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

• Type of Material: Article
• Authors: Binglan Li, Stanford University; Yogasudha Veturi, University of Pennsylvania; Anurag Verma, University of Pennsylvania; Yuki Bradford, University of Pennsylvania; Eric S. Daar, University of California Los Angeles; Roy M. Gulick, Weill Cornell Medical College; Sharon A. Riddler, University of Pittsburgh; Gregory K. Robbins, Massachusetts General Hospital; Jeffrey Lennox, Emory University; David W. Haas, Vanderbilt University; Marylyn D. Ritchie, University of Pennsylvania
• Language: English
• Date: 2021-04-01
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Li et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pgen.1009464
• Title of Journal or Parent Work: PLOS GENETICS
• Volume: 17
• Issue: 4
• Start Page: e1009464
• End Page: e1009464
• Grant/Funding Information: This project was supported by the National Institute of Allergy and Infectious Diseases (NIAID award number U01AI068636), the National Institute of Mental Health, and the National Institute of Dental and Craniofacial Research. Grant support included TR000124 (to E.S.D.); AI077505, TR000445, AI069439 (to D.W.H.); and the National Institute of Allergy and Infectious Disease (NIAID award AI077505 and AI116794 (to M.D.R.). Clinical research sites that participated in ACTG protocols ACTG 384, A5095, A5142, A5202 or A5257, and collected DNA under protocol A5128 were supported by the following grants from the National Institutes of Health (NIH): A1069412, A1069423, A1069424, A1069503, AI025859, AI025868, AI027658, AI027661, AI027666, AI027675, AI032782, AI034853, AI038858, AI045008, AI046370, AI046376, AI050409, AI050410, AI050410, AI058740, AI060354, AI068636, AI069412, AI069415, AI069418, AI069419, AI069423, AI069424, AI069428, AI069432, AI069432, AI069434, AI069439, AI069447, AI069450, AI069452, AI069465, AI069467, AI069470, AI069471, AI069472, AI069474, AI069477, AI069481, AI069484, AI069494, AI069495, AI069496, AI069501, AI069501, AI069502, AI069503, AI069511, AI069513, AI069532, AI069534, AI069556, AI072626, AI073961, RR000046, RR000425, RR023561, RR024156, RR024160, RR024996, RR025008, RR025747, RR025777, RR025780, TR000004, TR000058, TR000124, TR000170, TR000439, TR000445, TR000457, TR001079, TR001082, TR001111, and TR024160. NIH: https://www.nih.gov/ NIAID: https://www.niaid.nih.gov/
• Keywords: DISEASE; EFAVIRENZ; GENOME-WIDE ASSOCIATION; 3-DRUG; INFECTION; Genetics & Heredity; Science & Technology; INITIAL TREATMENT; GENE-EXPRESSION; ANTIRETROVIRAL REGIMENS; GWAS; Life Sciences & Biomedicine; CONTEXT
• Research Categories: Health Sciences, Immunology; Biology, Virology; Biology, Genetics

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.