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Shrine N, Izquierdo AG, Chen J, Packer R, Hall RJ, Guyatt AL, Batini C, Thompson RJ, Pavuluri C, Malik V, Hobbs BD, Moll M, Kim W, Tal-Singer R, Bakke P, Fawcett KA, John C, Coley K, Piga NN, Pozarickij A, Lin K, Millwood IY, Chen Z, Li L, Wijnant SRA, Lahousse L, Brusselle G, Uitterlinden AG, Manichaikul A, Oelsner EC, Rich SS, Barr RG, Kerr SM, Vitart V, Brown MR, Wielscher M, Imboden M, Jeong A, Bartz TM, Gharib SA, Flexeder C, Karrasch S, Gieger C, Peters A, Stubbe B, Hu X, Ortega VE, Meyers DA, Bleecker ER, Gabriel SB, Gupta N, Smith AV, Luan J, Zhao JH, Hansen AF, Langhammer A, Willer C, Bhatta L, Porteous D, Smith BH, Campbell A, Sofer T, Lee J, Daviglus ML, Yu B, Lim E, Xu H, O'Connor GT, Thareja G, Albagha OME, Suhre K, Granell R, Faquih TO, Hiemstra PS, Slats AM, Mullin BH, Hui J, James A, Beilby J, Patasova K, Hysi P, Koskela JT, Wyss AB, Jin J, Sikdar S, Lee M, May-Wilson S, Pirastu N, Kentistou KA, Joshi PK, Timmers PRHJ, Williams AT, Free RC, Wang X, Morrison JL, Gilliland FD, Chen Z, Wang CA, Foong RE, Harris SE, Taylor A, Redmond P, Cook JP, Mahajan A, Lind L, Palviainen T, Lehtimäki T, Raitakari OT, Kaprio J, Rantanen T, Pietiläinen KH, Cox SR, Pennell CE, Hall GL, Gauderman WJ, Brightling C, Wilson JF, Vasankari T, Laitinen T, Salomaa V, Mook-Kanamori DO, Timpson NJ, Zeggini E, Dupuis J, Hayward C, Brumpton B, Langenberg C, Weiss S, Homuth G, Schmidt CO, Probst-Hensch N, Jarvelin MR, Morrison AC, Polasek O, Rudan I, Lee JH, Sayers I, Rawlins EL, Dudbridge F, Silverman EK, Strachan DP, Walters RG, Morris AP, London SJ, Cho MH, Wain LV, Hall IP, Tobin MD. Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk. Nat Genet 2023; 55:410-422. [PMID: 36914875 PMCID: PMC10011137 DOI: 10.1038/s41588-023-01314-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 01/25/2023] [Indexed: 03/16/2023]
Abstract
Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.
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Affiliation(s)
- Nick Shrine
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
| | - Abril G Izquierdo
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Jing Chen
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Richard Packer
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert J Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Anna L Guyatt
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Chiara Batini
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Rebecca J Thompson
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Chandan Pavuluri
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Vidhi Malik
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Brian D Hobbs
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Per Bakke
- Department of Clinical Science, Unversity of Bergen, Bergen, Norway
| | - Katherine A Fawcett
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Catherine John
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Noemi Nicole Piga
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Alfred Pozarickij
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Sara R A Wijnant
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Lies Lahousse
- Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Respiratory Diseases, Ghent Universital Hospital, Ghent, Belgium
- Department of Epidemiology, Eramus Medical Center, Rotterdam, The Netherlands
| | - Andre G Uitterlinden
- Department of Internal Medicine, Eramus Medical Center, Rotterdam, The Netherlands
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Matthias Wielscher
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Medea Imboden
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Ayoung Jeong
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and Biostatistics, University of Washington, Seattle, WA, USA
| | - Sina A Gharib
- Computational Medicine Core, Center for Lung Biology, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Claudia Flexeder
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefan Karrasch
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
- Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig Maximilian University, Munich, Germany
| | - Beate Stubbe
- Department of Internal Medicine B-Cardiology, Intensive Care, Pulmonary Medicine and Infectious Diseases, University Medicine Greifswald, Greifswald, Germany
| | - Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Victor E Ortega
- Division of Respiratory Medicine, Department of Internal Medicine, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA
| | | | | | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Albert Vernon Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Jing-Hua Zhao
- Department of Public and Primary Care, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Ailin F Hansen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Cristen Willer
- Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Elise Lim
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Hanfei Xu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - George T O'Connor
- Pulmonary Center, School of Medicine, Boston University, Boston, MA, USA
| | - Gaurav Thareja
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, New York, NY, USA
| | - Raquel Granell
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tariq O Faquih
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Pieter S Hiemstra
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Annelies M Slats
- Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands
| | - Benjamin H Mullin
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jennie Hui
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
- PathWest Laboratory Medicine of WA, Nedlands, Western Australia, Australia
| | - Alan James
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
- Busselton Population Medical Research Institute, QEII Medical Centre, Nedlands, Western Australia, Australia
| | - Karina Patasova
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- Division of Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Pirro Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London School of Medicine, London, UK
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Jukka T Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Annah B Wyss
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | | | - Sinjini Sikdar
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
| | - Mikyeong Lee
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Sebastian May-Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul R H J Timmers
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Alexander T Williams
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Robert C Free
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Xueyang Wang
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - John L Morrison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Rachel E Foong
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Sarah E Harris
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland
| | - Taina Rantanen
- Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Obesity and Abdominal Centers, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Department of Maternity and Gynaecology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - Graham L Hall
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - W James Gauderman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chris Brightling
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Infection, Inflammation and Immunity, Institute for Lung Health, University of Leicester, Leicester, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Tuula Vasankari
- FILHA-Finnish Lung Health Association, Helsinki, Finland
- Department of Respiratory Diseases and Allergology, University of Turku, Turku, Finland
| | - Tarja Laitinen
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- ALSPAC, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleftheria Zeggini
- Wellcome Sanger Institute, Cambridge, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Ben Brumpton
- HUNT Research Centre, Department of Public Health and Nursing, NTNU Norwegian University of Science and Technology, Levanger, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Carsten Oliver Schmidt
- Institute for Community Medicine, SHIP-Clinical Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Nicole Probst-Hensch
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Department of Public Health, University of Basel, Basel, Switzerland
| | - Marjo-Riitta Jarvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Joo-Hyeon Lee
- Jeffrey Cheah Biomedical Centre, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ian Sayers
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Emma L Rawlins
- Wellcome Trust-CRUK Gurdon Institute and Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Edwin K Silverman
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David P Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Louise V Wain
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ian P Hall
- Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Martin D Tobin
- Department of Population Health Sciences, University of Leicester, Leicester, UK.
- Leicester National Institute for Health and Care Research, Biomedical Research Centre, Glenfield Hospital, Leicester, UK.
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Li Q, Perera D, Cao C, He J, Bian J, Chen X, Azeem F, Howe A, Au B, Wu J, Yan J, Long Q. Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism. Genomics 2023; 115:110575. [PMID: 36758877 DOI: 10.1016/j.ygeno.2023.110575] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 01/16/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.
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Affiliation(s)
- Qing Li
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Deshan Perera
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Chen Cao
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Jingni He
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Jiayi Bian
- Department of Mathematics and Statistics, University of Calgary, Alberta T2N 1N4, Canada
| | - Xingyu Chen
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Feeha Azeem
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada
| | - Aaron Howe
- Heritage Youth Researcher Summer Program, University of Calgary, Alberta T2N 1N4, Canada
| | - Billie Au
- Department of Medical Genetics, University of Calgary, Alberta T2N 1N4, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Alberta T2N 1N4, Canada
| | - Jingjing Wu
- Department of Mathematics and Statistics, University of Calgary, Alberta T2N 1N4, Canada
| | - Jun Yan
- Department of Physiology and Pharmacology, University of Calgary, Alberta T2N 1N4, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 1N4, Canada.
| | - Quan Long
- Department of Biochemistry and Molecular Biology, University of Calgary, Alberta T2N 1N4, Canada; Department of Medical Genetics, University of Calgary, Alberta T2N 1N4, Canada; Department of Mathematics and Statistics, University of Calgary, Alberta T2N 1N4, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Alberta T2N 1N4, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 1N4, Canada.
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Kim H, Westerman KE, Smith K, Chiou J, Cole JB, Majarian T, von Grotthuss M, Kwak SH, Kim J, Mercader JM, Florez JC, Gaulton K, Manning AK, Udler MS. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 2023; 66:495-507. [PMID: 36538063 PMCID: PMC10108373 DOI: 10.1007/s00125-022-05848-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. METHODS We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. RESULTS We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/INTERPRETATION Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.
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Affiliation(s)
- Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kenneth E Westerman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua Chiou
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Joanne B Cole
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Marcin von Grotthuss
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- GlaxoSmithKline, Cambridge, MA, USA
| | - Josep M Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jose C Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Alisa K Manning
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Miriam S Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Stankey CT, Lee JC. Translating non-coding genetic associations into a better understanding of immune-mediated disease. Dis Model Mech 2023; 16:297044. [PMID: 36897113 PMCID: PMC10040244 DOI: 10.1242/dmm.049790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
Genome-wide association studies have identified hundreds of genetic loci that are associated with immune-mediated diseases. Most disease-associated variants are non-coding, and a large proportion of these variants lie within enhancers. As a result, there is a pressing need to understand how common genetic variation might affect enhancer function and thereby contribute to immune-mediated (and other) diseases. In this Review, we first describe statistical and experimental methods to identify causal genetic variants that modulate gene expression, including statistical fine-mapping and massively parallel reporter assays. We then discuss approaches to characterise the mechanisms by which these variants modulate immune function, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based screens. We highlight examples of studies that, by elucidating the effects of disease variants within enhancers, have provided important insights into immune function and uncovered key pathways of disease.
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Affiliation(s)
- Christina T Stankey
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London NW1 1AT, UK
- Department of Immunology and Inflammation, Imperial College London, London W12 0NN, UK
| | - James C Lee
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London NW1 1AT, UK
- Institute of Liver and Digestive Health, Royal Free Hospital, University College London, London NW3 2PF, UK
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Yu X, Xiao J, Cai M, Jiao Y, Wan X, Liu J, Yang C. PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics 2023; 39:7028484. [PMID: 36744920 PMCID: PMC9950853 DOI: 10.1093/bioinformatics/btad068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/12/2023] [Accepted: 02/03/2023] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xinyi Yu
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China.,Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.,Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China
| | - Yuling Jiao
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China
| | - Jin Liu
- Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore.,School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen 518172, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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Zheng A, Shen Z, Glass CK, Gymrek M. Deep learning predicts the impact of regulatory variants on cell-type-specific enhancers in the brain. BIOINFORMATICS ADVANCES 2023; 3:vbad002. [PMID: 36726730 PMCID: PMC9887460 DOI: 10.1093/bioadv/vbad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/11/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Motivation Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization. Results Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of individual genetic variants in these regions. We show that the variants highlighted by our deep learning models are targeted by purifying selection in the human population, likely indicating a functional role. We integrate our deep learning predictions with statistical fine-mapping results for 8 brain-related traits, identifying 63 distinct candidate causal variants predicted to contribute to these traits by modulating enhancer activity, representing 6% of all genome-wide association study signals analyzed. Overall, our study provides a valuable computational method that can prioritize individual variants based on their estimated regulatory impact, but also highlights the limitations of existing methods for variant prioritization and fine-mapping. Availability and implementation The data underlying this article, nucleotide-level importance scores, and code for running the deep learning pipeline are available at https://github.com/Pandaman-Ryan/AgentBind-brain. Contact mgymrek@ucsd.edu. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- An Zheng
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Zeyang Shen
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Melissa Gymrek
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
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58
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Panyard DJ, Deming YK, Darst BF, Van Hulle CA, Zetterberg H, Blennow K, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:395-409. [PMID: 36744333 PMCID: PMC10050104 DOI: 10.3233/jad-220599] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.
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Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53726, United States of America
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, United States of America
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Wang Y, Chen L. DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome. Bioinformatics 2022; 38:5340-5351. [PMID: 36271868 PMCID: PMC9750124 DOI: 10.1093/bioinformatics/btac696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 09/04/2022] [Accepted: 10/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease. RESULTS By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server. AVAILABILITY AND IMPLEMENTATION https://github.com/lichen-lab/DeepPerVar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ye Wang
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USA
| | - Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL 32603, USA
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den Hollander AI, Mullins RF, Orozco LD, Voigt AP, Chen HH, Strunz T, Grassmann F, Haines JL, Kuiper JJW, Tumminia SJ, Allikmets R, Hageman GS, Stambolian D, Klaver CCW, Boeke JD, Chen H, Honigberg L, Katti S, Frazer KA, Weber BHF, Gorin MB. Systems genomics in age-related macular degeneration. Exp Eye Res 2022; 225:109248. [PMID: 36108770 PMCID: PMC10150562 DOI: 10.1016/j.exer.2022.109248] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/29/2022] [Accepted: 09/07/2022] [Indexed: 12/29/2022]
Abstract
Genomic studies in age-related macular degeneration (AMD) have identified genetic variants that account for the majority of AMD risk. An important next step is to understand the functional consequences and downstream effects of the identified AMD-associated genetic variants. Instrumental for this next step are 'omics' technologies, which enable high-throughput characterization and quantification of biological molecules, and subsequent integration of genomics with these omics datasets, a field referred to as systems genomics. Single cell sequencing studies of the retina and choroid demonstrated that the majority of candidate AMD genes identified through genomic studies are expressed in non-neuronal cells, such as the retinal pigment epithelium (RPE), glia, myeloid and choroidal cells, highlighting that many different retinal and choroidal cell types contribute to the pathogenesis of AMD. Expression quantitative trait locus (eQTL) studies in retinal tissue have identified putative causal genes by demonstrating a genetic overlap between gene regulation and AMD risk. Linking genetic data to complement measurements in the systemic circulation has aided in understanding the effect of AMD-associated genetic variants in the complement system, and supports that protein QTL (pQTL) studies in plasma or serum samples may aid in understanding the effect of genetic variants and pinpointing causal genes in AMD. A recent epigenomic study fine-mapped AMD causal variants by determing regulatory regions in RPE cells differentiated from induced pluripotent stem cells (iPSC-RPE). Another approach that is being employed to pinpoint causal AMD genes is to produce synthetic DNA assemblons representing risk and protective haplotypes, which are then delivered to cellular or animal model systems. Pinpointing causal genes and understanding disease mechanisms is crucial for the next step towards clinical translation. Clinical trials targeting proteins encoded by the AMD-associated genomic loci C3, CFB, CFI, CFH, and ARMS2/HTRA1 are currently ongoing, and a phase III clinical trial for C3 inhibition recently showed a modest reduction of lesion growth in geographic atrophy. The EYERISK consortium recently developed a genetic test for AMD that allows genotyping of common and rare variants in AMD-associated genes. Polygenic risk scores (PRS) were applied to quantify AMD genetic risk, and may aid in predicting AMD progression. In conclusion, genomic studies represent a turning point in our exploration of AMD. The results of those studies now serve as a driving force for several clinical trials. Expanding to omics and systems genomics will further decipher function and causality from the associations that have been reported, and will enable the development of therapies that will lessen the burden of AMD.
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Affiliation(s)
- Anneke I den Hollander
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands; AbbVie, Genomics Research Center, Cambridge, MA, USA.
| | - Robert F Mullins
- The University of Iowa Institute for Vision Research, Iowa City, IA, USA; Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | | | - Andrew P Voigt
- The University of Iowa Institute for Vision Research, Iowa City, IA, USA; Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
| | | | - Tobias Strunz
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | | | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Jonas J W Kuiper
- Department of Ophthalmology, University Medical Center Utrecht, Utrecht, the Netherlands; Center of Translational Immunology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Rando Allikmets
- Department of Ophthalmology, Columbia University, NY, USA; Department of Pathology and Cell Biology, Columbia University, NY, USA
| | - Gregory S Hageman
- Sharon Eccles Steele Center for Translational Medicine, John A. Moran Eye Center, Department of Ophthalmology & Visual Sciences, University of Utah, Salt Lake City, UT, USA
| | - Dwight Stambolian
- Departments of Ophthalmology and Human Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Caroline C W Klaver
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands; Departments of Ophthalmology and Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Jef D Boeke
- Institute for Systems Genetics, NYU Langone Health, NY, USA; Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, NY, USA; Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Hao Chen
- Genentech, South San Francisco, CA, USA
| | | | | | - Kelly A Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, USA
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany; Institute of Clinical Human Genetics, University Hospital Regensburg, Regensburg, Germany
| | - Michael B Gorin
- Departments of Ophthalmology and Human Genetics, University of California, Los Angeles, CA, USA
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Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS, Weeks EM, Wang M, Hindy G, Zhou W, Grace C, Roselli C, Marston NA, Kamanu FK, Surakka I, Venegas LM, Sherliker P, Koyama S, Ishigaki K, Åsvold BO, Brown MR, Brumpton B, de Vries PS, Giannakopoulou O, Giardoglou P, Gudbjartsson DF, Güldener U, Haider SMI, Helgadottir A, Ibrahim M, Kastrati A, Kessler T, Kyriakou T, Konopka T, Li L, Ma L, Meitinger T, Mucha S, Munz M, Murgia F, Nielsen JB, Nöthen MM, Pang S, Reinberger T, Schnitzler G, Smedley D, Thorleifsson G, von Scheidt M, Ulirsch JC, Arnar DO, Burtt NP, Costanzo MC, Flannick J, Ito K, Jang DK, Kamatani Y, Khera AV, Komuro I, Kullo IJ, Lotta LA, Nelson CP, Roberts R, Thorgeirsson G, Thorsteinsdottir U, Webb TR, Baras A, Björkegren JLM, Boerwinkle E, Dedoussis G, Holm H, Hveem K, Melander O, Morrison AC, Orho-Melander M, Rallidis LS, Ruusalepp A, Sabatine MS, Stefansson K, Zalloua P, Ellinor PT, Farrall M, Danesh J, Ruff CT, Finucane HK, Hopewell JC, Clarke R, Gupta RM, Erdmann J, Samani NJ, Schunkert H, Watkins H, Willer CJ, Deloukas P, Kathiresan S, Butterworth AS. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet 2022; 54:1803-1815. [PMID: 36474045 PMCID: PMC9729111 DOI: 10.1038/s41588-022-01233-6] [Citation(s) in RCA: 196] [Impact Index Per Article: 98.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/17/2022] [Indexed: 12/12/2022]
Abstract
The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.
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Grants
- MR/L003120/1 Medical Research Council
- BRC-1215-20014 Department of Health
- R01 HL125863 NHLBI NIH HHS
- UL1 RR025005 NCRR NIH HHS
- R01 HL059367 NHLBI NIH HHS
- U01 HG004402 NHGRI NIH HHS
- RG/14/5/30893 British Heart Foundation
- SP/13/2/30111 British Heart Foundation
- SP/16/4/32697 British Heart Foundation
- HHSN268201700001I NHLBI NIH HHS
- FS/14/55/30806 British Heart Foundation
- R01 HL087641 NHLBI NIH HHS
- MC_PC_17228 Medical Research Council
- MR/S502443/1 Medical Research Council
- R01 HL109946 NHLBI NIH HHS
- UM1 DK105554 NIDDK NIH HHS
- KL2 TR002542 NCATS NIH HHS
- 203141/Z/16/Z Wellcome Trust
- Department of Health
- FS/14/66/3129 British Heart Foundation
- R01 HL086694 NHLBI NIH HHS
- R35 HL135824 NHLBI NIH HHS
- RG/18/13/33946 British Heart Foundation
- T32 HG000040 NHGRI NIH HHS
- R01 HL146860 NHLBI NIH HHS
- HHSN268201700002C NHLBI NIH HHS
- SP/19/2/34462 British Heart Foundation
- HHSN268201700004I NHLBI NIH HHS
- RE/13/1/30181 British Heart Foundation
- K08 HL153950 NHLBI NIH HHS
- HHSN268201700005C NHLBI NIH HHS
- HHSN268201700001C NHLBI NIH HHS
- HHSN268201700003C NHLBI NIH HHS
- HHSN268201700004C NHLBI NIH HHS
- Wellcome Trust
- HHSN268201700002I NHLBI NIH HHS
- HHSN268201700005I NHLBI NIH HHS
- K08 HL153937 NHLBI NIH HHS
- HHSN268201700003I NHLBI NIH HHS
- RG/13/13/30194 British Heart Foundation
- T32 HL007604 NHLBI NIH HHS
- SP/09/002 British Heart Foundation
- G0800270 Medical Research Council
- K08 HG010155 NHGRI NIH HHS
- MC_QA137853 Medical Research Council
- K.G.A. has received support from the American Heart Association Institute for Precision Cardiovascular Medicine (17IFUNP3384001), a KL2/Catalyst Medical Research Investigator Training (CMeRIT) award from the Harvard Catalyst (KL2 TR002542), and the NIH (1K08HL153937).
- B.N.W is supported by the National Science Foundation Graduate Research Program (DGE 1256260).
- I.S. is supported by a Precision Health Scholars Award from the University of Michigan Medical School.
- I.K., S.Ko., and K.It. are funded by the Japan Agency for Medical Research and Development, AMED, under Grant Numbers JP16ek0109070h0003, JP18kk0205008h0003, JP18kk0205001s0703, JP20km0405209, and JP20ek0109487. The BioBank Japan is supported by AMED under Grant Number JP20km0605001.
- J.L.M.B. acknowledges research support from NIH R01HL125863, American Heart Association (A14SFRN20840000), the Swedish Research Council (2018-02529) and Heart Lung Foundation (20170265) and the Foundation Leducq (PlaqueOmics: Novel Roles of Smooth Muscle and Other Matrix Producing Cells in Atherosclerotic Plaque Stability and Rupture, 18CVD02.
- P.S.dV was supported by American Heart Association grant number 18CDA34110116 and National Heart, Lung, and Blood Institute grant R01HL146860. The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01HL087641, R01HL059367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research.
- O.G. has received funding from the British Heart Foundation (BHF) (FS/14/66/3129).
- T.K. is supported by the Corona-Foundation (Junior Research Group Translational Cardiovascular Genomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02).
- D.S.A. has received support from a training grant from the NIH (T32HL007604).
- N.P.B., M.C.C., J.F., and D.-K.J. have been funded by the National Institute of Diabetes and Digestive and Kidney Diseases (2UM1DK105554).
- A.V.K. has been funded by 1K08HG010155 from the National Human Genome Research Institute.
- C.P.N. and T.R.W received funding from the British Heart Foundation (SP/16/4/32697).
- The Trøndelag Health Study (The HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health. The K.G. Jebsen Center for Genetic Epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen; Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology; and Central Norway Regional Health Authority. Whole genome sequencing for the HUNT study was funded by HL109946.
- O.M. was funded by the Swedish Heart- and Lung Foundation, the Swedish Research Council, the European Research Council ERC-AdG-2019-885003 and Lund University Infrastructure grant ”Malmö population-based cohorts” (STYR 2019/2046).
- This work was supported by the European Commission (HEALTH-F2–2013-601456) and the TriPartite Immunometabolism Consortium [TrIC]- NovoNordisk Foundation (NNF15CC0018486), VIAgenomics (SP/19/2/344612), the British Heart Foundation, a Wellcome Trust core award (M.F., H.W., 203141/Z/16/Z) and support from the NIHR Oxford Biomedical Research Centre. M.F. and H.W. are members of the Oxford BHF Centre of Research Excellence (RE/13/1/30181). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
- J.D. is a British Heart Foundation Professor, European Research Council Senior Investigator, and National Institute for Health Research (NIHR) Senior Investigator.
- J.C.H. acknowledges personal funding from the British Heart Foundation (FS/14/55/30806) and is a member of the Oxford BHF Centre of Research Excellence (RE/13/1/30181).
- R.C. has received funding from the British Heart Foundation and British Heart Foundation Centre of Research Excellence.
- This research was supported by BHF (SP/13/2/30111) and conducted using the UK Biobank Resource (application number 9922).
- The GerMIFs gratefully acknowledge the support of the Bavarian State Ministry of Health and Care, furthermore founded this work within its framework of DigiMed Bayern (grant No: DMB-1805-0001), the German Federal Ministry of Education and Research (BMBF) within the framework of ERA-NET on Cardiovascular Disease (Druggable-MI-genes: 01KL1802), within the scheme of target validation (BlockCAD: 16GW0198K), within the framework of the e:Med research and funding concept (AbCD-Net: 01ZX1706C), the British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration (VIAgenomics) and the German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02) and the Sonderforschungsbereich SFB TRR 267 (B05).
- C.J.W. is funded by NIH grant R35-HL135824.
- This work was supported by the British Heart Foundation (BHF) grant RG/14/5/30893 (P.D.) and forms part of the research themes contributing to the translational research portfolios of the Barts Biomedical Research Centre funded by the UK National Institute for Health Research (NIHR).
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Affiliation(s)
- Krishna G Aragam
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Tao Jiang
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Anuj Goel
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Brooke N Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Deepak S Atri
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elle M Weeks
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Minxian Wang
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - George Hindy
- Department of Population Medicine, Qatar University College of Medicine, Doha, Qatar
| | - Wei Zhou
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher Grace
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Frederick K Kamanu
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Loreto Muñoz Venegas
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | - Paul Sherliker
- Medical Research Council Population Health Research Unit, CTSU-Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Satoshi Koyama
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama, Japan
| | - Bjørn O Åsvold
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim, Norway
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ben Brumpton
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Olga Giannakopoulou
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Panagiota Giardoglou
- Department of Nutrition-Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Ulrich Güldener
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
| | - Syed M Ijlal Haider
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | | | - Maysson Ibrahim
- CTSU-Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Adnan Kastrati
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany
| | - Thorsten Kessler
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany
| | | | - Tomasz Konopka
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ling Li
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
| | - Lijiang Ma
- Department of Genetics and Genomic Science, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Meitinger
- German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Klinikum Rechts der Isar, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Sören Mucha
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | - Matthias Munz
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | - Federico Murgia
- CTSU-Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Jonas B Nielsen
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Markus M Nöthen
- School of Medicine and University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Shichao Pang
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
| | - Tobias Reinberger
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | - Gavin Schnitzler
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Moritz von Scheidt
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany
| | - Jacob C Ulirsch
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - David O Arnar
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Internal Medicine, Division of Cardiology, Landspitali-National University Hospital of Iceland, Hringbraut, Reykjavik, Iceland
| | - Noël P Burtt
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maria C Costanzo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jason Flannick
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama, Japan
| | - Dong-Keun Jang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yoichiro Kamatani
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Amit V Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Luca A Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Christopher P Nelson
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Glenfield Hospital, Leicester, UK
| | - Robert Roberts
- Cardiovascular Genomics and Genetics, University of Arizona College of Medicin, Phoenix, AZ, USA
| | - Gudmundur Thorgeirsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Internal Medicine, Division of Cardiology, Landspitali-National University Hospital of Iceland, Hringbraut, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Thomas R Webb
- Department of Cardiovascular Sciences and NIHR Leicester Biomedical Research Centre, University of Leicester, Glenfield Hospital, Leicester, UK
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Integrated Cardio Metabolic Centre, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
- Clinical Gene Networks AB, Stockholm, Sweden
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - George Dedoussis
- Department of Nutrition-Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Kristian Hveem
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
- HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway
| | - Olle Melander
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Loukianos S Rallidis
- Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, University General Hospital Attikon, Athens, Greece
| | - Arno Ruusalepp
- Department of Cardiac Surgery, Tartu University Hospital and Institute of Clinical Medicine, Tartu University, Tartu, Estonia
| | - Marc S Sabatine
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Pierre Zalloua
- Harvard T.H.Chan School of Public Health, Boston, MA, USA
- College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, UAE
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Martin Farrall
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge, UK
- The National Institute for Health and Care Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Human Genetics, Wellcome Sanger Institute, Saffron Walden, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, UK
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jemma C Hopewell
- CTSU-Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Robert Clarke
- CTSU-Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Rajat M Gupta
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeanette Erdmann
- Institute for Cardiogenetics, University of Lübeck, Lübeck, Germany
- German Research Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Lübeck, Germany
| | - Nilesh J Samani
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Heribert Schunkert
- German Heart Centre Munich, Department of Cardiology, Technical University of Munich, Munich, Germany
- German Research Center for Cardiovascular Research (DZHK e.V.), Partner Site Munich Heart Alliance, Munich, Germany
| | - Hugh Watkins
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research Cambridge Biomedical Research Centre, Cambridge University Hospitals, Cambridge, UK.
- The National Institute for Health and Care Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke's Hospital, Cambridge, UK.
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62
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Finding causal genes underlying risk for coronary artery disease. Nat Genet 2022; 54:1768-1769. [PMID: 36474046 DOI: 10.1038/s41588-022-01094-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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63
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Rasooly D, Peloso GM, Giambartolomei C. Bayesian Genetic Colocalization Test of Two Traits Using coloc. Curr Protoc 2022; 2:e627. [PMID: 36515558 DOI: 10.1002/cpz1.627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome-wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non-coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics-based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses. © 2022 Wiley Periodicals LLC. Basic Protocol: Performing a genetic colocalization analysis using the 'coloc' package in R and summary-level data Support Protocol: Installing the 'coloc' R package.
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Affiliation(s)
- Danielle Rasooly
- Division of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Claudia Giambartolomei
- Non-Coding RNAs and RNA-Based Therapeutics, Istituto Italiano di Tecnologia, Via Morego, Genova, Italy
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64
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Chen J, Wang L, De Jager PL, Bennett DA, Buchman AS, Yang J. A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease. HGG ADVANCES 2022; 3:100143. [PMID: 36204489 PMCID: PMC9530673 DOI: 10.1016/j.xhgg.2022.100143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 09/14/2022] [Indexed: 11/30/2022] Open
Abstract
Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer's Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.
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Affiliation(s)
- Junyu Chen
- Department of Epidemiology, Emory University School of Public Health, Atlanta, GA 30322, USA
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lei Wang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Philip L. De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA
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65
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Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet 2022; 54:1479-1492. [PMID: 36175791 PMCID: PMC9910198 DOI: 10.1038/s41588-022-01187-9] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/18/2022] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.
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66
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Pervjakova N, Moen GH, Borges MC, Ferreira T, Cook JP, Allard C, Beaumont RN, Canouil M, Hatem G, Heiskala A, Joensuu A, Karhunen V, Kwak SH, Lin FTJ, Liu J, Rifas-Shiman S, Tam CH, Tam WH, Thorleifsson G, Andrew T, Auvinen J, Bhowmik B, Bonnefond A, Delahaye F, Demirkan A, Froguel P, Haller-Kikkatalo K, Hardardottir H, Hummel S, Hussain A, Kajantie E, Keikkala E, Khamis A, Lahti J, Lekva T, Mustaniemi S, Sommer C, Tagoma A, Tzala E, Uibo R, Vääräsmäki M, Villa PM, Birkeland KI, Bouchard L, Duijn CM, Finer S, Groop L, Hämäläinen E, Hayes GM, Hitman GA, Jang HC, Järvelin MR, Jenum AK, Laivuori H, Ma RC, Melander O, Oken E, Park KS, Perron P, Prasad RB, Qvigstad E, Sebert S, Stefansson K, Steinthorsdottir V, Tuomi T, Hivert MF, Franks PW, McCarthy MI, Lindgren CM, Freathy RM, Lawlor DA, Morris AP, Mägi R. Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Hum Mol Genet 2022; 31:3377-3391. [PMID: 35220425 PMCID: PMC9523562 DOI: 10.1093/hmg/ddac050] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/09/2022] [Accepted: 02/23/2022] [Indexed: 11/12/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10-8) with GDM, mapping to/near MTNR1B (P = 4.3 × 10-54), TCF7L2 (P = 4.0 × 10-16), CDKAL1 (P = 1.6 × 10-14), CDKN2A-CDKN2B (P = 4.1 × 10-9) and HKDC1 (P = 2.9 × 10-8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.
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Affiliation(s)
- Natalia Pervjakova
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Gunn-Helen Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Diamantina Institute, The University of Queensland, Woolloongabba QLD 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria-Carolina Borges
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Teresa Ferreira
- Big Data Institute, Li Ka Shing Center for Health for Health Information and Discovery, Oxford University, Oxford, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Catherine Allard
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Universite de Sherbrooke, Quebec, Canada
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
| | - Gad Hatem
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Anni Heiskala
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Anni Joensuu
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ville Karhunen
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Frederick T J Lin
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sheryl Rifas-Shiman
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Claudia H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | - Wing Hung Tam
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | | | - Toby Andrew
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Juha Auvinen
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Bishwajit Bhowmik
- Centre of Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Fabien Delahaye
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Section of Statistical Multi-omics, Department of Clinical and Experimental Research, University of Surrey, Surrey, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Kadri Haller-Kikkatalo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Hildur Hardardottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Livio Reykjavik, Reykjavik, Iceland
| | - Sandra Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany
- Forschergruppe Diabetes, Technical University Munich, at Klinikum rechts der Isar, Munich, Germany
| | - Akhtar Hussain
- Centre of Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
- Faculty of Health Sciences, Nord University, Bodø, Norway
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Elina Keikkala
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Amna Khamis
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille F-59000, France
- University of Lille, Lille University Hospital, Lille F-59000, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Tove Lekva
- Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway
| | - Sanna Mustaniemi
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Christine Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Aili Tagoma
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Evangelia Tzala
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Raivo Uibo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Marja Vääräsmäki
- PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
| | - Pia M Villa
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Hyvinkää Hospital, Helsinki and Uusimaa Hospital District, Hyvinkää, Finland
| | - Kåre I Birkeland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Universite de Sherbrooke, Quebec, Canada
- Department of Medical Biology, Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-St-Jean – Hôpital de Chicoutimi, Québec, Canada
| | - Cornelia M Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah Finer
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Leif Groop
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Hämäläinen
- Department of Clinical Chemistry, University of Eastern Finland, Kuopio, Finland
| | - Geoffrey M Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Anthropology, Northwestern University, Evanston, IL 60208, USA
| | - Graham A Hitman
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Hak C Jang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Marjo-Riitta Järvelin
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Hospital, London, UK
| | - Anne Karen Jenum
- General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, Faculty of Medicine, University of Oslo, Post Box 1130 Blindern, Oslo 0318, Norway
| | - Hannele Laivuori
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University, Hospital and Faculty of Medicine and Health Technology, Center for Child, Adolescent, and Maternal Health, Tampere University, Tampere, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Ronald C Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, The People's Republic of China
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Patrice Perron
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Universite de Sherbrooke, Quebec, Canada
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrook, Québec, Canada
| | - Rashmi B Prasad
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
| | - Elisabeth Qvigstad
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Sylvain Sebert
- Centre for Life-Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Kari Stefansson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund University Diabetes Centre, Malmö SE-20502, Sweden
- Department of Endocrinology, Abdominal Centre, Helsinki University Hospital, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Sherbrook, Québec, Canada
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Center for Health for Health Information and Discovery, Oxford University, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Boston, MA, USA
| | - Rachel M Freathy
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
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67
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Baca SC, Singler C, Zacharia S, Seo JH, Morova T, Hach F, Ding Y, Schwarz T, Huang CCF, Anderson J, Fay AP, Kalita C, Groha S, Pomerantz MM, Wang V, Linder S, Sweeney CJ, Zwart W, Lack NA, Pasaniuc B, Takeda DY, Gusev A, Freedman ML. Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation. Nat Genet 2022; 54:1364-1375. [PMID: 36071171 PMCID: PMC9784646 DOI: 10.1038/s41588-022-01168-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/19/2022] [Indexed: 12/25/2022]
Abstract
Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.
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Affiliation(s)
- Sylvan C. Baca
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA
| | - Cassandra Singler
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Soumya Zacharia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ji-Heui Seo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tunc Morova
- Vancouver Prostate Centre University of British Columbia, Vancouver, BC, Canada
| | - Faraz Hach
- Vancouver Prostate Centre University of British Columbia, Vancouver, BC, Canada
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA
| | | | - Jacob Anderson
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - André P. Fay
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Cynthia Kalita
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
| | - Stefan Groha
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA
| | - Mark M. Pomerantz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Victoria Wang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Simon Linder
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands,Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Wilbert Zwart
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands,Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Nathan A. Lack
- Vancouver Prostate Centre University of British Columbia, Vancouver, BC, Canada,School of Medicine, Koç University, Istanbul, Turkey
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA,Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA USA,Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - David Y. Takeda
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA,Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA,These authors jointly supervised this work. Correspondence should be directed to M.L.F or A.G. ()
| | - Matthew L. Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA,The Eli and Edythe L. Broad Institute, Cambridge, MA, USA,These authors jointly supervised this work. Correspondence should be directed to M.L.F or A.G. ()
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68
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Yang MG, Ling E, Cowley CJ, Greenberg ME, Vierbuchen T. Characterization of sequence determinants of enhancer function using natural genetic variation. eLife 2022; 11:76500. [PMID: 36043696 PMCID: PMC9662815 DOI: 10.7554/elife.76500] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 08/30/2022] [Indexed: 02/04/2023] Open
Abstract
Sequence variation in enhancers that control cell-type-specific gene transcription contributes significantly to phenotypic variation within human populations. However, it remains difficult to predict precisely the effect of any given sequence variant on enhancer function due to the complexity of DNA sequence motifs that determine transcription factor (TF) binding to enhancers in their native genomic context. Using F1-hybrid cells derived from crosses between distantly related inbred strains of mice, we identified thousands of enhancers with allele-specific TF binding and/or activity. We find that genetic variants located within the central region of enhancers are most likely to alter TF binding and enhancer activity. We observe that the AP-1 family of TFs (Fos/Jun) are frequently required for binding of TEAD TFs and for enhancer function. However, many sequence variants outside of core motifs for AP-1 and TEAD also impact enhancer function, including sequences flanking core TF motifs and AP-1 half sites. Taken together, these data represent one of the most comprehensive assessments of allele-specific TF binding and enhancer function to date and reveal how sequence changes at enhancers alter their function across evolutionary timescales.
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Affiliation(s)
- Marty G Yang
- Department of Neurobiology, Harvard Medical School, Boston, United States.,Program in Neuroscience, Harvard Medical School, Boston, United States
| | - Emi Ling
- Department of Neurobiology, Harvard Medical School, Boston, United States
| | | | | | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute for Cancer Research, New York, United States.,Center for Stem Cell Biology, Sloan Kettering Institute for Cancer Research, New York, United States
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69
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Francis CM, Futschik ME, Huang J, Bai W, Sargurupremraj M, Teumer A, Breteler MMB, Petretto E, Ho ASR, Amouyel P, Engelter ST, Bülow R, Völker U, Völzke H, Dörr M, Imtiaz MA, Aziz NA, Lohner V, Ware JS, Debette S, Elliott P, Dehghan A, Matthews PM. Genome-wide associations of aortic distensibility suggest causality for aortic aneurysms and brain white matter hyperintensities. Nat Commun 2022; 13:4505. [PMID: 35922433 PMCID: PMC9349177 DOI: 10.1038/s41467-022-32219-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 07/20/2022] [Indexed: 12/13/2022] Open
Abstract
Aortic dimensions and distensibility are key risk factors for aortic aneurysms and dissections, as well as for other cardiovascular and cerebrovascular diseases. We present genome-wide associations of ascending and descending aortic distensibility and area derived from cardiac magnetic resonance imaging (MRI) data of up to 32,590 Caucasian individuals in UK Biobank. We identify 102 loci (including 27 novel associations) tagging genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. Functional analyses highlight four signalling pathways associated with aortic distensibility (TGF-β, IGF, VEGF and PDGF). We identify distinct sex-specific associations with aortic traits. We develop co-expression networks associated with aortic traits and apply phenome-wide Mendelian randomization (MR-PheWAS), generating evidence for a causal role for aortic distensibility in development of aortic aneurysms. Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, mechanistically linking aortic traits and brain small vessel disease.
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Affiliation(s)
- Catherine M Francis
- National Heart and Lung Institute, Imperial College London, Programme in Cardiovascular Genetics and Genomics, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, SW3 6NP, UK
| | - Matthias E Futschik
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC London Institute of Medical Sciences (LMS), Imperial College London, London, W12 0NN, UK
| | - Jian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Wenjia Bai
- Department of Brain Sciences, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Muralidharan Sargurupremraj
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, 78229, USA
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Enrico Petretto
- Programme in Cardiovascular & Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Republic of Singapore
- Institute of Big Data and Artificial Intelligence, China Pharmaceutical University (CPU), 211198, Nanjing, China
- Computational Biology Programme, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Amanda S R Ho
- Computational Biology Programme, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Philippe Amouyel
- LabEx DISTALZ-U1167, RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases, University of Lille, Lille, France
- Inserm, U1167, Lille, France
- Centre Hospitalier Universitaire Lille, Lille, France
- Institut Pasteur de Lille, Lille, France
| | - Stefan T Engelter
- Department of Neurology and Stroke Center, University Hospital and University of Basel, Petersgraben 4, CH - 4031, Basel, Switzerland
- Department of Clinical Neurology and Neurorehabilitation, University Department of Geriatric Medicine FELIX PLATTER, University of Basel, Basel, Switzerland
| | - Robin Bülow
- Department of Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Marcus Dörr
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Mohammed-Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - N Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Valerie Lohner
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - James S Ware
- National Heart and Lung Institute, Imperial College London, Programme in Cardiovascular Genetics and Genomics, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, SW3 6NP, UK
- MRC London Institute of Medical Sciences (LMS), Imperial College London, London, W12 0NN, UK
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, 33000, Bordeaux, France
- Department of Neurology, Institute for Neurodegenerative Diseases, Bordeaux University Hospital - CHU Bordeaux, 33000, Bordeaux, France
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, London, UK
- Health Data Research (HDR) UK London at Imperial College London, London, UK
- Britsh Heart Foundation Centre of Research Excellence at Imperial College London, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- UK Dementia Research Institute at Imperial College London, London, UK.
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, UK.
- UK Dementia Research Institute at Imperial College London, London, UK.
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, London, UK.
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70
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Tang F, Duan C, Li R, Zhang H, Mo X. Identification of RNA modification-associated single nucleotide polymorphisms in genomic loci for low-density lipoprotein cholesterol concentrations. Pharmacogenomics 2022; 23:655-665. [PMID: 35880552 DOI: 10.2217/pgs-2022-0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Introduction: Genome-wide association studies have identified approximately 1000 lipid-associated loci, but functional variants are less known. Materials & methods: The authors identified RNA modification-related single nucleotide polymorphisms (RNAm-SNPs) in summary data from a genome-wide association study. By applying Mendelian randomization analysis, the authors identified gene expression levels involved in the regulation of RNAm-SNPs on low-density lipoprotein cholesterol (LDL-C) levels. Results: The authors identified 391 RNAm-SNPs that were significantly associated with LDL-C levels. RNAm-SNPs in NPC1L1, LDLR, APOB, MYLIP, LDLRAP1 and ABCA6 were identified. The RNAm-SNPs were associated with gene expression. The expression levels of 112 genes were associated with LDL-C levels, and some of them (e.g., APOB, SMARCA4 and SH2B3) were associated with coronary artery disease. Conclusion: This study identified many RNAm-SNPs in LDL-C loci and elucidated the relationship among the SNPs, gene expression and LDL-C.
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Affiliation(s)
- Fan Tang
- Department of Epidemiology, Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, China
| | - Chengcheng Duan
- Department of Epidemiology, Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, China
| | - Ru Li
- Department of Epidemiology, Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, China
| | - Huan Zhang
- Department of Epidemiology, Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, China
| | - Xingbo Mo
- Department of Epidemiology, Jiangsu Key Laboratory of Preventive & Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, China.,Center for Genetic Epidemiology & Genomics, School of Public Health, Medical College of Soochow University, China
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71
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Cheng W, Ramachandran S, Crawford L. Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks. iScience 2022; 25:104553. [PMID: 35769876 PMCID: PMC9234235 DOI: 10.1016/j.isci.2022.104553] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/09/2022] [Accepted: 06/01/2022] [Indexed: 02/07/2023] Open
Abstract
In this paper, we propose a new approach for variable selection using a collection of Bayesian neural networks with a focus on quantifying uncertainty over which variables are selected. Motivated by fine-mapping applications in statistical genetics, we refer to our framework as an "ensemble of single-effect neural networks" (ESNN) which generalizes the "sum of single effects" regression framework by both accounting for nonlinear structure in genotypic data (e.g., dominance effects) and having the capability to model discrete phenotypes (e.g., case-control studies). Through extensive simulations, we demonstrate our method's ability to produce calibrated posterior summaries such as credible sets and posterior inclusion probabilities, particularly for traits with genetic architectures that have significant proportions of non-additive variation driven by correlated variants. Lastly, we use real data to demonstrate that the ESNN framework improves upon the state of the art for identifying true effect variables underlying various complex traits.
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Affiliation(s)
- Wei Cheng
- Department of Computer Science, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Sohini Ramachandran
- Department of Computer Science, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research New England, Cambridge, MA, USA
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72
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/03/2021] [Accepted: 05/27/2022] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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73
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Xiao J, Cai M, Yu X, Hu X, Chen G, Wan X, Yang C. Leveraging the local genetic structure for trans-ancestry association mapping. Am J Hum Genet 2022; 109:1317-1337. [PMID: 35714612 PMCID: PMC9300880 DOI: 10.1016/j.ajhg.2022.05.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 05/23/2022] [Indexed: 01/09/2023] Open
Abstract
Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.
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Affiliation(s)
- Jiashun Xiao
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Mingxuan Cai
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xinyi Yu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen 518172, China; Pazhou Lab, Guangzhou 510330, China.
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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74
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Zou Y, Carbonetto P, Wang G, Stephens M. Fine-mapping from summary data with the "Sum of Single Effects" model. PLoS Genet 2022; 18:e1010299. [PMID: 35853082 PMCID: PMC9337707 DOI: 10.1371/journal.pgen.1010299] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 07/29/2022] [Accepted: 06/17/2022] [Indexed: 11/19/2022] Open
Abstract
In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.
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Affiliation(s)
- Yuxin Zou
- Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
| | - Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Research Computing Center, University of Chicago, Chicago, Illinois, United States of America
| | - Gao Wang
- Department of Neurology and the Gertrude. H. Sergievsky Center, Columbia University, New York, New York, United States of America
| | - Matthew Stephens
- Department of Statistics, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
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75
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Lorenz K, Thom CS, Adurty S, Voight BF. TSABL: Trait Specific Annotation Based Locus predictor. BMC Genomics 2022; 23:444. [PMID: 35705896 PMCID: PMC9202130 DOI: 10.1186/s12864-022-08654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The majority of Genome Wide Associate Study (GWAS) loci fall in the non-coding genome, making causal variants difficult to identify and study. We hypothesized that the regulatory features underlying causal variants are biologically specific, identifiable from data, and that the regulatory architecture that influences one trait is distinct compared to biologically unrelated traits. RESULTS To better characterize and identify these variants, we used publicly available GWAS loci and genomic annotations to build 17 Trait Specific Annotation Based Locus (TSABL) predictors to identify differences between GWAS loci associated with different phenotypic trait groups. We used a penalized binomial logistic regression model to select trait relevant annotations and tested all models on a holdout set of loci not used for training in any trait. We were able to successfully build models for autoimmune, electrocardiogram, lipid, platelet, red blood cell, and white blood cell trait groups. We used these models both to prioritize variants in existing loci and to identify new genomic regions of interest. CONCLUSIONS We found that TSABL models identified biologically relevant regulatory features, and anticipate their future use to enhance the design and interpretation of genetic studies.
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Affiliation(s)
- Kim Lorenz
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher S Thom
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Benjamin F Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
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76
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Yang M, Huang L, Huang H, Tang H, Zhang N, Yang H, Wu J, Mu F. Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution. Nucleic Acids Res 2022; 50:e81. [PMID: 35536244 PMCID: PMC9371931 DOI: 10.1093/nar/gkac326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/22/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.
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Affiliation(s)
- Meng Yang
- MGI, BGI-Shenzhen, Shenzhen 518083, China.,Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark
| | | | | | - Hui Tang
- MGI, BGI-Shenzhen, Shenzhen 518083, China
| | - Nan Zhang
- MGI, BGI-Shenzhen, Shenzhen 518083, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen 518083, China.,Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, BGI-Shenzhen, Shenzhen, 518120, China
| | - Jihong Wu
- Department of Ophthalmology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Visual Impairment and Restoration, Science and Technology Commission of Shanghai Municipality, Shanghai, China.,Key Laboratory of Myopia (Fudan University), Chinese Academy of Medical Sciences, National Health Commission, Shanghai, China
| | - Feng Mu
- MGI, BGI-Shenzhen, Shenzhen 518083, China
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Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak SH, Robertson NR, Rayner NW, Loh M, Kim BJ, Chiou J, Miguel-Escalada I, Della Briotta Parolo P, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee JJ, Huerta-Chagoya A, Graff M, Chai JF, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen WM, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Lecoeur C, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe EK, An P, Xiang AH, Choi HS, Cade BE, Tan J, Flanagan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman BI, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen ME, Jørgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KR, Meitinger T, Metspalu A, Morris AD, Nadkarni GN, Nadler JL, Nalls MA, Nayak U, Nongmaithem SS, Ntalla I, Okada Y, Orozco L, Patel SR, Pereira MA, Peters A, Pirie FJ, Porneala B, Prasad G, Preissl S, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sander M, Sandow K, Sattar N, Schönherr S, Schurmann C, Shahriar M, Shi J, Shin DM, Shriner D, Smith JA, So WY, Stančáková A, Stilp AM, Strauch K, Suzuki K, Takahashi A, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tomlinson B, Torres JM, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Vujkovic M, Wacher-Rodarte N, Wheeler E, Whitsel EA, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamauchi T, Yengo L, Yoon K, Yu C, Yuan JM, Yusuf S, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Hanis CL, Peyser PA, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Zeggini E, Yokota M, Rich SS, Kooperberg C, Pankow JS, Engert JC, Chen YDI, Froguel P, Wilson JG, Sheu WHH, Kardia SLR, Wu JY, Hayes MG, Ma RCW, Wong TY, Groop L, Mook-Kanamori DO, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, McKean-Cowdin R, Grallert H, Cheng CY, Bottinger EP, Dehghan A, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Palmer CNA, Liu S, Abecasis G, Kooner JS, Loos RJF, North KE, Haiman CA, Florez JC, Saleheen D, Hansen T, Pedersen O, Mägi R, Langenberg C, Wareham NJ, Maeda S, Kadowaki T, Lee J, Millwood IY, Walters RG, Stefansson K, Myers SR, Ferrer J, Gaulton KJ, Meigs JB, Mohlke KL, Gloyn AL, Bowden DW, Below JE, Chambers JC, Sim X, Boehnke M, Rotter JI, McCarthy MI, Morris AP. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 2022; 54:560-572. [PMID: 35551307 PMCID: PMC9179018 DOI: 10.1038/s41588-022-01058-3] [Citation(s) in RCA: 273] [Impact Index Per Article: 136.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/23/2022] [Indexed: 02/02/2023]
Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
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Affiliation(s)
- Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Genentech, South San Francisco, CA, USA.
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology and Biostatistics, University of Massachusetts Amherst, Amherst, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Maggie C Y Ng
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hidetoshi Kitajima
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, Sendai, Japan
- Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Cancer Center, Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Grace Z Yu
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Sina Rüeger
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Leo Speidel
- Genetics Institute, University College London, London, UK
- Francis Crick Institute, London, UK
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Sanghoon Moon
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Soo-Heon Kwak
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Neil R Robertson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nigel W Rayner
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR) and National University of Singapore (NUS), Singapore, Singapore
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Joshua Chiou
- Biomedical Sciences Graduate Studies Program, University of California San Diego, La Jolla, CA, USA
- Internal Medicine Research Unit, Pfizer Worldwide Research, Cambridge, MA, USA
| | - Irene Miguel-Escalada
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, the Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Madrid, Spain
| | | | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Alicia Huerta-Chagoya
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxicologí, a AmbientalInstituto de Investigaciones Biomédicas, UNAM, Ciudad de Mexico, Mexico, Mexico
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ellen M Schmidt
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloe Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Human Genetics, and Environmental Sciences, the University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA
| | - Christian Gieger
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Meraj Ahmad
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Victor J Y Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Institute of Data Science, Korea University, Seoul, South Korea
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cécile Lecoeur
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Richard A Jensen
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Salman Tajuddin
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Edmond K Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Anny H Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E Cade
- Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jack Flanagan
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Fernando Abaitua
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Vertex Pharmaceuticals Ltd, Oxford, UK
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Masato Akiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Chad M Brummett
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, the Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Wellcome Sanger Institute, Hinxton, UK
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - Swapan K Das
- Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Oscar H Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David Gorkin
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, the Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Jost B Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Marit E Jørgensen
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- National Institute of Public Health, Southern Denmark University, Copenhagen, Denmark
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology & Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M Keaton
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G Kibriya
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Katsuhiko Kohara
- Department of Regional Resource Management, Ehime University Faculty of Collaborative Regional Innovation, Ehime, Japan
- Ibusuki Kozenkai Hospital, Ibusuki, Japan
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Myung-Shik Lee
- Severance Biomedical Science Institute and Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, Inc., University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Andrea O Luk
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - K Radha Mani
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrew D Morris
- The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- The Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Baltimore, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Fraser J Pirie
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Centre Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Maike Sander
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohammad Shahriar
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, the University of Chicago, Chicago, IL, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Ken Suzuki
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genomic Medicine, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Reykjavik, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Jason M Torres
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Department of Health, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Neuroscience and Preventive Medicine, Danube University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología, Ambiental Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Marijana Vujkovic
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Loïc Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, Peking University Health Science Centre, Peking University, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Munich, Germany
| | | | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | | | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C Engert
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille, France
- University of Lille, Lille University Hospital, Lille, France
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Wayne H H Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, the Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, the Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, the Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, the Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Leif Groop
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Centre Campus, Ghaziabad, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michèle M Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health, the University of Chicago, Chicago, IL, USA
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Digital Engineering Faculty of Hasso Plattner Institue and University Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Goncalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Danish Saleheen
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité Universitätsmedizin, Berlin, Germany
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Republic of Korea
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Reykjavik, Reykjavik, Iceland
| | - Simon R Myers
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jorge Ferrer
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, the Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas asociadas (CIBERDEM), Madrid, Spain
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Kyle J Gaulton
- Department of Pediatrics, Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
- Genentech, South San Francisco, CA, USA.
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Department of Health Data Science, University of Liverpool, Liverpool, UK.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK.
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78
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Lai B, Qian S, Zhang H, Zhang S, Kozlova A, Duan J, Xu J, He X. Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning. PLoS Comput Biol 2022; 18:e1010011. [PMID: 35576194 PMCID: PMC9135341 DOI: 10.1371/journal.pcbi.1010011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 05/26/2022] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap, we collected a large collection of neurodevelopment-related cell/tissue types, and trained deep Convolutional Neural Networks (ResNet) using such data. Furthermore, our model, called MetaChrom, borrows information from public epigenomic consortium to improve the accuracy via transfer learning. We show that MetaChrom is substantially better in predicting experimentally determined chromatin accessibility variants than popular variant annotation tools such as CADD and delta-SVM. By combining GWAS data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia, suggesting potential risk genes and the biological contexts where they act. In summary, MetaChrom provides functional annotations of any DNA variants in the neuro-development context and the general method of MetaChrom can also be extended to other disease-related cell or tissue types.
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Affiliation(s)
- Boqiao Lai
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Sheng Qian
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Hanwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Siwei Zhang
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Alena Kozlova
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, Illinois, United States of America
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
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79
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Burch KS, Hou K, Ding Y, Wang Y, Gazal S, Shi H, Pasaniuc B. Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes. Am J Hum Genet 2022; 109:692-709. [PMID: 35271803 PMCID: PMC9069080 DOI: 10.1016/j.ajhg.2022.02.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/15/2022] [Indexed: 11/15/2022] Open
Abstract
Recent works have shown that SNP heritability-which is dominated by low-effect common variants-may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ("gene-level heritability"). We partition gene-level heritability by minor allele frequency (MAF) to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% ≤ MAF < 1%). Applying our method to ∼16K protein-coding genes and 25 quantitative traits in the UK Biobank (N = 290K "White British"), we find that, on average across traits, ∼2.5% of nonzero-heritability genes have a rare-variant component and only ∼0.8% (327 gene-trait pairs) have heritability exclusively from rare variants. Of these 327 gene-trait pairs, 114 (35%) were not detected by existing gene-level association testing methods. The additional genes we identify are significantly enriched for known disease genes, and we find several examples of genes that have been previously implicated in phenotypically related Mendelian disorders. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. While nonzero gene-level heritability does not imply causality, if interpreted in the correct context, gene-level heritability can reveal useful insights into complex-trait genetic architecture.
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Affiliation(s)
- Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yifei Wang
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; OMNI Bioinformatics, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
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80
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Zhang S, Cooper-Knock J, Weimer AK, Shi M, Moll T, Marshall JNG, Harvey C, Nezhad HG, Franklin J, Souza CDS, Ning K, Wang C, Li J, Dilliott AA, Farhan S, Elhaik E, Pasniceanu I, Livesey MR, Eitan C, Hornstein E, Kenna KP, Veldink JH, Ferraiuolo L, Shaw PJ, Snyder MP. Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis. Neuron 2022; 110:992-1008.e11. [PMID: 35045337 PMCID: PMC9017397 DOI: 10.1016/j.neuron.2021.12.019] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/07/2021] [Accepted: 12/13/2021] [Indexed: 02/01/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases.
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Affiliation(s)
- Sai Zhang
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Annika K Weimer
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Minyi Shi
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tobias Moll
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Jack N G Marshall
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Calum Harvey
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Helia Ghahremani Nezhad
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - John Franklin
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Cleide Dos Santos Souza
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Ke Ning
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Cheng Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jingjing Li
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Allison A Dilliott
- Department of Neurology and Neurosurgery, the Montreal Neurological Institute, McGill University, Montreal, QC H3A 1A1, Canada
| | - Sali Farhan
- Department of Neurology and Neurosurgery, the Montreal Neurological Institute, McGill University, Montreal, QC H3A 1A1, Canada
| | - Eran Elhaik
- Department of Biology, Lunds Universitet, Lund 223 62, Sweden
| | - Iris Pasniceanu
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Matthew R Livesey
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Chen Eitan
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Eran Hornstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Kevin P Kenna
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, the Netherlands
| | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, the Netherlands
| | - Laura Ferraiuolo
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK
| | - Michael P Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
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81
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Wang Z, Chivu AG, Choate LA, Rice EJ, Miller DC, Chu T, Chou SP, Kingsley NB, Petersen JL, Finno CJ, Bellone RR, Antczak DF, Lis JT, Danko CG. Prediction of histone post-translational modification patterns based on nascent transcription data. Nat Genet 2022; 54:295-305. [PMID: 35273399 PMCID: PMC9444190 DOI: 10.1038/s41588-022-01026-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 01/24/2022] [Indexed: 01/01/2023]
Abstract
The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription.
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Affiliation(s)
- Zhong Wang
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- School of Software Technology, Dalian University of Technology, Dalian, China
| | - Alexandra G Chivu
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Lauren A Choate
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Edward J Rice
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Donald C Miller
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Tinyi Chu
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Shao-Pei Chou
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - Nicole B Kingsley
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Jessica L Petersen
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Carrie J Finno
- Department of Population Health and Reproduction, University of California, Davis, Davis, CA, USA
| | - Rebecca R Bellone
- Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Douglas F Antczak
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
| | - John T Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | - Charles G Danko
- Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
- Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
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82
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Yang Y, Sun Q, Huang L, Broome JG, Correa A, Reiner A, Raffield LM, Yang Y, Li Y. eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data. Brief Bioinform 2022; 23:bbab497. [PMID: 34882196 PMCID: PMC8898002 DOI: 10.1093/bib/bbab497] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/25/2021] [Accepted: 10/30/2021] [Indexed: 02/07/2023] Open
Abstract
Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.
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Affiliation(s)
- Yingxi Yang
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06511, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Le Huang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jai G Broome
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA
| | - Adolfo Correa
- Department of Medicine and Population Health Science, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Alexander Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
- Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, 98195, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yuchen Yang
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, 510275 Guangzhou, China
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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83
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Orozco G. Fine mapping with epigenetic information and 3D structure. Semin Immunopathol 2022; 44:115-125. [PMID: 35022890 PMCID: PMC8837508 DOI: 10.1007/s00281-021-00906-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. .,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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84
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Cheng Q, Qiu T, Chai X, Sun B, Xia Y, Shi X, Liu J. MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants. Bioinformatics 2022; 38:303-310. [PMID: 34499127 DOI: 10.1093/bioinformatics/btab646] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 08/04/2021] [Accepted: 09/06/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. RESULTS To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure-outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. AVAILABILITY AND IMPLEMENTATION The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qing Cheng
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.,Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, 169857 Singapore
| | - Tingting Qiu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Xiaoran Chai
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | - Baoluo Sun
- Department of Statistics and Applied Probability, NUS, 117546 Singapore
| | - Yingcun Xia
- Department of Statistics and Applied Probability, NUS, 117546 Singapore
| | - Xingjie Shi
- Academy of Statistics and Interdisciplinary Sciences, Faculty of Economics and Management, East China Normal University, Shanghai 200062, China
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, 169857 Singapore
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85
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OUP accepted manuscript. Brain 2022; 145:3214-3224. [DOI: 10.1093/brain/awac105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/11/2022] [Accepted: 03/04/2022] [Indexed: 11/15/2022] Open
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86
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Wang QS, Huang H. Methods for statistical fine-mapping and their applications to auto-immune diseases. Semin Immunopathol 2022; 44:101-113. [PMID: 35041074 PMCID: PMC8837575 DOI: 10.1007/s00281-021-00902-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Although genome-wide association studies (GWAS) have identified thousands of loci in the human genome that are associated with different traits, understanding the biological mechanisms underlying the association signals identified in GWAS remains challenging. Statistical fine-mapping is a method aiming to refine GWAS signals by evaluating which variant(s) are truly causal to the phenotype. Here, we review the types of statistical fine-mapping methods that have been widely used to date, with a focus on recently developed functionally informed fine-mapping (FIFM) methods that utilize functional annotations. We then systematically review the applications of statistical fine-mapping in autoimmune disease studies to highlight the value of statistical fine-mapping in biological contexts.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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87
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Xiang B, Deng C, Qiu F, Li J, Li S, Zhang H, Lin X, Huang Y, Zhou Y, Su J, Lu M, Ma Y. Single cell sequencing analysis identifies genetics-modulated ORMDL3 + cholangiocytes having higher metabolic effects on primary biliary cholangitis. J Nanobiotechnology 2021; 19:406. [PMID: 34872583 PMCID: PMC8647381 DOI: 10.1186/s12951-021-01154-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/21/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. RESULTS We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3- cholangiocytes (P = 1.38 × 10-15). Compared with ORMDL3- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. CONCLUSIONS To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.
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Affiliation(s)
- Bingyu Xiang
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150080, China
| | - Fei Qiu
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jingjing Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China
| | - Shanshan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Huifang Zhang
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Xiuli Lin
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yukuan Huang
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Yijun Zhou
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jianzhong Su
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, Zhejiang, China
| | - Mingqin Lu
- Department of Infectious Diseases, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Yunlong Ma
- Institute of Biomedical Big Data, School of Ophthalmology and Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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88
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Wang X, Cheng H, Yang Y, Zuo X, Shao L, Yu D, Yang N, Zhang Y, Li R, Wang X, Shen B, Wang J, Shi X, Cao P, Sun L, Han X, Sun Y. The enhancer rare germline variation rs548071605 contributes to lung cancer development. Hum Mutat 2021; 43:200-214. [PMID: 34859522 DOI: 10.1002/humu.24310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 10/21/2021] [Accepted: 11/28/2021] [Indexed: 11/05/2022]
Abstract
Rare germline variations contribute to the missing heritability of human complex diseases including cancers. Given their very low frequency, discovering and testing disease-causing rare germline variations remains challenging. The tag-single nucleotide polymorphism rs17728461 in 22q12.2 is highly associated with lung cancer risk. Here, we identified a functional rare germline variation rs548071605 (A>G) in a p65-responsive enhancer located within 22q12.2. The enhancer significantly promoted lung cancer cell proliferation in vitro and in a xenograft mouse model by upregulating the leukemia inhibitory factor (LIF) gene via the formation of a chromatin loop. Differential expression of LIF and its significant correlation with first progression survival time of patients further supported the lung cancer-driving effects of the 22q-Enh enhancer. Importantly, the rare variation was harbored in the p65 binding sequence and dramatically increased the enhancer activity by increasing responsiveness of the enhancer to p65 and B-cell lymphoma 3 protein, an oncoprotein that assisted the p65 binding. Our study revealed a regulatory rare germline variation with a potential lung cancer-driving role in the 22q12.2 risk region, providing intriguing clues for investigating the "missing heritability" of cancers, and also offered a useful experimental model for identifying causal rare variations.
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Affiliation(s)
- Xuchun Wang
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - He Cheng
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Yin Yang
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Xianglin Zuo
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Lipei Shao
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Dawei Yu
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Nan Yang
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
| | - Yu Zhang
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Ruilei Li
- Department of Cancer Biotherapy Center, The Third Affiliated Hospital of Kunming Medical University (Tumor Hospital of Yunnan Province), Kunming, Yunnan, China
| | - Xinyuan Wang
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Bin Shen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Jianying Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Xiao Shi
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Pingping Cao
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Luan Sun
- Department of Cell Biology, Nanjing Medical University, Nanjing, China
| | - Xiao Han
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
| | - Yujie Sun
- Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Personalized Cancer Medicine, Nanjing Medical University, Nanjing, China.,Department of Cell Biology, Nanjing Medical University, Nanjing, China
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89
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Castano-Duque L, Gilbert MK, Mack BM, Lebar MD, Carter-Wientjes CH, Sickler CM, Cary JW, Rajasekaran K. Flavonoids Modulate the Accumulation of Toxins From Aspergillus flavus in Maize Kernels. FRONTIERS IN PLANT SCIENCE 2021; 12:761446. [PMID: 34899785 PMCID: PMC8662736 DOI: 10.3389/fpls.2021.761446] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/15/2021] [Indexed: 06/14/2023]
Abstract
Aspergillus flavus is an opportunistic fungal pathogen capable of producing aflatoxins, potent carcinogenic toxins that accumulate in maize kernels after infection. To better understand the molecular mechanisms of maize resistance to A. flavus growth and aflatoxin accumulation, we performed a high-throughput transcriptomic study in situ using maize kernels infected with A. flavus strain 3357. Three maize lines were evaluated: aflatoxin-contamination resistant line TZAR102, semi-resistant MI82, and susceptible line Va35. A modified genotype-environment association method (GEA) used to detect loci under selection via redundancy analysis (RDA) was used with the transcriptomic data to detect genes significantly influenced by maize line, fungal treatment, and duration of infection. Gene ontology enrichment analysis of genes highly expressed in infected kernels identified molecular pathways associated with defense responses to fungi and other microbes such as production of pathogenesis-related (PR) proteins and lipid bilayer formation. To further identify novel genes of interest, we incorporated genomic and phenotypic field data from a genome wide association analysis with gene expression data, allowing us to detect significantly expressed quantitative trait loci (eQTL). These results identified significant association between flavonoid biosynthetic pathway genes and infection by A. flavus. In planta fungal infections showed that the resistant line, TZAR102, has a higher fold increase of the metabolites naringenin and luteolin than the susceptible line, Va35, when comparing untreated and fungal infected plants. These results suggest flavonoids contribute to plant resistance mechanisms against aflatoxin contamination through modulation of toxin accumulation in maize kernels.
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90
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Genetic overlap and causality between blood metabolites and migraine. Am J Hum Genet 2021; 108:2086-2098. [PMID: 34644541 DOI: 10.1016/j.ajhg.2021.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/17/2021] [Indexed: 12/11/2022] Open
Abstract
The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.
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91
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Ma Y, Zhou X. Genetic prediction of complex traits with polygenic scores: a statistical review. Trends Genet 2021; 37:995-1011. [PMID: 34243982 PMCID: PMC8511058 DOI: 10.1016/j.tig.2021.06.004] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 01/03/2023]
Abstract
Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.
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Affiliation(s)
- Ying Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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92
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Márquez-Luna C, Gazal S, Loh PR, Kim SS, Furlotte N, Auton A, Price AL. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat Commun 2021; 12:6052. [PMID: 34663819 PMCID: PMC8523709 DOI: 10.1038/s41467-021-25171-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 07/16/2021] [Indexed: 12/23/2022] Open
Abstract
Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2 = 0.144; highest R2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (N = 1107 K) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.
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Affiliation(s)
- Carla Márquez-Luna
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Steven Gazal
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Charles R. Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Samuel S Kim
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Alkes L Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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93
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Thorp JG, Campos AI, Grotzinger AD, Gerring ZF, An J, Ong JS, Wang W, Shringarpure S, Byrne EM, MacGregor S, Martin NG, Medland SE, Middeldorp CM, Derks EM. Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. Nat Hum Behav 2021; 5:1432-1442. [PMID: 33859377 DOI: 10.1038/s41562-021-01094-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 03/01/2021] [Indexed: 02/02/2023]
Abstract
Depression and anxiety are highly prevalent and comorbid psychiatric traits that cause considerable burden worldwide. Here we use factor analysis and genomic structural equation modelling to investigate the genetic factor structure underlying 28 items assessing depression, anxiety and neuroticism, a closely related personality trait. Symptoms of depression and anxiety loaded on two distinct, although highly genetically correlated factors, and neuroticism items were partitioned between them. We used this factor structure to conduct genome-wide association analyses on latent factors of depressive symptoms (89 independent variants, 61 genomic loci) and anxiety symptoms (102 variants, 73 loci) in the UK Biobank. Of these associated variants, 72% and 78%, respectively, replicated in an independent cohort of approximately 1.9 million individuals with self-reported diagnosis of depression and anxiety. We use these results to characterize shared and trait-specific genetic associations. Our findings provide insight into the genetic architecture of depression and anxiety and comorbidity between them.
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Affiliation(s)
- Jackson G Thorp
- Translational Neurogenomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.
| | - Adrian I Campos
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Zachary F Gerring
- Translational Neurogenomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jiyuan An
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Jue-Sheng Ong
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | | | - Enda M Byrne
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Christel M Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Queensland, Australia
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Eske M Derks
- Translational Neurogenomics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
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94
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Hutchinson A, Reales G, Willis T, Wallace C. Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR. PLoS Genet 2021; 17:e1009853. [PMID: 34669738 PMCID: PMC8559959 DOI: 10.1371/journal.pgen.1009853] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/01/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
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Affiliation(s)
- Anna Hutchinson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Guillermo Reales
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Willis
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), University of Cambridge, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
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95
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Kolosov N, Daly MJ, Artomov M. Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning. Eur J Hum Genet 2021; 29:1527-1535. [PMID: 34276057 PMCID: PMC8484264 DOI: 10.1038/s41431-021-00930-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 05/23/2021] [Accepted: 06/21/2021] [Indexed: 02/07/2023] Open
Abstract
A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.
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Affiliation(s)
- Nikita Kolosov
- ITMO University, St. Petersburg, Russia
- Almazov National Medical Research Center, St. Petersburg, Russia
- Broad Institute, Cambridge, MA, USA
| | - Mark J Daly
- Broad Institute, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland.
| | - Mykyta Artomov
- ITMO University, St. Petersburg, Russia.
- Almazov National Medical Research Center, St. Petersburg, Russia.
- Broad Institute, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland.
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96
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Genetic variations of DNA bindings of FOXA1 and co-factors in breast cancer susceptibility. Nat Commun 2021; 12:5318. [PMID: 34518541 PMCID: PMC8438084 DOI: 10.1038/s41467-021-25670-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 08/25/2021] [Indexed: 11/21/2022] Open
Abstract
Identifying transcription factors (TFs) whose DNA bindings are altered by genetic variants that regulate susceptibility genes is imperative to understand transcriptional dysregulation in disease etiology. Here, we develop a statistical framework to analyze extensive ChIP-seq and GWAS data and identify 22 breast cancer risk-associated TFs. We find that, by analyzing genetic variations of TF-DNA bindings, the interaction of FOXA1 with co-factors such as ESR1 and E2F1, and the interaction of TFs with chromatin features (i.e., enhancers) play a key role in breast cancer susceptibility. Using genetic variants occupied by the 22 TFs, transcriptome-wide association analyses identify 52 previously unreported breast cancer susceptibility genes, including seven with evidence of essentiality from functional screens in breast relevant cell lines. We show that FOXA1 and co-factors form a core TF-transcriptional network regulating the susceptibility genes. Our findings provide additional insights into genetic variations of TF-DNA bindings (particularly for FOXA1) underlying breast cancer susceptibility. The identification of transcription factors (TFs) whose binding sites are affected by risk genetic variants remains crucial. Here, the authors develop a statistical framework to analyse ChIP-seq and GWAS data, identify 22 breast cancer risk-associated TFs and a core TF-transcriptional network for FOXA1 and co-factors.
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97
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Findley AS, Zhang X, Boye C, Lin YL, Kalita CA, Barreiro L, Lohmueller KE, Pique-Regi R, Luca F. A signature of Neanderthal introgression on molecular mechanisms of environmental responses. PLoS Genet 2021; 17:e1009493. [PMID: 34570765 PMCID: PMC8509894 DOI: 10.1371/journal.pgen.1009493] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 10/12/2021] [Accepted: 08/18/2021] [Indexed: 12/17/2022] Open
Abstract
Ancient human migrations led to the settlement of population groups in varied environmental contexts worldwide. The extent to which adaptation to local environments has shaped human genetic diversity is a longstanding question in human evolution. Recent studies have suggested that introgression of archaic alleles in the genome of modern humans may have contributed to adaptation to environmental pressures such as pathogen exposure. Functional genomic studies have demonstrated that variation in gene expression across individuals and in response to environmental perturbations is a main mechanism underlying complex trait variation. We considered gene expression response to in vitro treatments as a molecular phenotype to identify genes and regulatory variants that may have played an important role in adaptations to local environments. We investigated if Neanderthal introgression in the human genome may contribute to the transcriptional response to environmental perturbations. To this end we used eQTLs for genes differentially expressed in a panel of 52 cellular environments, resulting from 5 cell types and 26 treatments, including hormones, vitamins, drugs, and environmental contaminants. We found that SNPs with introgressed Neanderthal alleles (N-SNPs) disrupt binding of transcription factors important for environmental responses, including ionizing radiation and hypoxia, and for glucose metabolism. We identified an enrichment for N-SNPs among eQTLs for genes differentially expressed in response to 8 treatments, including glucocorticoids, caffeine, and vitamin D. Using Massively Parallel Reporter Assays (MPRA) data, we validated the regulatory function of 21 introgressed Neanderthal variants in the human genome, corresponding to 8 eQTLs regulating 15 genes that respond to environmental perturbations. These findings expand the set of environments where archaic introgression may have contributed to adaptations to local environments in modern humans and provide experimental validation for the regulatory function of introgressed variants.
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Affiliation(s)
- Anthony S. Findley
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Xinjun Zhang
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
| | - Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Yen Lung Lin
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Cynthia A. Kalita
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
| | - Luis Barreiro
- Genetics Section, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Kirk E. Lohmueller
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, United States of America
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98
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Elmentaite R, Kumasaka N, Roberts K, Fleming A, Dann E, King HW, Kleshchevnikov V, Dabrowska M, Pritchard S, Bolt L, Vieira SF, Mamanova L, Huang N, Perrone F, Goh Kai'En I, Lisgo SN, Katan M, Leonard S, Oliver TRW, Hook CE, Nayak K, Campos LS, Domínguez Conde C, Stephenson E, Engelbert J, Botting RA, Polanski K, van Dongen S, Patel M, Morgan MD, Marioni JC, Bayraktar OA, Meyer KB, He X, Barker RA, Uhlig HH, Mahbubani KT, Saeb-Parsy K, Zilbauer M, Clatworthy MR, Haniffa M, James KR, Teichmann SA. Cells of the human intestinal tract mapped across space and time. Nature 2021; 597:250-255. [PMID: 34497389 PMCID: PMC8426186 DOI: 10.1038/s41586-021-03852-1] [Citation(s) in RCA: 266] [Impact Index Per Article: 88.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/26/2021] [Indexed: 12/12/2022]
Abstract
The cellular landscape of the human intestinal tract is dynamic throughout life, developing in utero and changing in response to functional requirements and environmental exposures. Here, to comprehensively map cell lineages, we use single-cell RNA sequencing and antigen receptor analysis of almost half a million cells from up to 5 anatomical regions in the developing and up to 11 distinct anatomical regions in the healthy paediatric and adult human gut. This reveals the existence of transcriptionally distinct BEST4 epithelial cells throughout the human intestinal tract. Furthermore, we implicate IgG sensing as a function of intestinal tuft cells. We describe neural cell populations in the developing enteric nervous system, and predict cell-type-specific expression of genes associated with Hirschsprung's disease. Finally, using a systems approach, we identify key cell players that drive the formation of secondary lymphoid tissue in early human development. We show that these programs are adopted in inflammatory bowel disease to recruit and retain immune cells at the site of inflammation. This catalogue of intestinal cells will provide new insights into cellular programs in development, homeostasis and disease.
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Affiliation(s)
- Rasa Elmentaite
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Kenny Roberts
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aaron Fleming
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Hamish W King
- Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK
| | | | | | | | - Liam Bolt
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Sara F Vieira
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Lira Mamanova
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Ni Huang
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Issac Goh Kai'En
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Steven N Lisgo
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Matilda Katan
- Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Steven Leonard
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Thomas R W Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - C Elizabeth Hook
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Komal Nayak
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Lia S Campos
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Emily Stephenson
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Justin Engelbert
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachel A Botting
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Minal Patel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Michael D Morgan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Holm H Uhlig
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Paediatrics, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Kourosh Saeb-Parsy
- Department of Surgery, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Matthias Zilbauer
- Department of Paediatrics, University of Cambridge, Cambridge, UK
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals Trust, Cambridge, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Anne McLaren Laboratory, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Kylie R James
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK.
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99
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Hao X, Wang K, Dai C, Ding Z, Yang W, Wang C, Cheng S. Integrative analysis of scRNA-seq and GWAS data pinpoints periportal hepatocytes as the relevant liver cell types for blood lipids. Hum Mol Genet 2021; 29:3145-3153. [PMID: 32821946 DOI: 10.1093/hmg/ddaa188] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 08/10/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022] Open
Abstract
Liver, a heterogeneous tissue consisting of various cell types, is known to be relevant for blood lipid traits. By integrating summary statistics from genome-wide association studies (GWAS) of lipid traits and single-cell transcriptome data of the liver, we sought to identify specific cell types in the liver that were most relevant for blood lipid levels. We conducted differential expression analyses for 40 cell types from human and mouse livers in order to construct the cell-type specifically expressed gene sets, which we refer to as construction of the liver cell-type specifically expressed gene sets (CT-SEGS). Under the assumption that CT-SEGS represented specific functions of each cell type, we applied stratified linkage disequilibrium score regression to determine cell types that were most relevant for complex traits and diseases. We first confirmed the validity of this method (of delineating functionally relevant cell types) by identifying the immune cell types as relevant for autoimmune diseases. We further showed that lipid GWAS signals were enriched in the human and mouse periportal hepatocytes. Our results provide important information to facilitate future cellular studies of the metabolic mechanism affecting blood lipid levels.
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Affiliation(s)
- Xingjie Hao
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | - Kai Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | - Chengguqiu Dai
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
| | | | - Wei Yang
- Department of Nutrition and Food Hygiene, School of Public Health
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health.,Department of Orthopedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Shanshan Cheng
- Department of Epidemiology and Biostatistics, Key Laboratory for Environment and Health, School of Public Health
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100
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Hu Z, Zhou Y, Tong T. Meta-Analyzing Multiple Omics Data With Robust Variable Selection. Front Genet 2021; 12:656826. [PMID: 34290735 PMCID: PMC8288516 DOI: 10.3389/fgene.2021.656826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/24/2021] [Indexed: 12/03/2022] Open
Abstract
High-throughput omics data are becoming more and more popular in various areas of science. Given that many publicly available datasets address the same questions, researchers have applied meta-analysis to synthesize multiple datasets to achieve more reliable results for model estimation and prediction. Due to the high dimensionality of omics data, it is also desirable to incorporate variable selection into meta-analysis. Existing meta-analyzing variable selection methods are often sensitive to the presence of outliers, and may lead to missed detections of relevant covariates, especially for lasso-type penalties. In this paper, we develop a robust variable selection algorithm for meta-analyzing high-dimensional datasets based on logistic regression. We first search an outlier-free subset from each dataset by borrowing information across the datasets with repeatedly use of the least trimmed squared estimates for the logistic model and together with a hierarchical bi-level variable selection technique. We then refine a reweighting step to further improve the efficiency after obtaining a reliable non-outlier subset. Simulation studies and real data analysis show that our new method can provide more reliable results than the existing meta-analysis methods in the presence of outliers.
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Affiliation(s)
- Zongliang Hu
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
| | - Yan Zhou
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
| | - Tiejun Tong
- Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
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