Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults

As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59x10(-12)), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49x10(-12)). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits. Author summary Transcriptome-wide association studies (TWAS) are a type of bioinformatics methodology for identifying complex trait-associated genes. There have been various TWAS methods, each developed under distinct biological assumptions of how genes contribute to complex traits. It is unclear, however, how powerful different TWAS methods are under a variety of biological scenarios. Here, we design an unbiased simulation strategy to evaluate the performance of multiple representative TWAS methods. We find that no one method fits all. Different TWAS methods are advantageous at dealing with different biological scenarios and answering different research questions. Thus, we propose a novel TWAS analytic framework that integrates and maximizes the performance of multiple TWAS methods, and validate its capability using a well-studied real-world dataset. In a word, our study provides quantitative evaluation of method performance to aid future TWAS experimental design and understanding of genes underlying complex human traits. The TWAS evaluation tool is made publicly available.

Automated summary

The transcriptome-wide association study (TWAS) is a bioinformatics methodology for identifying complex trait-associated genes. Different TWAS methods have varying advantages in dealing with different biological scenarios and research questions. Thus, a novel TWAS analytic framework has been proposed to integrate and maximize the performance of multiple TWAS methods, with validation using a well-studied real-world dataset.