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
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Medical Specialties Commons
Comments
Supplementary Materials
PMID: 37099718