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      Predicted expression of genes involved in the thiopurine metabolic pathway and azathioprine discontinuation due to myelotoxicity

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          Abstract

          TPMT and NUDT15 variants explain less than 25% of azathioprine‐associated myelotoxicity. There are 25 additional genes in the thiopurine pathway that could also contribute to azathioprine myelotoxicity. We hypothesized that among TPMT and NUDT15 normal metabolizers, a score combining the genetically predicted expression of other proteins in the thiopurine pathway would be associated with a higher risk for azathioprine discontinuation due to myelotoxicity. We conducted a retrospective cohort study of new users of azathioprine who were normal TPMT and NUDT15 metabolizers. In 1201 White patients receiving azathioprine for an inflammatory disease, we used relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression to select genes that built a score for discontinuing azathioprine due to myelotoxicity. The score incorporated the predicted expression of AOX1 and NME1. Patients in the highest score tertile had a higher risk of discontinuing azathioprine compared to those in the lowest tertile (hazard ratio [HR] = 2.15, 95% confidence interval [CI] = 1.11–4.19, p = 0.024). Results remained significant after adjusting for a propensity score, including sex, tertile of calendar year at initial dose, initial dose, age at baseline, indication, prior TPMT testing, and the first 10 principal components of the genetic data (HR = 2.11, 95% CI = 1.08–4.13, p = 0.030). We validated the results in a cohort ( N = 517 non‐White patients and those receiving azathioprine to prevent transplant rejection) that included all other patients receiving azathioprine (HR = 2.00, (95% CI = 1.09–3.65, p = 0.024). In conclusion, among patients who were TPMT and NUDT15 normal metabolizers, a score combining the predicted expression of AOX1 and NME1 was associated with an increased risk for discontinuing azathioprine due to myelotoxicity.

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          Most cited references29

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            The Genotype-Tissue Expression (GTEx) project.

            Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
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              Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.
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                Author and article information

                Contributors
                c.chung@vumc.org
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                20 February 2022
                April 2022
                : 15
                : 4 ( doiID: 10.1111/cts.v15.4 )
                : 859-865
                Affiliations
                [ 1 ] Department of Medicine Vanderbilt University Medical Center Nashville Tennessee USA
                [ 2 ] Department of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA
                [ 3 ] Department of Biostatistics Vanderbilt University Medical Center Nashville Tennessee USA
                [ 4 ] Tennessee Valley Healthcare System Nashville Virginia USA
                Author notes
                [*] [* ] Correspondence

                Cecilia P. Chung, Department of Medicine, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN 37232, USA.

                Email: c.chung@ 123456vumc.org

                Author information
                https://orcid.org/0000-0003-3143-5915
                https://orcid.org/0000-0002-0398-5162
                https://orcid.org/0000-0001-5908-9423
                Article
                CTS13243
                10.1111/cts.13243
                9010278
                35118815
                5f08bebf-09e2-4b9b-9092-f90c1b5107b7
                © 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 13 January 2022
                : 16 November 2021
                : 24 January 2022
                Page count
                Figures: 0, Tables: 2, Pages: 7, Words: 4287
                Funding
                Funded by: National Center for Advancing Translational Science , doi 10.13039/100006108;
                Award ID: 2UL1TR000445‐06
                Funded by: NIH/NIGMS , doi 10.13039/100000057;
                Award ID: R01AR076516
                Funded by: Lupus Research Alliance BMS
                Funded by: NIH , doi 10.13039/100000002;
                Award ID: S10OD017985
                Award ID: S10RR025141
                Funded by: CTSA , doi 10.13039/100016220;
                Award ID: UL1TR002243
                Award ID: UL1TR000445
                Award ID: UL1RR024975
                Categories
                Brief Report
                Research
                Brief Reports
                Custom metadata
                2.0
                April 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.4 mode:remove_FC converted:14.04.2022

                Medicine
                Medicine

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