Meta-Analysis

. 2020 Sep 3;182(5):1214-1231.e11.

doi: 10.1016/j.cell.2020.08.008.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Dragana Vuckovic¹, Erik L Bao², Parsa Akbari³, Caleb A Lareau⁴, Abdou Mousas⁵, Tao Jiang⁶, Ming-Huei Chen⁷, Laura M Raffield⁸, Manuel Tardaguila⁹, Jennifer E Huffman¹⁰, Scott C Ritchie¹¹, Karyn Megy¹², Hannes Ponstingl⁹, Christopher J Penkett¹³, Patrick K Albers⁹, Emilie M Wigdor⁹, Saori Sakaue¹⁴, Arden Moscati¹⁵, Regina Manansala¹⁶, Ken Sin Lo⁵, Huijun Qian¹⁷, Masato Akiyama¹⁸, Traci M Bartz¹⁹, Yoav Ben-Shlomo²⁰, Andrew Beswick²¹, Jette Bork-Jensen²², Erwin P Bottinger²³, Jennifer A Brody²⁴, Frank J A van Rooij²⁵, Kumaraswamy N Chitrala²⁶, Peter W F Wilson²⁷, Hélène Choquet²⁸, John Danesh²⁹, Emanuele Di Angelantonio²⁹, Niki Dimou³⁰, Jingzhong Ding³¹, Paul Elliott³², Tõnu Esko³³, Michele K Evans²⁶, Stephan B Felix³⁴, James S Floyd³⁵, Linda Broer³⁶, Niels Grarup²², Michael H Guo³⁷, Qi Guo³⁸, Andreas Greinacher³⁹, Jeff Haessler⁴⁰, Torben Hansen²², Joanna M M Howson⁴¹, Wei Huang⁴², Eric Jorgenson²⁸, Tim Kacprowski⁴³, Mika Kähönen⁴⁴, Yoichiro Kamatani⁴⁵, Masahiro Kanai⁴⁶, Savita Karthikeyan³⁸, Fotios Koskeridis⁴⁷, Leslie A Lange⁴⁸, Terho Lehtimäki⁴⁹, Allan Linneberg⁵⁰, Yongmei Liu⁵¹, Leo-Pekka Lyytikäinen⁴⁹, Ani Manichaikul⁵², Koichi Matsuda⁵³, Karen L Mohlke⁸, Nina Mononen⁴⁹, Yoshinori Murakami⁵⁴, Girish N Nadkarni¹⁵, Kjell Nikus⁵⁵, Nathan Pankratz⁵⁶, Oluf Pedersen²², Michael Preuss¹⁵, Bruce M Psaty⁵⁷, Olli T Raitakari⁵⁸, Stephen S Rich⁵², Benjamin A T Rodriguez⁷, Jonathan D Rosen⁵⁹, Jerome I Rotter⁶⁰, Petra Schubert⁶¹, Cassandra N Spracklen⁶², Praveen Surendran⁶³, Hua Tang⁶⁴, Jean-Claude Tardif⁶⁵, Mohsen Ghanbari⁶⁶, Uwe Völker⁶⁷, Henry Völzke⁶⁸, Nicholas A Watkins⁶⁹, Stefan Weiss⁶⁷; VA Million Veteran Program; Na Cai⁹, Kousik Kundu⁷⁰, Stephen B Watt⁹, Klaudia Walter⁹, Alan B Zonderman²⁶, Kelly Cho⁷¹, Yun Li⁷², Ruth J F Loos¹⁵, Julian C Knight⁷³, Michel Georges⁷⁴, Oliver Stegle⁷⁵, Evangelos Evangelou⁷⁶, Yukinori Okada⁷⁷, David J Roberts⁷⁸, Michael Inouye⁷⁹, Andrew D Johnson⁷, Paul L Auer¹⁶, William J Astle⁸⁰, Alexander P Reiner⁸¹, Adam S Butterworth²⁹, Willem H Ouwehand⁸², Guillaume Lettre⁶⁵, Vijay G Sankaran⁸³, Nicole Soranzo⁸⁴

Affiliations

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

PMID: 32888494
PMCID: PMC7482360
DOI: 10.1016/j.cell.2020.08.008

Meta-Analysis

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Dragana Vuckovic et al. Cell. 2020.

. 2020 Sep 3;182(5):1214-1231.e11.

doi: 10.1016/j.cell.2020.08.008.

Authors

Affiliations

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

PMID: 32888494
PMCID: PMC7482360
DOI: 10.1016/j.cell.2020.08.008

Abstract

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.

Keywords: UK Biobank; blood; chromatin; fine-mapping; genetics; hematopoiesis; omnigenic; polygenic risk; rare disease; splicing.

PubMed Disclaimer

Conflict of interest statement

Declaration of Interests Adam Butterworth has received grants (outside of this work) from AstraZeneca, Biogen, BioMarin, Bioverativ, Merck, Novartis, and Sanofi; James Floyd has consulted for Shionogi; Qi Guo is a full-time employee of BenevolentAI; Joanna Howson is a full-time employee of Novo Nordisk. Parsa Akbari is a full-time employee of Regeneron Pharmaceuticals.

Figures

**Figure 1**
GWAS Study Design and Results (A–E) (A) Study design, (B) illustration for fine-mapping (FM) strategy showing how the FM blocks and the relevant number of causative signals were defined, (C) distribution of FM results by MAF, (D) distribution of FM results by sentinel annotation and MAF, and (E) FM 95% credible set size distribution for each sentinel, across all traits: different colors indicate different cell type groups.

**Figure S1**
Replication and Mendelian Genes, Related to Figures 1 and 2 A, Comparison of replication effect size estimates, the x-axes shows effect sizes in MVP, the y-axes shows effect sizes in UK Biobank. The zoom-in panel highlights non-replicating variants in red. B, Proportions of correct gene to variant assignments for VEP worst consequence and VEP all consequences divided by functional annotation. Only known eQTLs in matched cell-types are shown and the correct gene is assumed to be the one identified by the eQTL experiment (eGene). C, Variants assigned by VEP to Mendelian genes across different functional annotations have higher effect sizes compared to other variants, after matching for MAF. The top 5 panels show absolute effect size distributions across all sentinel variants, where sentinels associated with multiple traits were included only once with the highest effect size. The middle 5 panels show the same distributions but after matching the non-Mendelian variants to the Mendelian ones by MAF. Stars denote significance: ^∗ 0.005 < p value < 0.05; ^∗∗ 0.0005 < p value < 0.005; ^∗∗∗p value < 0.0005; FC = median fold change. The bottom 5 panels show the distributions of minor allele frequencies after matching.

**Figure 2**
Network Connectivity (A–B) Coexpression network in whole blood. For illustrative purposes, a subset of highly coexpressed genes is shown (correlation > 0.7). Edges are omitted for clarity, and the node size summarizes the number and strength of coexpression links. Blue dots represent genes detected by GWAS, violet dots are Mendelian genes, and red dots show the intersection. Grey dots are genes in the coexpression network that do not belong to any of the previous categories. GWAS genes are defined by two different variant annotation approaches: VEP all consequences (A) and 500kb FM regions (B). (C) Diagram showing the hypothesized genetic architecture of healthy blood traits. At the core of the underlying molecular network is the set of Mendelian genes which cause blood disorders when mutated. Peripherally to the core lie regulatory genes which affect the phenotype through core genes. Cis and trans-eQTLs can give insights about cell-type specificity and can identify master regulators, i.e., genes that trans-regulate several core genes simultaneously. (D) Enrichment of sets of genes in the coexpression network at different correlation cut-offs. Whiskers indicate 95% CI for the fold enrichment estimate. (E) Proportion of network genes among Mendelian, GWAS, or other genes with > 1 edge, or average number of edges, at different correlation cut-offs. (F) Example of a sub-network containing 3 Mendelian genes involved in platelets (*GP9*, *ITGA2B*, *GP1BB*). As in (A), blue dots are GWAS genes, red dots are GWAS and previously known Mendelian genes, and gray dots are other coexpressed genes.

**Figure S2**
Mendelian and Peripheral Enrichment Q-Q Plots, Related to Figure 2 Each Q-Q plot shows the enrichment for variants assigned to a 100kb interval surrounding Mendelian genes. Different GWAS traits are included: 4 exemplar blood traits and 8 unrelated traits, selected to have at least 500 significant GWAS associations. Overall, with the exception of the “Intelligence” trait, most non-blood phenotypes do not show enrichment for variants mapped to Mendelian blood disorder genes. Conversely, peripheral associations were more likely to be enriched in non-blood traits, showing enrichment for six out of eight traits. SMD = stem cell and myeloid disorders, BPD = bleeding and thrombotic disorders, BMF = bone-marrow failure; GW = genome-wide.

**Figure S3**
Network Examples and Functional Annotation, Related to Figure 2 A, A zoom-in example of the coexpression network, including connected genes with a very high correlation cut-off (0.8). Blue dots represent genes detected by GWAS, according to VEP worst-consequence annotation, red dots represent GWAS genes that are also Mendelian genes for blood disorders. Three Mendelian genes are identified, all of them involved in spherocytosis and other red-cell disorders. **B-C,** Receiver operating characteristic (ROC) curves for measuring classification performance of deltaSVM in two datasets: B) 18 hematopoietic populations sorted from bone marrow, and C) 8 stages of primary erythroid differentiation. D, Association between variant absolute deltaSVM score (maxSVM), reflecting a variant’s predicted disruption of chromatin accessibility, and bins of MAF. Dotted line indicates the median maxSVM score for the MAF 0.3-0.5 bin. E, Rare variants (MAC > 20, MAF < 1%, PP_FM > 0.50, conditionally independent) grouped by genomic annotation. F, Flow-chart depicting the steps involved in the identification and validation of blood trait-associated splice variants. G, Density distribution of variant MAF, comparing 109 putative splice variants to all fine-mapped blood trait variants. H, Violin plot of the fine-mapped posterior probability (PP_FM) for putative splice variants versus all fine-mapped variants. For variants fine-mapped to multiple blood traits, we used the maximum PP_FM.

**Figure 3**
Functional Annotation of Blood Trait Variants (A) g-chromVAR results for FM variants (PP_FM>0.1%) across 22 hematological traits. The Bonferroni-adjusted significance level (p = 0.05/22 traits ×18 cell types) is indicated by the dotted line. New traits are labeled in red. Novel enrichments are starred. The color legend for cell types is shared by panels (A), (B), and the trackplot in (H). mono = monocyte; gran = granulocyte; ery = erythroid; mega = megakaryocyte; CD4 = CD4+ T cell; CD8 = CD8+ T cell; B = B cell; NK = natural killer cell; mDC = myeloid dendritic cell; pDC, = plasmacytoid dendritic cell; MPP = multipotent progenitor; LMPP = lymphoid-primed multipotent progenitor; CMP = common myeloid progenitor; CLP = common lymphoid progenitor; GMP = granulocyte-macrophage progenitor; MEP = megakaryocyte–erythroid progenitor. (B) g-chromVAR enrichment results across 4 platelet traits (MPV, mean platelet volume; PCT, platelet crit; PDW, platelet distribution width; PLT, platelet count), using either all trait-associated variants (all), variants with any gene assignment (any gene), or only variants assigned to genes causative for BPD. The original Bonferroni-adjusted significance level is indicated by the dotted line. (C) The allelic effects of blood trait variants with (1) high (> 99th percentile) versus low (< 1st percentile) deltaSVM scores and (2) one or more predicted motif disruptions, on normalized motif scores. The normalized motif score represents the score for a variant-containing sequence as a percentage of the best score that motif could achieve on an ideal sequence. (D–F) Cell type-specific deltaSVM scores for variants disrupting the (D) GATA1, (E) CEBPA, or (F) GABPA motif compared to scores in non-motif-disrupting controls and non-lineage-specific cell types. Non-motif group indicates all other variants that do not disrupt the target TF. Gain or lost motif group contains variants predicted to create or disrupt the target TF motif, respectively, with the deltaSVM score for a lineage-specific cell type (erythroblast for GATA1, GMP for CEBPA, CD8 for GABPA). Non-lineage gain or lost indicates variants predicted to create or disrupt the target TF motif, but with the deltaSVM score for non-lineage-specific populations (CD8, CD4, and B cells for GATA1 and CEBPA; erythroblast and megakaryocytes for GABPA). (G) Lymphocyte count-associated variant rs72928038 has high chromatin accessibility (left) and deltaSVM score (right) in CD4 and CD8 populations. (H) rs72928038 is located within intron 1 of *BACH2*, and its minor allele A is predicted to break the motifs of TFs ETS1 and STAT3. In the bottom ATAC-seq plot, stacked colors represent accessibility for 18 hematopoietic cell types shown in (A).

**Figure 4**
Characterization of Rare Blood Trait Variants (A) Distribution of coding consequences of 456 rare variants (MAC > 20, MAF < 1%), annotated using VEP. (B) Phenome-wide association study of these 456 rare variants across 529 well-represented clinical phenotypes in the UK Biobank (n up to 408,961). Variants are grouped by the hematopoietic lineage with which they are associated (BASO, basophil; EO, eosinophil; LYMPH, lymphocyte; MONO, monocyte; NEUT, neutrophil; PLT, platelet; RBC, red blood cell; WBC, white blood cell). Some variants appear in more than one category if they are associated with traits from distinct lineages. Text labels indicate the clinical outcomes with the strongest association per category. The dotted line denotes the Bonferroni-adjusted significance level (corrected for 529 phenotypes). (C–E) Sashimi plots depicting splice alterations at 3 loci as determined by RNA-sequencing analysis, comparing carriers of a specified blood trait variant (top track) versus non-carriers (bottom track). (C) Intronic donor gain splicing event in *CD3EAP* among carriers of rs8113779 (PP_FM = 0.23 for PLT, 2nd highest in credible set). Numbers within the splice junctions represent the number of reads supporting the junction. The x axis marks genomic coordinates. (D) Exonic donor gain splicing alteration in *ULK3* associated with rs12898397 (PP_FM = 0.071 for lymphocyte percent, 5th highest in credible set). (E) Donor loss splicing event in the *TFR2* locus, induced by variant rs139178017 (PP_FM = 0.73 for RDW, highest in credible set; PP_FM = 0.4 for MCV, 2nd highest in credible set).

**Figure 5**
Polygenic Prediction of Blood Traits and Contribution to Common Diseases (A) Portability of the PGS across populations with European ancestry for 15 available traits. The red bar represents the Pearson’s correlation (R) between the score and the trait in the validation cohort (INTERVAL). Blue bars show the same in a French Canadian cohort called CARTAgENE. (B and C) Saturation analysis showing the number of discovered variants (B) and the proportion of heritability explained (C) as a function of GWAS sample size for mean platelet volume. The black dotted line is a linear projection of the first 3 points, the red dotted line is a linear interpolation of all points, and the red solid curve is the best model fitting the 4 points. (D) Number of loci with multiple sentinel variants, stratified by trait group. (E) Number of disease loci colocalizing (posterior probability > 99%) with at least one blood count locus, colored by known vs. new loci. (F–K) Examples of loci with multiple sentinels associated with blood cell counts, and with at least one disease-colocalization (red diamond) or PheWAS association (green diamond) for the following genes and diseases: *ITGA4* and Inflammatory Bowel Disease (IBD) (F), *RUNX1* and Rheumatoid Arthritis (G), *NFKB1* and IBD (H), *C1QTNF6* and Type-1 Diabetes (I), *JAK2* and IBD (J), *IL4* and asthma (K). In each panel, black dots show MAF (right y axis) and red dots show the effect size (in SD for the phenotype between brackets, left y axis) of each variant as a function of the variant’s position in the genomic interval.

**Figure S4**
Saturation Models, Related to Figure 5 A, For each trait, we show the number of conditionally independent variants (y-axes) discovered by GWAS in four cohorts of increasing sample size. The sample size is shown on x-axes in 10,000 s. Two linear regression lines are shown: the full black line represents a regression including all 4 data points, the dotted black line represents a linear projection of the first three data points for comparison. A decreasing trend can be observed for almost all traits. B, Similarly to panel a, the number of GWAS-identified genes is shown on the y-axes. Genes were identified by VEP worst-consequence annotations. C, The same data points as in panel a are now shown with the best fitting model line in red, which correspond to a square-root growth model. D, The same data points as in panel b are now shown with the best fitting model line in red, which corresponds to a square-root growth model. E, The plot shows the saturation analysis of the number of discovered Mendelian genes (red color) and peripheral genes (black color) as a function of the discovery sample size. Both lines represent the best fitting model interpolating the dots and are defined as a function of the square-root of the sample size.

**Figure 6**
Contribution of Polygenic and Rare Variation to Blood Diseases (A) Density distribution of PLT (10⁹/liter) for UK Biobank participants who are heterozygous carriers (HET, red line) or wild-type (WT, black line) of the *GP9* rs5030764 c.182A>G (p.Asn61Ser) variant pathogenic for Bernard-Soulier syndrome, plotted for participants whose PGS is above or below 2 SDs of the population platelet PGS. (B) Proportion of participants below the normal range for PLT (150×10⁹/l) depending on PGS quintiles and *GP9* rs5030764 carriage status. (C) Absolute effect sizes comparison between different rare variant annotations and the common polygenic score. A subset of previously unreported missense variants shows high effect sizes comparable to known pathogenic ones, nominating them as putative new pathogenic candidates. The contribution of the polygenic score is comparable to that of a pathogenic variant in heterozygosity. Diamond shapes represent median values. (D) Forest plot showing the association of PGS with rare blood disorders, top 30 results (by p-value) are shown. Significant associations, after Bonferroni correction, are indicated by the ^∗ symbol for the discovery stage, while replication effects shown are all nominally significant. Diamonds represent odds ratios and whiskers show the 95% confidence interval.

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Comment in

Exploring the genetic basis of blood cell traits.
Clyde D. Clyde D. Nat Rev Genet. 2020 Nov;21(11):650. doi: 10.1038/s41576-020-00289-6. Nat Rev Genet. 2020. PMID: 32943745 No abstract available.

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The Polygenic and Monogenic Basis of Blood Traits and Diseases

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The Polygenic and Monogenic Basis of Blood Traits and Diseases

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