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

Abstract

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, had high false positive rates for genes that are expressed in multiple tissues. 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 glucuronosyl transferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). 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.publicly available.

Multi-ancestry gene-trait connection landscape using electronic health record (EHR) linked biobank data

“…In order to depict a multi-ancestry, gene-disease connection landscape, we designed and implemented a high-throughput phenome-wide TWAS framework based on our previous evaluation of multiple representative TWAS approaches [16] (Figure 1). As individual-level data phenome-wide TWAS takes a substantial amount of computing resources, so much that the analyses would be nearly impossible to finish, our phenome-wide TWAS framework used GWAS summary statistics as input.…”

“…Taking advantage of multi-ancestry EHR-linked biobanks through eMERGE III and PMBB, we investigated ancestry-specific and cross-ancestry complex disease associated genes on a phenome-wide scale for European ancestry and African ancestry populations. Here, based on our previous work [16], we designed a GWAS summary statistics-based phenome-wide TWAS framework that performed a combination of tissue-specific and cross-tissue TWAS to comprehensively capture gene-disease connections. We first applied phenome-wide TWAS to the eMERGE III individuals of European ancestry (N = 68,813) and to individuals of African ancestry (N = 12,658), separately, to investigate ancestryspecific gene-disease associations.…”