we present twas_sim, a computationally scalable and easily extendable tool for simplified performance evaluation and power analysis for TWAS methods.

Software and documentation are available at https://github.com/mancusolab/twas_sim.

Keywords

Humans, Transcriptome, Genome-Wide Association Study, Gene Expression Profiling, Computer Simulation, Software, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease

Comments

Supplementary Materials

PMID: 37099718