Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States.
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States.
Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad288.
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.
全基因组关联研究（GWAS）已经确定了许多与复杂疾病风险相关的遗传变异；然而，这些关联大多数是非编码的，这使得鉴定它们的近端靶基因变得复杂。全转录组关联研究（TWAS）已经被提出，通过将表达数量性状基因座（eQTL）数据与 GWAS 数据整合，来缓解这一差距。已经提出了许多 TWAS 的方法学进展，但是每种方法都需要特定的模拟来证明其可行性。在这里，我们提出了 twas_sim，这是一个计算上可扩展的、易于扩展的工具，用于简化 TWAS 方法的性能评估和功效分析。
软件和文档可在 https://github.com/mancusolab/twas_sim 上获得。
