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

Autoři: Binglan Li aff001;  Yogasudha Veturi aff002;  Anurag Verma aff002;  Yuki Bradford aff002;  Eric S. Daar aff003;  Roy M. Gulick aff004;  Sharon A. Riddler aff005;  Gregory K. Robbins aff006;  Jeffrey L. Lennox aff007;  David W. Haas aff008;  Marylyn D. Ritchie aff002
Působiště autorů: Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America aff001;  Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America aff002;  Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America aff003;  Weill Cornell Medicine, New York City, New York, United States of America aff004;  Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America aff005;  Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America aff006;  Emory University School of Medicine, Atlanta, Georgia, United States of America aff007;  Departments of Medicine, Pharmacology, Pathology, Microbiology & Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America aff008;  Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, United States of America aff009;  Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America aff010
Vyšlo v časopise: Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults. PLoS Genet 17(4): e1009464. doi:10.1371/journal.pgen.1009464
Kategorie: Research Article
doi: 10.1371/journal.pgen.1009464


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.

Klíčová slova:

Blood counts – Gene expression – Genetic loci – Genome-wide association studies – Heredity – Research errors – Total cell counting – Viral load


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