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      The Polygenic and Monogenic Basis of Blood Traits and Diseases

      research-article
      1 , 2 , 101 , 4 , 5 , 6 , 101 , 7 , 2 , 8 , 1 , 101 , 4 , 5 , 101 , 9 , 7 , 10 , 11 , 12 , 13 , 1 , 14 , 15 , 16 , 7 , 17 , 10 , 18 , 19 , 20 , 1 , 19 , 18 , 1 , 1 , 21 , 22 , 23 , 24 , 9 , 25 , 22 , 26 , 27 , 28 , 29 , 30 , 31 , 23 , 32 , 33 , 34 , 35 , 36 , 7 , 37 , 2 , 1 , 10 , 38 , 7 , 37 , 2 , 1 , 10 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 5 , 34 , 47 , 48 , 32 , 49 , 50 , 30 , 5 , 51 , 7 , 52 , 53 , 30 , 7 , 10 , 54 , 55 , 36 , 56 , 57 , 48 , 58 , 59 , 22 , 60 , 22 , 61 , 7 , 40 , 62 , 63 , 64 , 65 , 66 , 67 , 63 , 64 , 68 , 69 , 13 , 63 , 64 , 70 , 23 , 71 , 72 , 73 , 30 , 23 , 74 , 32 , 75 , 76 , 77 , 78 , 79 , 68 , 11 , 12 , 80 , 81 , 82 , 13 , 83 , 7 , 38 , 84 , 85 , 86 , 9 , 87 , 33 , 88 , 56 , 48 , 89 , 48 , 20 , 56 , 48 , VA Million Veteran Program 100 , 1 , 1 , 18 , 1 , 1 , 34 , 82 , 91 , 92 , 80 , 13 , 93 , 23 , 94 , 95 , 96 , 42 , 40 , 21 , 97 , 98 , 99 , 15 , 16 , 7 , 38 , 10 , 37 , 90 , 11 , 12 , 24 , 8 , 2 , 20 , 3 , 7 , 37 , 2 , 1 , 10 , 38 , 18 , 20 , 1 , 2 , 38 , 9 , 87 , 4 , 5 , 102 , ∗∗ , 1 , 2 , 17 , 18 , 102 , 103 ,
      Cell
      Cell Press
      blood, genetics, hematopoiesis, rare disease, polygenic risk, fine-mapping, splicing, UK Biobank, omnigenic, chromatin

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Summary

          Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

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          Highlights

          • Largest genome-wide association study of blood cell traits to date

          • Empiric assessments of omnigenic and infinitesimal models of polygenic variation

          • Functional insights into how genetic variants impact human hematopoiesis

          • Assessment of the effect of polygenic trait scores upon blood diseases

          Abstract

          Analysis of blood cell traits in the UK Biobank and other cohorts illuminates the full genetic architecture of hematopoietic phenotypes, with evidence supporting the omnigenic model for complex traits and linking polygenic burden with monogenic blood diseases.

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          Most cited references39

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          Annotation-free quantification of RNA splicing using LeafCutter

          The excision of introns from pre-mRNA is an essential step in mRNA processing. We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both for detecting differential splicing between sample groups, and for mapping splicing quantitative trait loci (sQTLs). Compared to contemporary methods, we find 1.4–2.1 times more sQTLs, many of which help us ascribe molecular effects to disease-associated variants. Strikingly, transcriptome-wide associations between LeafCutter intron quantifications and 40 complex traits increased the number of associated disease genes at 5% FDR by an average of 2.1-fold as compared to using gene expression levels alone. LeafCutter is fast, scalable, easy to use, and available online.
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            Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

            Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic – the majority of inherited susceptibility is related to the cumulative impact of many common DNA variants. Here, we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in >300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13 kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal difference in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by age 18 years. This new approach to quantify inherited susceptibility to obesity using affords new opportunities for clinical prevention and mechanistic assessment. A genome-wide polygenic score quantifies inherited susceptibility to obesity, integrating information from 2.1 million common genetic variants to identify adults at risk of severe obesity.
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              • Abstract: found
              • Article: not found

              Hierarchical organization in complex networks.

              Many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. We show that these two features are the consequence of a hierarchical organization, implying that small groups of nodes organize in a hierarchical manner into increasingly large groups, while maintaining a scale-free topology. In hierarchical networks, the degree of clustering characterizing the different groups follows a strict scaling law, which can be used to identify the presence of a hierarchical organization in real networks. We find that several real networks, such as the Worldwideweb, actor network, the Internet at the domain level, and the semantic web obey this scaling law, indicating that hierarchy is a fundamental characteristic of many complex systems.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                03 September 2020
                03 September 2020
                : 182
                : 5
                : 1214-1231.e11
                Affiliations
                [1 ]Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
                [2 ]National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, UK
                [3 ]Department of Epidemiology, University of Washington, Seattle, WA, 98109, USA
                [4 ]Division of Hematology/Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA
                [5 ]Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
                [6 ]Harvard-MIT Health Sciences and Technology, Harvard Medical School, Boston, MA, 02142, USA
                [7 ]Department of Public Health and Primary Care, British Heart Foundation Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, CB1 8RN, UK
                [8 ]MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
                [9 ]Montreal Heart Institute, Montreal, Quebec, H1T 1C8, Canada
                [10 ]National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
                [11 ]The Framingham Heart Study, National Heart, Lung and Blood Institute, Framingham, MA, 01702, USA
                [12 ]Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, 01702, USA
                [13 ]Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
                [14 ]Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
                [15 ]Department of Public Health and Primary Care, Cambridge Baker Systems Genomics Initiative, University of Cambridge, Cambridge, CB1 8RN, UK
                [16 ]Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, VIC 3004, Australia
                [17 ]British Heart Foundation Centre of Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, Cambridge, CB2 0QQ, UK
                [18 ]Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, UK
                [19 ]National Institute for Health Research (NIHR) BioResource, Cambridge University Hospitals, Cambridge, CB2 0PT, UK
                [20 ]National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, UK
                [21 ]Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
                [22 ]Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
                [23 ]Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, NY, 10029, USA
                [24 ]Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA
                [25 ]Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC, 27599, USA
                [26 ]Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8581, Japan
                [27 ]Department of Biostatistics, University of Washington, Seattle, WA, 98101, USA
                [28 ]Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 1QU, UK
                [29 ]Translational Health Sciences, Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, BS10 5NB, UK
                [30 ]Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
                [31 ]Hasso-Plattner-Institut, Universität Potsdam, Potsdam, 14469, Germany
                [32 ]Department of Medicine, University of Washington, Seattle, WA, 98101, USA
                [33 ]Department of Epidemiology, Erasmus University Medical Center Rotterdam, Rotterdam, 3015 GE, the Netherlands
                [34 ]Laboratory of Epidemiology and Population Science, National Institute on Aging/NIH, Baltimore, MD, 21224, USA
                [35 ]Atlanta VA Medical Center, Decatur, GA, 30033, USA
                [36 ]Division of Research, Kaiser Permanente Northern California, Oakland, CA, 94612, USA
                [37 ]Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, UK
                [38 ]British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, UK
                [39 ]Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, 69008, France
                [40 ]Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece
                [41 ]Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA
                [42 ]Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
                [43 ]Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London, W2 1NY, UK
                [44 ]Medical Research Council Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
                [45 ]UK Dementia Research Institute, Imperial College London, London, WC1E 6BT, UK
                [46 ]Health Data Research UK London, London, W2 1PG, UK
                [47 ]Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475, Germany
                [48 ]German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, 17475, Germany
                [49 ]Department of Epidemiology, University of Washington, Seattle, WA, 98101, USA
                [50 ]Department of Internal Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, 3015 GE, the Netherlands
                [51 ]Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
                [52 ]Institute for Immunology and Transfusion Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
                [53 ]Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98101, USA
                [54 ]Novo Nordisk Research Centre Oxford, Oxford, OX3 7FZ, UK
                [55 ]Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute (SITI), Shanghai, 201203, China
                [56 ]Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany
                [57 ]Chair of Experimental Bioinformatics, Research Group Computational Systems Medicine, Technical University of Munich, Freising-Weihenstephan, 85354, Germany
                [58 ]Department of Clinical Physiology, Tampere University Hospital, Tampere, 33521, Finland
                [59 ]Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33014, Finland
                [60 ]Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639, Japan
                [61 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
                [62 ]Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, 80045, USA
                [63 ]Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33520, Finland
                [64 ]Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33014, Finland
                [65 ]Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, 2000, Denmark
                [66 ]Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
                [67 ]Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
                [68 ]Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22903, USA
                [69 ]Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Tokyo, 108-8639, Japan
                [70 ]Division of Molecular Pathology, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
                [71 ]Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, 33521, Finland
                [72 ]Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33014, Finland
                [73 ]Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55455, USA
                [74 ]Departments of Epidemiology, University of Washington, Seattle, WA, 98101, USA
                [75 ]Department of Health Services, University of Washington, Seattle, WA, 98101, USA
                [76 ]Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
                [77 ]Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, 20521, Finland
                [78 ]Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20521, Finland
                [79 ]Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland
                [80 ]Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA
                [81 ]Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
                [82 ]Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
                [83 ]Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA, 01002, USA
                [84 ]Health Data Research UK Cambridge, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
                [85 ]Department of Public Health and Primary Care, Rutherford Fund Fellow, University of Cambridge, Cambridge, CB1 8RN, UK
                [86 ]Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
                [87 ]Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, H3T 1J4, Canada
                [88 ]Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, 9177948564, Iran
                [89 ]Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
                [90 ]The Alan Turing Institute, London, NW1 2DB, UK
                [91 ]Department of Medicine, Division on Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
                [92 ]Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
                [93 ]Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
                [94 ]Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
                [95 ]Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Liège, B-4000, Belgium
                [96 ]European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, CB10 1SA, UK
                [97 ]Laboratory of Statistical Immunology, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
                [98 ]BRC Haematology Theme and Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
                [99 ]NHSBT Blood and Transplant - Oxford Center, John Radcliffe Hospital, Oxford, OX3 9BQ, UK
                Author notes
                []Corresponding author ns6@ 123456sanger.ac.uk
                [∗∗ ]Corresponding author sankaran@ 123456broadinstitute.org
                [100]

                A list of members and their affiliations appears in the Extended Acknowledgments and Author Contributions

                [101]

                These authors contributed equally

                [102]

                These authors contributed equally

                [103]

                Lead Contact

                Article
                S0092-8674(20)30999-5
                10.1016/j.cell.2020.08.008
                7482360
                32888494
                b8d5f1fe-a470-41d9-a546-f2a49f7478ba
                © 2020 The Authors

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

                History
                : 18 January 2020
                : 29 June 2020
                : 3 August 2020
                Categories
                Article

                Cell biology
                blood,genetics,hematopoiesis,rare disease,polygenic risk,fine-mapping,splicing,uk biobank,omnigenic,chromatin

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