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      Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

      research-article
      , MD a , b , * , , MD a , c , * , , PhD d , , MD a , b , , MD a , b , , MBBChir d , , PhD d , , PhD a , , MD e , f , , PhD a , h , , PhD i , k , , MSc i , k , l , , Prof, MD i , j , l , , Prof, PhD m , , Prof, MD m , , PhD m , , PhD n , , PhD o , p , , Prof, PhD o , p , , PhD q , r , s , , MD t , , MS r , , MD b , , Prof, MD u , , Prof, MD g , v , , MS g , , Prof, MD w , , Prof, PhD x , , Prof, PhD y , , Prof, MD z , , Prof, MB aa , , Prof, MD ab , , Prof, MD ac , , Prof, FMedSci ad , , Prof, PhD d , ad , International COPD Genetics Consortium , SpiroMeta Consortium , , Prof, MD a , , Prof, MD a , b , ae , , Prof, PhD d , , Prof, PhD d , ad , , * , , MD a , b , ** ,
      The Lancet. Respiratory Medicine
      Elsevier

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          Summary

          Background

          Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes.

          Methods

          We constructed a polygenic risk score using a genome-wide association study of lung function (FEV 1 and FEV 1/forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV 1/FVC <0·7 and FEV 1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth.

          Findings

          The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern.

          Interpretation

          A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth.

          Funding

          US National Institutes of Health, Wellcome Trust.

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          Most cited references78

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          Index for rating diagnostic tests

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            Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

            A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation. 1 Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature, 2–5 it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0%, 6.1%, 3.5%, 3.2% and 1.5% of the population at greater than three-fold increased risk for coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For CAD, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk. 6 We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care and discuss relevant issues.
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              Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary

              American Journal of Respiratory and Critical Care Medicine, 195(5), 557-582
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                Author and article information

                Contributors
                Journal
                Lancet Respir Med
                Lancet Respir Med
                The Lancet. Respiratory Medicine
                Elsevier
                2213-2600
                2213-2619
                1 July 2020
                July 2020
                : 8
                : 7
                : 696-708
                Affiliations
                [a ]Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
                [b ]Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
                [c ]Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
                [d ]Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester, UK
                [e ]Computational Medicine Core, Center for Lung Biology, Department of Medicine, University of Washington, Seattle, WA, USA
                [f ]Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
                [g ]Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
                [h ]University of British Columbia Center for Heart Lung Innovation, St Paul's Hospital, Vancouver, BC, Canada
                [i ]Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands
                [j ]Department of Respiratory Medicine, Erasmus Medical Centre, Rotterdam, Netherlands
                [k ]Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
                [l ]Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
                [m ]Department of Medicine, University of Arizona, Tucson, AZ, USA
                [n ]GlaxoSmithKline Research and Development, Collegeville, PA, USA
                [o ]Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
                [p ]Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
                [q ]Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea
                [r ]Interdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National University, Seoul, South Korea
                [s ]Institute of Health and Environment, Seoul National University, Seoul, South Korea
                [t ]Department of Internal Medicine, Kangwon National University, Chuncheon, South Korea
                [u ]Department of Medicine and Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
                [v ]Kaiser Permanente Washington Health Research Institute, Seattle, WA
                [w ]School of Medicine, Johns Hopkins University, Baltimore, MD, USA
                [x ]Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
                [y ]School of Public Health, University of Colorado, Denver
                [z ]Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, USA
                [aa ]Department of Radiology, National Jewish Health, Denver, CO, USA
                [ab ]Department of Clinical Science, University of Bergen, Bergen, Norway
                [ac ]Division of Respiratory Medicine, Queen's Medical Centre, Nottingham, UK
                [ad ]National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
                [ae ]Harvard Medical School, Boston, MA, USA
                Author notes
                [* ]Correspondence to: Prof Martin D Tobin, Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK martin.tobin@ 123456leicester.ac.uk
                [** ]Dr M H Cho, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA remhc@ 123456channing.harvard.edu
                [*]

                Contributed equally

                [†]

                Consortium members are listed in the appendix

                [‡]

                Contributed equally

                Article
                S2213-2600(20)30101-6
                10.1016/S2213-2600(20)30101-6
                7429152
                32649918
                acfd95d5-8c16-406e-9905-2d4374f4dcfc
                © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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