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, 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 = 3.59×10−12), 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.

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

“…It is hard to quantify TWAS power due to the complexity of transcriptional regulation and varied genetic backgrounds of different complex diseases or traits ( Veturi and Ritchie, 2018 ; Li et al, 2021 ). For example, TWAS power can be influenced by the quality of gene expression prediction (sample sizes used for eQTL detection, concordance between transcriptome reference population and testing populations, coverage of eQTLs in the test dataset, etc.…”

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer

“…Several recent studies have explored context-specific TWAS [35][36][37] . Li et al proposed a tissue-specificity-aware TWAS framework that uses prior knowledge of trait-related tissue types for accurate detection of single-tissue and cross-tissue TWAS 35 .…”

Transcriptome-wide association study of HIV-1 acquisition identifies HERC1 as a susceptibility gene

“…The TWAS identified 15 genes regulated in association with HIV-1 acquisition, of which three genes had their expression considered independent from other genes in their loci ( UEVLD , HERC1 , and HIST1H4K ). None of these genes were identified in a recent TWAS of HIV-1 viral control ( Li et al., 2021 ). Using an agnostic TWAS fine-mapping approach, we found evidence corroborating an association between HIV-1 acquisition and HERC1 expression in EBV-transformed B lymphocytes and RTP4 expression in the aorta.…”