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      Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease

      research-article
      1 , 2 , 3 , 1 , 4 , 5 , 6 , 6 , 7 , 8 , 9 , 10 , 11 , 6 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 8 , 9 , 7 , 24 , 25 , 26 , 27 , 28 , 6 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 12 , 6 , 38 , 39 , 40 , 21 , 41 , 24 , 6 , 42 , 7 , 28 , 19 , 22 , 43 , 44 , 6 , 35 , 45 , 4 , 2 , 3 ,   2 , 3 , 1 , 46 , Rotterdam Study , Vis (Croatia) , Korcula (Croatia) , Split (Croatia) , ORCADES , Helsinki Birth Cohort (1934-44) Study , North Finland Birth Cohort (1966) Study , ALSPAC , PREVEND , PIVUS , Edinburgh type 2 Diabetes Study , MrOS Sweden , The Raine Study , TwinsUK , KORA , SHIP , VIKING , ORCADES
      Journal of human genetics

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          Abstract

          The stress hormone cortisol modulates fuel metabolism, cardiovascular homeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2M to ~7M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomization analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06 to 0.59) and myocardial infarction (0.21, 95% CI 0.00 to 0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.

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          Most cited references44

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          Is Open Access

          The MR-Base platform supports systematic causal inference across the human phenome

          Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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            LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

            Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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              Is Open Access

              In situ click chemistry generation of cyclooxygenase-2 inhibitors

              Cyclooxygenase-2 isozyme is a promising anti-inflammatory drug target, and overexpression of this enzyme is also associated with several cancers and neurodegenerative diseases. The amino-acid sequence and structural similarity between inducible cyclooxygenase-2 and housekeeping cyclooxygenase-1 isoforms present a significant challenge to design selective cyclooxygenase-2 inhibitors. Herein, we describe the use of the cyclooxygenase-2 active site as a reaction vessel for the in situ generation of its own highly specific inhibitors. Multi-component competitive-binding studies confirmed that the cyclooxygenase-2 isozyme can judiciously select most appropriate chemical building blocks from a pool of chemicals to build its own highly potent inhibitor. Herein, with the use of kinetic target-guided synthesis, also termed as in situ click chemistry, we describe the discovery of two highly potent and selective cyclooxygenase-2 isozyme inhibitors. The in vivo anti-inflammatory activity of these two novel small molecules is significantly higher than that of widely used selective cyclooxygenase-2 inhibitors.

                Author and article information

                Journal
                9808008
                J Hum Genet
                J Hum Genet
                Journal of human genetics
                1434-5161
                1435-232X
                26 January 2021
                01 June 2021
                20 January 2021
                27 May 2021
                : 66
                : 6
                : 625-636
                Affiliations
                [1 ]BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
                [2 ]MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
                [3 ]Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
                [4 ]Division of Genetics and Genomics, The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK
                [5 ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
                [6 ]Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
                [7 ]Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
                [8 ]Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, 17475 Greifswald, Germany
                [9 ]German Center for Cardiovascular Disease (DZHK e.V.), partner site Greifswald, 17475 Greifswald, Germany
                [10 ]Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
                [11 ]University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, PO box 30.001, 9700 RB, The Netherlands
                [12 ]Department of Epidemiology and Biostatistics, Medical Research Council–Public Health England Centre for Environment and Health, Imperial College London, London, UK
                [13 ]Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
                [14 ]Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
                [15 ]Turku Institute of Advanced Studies, University of Turku, Turku, Finland
                [16 ]Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
                [17 ]Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
                [18 ]Department of Twin Research and Genetic Epidemiology, King’s College, London, Lambeth Palace Road, SE1 7EH, UK
                [19 ]NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
                [20 ]Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
                [21 ]Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
                [22 ]Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center Rotterdam, the Netherlands
                [23 ]Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
                [24 ]School of Medicine and Public Health, Faculty of Medicine and Health, University of Newcastle, Newcastle, NSW, 2308, Australia
                [25 ]Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
                [26 ]Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
                [27 ]Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
                [28 ]Clinical Gene Networks AB, Stockholm, Sweden
                [29 ]Folkhälsan Research Center, Helsinki, Finland
                [30 ]Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, University of Helsinki, Helsinki, Finland
                [31 ]Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Helsinki, Singapore
                [32 ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
                [33 ]Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
                [34 ]German Center for Diabetes Research (DZD), Neuherberg, Germany
                [35 ]MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, Scotland
                [36 ]Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
                [37 ]Unit of Primary Health Care and Medical Research Center, Oulu University Hospital, Oulu, Finland
                [38 ]Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
                [39 ]Department of Biostatistics, University of Liverpool, Liverpool, UK
                [40 ]Wellcome Centre for Human genetics, University of Oxford, Oxford, UK
                [41 ]Sahlgrenska University Hospital, Department of Drug Treatment, Gothenburg, Sweden
                [42 ]Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
                [43 ]Department of Social and Behavioural Science, Harvard TH Chan School of Public Health, Boston, MA, USA
                [44 ]Institute for Community Medicine, University Medicine Greifswald, Walther-Rathenau-Str. 48, 17489, Greifswald, Germany
                [45 ]Computational Biology Unit, Department of Informatics, University of Bergen, PO Box 7803, 5020 Bergen, Norway
                [46 ]Clinical and Translational Research Institute, Newcastle University, Newcastle upon Tyne, UK
                Author notes
                Correspondence: Prof Brian R Walker, Newcastle University, Executive Office, King’s Gate, Newcastle upon Tyne, NE1 7RU, UK, Tel +44 (0)191 208 7701, Brian.Walker@ 123456ncl.ac.uk
                Article
                EMS114696
                10.1038/s10038-020-00895-6
                8144017
                33469137
                ec59e41b-bc66-44fe-b4dc-8af5f8a7f992

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