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Ueland TE, Mosley JD, Neylan C, Shelley JP, Robinson J, Gamazon ER, Maguire L, Peek R, Hawkins AT. Multiancestry transferability of a polygenic risk score for diverticulitis. BMJ Open Gastroenterol 2024; 11:e001474. [PMID: 39313293 PMCID: PMC11418579 DOI: 10.1136/bmjgast-2024-001474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Accepted: 09/06/2024] [Indexed: 09/25/2024] Open
Abstract
OBJECTIVE Polygenic risk scores (PRS) for diverticular disease must be evaluated in diverse cohorts. We sought to explore shared genetic predisposition across the phenome and to assess risk stratification in individuals genetically similar to European, African and Admixed-American reference samples. METHODS A 44-variant PRS was applied to the All of Us Research Program. Phenome-wide association studies (PheWAS) identified conditions linked with heightened genetic susceptibility to diverticular disease. To evaluate the PRS in risk stratification, logistic regression models for symptomatic and for severe diverticulitis were compared with base models with covariates of age, sex, body mass index, smoking and principal components. Performance was assessed using area under the receiver operating characteristic curves (AUROC) and Nagelkerke's R2. RESULTS The cohort comprised 181 719 individuals for PheWAS and 50 037 for risk modelling. PheWAS identified associations with diverticular disease, connective tissue disease and hernias. Across ancestry groups, one SD PRS increase was consistently associated with greater odds of severe (range of ORs (95% CI) 1.60 (1.27 to 2.02) to 1.86 (1.42 to 2.42)) and of symptomatic diverticulitis ((95% CI) 1.27 (1.10 to 1.46) to 1.66 (1.55 to 1.79)) relative to controls. European models achieved the highest AUROC and Nagelkerke's R2 (AUROC (95% CI) 0.78 (0.75 to 0.81); R2 0.25). The PRS provided a maximum R2 increase of 0.034 and modest AUROC improvement. CONCLUSION Associations between a diverticular disease PRS and severe presentations persisted in diverse cohorts when controlling for known risk factors. Relative improvements in model performance were observed, but absolute change magnitudes were modest.
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Affiliation(s)
- Thomas E Ueland
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christopher Neylan
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John P Shelley
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Jamie Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lillias Maguire
- Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Richard Peek
- Division of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alexander T Hawkins
- Division of General Surgery, Section of Colon & Rectal Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Koromina M, Ravi A, Panagiotaropoulou G, Schilder BM, Humphrey J, Braun A, Bidgeli T, Chatzinakos C, Coombes B, Kim J, Liu X, Terao C, O.’Connell KS, Adams M, Adolfsson R, Alda M, Alfredsson L, Andlauer TFM, Andreassen OA, Antoniou A, Baune BT, Bengesser S, Biernacka J, Boehnke M, Bosch R, Cairns M, Carr VJ, Casas M, Catts S, Cichon S, Corvin A, Craddock N, Dafnas K, Dalkner N, Dannlowski U, Degenhardt F, Di Florio A, Dikeos D, Fellendorf FT, Ferentinos P, Forstner AJ, Forty L, Frye M, Fullerton JM, Gawlik M, Gizer IR, Gordon-Smith K, Green MJ, Grigoroiu-Serbanescu M, Guzman-Parra J, Hahn T, Henskens F, Hillert J, Jablensky AV, Jones L, Jones I, Jonsson L, Kelsoe JR, Kircher T, Kirov G, Kittel-Schneider S, Kogevinas M, Landén M, Leboyer M, Lenger M, Lissowska J, Lochner C, Loughland C, MacIntyre D, Martin NG, Maratou E, Mathews CA, Mayoral F, McElroy SL, McGregor NW, McIntosh A, McQuillin A, Michie P, Milanova V, Mitchell PB, Moutsatsou P, Mowry B, Müller-Myhsok B, Myers R, Nenadić I, Nöthen MM, O’Donovan C, O’Donovan M, Ophoff RA, Owen MJ, Pantelis C, Pato C, Pato MT, Patrinos GP, Pawlak JM, Perlis RH, Porichi E, Posthuma D, Ramos-Quiroga JA, Reif A, Reininghaus EZ, Ribasés M, Rietschel M, Schall U, Schulze TG, Scott L, Scott RJ, Serretti A, Weickert CS, Smoller JW, Artigas MS, Stein DJ, Streit F, Toma C, Tooney P, Vieta E, Vincent JB, Waldman ID, Weickert T, Witt SH, Hong KS, Ikeda M, Iwata N, Świątkowska B, Won HH, Edenberg HJ, Ripke S, Raj T, Coleman JRI, Mullins N. Fine-mapping genomic loci refines bipolar disorder risk genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302716. [PMID: 38405768 PMCID: PMC10889003 DOI: 10.1101/2024.02.12.24302716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453 . 3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline ( https://github.com/mkoromina/SAFFARI ).
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Affiliation(s)
- Maria Koromina
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashvin Ravi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Brian M. Schilder
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jack Humphrey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alice Braun
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
| | | | | | - Brandon Coombes
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jaeyoung Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Kevin S. O.’Connell
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
| | - Mark Adams
- Division of Psychiatry, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Rolf Adolfsson
- Department of Clinical Sciences, Psychiatry, Umeå, University Medical Faculty, Umeå, Sweden
| | - Martin Alda
- Department 20 of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Till F. M. Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ole A. Andreassen
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- NORMENT, University of Oslo, Oslo, Norway
| | - Anastasia Antoniou
- National Kapodistrian University of Athens, 2nd Department of Psychiatry, Attikon General Hospital, Athens, Greece
| | - Bernhard T. Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Susanne Bengesser
- Medical University of Graz, Division of Psychiatry and Psychotherapeutic Medicine, Graz, Austria
| | - Joanna Biernacka
- Department of Quantitative Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Michael Boehnke
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Rosa Bosch
- Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Programa SJD MIND Escoles, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | | | - Vaughan J. Carr
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, Australia
| | - Miquel Casas
- Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Programa SJD MIND Escoles, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Spain
| | | | - Sven Cichon
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Dept of Psychiatry and Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Nicholas Craddock
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Konstantinos Dafnas
- National Kapodistrian University of Athens, 2nd Department of Psychiatry, Attikon General Hospital, Athens, Greece
| | - Nina Dalkner
- Medical University of Graz, Division of Psychiatry and Psychotherapeutic Medicine, Graz, Austria
| | - Udo Dannlowski
- Institute for Translatiol Psychiatry, University of Münster, Münster, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Duisburg, Germany
| | - Arianna Di Florio
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Psychiatry, University of North Caroli at Chapel Hill, Chapel Hill, NC, USA
| | - Dimitris Dikeos
- National Kapodistrian University of Athens, 2nd Department of Psychiatry, Attikon General Hospital, Athens, Greece
| | | | - Panagiotis Ferentinos
- National Kapodistrian University of Athens, 2nd Department of Psychiatry, Attikon General Hospital, Athens, Greece
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
| | - Andreas J. Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Liz Forty
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Mark Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Micha Gawlik
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany
| | - Ian R. Gizer
- Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
| | | | - Melissa J. Green
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - José Guzman-Parra
- Mental Health Department, University Regional Hospital, Biomedicine Institute (IBIMA), Málaga, Spain
| | - Tim Hahn
- Institute for Translatiol Psychiatry, University of Münster, Münster, Germany
| | | | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Lisa Jones
- Psychological Medicine, University of Worcester, Worcester, UK
| | - Ian Jones
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Lina Jonsson
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - John R. Kelsoe
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Germany
| | - George Kirov
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
- Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland
| | | | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marion Leboyer
- Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, France
- Department of Psychiatry and Addiction Medicine, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Melanie Lenger
- Medical University of Graz, Division of Psychiatry and Psychotherapeutic Medicine, Graz, Austria
| | - Jolanta Lissowska
- Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | | | - Donald MacIntyre
- Division of Psychiatry, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Nicholas G. Martin
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Eirini Maratou
- National and Kapodistrian University of Athens, Medical School, Clinical Biochemistry Laboratory, Attikon General Hospital, Athens, Greece
| | - Carol A. Mathews
- Department of Psychiatry and Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Fermin Mayoral
- Mental Health Department, University Regional Hospital, Biomedicine Institute (IBIMA), Málaga, Spain
| | | | - Nathaniel W. McGregor
- Systems Genetics Working Group, Department of Genetics, Stellenbosch University, Stellenbosch, South Africa
| | - Andrew McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | | | | | - Vihra Milanova
- Psychiatric Clinic, Alexander University Hospital, Bulgaria
| | - Philip B. Mitchell
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Paraskevi Moutsatsou
- National Kapodistrian University of Athens, Medical School, Clinical Biochemistry Laboratory, Attikon General Hospital, Athens, Greece
| | - Bryan Mowry
- University of Queensland, Brisbane, QLD, Australia
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Richard Myers
- Hudsolpha Institute for Biotechnology, Huntsville, AL, USA
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Claire O’Donovan
- Department 20 of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Michael O’Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Roel A. Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Psychiatry and Biobehavioral Science, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | | | - Carlos Pato
- Institute for Genomic Health, SUNY Downstate Medical Center College of Medicine, Brooklyn, NY, USA
| | - Michele T. Pato
- Institute for Genomic Health, SUNY Downstate Medical Center College of Medicine, Brooklyn, NY, USA
| | - George P. Patrinos
- University of Patras, School of Health Sciences, Department of Pharmacy, Laboratory of Pharmacogenomics and Individualized Therapy, Patras, Greece
- United Arab Emirates University, College of Medicine and Health Sciences, Department of Genetics and Genomics, Al-Ain, United Arab Emirates
- United Arab Emirates University, Zayed Center for Health Sciences, Al-Ain, United Arab Emirates
| | - Joanna M. Pawlak
- Department of Psychiatry, Departmet of Psychiatric Genetics, Poznan University of Medical Sciences, Poznan, Poland
| | - Roy H. Perlis
- Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Clinical Research, Massachusetts General Hospital, Boston, MA, USA
| | - Evgenia Porichi
- National and Kapodistrian University of Athens, 2nd Department of Psychiatry, Attikon General Hospital, Athens, Greece
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Josep Antoni Ramos-Quiroga
- Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital Universitari Vall d´Hebron, Barcelo, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelo, Barcelo, Spain
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addictions, Vall d´Hebron Research Institut (VHIR), Universitat Autònoma de Barcelo, Barcelo, Spain
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Eva Z. Reininghaus
- Medical University of Graz, Division of Psychiatry and Psychotherapeutic Medicine, Graz, Austria
| | - Marta Ribasés
- Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital Universitari Vall d´Hebron, Barcelona, Spain
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addictions, Vall d´Hebron Research Institut (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain. Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Thomas G. Schulze
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Laura Scott
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | | | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Cynthia Shannon Weickert
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA
| | - Maria Soler Artigas
- Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital Universitari Vall d´Hebron, Barcelo, Spain
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addictions, Vall d´Hebron Research Institut (VHIR), Universitat Autònoma de Barcelo, Barcelo, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelo, Barcelo, Spain
| | - Dan J. Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Claudio Toma
- Neuroscience Research Australia, Sydney, NSW, Australia
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Centro de Biología Molecular Severo Ochoa, Universidad Autónoma de Madrid and CSIC, Madrid, Spain
| | - Paul Tooney
- University of Newcastle, Newcastle, NSW, Australia
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - John B. Vincent
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Thomas Weickert
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kyung Sue Hong
- Department of Psychiatry, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea
| | - Masashi Ikeda
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Beata Świątkowska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Howard J. Edenberg
- Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Towfique Raj
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonathan R. I. Coleman
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Niamh Mullins
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Mitjans M, Papiol S, Fatjó-Vilas M, González-Peñas J, Acosta-Díez M, Zafrilla-López M, Costas J, Arango C, Vilella E, Martorell L, Moltó MD, Bobes J, Crespo-Facorro B, González-Pinto A, Fañanás L, Rosa A, Arias B. Shared vulnerability and sex-dependent polygenic burden in psychotic disorders. Eur Neuropsychopharmacol 2024; 86:49-54. [PMID: 38941950 DOI: 10.1016/j.euroneuro.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/21/2024] [Accepted: 04/27/2024] [Indexed: 06/30/2024]
Abstract
Evidence suggests a remarkable shared genetic susceptibility between psychiatric disorders. However, sex-dependent differences have been less studied. We explored the contribution of schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) polygenic scores (PGSs) on the risk for psychotic disorders and whether sex-dependent differences exist (CIBERSAM sample: 1826 patients and 1372 controls). All PGSs were significantly associated with psychosis. Sex-stratified analyses showed that the variance explained in psychotic disorders risk was significantly higher in males than in females for all PGSs. Our results confirm the shared genetic architecture across psychotic disorders and demonstrate sex-dependent differences in the vulnerability to psychotic disorders.
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Affiliation(s)
- Marina Mitjans
- Departament Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany; Max Planck Institute of Psychiatry, Munich, Germany; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense de Madrid, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Miriam Acosta-Díez
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Marina Zafrilla-López
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Javier Costas
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Galicia, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense de Madrid, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Reus, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, Reus, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - M Dolores Moltó
- INCLIVA Biomedical Research Institute, Fundación Investigación Hospital Clínico de Valencia; Department of Genetics, Universitat de València, Valencia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Julio Bobes
- Department of Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio/IBiS/CSIC-Department of Psychiatry, School of Medicine, University of Sevilla, Sevilla, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ana González-Pinto
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Lourdes Fañanás
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Araceli Rosa
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Bárbara Arias
- Departament Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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Monti R, Eick L, Hudjashov G, Läll K, Kanoni S, Wolford BN, Wingfield B, Pain O, Wharrie S, Jermy B, McMahon A, Hartonen T, Heyne H, Mars N, Lambert S, Hveem K, Inouye M, van Heel DA, Mägi R, Marttinen P, Ripatti S, Ganna A, Lippert C. Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning. Am J Hum Genet 2024; 111:1431-1447. [PMID: 38908374 PMCID: PMC11267524 DOI: 10.1016/j.ajhg.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/24/2024] Open
Abstract
Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.
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Affiliation(s)
- Remo Monti
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Eick
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Georgi Hudjashov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - 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
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Benjamin Wingfield
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience; Institute of Psychiatry, Psychology and Neuroscience; King's College London, London, UK
| | - Sophie Wharrie
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Bradley Jermy
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Henrike Heyne
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Nina Mars
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel Lambert
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pekka Marttinen
- Aalto University, Department of Computer Science, Espoo, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Massachusetts General Hospital and Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Lippert
- Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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5
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Grigoroiu-Serbanescu M, van der Veen T, Bigdeli T, Herms S, Diaconu CC, Neagu AI, Bass N, Thygesen J, Forstner AJ, Nöthen MM, McQuillin A. Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder. J Affect Disord 2024; 356:507-518. [PMID: 38640977 DOI: 10.1016/j.jad.2024.04.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 04/21/2024]
Abstract
AIM We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. METHODS We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. RESULTS SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LIMITATIONS The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. CONCLUSION Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.
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Affiliation(s)
- Maria Grigoroiu-Serbanescu
- Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania.
| | - Tracey van der Veen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Tim Bigdeli
- SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Stefan Herms
- Department of Biomedicine, University of Basel, Basel, Switzerland; Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | | | | | - Nicholas Bass
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
| | - Johan Thygesen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine, University Hospital Bonn, Germany
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London, UK
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6
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Høberg A, Solberg BS, Hegvik TA, Haavik J. Using polygenic scores in combination with symptom rating scales to identify attention-deficit/hyperactivity disorder. BMC Psychiatry 2024; 24:471. [PMID: 38937684 PMCID: PMC11210094 DOI: 10.1186/s12888-024-05925-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 06/20/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. METHODS Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. RESULTS The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2ASRS = 61.11%, R2ASRS + PGS=61.69%), the WURS by 0.61pp (R2WURS = 77.33%, R2WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2ASRS + WURS=80.84%, R2ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2family = 28.06%, R2family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. CONCLUSION We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined.
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Affiliation(s)
- André Høberg
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway.
| | - Berit Skretting Solberg
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway
- Child- and adolescent psychiatric outpatient unit, Hospital Betanien, Bergen, Norway
| | - Tor-Arne Hegvik
- Clinic of Surgery, St. Olavs Hospital, Trondheim, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, 5009, Norway
- Bergen Center for Brain Plasticity, Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
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7
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Momin MM, Wray NR, Lee SH. R2ROC: an efficient method of comparing two or more correlated AUC from out-of-sample prediction using polygenic scores. Hum Genet 2024:10.1007/s00439-024-02682-1. [PMID: 38902498 DOI: 10.1007/s00439-024-02682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 05/29/2024] [Indexed: 06/22/2024]
Abstract
Polygenic risk scores (PRSs) enable early prediction of disease risk. Evaluating PRS performance for binary traits commonly relies on the area under the receiver operating characteristic curve (AUC). However, the widely used DeLong's method for comparative significance tests suffer from limitations, including computational time and the lack of a one-to-one mapping between test statistics based on AUC and R 2 . To overcome these limitations, we propose a novel approach that leverages the Delta method to derive the variance and covariance of AUC values, enabling a comprehensive and efficient comparative significance test. Our approach offers notable advantages over DeLong's method, including reduced computation time (up to 150-fold), making it suitable for large-scale analyses and ideal for integration into machine learning frameworks. Furthermore, our method allows for a direct one-to-one mapping between AUC and R 2 values for comparative significance tests, providing enhanced insights into the relationship between these measures and facilitating their interpretation. We validated our proposed approach through simulations and applied it to real data comparing PRSs for diabetes and coronary artery disease (CAD) prediction in a cohort of 28,880 European individuals. The PRSs were derived using genome-wide association study summary statistics from two distinct sources. Our approach enabled a comprehensive and informative comparison of the PRSs, shedding light on their respective predictive abilities for diabetes and CAD. This advancement contributes to the assessment of genetic risk factors and personalized disease prediction, supporting better healthcare decision-making.
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Affiliation(s)
- Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
| | - Naomi R Wray
- Department of Psychiatry, Medical Sciences Division, University of Oxford, Oxford, OX3 7JX, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
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8
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Thorpe HHA, Fontanillas P, Meredith JJ, Jennings MV, Cupertino RB, Pakala S, Elson SL, Khokhar JY, Davis LK, Johnson EC, Palmer AA, Sanchez-Roige S. Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308946. [PMID: 38947071 PMCID: PMC11213095 DOI: 10.1101/2024.06.14.24308946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Cannabis is one of the most widely used drugs globally. Decriminalization of cannabis is further increasing cannabis consumption. We performed genome-wide association studies (GWASs) of lifetime (N=131,895) and frequency (N=73,374) of cannabis use. Lifetime cannabis use GWAS identified two loci, one near CADM2 (rs11922956, p=2.40E-11) and another near GRM3 (rs12673181, p=6.90E-09). Frequency of use GWAS identified one locus near CADM2 (rs4856591, p=8.10E-09; r2 =0.76 with rs11922956). Both traits were heritable and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use associated cannabis use phenotypes in AllofUs participants. Phenome-wide association study of lifetime cannabis use PGS in a hospital cohort replicated associations with substance use and mood disorders, and uncovered associations with celiac and infectious diseases. This work demonstrates the value of GWASs of CUD transition risk factors.
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Affiliation(s)
- Hayley H A Thorpe
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Renata B Cupertino
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Shreya Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | | | | | - Jibran Y Khokhar
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
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9
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Ojima T, Namba S, Suzuki K, Yamamoto K, Sonehara K, Narita A, Kamatani Y, Tamiya G, Yamamoto M, Yamauchi T, Kadowaki T, Okada Y. Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses. Nat Genet 2024; 56:1100-1109. [PMID: 38862855 DOI: 10.1038/s41588-024-01782-y] [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: 07/17/2022] [Accepted: 04/26/2024] [Indexed: 06/13/2024]
Abstract
Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.
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Affiliation(s)
- Takafumi Ojima
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ken Suzuki
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Laboratory of Children's Health and Genetics, Division of Health Science, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Osaka, Japan.
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10
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024; 56:827-837. [PMID: 38632349 PMCID: PMC11096111 DOI: 10.1038/s41588-024-01720-y] [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] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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11
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Holmgren A, Akkouh I, O'Connell KS, Osete JR, Bjørnstad PM, Djurovic S, Hughes T. Bipolar patients display stoichiometric imbalance of gene expression in post-mortem brain samples. Mol Psychiatry 2024; 29:1128-1138. [PMID: 38351171 PMCID: PMC11176081 DOI: 10.1038/s41380-023-02398-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/19/2024]
Abstract
Bipolar disorder is a severe neuro-psychiatric condition where genome-wide association and sequencing studies have pointed to dysregulated gene expression as likely to be causal. We observed strong correlation in expression between GWAS-associated genes and hypothesised that healthy function depends on balance in the relative expression levels of the associated genes and that patients display stoichiometric imbalance. We developed a method for quantifying stoichiometric imbalance and used this to predict each sample's diagnosis probability in four cortical brain RNAseq datasets. The percentage of phenotypic variance on the liability-scale explained by these probabilities ranged from 10.0 to 17.4% (AUC: 69.4-76.4%) which is a multiple of the classification performance achieved using absolute expression levels or GWAS-based polygenic risk scores. Most patients display stoichiometric imbalance in three to ten genes, suggesting that dysregulation of only a small fraction of associated genes can trigger the disorder, with the identity of these genes varying between individuals.
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Affiliation(s)
- Asbjørn Holmgren
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Ibrahim Akkouh
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin Sean O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jordi Requena Osete
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Timothy Hughes
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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12
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Gunn S, Lunetta KL. Correlation-based tests for the formal comparison of polygenic scores in multiple populations. PLoS Genet 2024; 20:e1011249. [PMID: 38669290 PMCID: PMC11078427 DOI: 10.1371/journal.pgen.1011249] [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: 10/24/2023] [Revised: 05/08/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
Polygenic scores (PGS) are measures of genetic risk, derived from the results of genome wide association studies (GWAS). Previous work has proposed the coefficient of determination (R2) as an appropriate measure by which to compare PGS performance in a validation dataset. Here we propose correlation-based methods for evaluating PGS performance by adapting previous work which produced a statistical framework and robust test statistics for the comparison of multiple correlation measures in multiple populations. This flexible framework can be extended to a wider variety of hypothesis tests than currently available methods. We assess our proposed method in simulation and demonstrate its utility with two examples, assessing previously developed PGS for low-density lipoprotein cholesterol and height in multiple populations in the All of Us cohort. Finally, we provide an R package 'coranova' with both parametric and nonparametric implementations of the described methods.
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Affiliation(s)
- Sophia Gunn
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
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13
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Kiltschewskij DJ, Reay WR, Geaghan MP, Atkins JR, Xavier A, Zhang X, Watkeys OJ, Carr VJ, Scott RJ, Green MJ, Cairns MJ. Alteration of DNA Methylation and Epigenetic Scores Associated With Features of Schizophrenia and Common Variant Genetic Risk. Biol Psychiatry 2024; 95:647-661. [PMID: 37480976 DOI: 10.1016/j.biopsych.2023.07.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Unpacking molecular perturbations associated with features of schizophrenia is a critical step toward understanding phenotypic heterogeneity in this disorder. Recent epigenome-wide association studies have uncovered pervasive dysregulation of DNA methylation in schizophrenia; however, clinical features of the disorder that account for a large proportion of phenotypic variability are relatively underexplored. METHODS We comprehensively analyzed patterns of DNA methylation in a cohort of 381 individuals with schizophrenia from the deeply phenotyped Australian Schizophrenia Research Bank. Epigenetic changes were investigated in association with cognitive status, age of onset, treatment resistance, Global Assessment of Functioning scores, and common variant polygenic risk scores for schizophrenia. We subsequently explored alterations within genes previously associated with psychiatric illness, phenome-wide epigenetic covariance, and epigenetic scores. RESULTS Epigenome-wide association studies of the 5 primary traits identified 662 suggestively significant (p < 6.72 × 10-5) differentially methylated probes, with a further 432 revealed after controlling for schizophrenia polygenic risk on the remaining 4 traits. Interestingly, we uncovered many probes within genes associated with a variety of psychiatric conditions as well as significant epigenetic covariance with phenotypes and exposures including acute myocardial infarction, C-reactive protein, and lung cancer. Epigenetic scores for treatment-resistant schizophrenia strikingly exhibited association with clozapine administration, while epigenetic proxies of plasma protein expression, such as CCL17, MMP10, and PRG2, were associated with several features of schizophrenia. CONCLUSIONS Our findings collectively provide novel evidence suggesting that several features of schizophrenia are associated with alteration of DNA methylation, which may contribute to interindividual phenotypic variation in affected individuals.
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Affiliation(s)
- Dylan J Kiltschewskij
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Michael P Geaghan
- Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
| | - Alexandre Xavier
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Xiajie Zhang
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Oliver J Watkeys
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia; Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton, New South Wales, Australia
| | - Melissa J Green
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia; Precision Medicine Program, Hunter Medical Research Institute, New Lambton, New South Wales, Australia.
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14
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Wong THT, Mo JMY, Zhou M, Zhao JV, Schooling CM, He B, Luo S, Au Yeung SL. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits. Commun Biol 2024; 7:293. [PMID: 38459184 PMCID: PMC10923832 DOI: 10.1038/s42003-024-05977-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.
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Affiliation(s)
- Tommy H T Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jacky M Y Mo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mingqi Zhou
- Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, CA, USA
- Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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15
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Hess JL, Mattheisen M, Greenwood TA, Tsuang MT, Edenberg HJ, Holmans P, Faraone SV, Glatt SJ. A polygenic resilience score moderates the genetic risk for schizophrenia: Replication in 18,090 cases and 28,114 controls from the Psychiatric Genomics Consortium. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32957. [PMID: 37551635 PMCID: PMC10850427 DOI: 10.1002/ajmg.b.32957] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023]
Abstract
Identifying heritable factors that moderate the genetic risk for schizophrenia (SCZ) could help clarify why some individuals remain unaffected despite having relatively high genetic liability. Previously, we developed a framework to mine genome-wide association (GWAS) data for common genetic variants that protect high-risk unaffected individuals from SCZ, leading to derivation of the first-ever "polygenic resilience score" for SCZ (resilient controls n = 3786; polygenic risk score-matched SCZ cases n = 18,619). Here, we performed a replication study to verify the moderating effect of our polygenic resilience score on SCZ risk (OR = 1.09, p = 4.03 × 10-5 ) using newly released GWAS data from 23 independent case-control studies collated by the Psychiatric Genomics Consortium (PGC) (resilient controls n = 2821; polygenic risk score-matched SCZ cases n = 5150). Additionally, we sought to optimize our polygenic resilience-scoring formula to improve subsequent modeling of resilience to SCZ and other complex disorders. We found significant replication of the polygenic resilience score, and found that strict pruning of SNPs based on linkage disequilibrium to known risk SNPs and their linked loci optimizes the performance of the polygenic resilience score.
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Affiliation(s)
- Jonathan L. Hess
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Manuel Mattheisen
- Departments of Psychiatry and Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada
| | | | | | - Ming T. Tsuang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Peter Holmans
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Stephen V. Faraone
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J. Glatt
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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16
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Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, Price AL. Distinguishing different psychiatric disorders using DDx-PRS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302228. [PMID: 38352307 PMCID: PMC10862992 DOI: 10.1101/2024.02.02.24302228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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17
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Cornish N, Haycock P, Brenner H, Figueiredo JC, Galesloot TE, Grant RC, Johansson M, Mariosa D, McKay J, Pai R, Pellatt AJ, Samadder NJ, Shi J, Thibord F, Trégouët DA, Voegele C, Thirlwell C, Mumford A, Langdon R. Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomization analysis. Int J Epidemiol 2024; 53:dyad170. [PMID: 38124529 PMCID: PMC10859161 DOI: 10.1093/ije/dyad170] [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] [Received: 05/16/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND People with cancer experience high rates of venous thromboembolism (VTE). Risk of subsequent cancer is also increased in people experiencing their first VTE. The causal mechanisms underlying this association are not completely understood, and it is unknown whether VTE is itself a risk factor for cancer. METHODS We used data from large genome-wide association study meta-analyses to perform bidirectional Mendelian randomization analyses to estimate causal associations between genetic liability to VTE and risk of 18 different cancers. RESULTS We found no conclusive evidence that genetic liability to VTE was causally associated with an increased incidence of cancer, or vice versa. We observed an association between liability to VTE and pancreatic cancer risk [odds ratio for pancreatic cancer: 1.23 (95% confidence interval: 1.08-1.40) per log-odds increase in VTE risk, P = 0.002]. However, sensitivity analyses revealed this association was predominantly driven by a variant proxying non-O blood group, with inadequate evidence to suggest a causal relationship. CONCLUSIONS These findings do not support the hypothesis that genetic liability to VTE is a cause of cancer. Existing observational epidemiological associations between VTE and cancer are therefore more likely to be driven by pathophysiological changes which occur in the setting of active cancer and anti-cancer treatments. Further work is required to explore and synthesize evidence for these mechanisms.
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Affiliation(s)
- Naomi Cornish
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Philip Haycock
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tessel E Galesloot
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert C Grant
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Daniela Mariosa
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - James McKay
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rish Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, AZ, USA
| | - Andrew J Pellatt
- Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Florian Thibord
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA
| | | | - Catherine Voegele
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Chrissie Thirlwell
- University of Exeter Medical School, University of Exeter, Exeter, UK
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew Mumford
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ryan Langdon
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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18
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Uffelmann E, Price AL, Posthuma D, Peyrot WJ. Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301157. [PMID: 38260678 PMCID: PMC10802765 DOI: 10.1101/2024.01.12.24301157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.
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Affiliation(s)
- Emil Uffelmann
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
| | | | | | - Alkes L. Price
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex, Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Wouter J. Peyrot
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam
- Department of Psychiatry, Amsterdam UMC, The Netherlands
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Chung RH, Chuang SY, Zhuang YS, Jhang YS, Huang TH, Li GH, Chang IS, Hsiung CA, Chiou HY. Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank. HGG ADVANCES 2024; 5:100260. [PMID: 38053338 PMCID: PMC10777116 DOI: 10.1016/j.xhgg.2023.100260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 12/07/2023] Open
Abstract
Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.
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Affiliation(s)
- Ren-Hua Chung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
| | - Shao-Yuan Chuang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Yong-Sheng Zhuang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Yi-Syuan Jhang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Tsung-Hsien Huang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Guo-Hung Li
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Chao A Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan; School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
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20
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Nakase T, Guerra G, Ostrom QT, Ge T, Melin B, Wrensch M, Wiencke JK, Jenkins RB, Eckel-Passow JE, Bondy ML, Francis SS, Kachuri L. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301112. [PMID: 38260701 PMCID: PMC10802631 DOI: 10.1101/2024.01.10.24301112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.
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Affiliation(s)
- Taishi Nakase
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Geno Guerra
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Robert B. Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Melissa L. Bondy
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Francis
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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21
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Riesmeijer SA, Kamali Z, Ng M, Drichel D, Piersma B, Becker K, Layton TB, Nanchahal J, Nothnagel M, Vaez A, Hennies HC, Werker PMN, Furniss D, Nolte IM. A genome-wide association meta-analysis implicates Hedgehog and Notch signaling in Dupuytren's disease. Nat Commun 2024; 15:199. [PMID: 38172110 PMCID: PMC10764787 DOI: 10.1038/s41467-023-44451-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
Dupuytren's disease (DD) is a highly heritable fibrotic disorder of the hand with incompletely understood etiology. A number of genetic loci, including Wnt signaling members, have been previously identified. Our overall aim was to identify novel genetic loci, to prioritize genes within the loci for functional studies, and to assess genetic correlation with associated disorders. We performed a meta-analysis of six DD genome-wide association studies from three European countries and extensive bioinformatic follow-up analyses. Leveraging 11,320 cases and 47,023 controls, we identified 85 genome-wide significant single nucleotide polymorphisms in 56 loci, of which 11 were novel, explaining 13.3-38.1% of disease variance. Gene prioritization implicated the Hedgehog and Notch signaling pathways. We also identified a significant genetic correlation with frozen shoulder. The pathways identified highlight the potential for new therapeutic targets and provide a basis for additional mechanistic studies for a common disorder that can severely impact hand function.
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Affiliation(s)
- Sophie A Riesmeijer
- University of Groningen, University Medical Center Groningen, Department of Plastic Surgery, Groningen, The Netherlands.
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands.
| | - Zoha Kamali
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
- Department of bioinformatics, School of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Michael Ng
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Dmitriy Drichel
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
- Faculty of Medicine and the Cologne University Hospital, Cologne, Germany
| | - Bram Piersma
- University of Groningen, Groningen, The Netherlands
| | - Kerstin Becker
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | | | | | - Michael Nothnagel
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
- Faculty of Medicine and the Cologne University Hospital, Cologne, Germany
| | - Ahmad Vaez
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
- Department of bioinformatics, School of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hans Christian Hennies
- Faculty of Medicine and the Cologne University Hospital, Cologne, Germany
- Department of Biological Sciences, University of Huddersfield, Huddersfield, UK
| | - Paul M N Werker
- University of Groningen, University Medical Center Groningen, Department of Plastic Surgery, Groningen, The Netherlands
| | - Dominic Furniss
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Science, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Ilja M Nolte
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
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22
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Fang F, Quach B, Lawrence KG, van Dongen J, Marks JA, Lundgren S, Lin M, Odintsova VV, Costeira R, Xu Z, Zhou L, Mandal M, Xia Y, Vink JM, Bierut LJ, Ollikainen M, Taylor JA, Bell JT, Kaprio J, Boomsma DI, Xu K, Sandler DP, Hancock DB, Johnson EO. Trans-ancestry epigenome-wide association meta-analysis of DNA methylation with lifetime cannabis use. Mol Psychiatry 2024; 29:124-133. [PMID: 37935791 PMCID: PMC11078760 DOI: 10.1038/s41380-023-02310-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9436 participants (7795 European and 1641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 ( p < 5.85 × 10 - 7 ) : cg22572071 near gene ADGRF1, cg15280358 in ADAM12, cg00813162 in ACTN1, and cg01101459 near LINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated to APOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.
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Affiliation(s)
- Fang Fang
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA.
| | - Bryan Quach
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Kaitlyn G Lawrence
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jesse A Marks
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Sara Lundgren
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Mingkuan Lin
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ricardo Costeira
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Zongli Xu
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Linran Zhou
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Meisha Mandal
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Yujing Xia
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jacqueline M Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, St. Louis, MO, USA
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jordana T Bell
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Dana B Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Eric O Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
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23
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Hübel C, Abdulkadir M, Herle M, Palmos AB, Loos RJF, Breen G, Micali N, Bulik CM. Persistent thinness and anorexia nervosa differ on a genomic level. Eur J Hum Genet 2024; 32:117-124. [PMID: 37474786 PMCID: PMC10772076 DOI: 10.1038/s41431-023-01431-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 06/15/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023] Open
Abstract
Thinness and anorexia nervosa are both characterised by persistent low weight. Individuals with anorexia nervosa concurrently report distorted perceptions of their body and engage in weight-loss behaviours, whereas individuals with thinness often wish to gain weight. Both conditions are heritable and share genomics with BMI, but are not genetically correlated with each other. Based on their pattern of genetic associations with other traits, we explored differences between thinness and anorexia nervosa on a genomic level. In Part 1, using publicly available data, we compared genetic correlations of persistent thinness/anorexia nervosa with eleven psychiatric disorders. In Part 2, we identified individuals with adolescent persistent thinness in the Avon Longitudinal Study of Parents and Children (ALSPAC) by latent class growth analysis of measured BMI from 10 to 24 years (n = 6594) and evaluated associations with psychiatric and anthropometric polygenic scores. In Part 1, in contrast to the positive genetic correlations of anorexia nervosa with various psychiatric disorders, persistent thinness showed negative genetic correlations with attention deficit hyperactivity disorder (rgAN = 0.08 vs. rgPT = -0.30), alcohol dependence (rgAN = 0.07 vs. rgPT = -0.44), major depressive disorder (rgAN = 0.27 vs. rgPT = -0.18) and post-traumatic stress disorder (rgAN = 0.26 vs. rgPT = -0.20). In Part 2, individuals with adolescent persistent thinness in the ALSPAC had lower borderline personality disorder polygenic scores (OR = 0.77; Q = 0.01). Overall, results suggest that genetic variants associated with thinness are negatively associated with psychiatric disorders and therefore thinness may be differentiable from anorexia nervosa on a genomic level.
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Affiliation(s)
- Christopher Hübel
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK.
- National Centre for Register-based Research, Aarhus Business and Social Sciences, Aarhus University, Aarhus, Denmark.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Department of Pediatric Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Mohamed Abdulkadir
- National Centre for Register-based Research, Aarhus Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Moritz Herle
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alish B Palmos
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Gerome Breen
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Nadia Micali
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Mental Health Services in the Capital Region of Denmark, Eating Disorders Research Unit, Psychiatric Centre Ballerup, Ballerup, Denmark
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Al Ali L, van de Vegte YJ, Said MA, Groot HE, Hendriks T, Yeung MW, Lipsic E, van der Harst P. Fetuin-A and its genetic association with cardiometabolic disease. Sci Rep 2023; 13:21469. [PMID: 38052855 PMCID: PMC10697970 DOI: 10.1038/s41598-023-48600-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 11/28/2023] [Indexed: 12/07/2023] Open
Abstract
Fetuin-A acts as both an inhibitor of calcification and insulin signaling. Previous studies reported conflicting results on the association between fetuin-A and cardiometabolic diseases. We aim to provide further insights into the association between genetically predicted levels of fetuin-A and cardiometabolic diseases using a Mendelian randomization strategy. Genetic variants associated with fetuin-A and their effect sizes were obtained from previous genetic studies. A series of two-sample Mendelian randomization analyses in 412,444 unrelated individuals from the UK Biobank did not show evidence for an association of genetically predicted fetuin-A with any stroke, ischemic stroke, or myocardial infarction. We do find that increased levels of genetically predicted fetuin-A are associated with increased risk of type 2 diabetes (OR = 1.21, 95%CI 1.13-1.30, P = < 0.01). Furthermore, genetically predicted fetuin-A increases the risk of coronary artery disease in individuals with type 2 diabetes, but we did not find evidence for an association between genetically predicted fetuin-A and coronary artery disease in those without type 2 diabetes (P for interaction = 0.03). One SD increase in genetically predicted fetuin-A decreases risk of myocardial infarction in women, but we do not find evidence for an association between genetically predicted fetuin-A and myocardial infarction in men (P for interaction = < 0.01). Genetically predicted fetuin-A is associated with type 2 diabetes. Furthermore, type 2 diabetes status modifies the association of genetically predicted fetuin-A with coronary artery disease, indicating that fetuin-A increases risk in individuals with type 2 diabetes. Finally, higher genetically predicted fetuin-A reduces the risk of myocardial infarction in women, but we do not find evidence for an association between genetically predicted fetuin-A and myocardial infarction in men.
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Affiliation(s)
- Lawien Al Ali
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands.
| | - Yordi J van de Vegte
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - M Abdullah Said
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Hilde E Groot
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Tom Hendriks
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Ming Wai Yeung
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Erik Lipsic
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, the Netherlands
- Department of Heart and Lungs, University of Utrecht, University Medical Center Utrecht, Utrecht, the Netherlands
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25
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Moll M, Sordillo JE, Ghosh AJ, Hayden LP, McDermott G, McGeachie MJ, Dahlin A, Tiwari A, Manmadkar MG, Abston ED, Pavuluri C, Saferali A, Begum S, Ziniti JP, Gulsvik A, Bakke PS, Aschard H, Iribarren C, Hersh CP, Sparks JA, Hobbs BD, Lasky-Su JA, Silverman EK, Weiss ST, Wu AC, Cho MH. Polygenic risk scores identify heterogeneity in asthma and chronic obstructive pulmonary disease. J Allergy Clin Immunol 2023; 152:1423-1432. [PMID: 37595761 PMCID: PMC10841234 DOI: 10.1016/j.jaci.2023.08.002] [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] [Received: 01/11/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND Asthma and chronic obstructive pulmonary disease (COPD) have distinct and overlapping genetic and clinical features. OBJECTIVE We sought to test the hypothesis that polygenic risk scores (PRSs) for asthma (PRSAsthma) and spirometry (FEV1 and FEV1/forced vital capacity; PRSspiro) would demonstrate differential associations with asthma, COPD, and asthma-COPD overlap (ACO). METHODS We developed and tested 2 asthma PRSs and applied the higher performing PRSAsthma and a previously published PRSspiro to research (Genetic Epidemiology of COPD study and Childhood Asthma Management Program, with spirometry) and electronic health record-based (Mass General Brigham Biobank and Genetic Epidemiology Research on Adult Health and Aging [GERA]) studies. We assessed the association of PRSs with COPD and asthma using modified random-effects and binary-effects meta-analyses, and ACO and asthma exacerbations in specific cohorts. Models were adjusted for confounders and genetic ancestry. RESULTS In meta-analyses of 102,477 participants, the PRSAsthma (odds ratio [OR] per SD, 1.16 [95% CI, 1.14-1.19]) and PRSspiro (OR per SD, 1.19 [95% CI, 1.17-1.22]) both predicted asthma, whereas the PRSspiro predicted COPD (OR per SD, 1.25 [95% CI, 1.21-1.30]). However, results differed by cohort. The PRSspiro was not associated with COPD in GERA and Mass General Brigham Biobank. In the Genetic Epidemiology of COPD study, the PRSAsthma (OR per SD: Whites, 1.3; African Americans, 1.2) and PRSspiro (OR per SD: Whites, 2.2; African Americans, 1.6) were both associated with ACO. In GERA, the PRSAsthma was associated with asthma exacerbations (OR, 1.18) in Whites; the PRSspiro was associated with asthma exacerbations in White, LatinX, and East Asian participants. CONCLUSIONS PRSs for asthma and spirometry are both associated with ACO and asthma exacerbations. Genetic prediction performance differs in research versus electronic health record-based cohorts.
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Affiliation(s)
- Matthew Moll
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Joanne E Sordillo
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass
| | - Auyon J Ghosh
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, SUNY Upstate Medical Center, Syracuse, NY
| | - Lystra P Hayden
- Department of Pediatrics, Division of Pulmonary Medicine, Boston Children's Hospital, Harvard Medical School, Massachusetts General Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Gregory McDermott
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Mass
| | - Michael J McGeachie
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Amber Dahlin
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Anshul Tiwari
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Monica G Manmadkar
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Eric D Abston
- Department of Thoracic Surgery, Massachusetts General Hospital, Boston, Mass
| | - Chandan Pavuluri
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Aabida Saferali
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Sofina Begum
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - John P Ziniti
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Amund Gulsvik
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Universit de Paris, Paris, France
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente Northern California, Oakland, Calif
| | - Craig P Hersh
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Jeffrey A Sparks
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Mass
| | - Brian D Hobbs
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass
| | - Jessica A Lasky-Su
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Edwin K Silverman
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Scott T Weiss
- Harvard Medical School, Brigham and Women's Hospital, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Mass
| | - Ann Chen Wu
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass
| | - Michael H Cho
- Department of Medicine, Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Harvard Medical School, Boston, Mass; Harvard Medical School, Brigham and Women's Hospital, Boston, Mass.
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He Y, Koido M, Sutoh Y, Shi M, Otsuka-Yamasaki Y, Munter HM, Morisaki T, Nagai A, Murakami Y, Tanikawa C, Hachiya T, Matsuda K, Shimizu A, Kamatani Y. East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease. Nat Genet 2023; 55:2129-2138. [PMID: 38036781 PMCID: PMC10703676 DOI: 10.1038/s41588-023-01569-7] [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] [Received: 11/01/2022] [Accepted: 10/12/2023] [Indexed: 12/02/2023]
Abstract
Peptic ulcer disease (PUD) refers to acid-induced injury of the digestive tract, occurring mainly in the stomach (gastric ulcer (GU)) or duodenum (duodenal ulcer (DU)). In the present study, we conducted a large-scale, cross-ancestry meta-analysis of PUD combining genome-wide association studies with Japanese and European studies (52,032 cases and 905,344 controls), and discovered 25 new loci highly concordant across ancestries. An examination of GU and DU genetic architecture demonstrated that GUs shared the same risk loci as DUs, although with smaller genetic effect sizes and higher polygenicity than DUs, indicating higher heterogeneity of GUs. Helicobacter pylori (HP)-stratified analysis found an HP-related host genetic locus. Integrative analyses using bulk and single-cell transcriptome profiles highlighted the genetic factors of PUD being enriched in the highly expressed genes in stomach tissues, especially in somatostatin-producing D cells. Our results provide genetic evidence that gastrointestinal cell differentiations and hormone regulations are critical in PUD etiology.
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Affiliation(s)
- Yunye He
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoichi Sutoh
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Mingyang Shi
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine and Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Takayuki Morisaki
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Akiko Nagai
- Department of Public Policy, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Hachiya
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
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27
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Gharahkhani P, He W, Han X, Ong JS, Rentería ME, Wiggs JL, Khawaja AP, Trzaskowski M, Mackey DA, Craig JE, Hewitt AW, MacGregor S, Wu Y. WITHDRAWN: Genome-wide risk prediction of primary open-angle glaucoma across multiple ancestries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.08.23298255. [PMID: 37986775 PMCID: PMC10659472 DOI: 10.1101/2023.11.08.23298255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
This manuscript has been withdrawn by medRxiv following a formal request by the QIMR Berghofer Medical Research Institute Research Integrity Office owing to lack of author consent.
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28
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Wu D, Liu B, Xian W, Yang Y, Li J, Hong S, Li Y, Xiao H. New insight into the causal relationship between Graves' disease liability and drug eruption: a Mendelian randomization study. Front Immunol 2023; 14:1267814. [PMID: 38077385 PMCID: PMC10703291 DOI: 10.3389/fimmu.2023.1267814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
Background Graves' disease (GD) and drug eruption are closely associated and frequently observed in the clinical setting. However, it remains unclear whether a causal relationship exists between these two conditions. The aim of the study is to investigate whether GD is causal to drug eruptions using two-sample Mendelian randomization. Methods We launched a two-sample MR to investigate whether GD is causal to drug eruption using Genome-wide association study (GWAS) summary data from Biobank Japan and FinnGen. Genetic variants were used as instrumental variables to avoid confounding bias. Statistical methods including inverse variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO were conducted to identify the robustness of the causal effect. Results Genetically predicted GD may increase the risk of drug eruption by 30.3% (OR=1.303, 95% CI 1.119-1.516, p<0.001) in the Asian population. In European populations, GD may increase the generalized drug eruption by 15.9% (OR=1.159, 95%CI 0.982-1.367, p=0.080). Conclusions We found GD is potentially causal to drug eruption. This finding expanded the view of the frequently observed co-existence of GD and adverse drug reactions involving the skin. The mechanism remains for further investigation.
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Affiliation(s)
- Dide Wu
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Boyuan Liu
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wei Xian
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuxin Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jinjian Li
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shubin Hong
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanbing Li
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Haipeng Xiao
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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29
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Patel AP, Fahed AC. Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations. Curr Protoc 2023; 3:e911. [PMID: 37921506 PMCID: PMC11196001 DOI: 10.1002/cpz1.911] [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: 11/04/2023]
Abstract
Polygenic risk scores (PRS) estimate genetic susceptibility of an individual to disease and have the potential of providing utility in multiple clinical contexts. However, their performance, computation, and reporting in diverse populations remain challenging. Here, we present a pragmatic approach to optimize a PRS for a population of interest that leverages publicly available data and methods and consists of seven steps that are easily implemented without the requirement of expertise in complex genetics: step 1, selecting source genome-wide association studies (GWAS) and imputation; step 2, selecting methods to compute polygenic score; step 3, adjusting scores using principal components of genetic ancestry; step 4, selecting the best performing score; step 5, defining percentiles of a population distribution; step 6, validating performance of the optimized polygenic score; and step 7, implementing the optimized polygenic score in clinical practice. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Aniruddh P Patel
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Akl C Fahed
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
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30
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Dapas M, Lee YL, Wentworth-Sheilds W, Im HK, Ober C, Schoettler N. Revealing polygenic pleiotropy using genetic risk scores for asthma. HGG ADVANCES 2023; 4:100233. [PMID: 37663543 PMCID: PMC10474095 DOI: 10.1016/j.xhgg.2023.100233] [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] [Received: 06/02/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Yu Lin Lee
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Nathan Schoettler
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
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31
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Cuellar-Barboza AB, Prieto ML, Coombes BJ, Gardea-Resendez M, Núñez N, Winham SJ, Romo-Nava F, González S, McElroy SL, Frye MA, Biernacka JM. Polygenic prediction of bipolar disorder in a Latin American sample. Am J Med Genet B Neuropsychiatr Genet 2023; 192:139-146. [PMID: 36919637 DOI: 10.1002/ajmg.b.32936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/31/2023] [Accepted: 03/01/2023] [Indexed: 03/16/2023]
Abstract
To date, bipolar disorder (BD) genetic studies and polygenic risk scores (PRSs) for BD are based primarily on populations of European descent (EUR) and lack representation from other ancestries including Latin American (LAT). Here, we describe a new LAT cohort from the Mayo Clinic Bipolar Biobank (MCBB), a multisite collaboration with recruitment sites in the United States (EUR; 1,443 cases and 777 controls) and Mexico and Chile (LAT; 211 cases and 161 controls) and use the sample to explore the performance of a BD-PRS in a LAT population. Using results from the largest genome-wide association study of BD in EUR individuals, PRSice2 and LDpred2 were used to compute BD-PRSs in the LAT and EUR samples from the MCBB. PRSs explained up to 1.4% (PRSice) and 4% (LDpred2) of the phenotypic variance on the liability scale in the LAT sample compared to 3.8% (PRSice2) and 3.4% (LDpred2) in the EUR samples. Future larger studies should further explore the differential performance of different PRS approaches across ancestries. International multisite studies, such as this one, have the potential to address diversity-related limitations of prior genomic studies and ultimately contribute to the reduction of health disparities.
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Affiliation(s)
- Alfredo B Cuellar-Barboza
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Psychiatry, Universidad Autonoma de Nuevo Leon, Monterrey, Mexico
| | - Miguel L Prieto
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Psychiatry, Universidad de los Andes, Santiago, Chile
- Mental Health Service, Clinica Universidad de los Andes, Santiago, Chile
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Nicolás Núñez
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Stacey J Winham
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Sarai González
- Department of Psychiatry, Universidad Autonoma de Nuevo Leon, Monterrey, Mexico
| | - Susan L McElroy
- Lindner Center of HOPE/University of Cincinnati, Cincinnati, Ohio, USA
| | - Mark A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
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32
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Mohammad S, de Ruijter MJT, Rukh G, Rask-Andersen M, Mwinyi J, Schiöth HB. Well-being spectrum traits are associated with polygenic scores for autism. Autism Res 2023; 16:1891-1902. [PMID: 37602645 DOI: 10.1002/aur.3011] [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] [Received: 04/27/2023] [Accepted: 07/29/2023] [Indexed: 08/22/2023]
Abstract
Individuals with autism spectrum disorder (ASD) tend to experience lower well-being as demonstrated mostly for children and adolescents in epidemiological studies. A further investigation of inclusive well-being, in terms of five well-being spectrum (5-WBS) traits including neuroticism, depression, loneliness, life satisfaction, and positive affect, among adults with ASD may deepen our understanding of their well-being, and lead to the possibility to further modify societal supportive mechanisms for individuals with ASD. This study aims to investigate if a genetic predisposition for ASD is associated with 5-WBS traits using polygenic risk score (PRS) analysis. PRS for ASD were calculated based on the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and were created in the independent cohort UK Biobank. Regression analyses were performed to investigate the association between ASD PRS and 5-WBS traits in the UK Biobank population including 337,423 individuals. ASD PRS were significantly associated with all 5-WBS traits, showing a positive association with the negative WBS traits, neuroticism (max R2 = 0.04%, p < 1 × 10-4 ), depression (max R2 = 0.06%, p < 1 × 10-4 ), loneliness (max R2 = 0.04%, p < 1 × 10-4 ), and a negative association with the positive WBS traits, life satisfaction (max R2 = 0.08%, p < 1 × 10-4 ), positive affect (max R2 = 0.10%, p < 1 × 10-4 ). The findings suggest that adults carrying a high load of risk single nucleotide peptides (SNPs) for ASD are more likely to report decreased well-being. The study demonstrates a considerable connection between susceptibility to ASD, its underlying genetic etiology and well-being.
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Affiliation(s)
- Salahuddin Mohammad
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Markus J T de Ruijter
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Mathias Rask-Andersen
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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Blostein F, Zou T, Bhaumik D, Salzman E, Bakulski K, Shaffer J, Marazita M, Foxman B. Bacterial Community Modifies Host Genetics Effect on Early Childhood Caries. J Dent Res 2023; 102:1098-1105. [PMID: 37395259 PMCID: PMC10552462 DOI: 10.1177/00220345231175356] [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: 07/04/2023] Open
Abstract
By age 5, approximately one-fifth of children have early childhood caries (ECC). Both the oral microbiome and host genetics are thought to influence susceptibility. Whether the oral microbiome modifies genetic susceptibility to ECC has not been tested. We test whether the salivary bacteriome modifies the association of a polygenic score (PGS, a score derived from genomic data that summarizes genetic susceptibility to disease) for primary tooth decay on ECC in the Center for Oral Health Research in Appalachia 2 longitudinal birth cohort. Children were genotyped using the Illumina Multi-Ethnic Genotyping Array and underwent annual dental examinations. We constructed a PGS for primary tooth decay using weights from an independent, genome-wide association meta-analysis. Using Poisson regression, we tested for associations between the PGS (high versus low) and ECC incidence, adjusting for demographic characteristics (n = 783). An incidence-density sampled subset of the cohort (n = 138) had salivary bacteriome data at 24 mo of age. We tested for effect modification of the PGS on ECC case status by salivary bacterial community state type (CST). By 60 mo, 20.69% of children had ECC. High PGS was not associated with an increased rate of ECC (incidence rate ratio, 1.09; 95% confidence interval [CI], 0.83-1.42). However, having a cariogenic salivary bacterial CST at 24 mo was associated with ECC (odds ratio [OR], 7.48; 95% CI, 3.06-18.26), which was robust to PGS adjustment. An interaction existed between the salivary bacterial CST and the PGS on the multiplicative scale (P = 0.04). The PGS was associated with ECC (OR, 4.83; 95% CI, 1.29-18.17) only among individuals with a noncariogenic salivary bacterial CST (n = 70). Genetic causes of caries may be harder to detect when not accounting for cariogenic oral microbiomes. As certain salivary bacterial CSTs increased ECC risk across genetic risk strata, preventing colonization of cariogenic microbiomes would be universally beneficial.
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Affiliation(s)
- F. Blostein
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - T. Zou
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - D. Bhaumik
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - E. Salzman
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - K.M. Bakulski
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - J.R. Shaffer
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - M.L. Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Sciences Institute, and Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - B. Foxman
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, MI, USA
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34
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Momen M, Brauer K, Patterson MM, Sample SJ, Binversie EE, Davis BW, Cothran EG, Rosa GJM, Brounts SH, Muir P. Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse. Front Genet 2023; 14:1201628. [PMID: 37645058 PMCID: PMC10460910 DOI: 10.3389/fgene.2023.1201628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/07/2023] [Indexed: 08/31/2023] Open
Abstract
Introduction: Spontaneous rupture of tendons and ligaments is common in several species including humans. In horses, degenerative suspensory ligament desmitis (DSLD) is an important acquired idiopathic disease of a major energy-storing tendon-like structure. DSLD risk is increased in several breeds, including the Peruvian Horse. Affected horses have often been used for breeding before the disease is apparent. Breed predisposition suggests a substantial genetic contribution, but heritability and genetic architecture of DSLD have not been determined. Methods: To identify genomic regions associated with DSLD, we recruited a reference population of 183 Peruvian Horses, phenotyped as DSLD cases or controls, and undertook a genome-wide association study (GWAS), a regional window variance analysis using local genomic partitioning, a signatures of selection (SOS) analysis, and polygenic risk score (PRS) prediction of DSLD risk. We also estimated trait heritability from pedigrees. Results: Heritability was estimated in a population of 1,927 Peruvian horses at 0.22 ± 0.08. After establishing a permutation-based threshold for genome-wide significance, 151 DSLD risk single nucleotide polymorphisms (SNPs) were identified by GWAS. Multiple regions of enriched local heritability were identified across the genome, with strong enrichment signals on chromosomes 1, 2, 6, 10, 13, 16, 18, 22, and the X chromosome. With SOS analysis, there were 66 genes with a selection signature in DSLD cases that was not present in the control group that included the TGFB3 gene. Pathways enriched in DSLD cases included proteoglycan metabolism, extracellular matrix homeostasis, and signal transduction pathways that included the hedgehog signaling pathway. The best PRS predictive performance was obtained when we fitted 1% of top SNPs using a Bayesian Ridge Regression model which achieved the highest mean of R2 on both the probit and logit liability scales, indicating a strong predictive performance. Discussion: We conclude that within-breed GWAS of DSLD in the Peruvian Horse has further confirmed that moderate heritability and a polygenic architecture underlies the trait and identified multiple DSLD SNP associations in novel tendinopathy candidate genes influencing disease risk. Pathways enriched with DSLD risk variants include ones that influence glycosaminoglycan metabolism, extracellular matrix homeostasis, signal transduction pathways.
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Affiliation(s)
- Mehdi Momen
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Kiley Brauer
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Margaret M. Patterson
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Susannah J. Sample
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Emily E. Binversie
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Brian W. Davis
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - E. Gus Cothran
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Guilherme J. M. Rosa
- Department of Animal and Dairy Sciences, College of Agriculture and Life Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Sabrina H. Brounts
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Peter Muir
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States
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35
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Albiñana C, Zhu Z, Schork AJ, Ingason A, Aschard H, Brikell I, Bulik CM, Petersen LV, Agerbo E, Grove J, Nordentoft M, Hougaard DM, Werge T, Børglum AD, Mortensen PB, McGrath JJ, Neale BM, Privé F, Vilhjálmsson BJ. Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nat Commun 2023; 14:4702. [PMID: 37543680 PMCID: PMC10404269 DOI: 10.1038/s41467-023-40330-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/21/2023] [Indexed: 08/07/2023] Open
Abstract
The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.
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Affiliation(s)
- Clara Albiñana
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark.
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark.
| | - Zhihong Zhu
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Andrew J Schork
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
- The Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Andrés Ingason
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université de Paris, 25-28 Rue du Dr Roux, 75015, Paris, France
| | - Isabell Brikell
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
| | - Liselotte V Petersen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Center for Genomics and Personalized Medicine, Aarhus University, 8000, Aarhus C, Denmark
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Copenhagen Research Centre on Mental Health (CORE), University of Copenhagen, Copenhagen, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, 2300, Copenhagen S, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Copenhagen, 2100, Denmark
- Lundbeck Foundation Centre for GeoGenetics, GLOBE Institute, University of Copenhagen, 1350, Copenhagen K, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- Department of Biomedicine and Center for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus C, Denmark
- Center for Genomics and Personalized Medicine, Aarhus University, 8000, Aarhus C, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD, 4076, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Benjamin M Neale
- 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
| | - Florian Privé
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210, Aarhus V, Denmark.
- National Centre for Register-Based Research, Aarhus University, 8210, Aarhus V, Denmark.
- Bioinformatics Research Centre, Aarhus University, 8000, Aarhus C, Denmark.
- Novo Nordisk Foundation Center for Genomic Mechanisms, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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36
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van de Vegte YJ, Eppinga RN, van der Ende MY, Hagemeijer YP, Mahendran Y, Salfati E, Smith AV, Tan VY, Arking DE, Ntalla I, Appel EV, Schurmann C, Brody JA, Rueedi R, Polasek O, Sveinbjornsson G, Lecoeur C, Ladenvall C, Zhao JH, Isaacs A, Wang L, Luan J, Hwang SJ, Mononen N, Auro K, Jackson AU, Bielak LF, Zeng L, Shah N, Nethander M, Campbell A, Rankinen T, Pechlivanis S, Qi L, Zhao W, Rizzi F, Tanaka T, Robino A, Cocca M, Lange L, Müller-Nurasyid M, Roselli C, Zhang W, Kleber ME, Guo X, Lin HJ, Pavani F, Galesloot TE, Noordam R, Milaneschi Y, Schraut KE, den Hoed M, Degenhardt F, Trompet S, van den Berg ME, Pistis G, Tham YC, Weiss S, Sim XS, Li HL, van der Most PJ, Nolte IM, Lyytikäinen LP, Said MA, Witte DR, Iribarren C, Launer L, Ring SM, de Vries PS, Sever P, Linneberg A, Bottinger EP, Padmanabhan S, Psaty BM, Sotoodehnia N, Kolcic I, Arnar DO, Gudbjartsson DF, Holm H, Balkau B, Silva CT, Newton-Cheh CH, Nikus K, Salo P, Mohlke KL, Peyser PA, Schunkert H, Lorentzon M, Lahti J, Rao DC, Cornelis MC, Faul JD, Smith JA, Stolarz-Skrzypek K, Bandinelli S, Concas MP, Sinagra G, Meitinger T, Waldenberger M, Sinner MF, Strauch K, Delgado GE, Taylor KD, Yao J, Foco L, Melander O, de Graaf J, de Mutsert R, de Geus EJC, Johansson Å, Joshi PK, Lind L, Franke A, Macfarlane PW, Tarasov KV, Tan N, Felix SB, Tai ES, Quek DQ, Snieder H, Ormel J, Ingelsson M, Lindgren C, Morris AP, Raitakari OT, Hansen T, Assimes T, Gudnason V, Timpson NJ, Morrison AC, Munroe PB, Strachan DP, Grarup N, Loos RJF, Heckbert SR, Vollenweider P, Hayward C, Stefansson K, Froguel P, Groop L, Wareham NJ, van Duijn CM, Feitosa MF, O'Donnell CJ, Kähönen M, Perola M, Boehnke M, Kardia SLR, Erdmann J, Palmer CNA, Ohlsson C, Porteous DJ, Eriksson JG, Bouchard C, Moebus S, Kraft P, Weir DR, Cusi D, Ferrucci L, Ulivi S, Girotto G, Correa A, Kääb S, Peters A, Chambers JC, Kooner JS, März W, Rotter JI, Hicks AA, Smith JG, Kiemeney LALM, Mook-Kanamori DO, Penninx BWJH, Gyllensten U, Wilson JF, Burgess S, Sundström J, Lieb W, Jukema JW, Eijgelsheim M, Lakatta ELM, Cheng CY, Dörr M, Wong TY, Sabanayagam C, Oldehinkel AJ, Riese H, Lehtimäki T, Verweij N, van der Harst P. Genetic insights into resting heart rate and its role in cardiovascular disease. Nat Commun 2023; 14:4646. [PMID: 37532724 PMCID: PMC10397318 DOI: 10.1038/s41467-023-39521-2] [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] [Received: 06/06/2022] [Accepted: 06/16/2023] [Indexed: 08/04/2023] Open
Abstract
Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.
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Affiliation(s)
- Yordi J van de Vegte
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands
| | - Ruben N Eppinga
- Department of Cardiology, Isala Zwolle ziekenhuis, Zwolle, 8025 AB, the Netherlands
| | - M Yldau van der Ende
- Department of Cardiology, University medical Center Utrecht, Utrecht, 3584 Cx, the Netherlands
| | - Yanick P Hagemeijer
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands
- Analytical Biochemistry, University of Groningen, Groningen, 9713 AV, the Netherlands
| | - Yuvaraj Mahendran
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine Science, University of Copenhagen, Copenhagen Ø, 2100, Denmark
| | - Elias Salfati
- Department of Medicine, Stanford University School of Medicine, Stanford, 94305, USA
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI48109, USA
| | - Vanessa Y Tan
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, BS82BN, UK
- MRC Integrative Epidemiology, University of Bristol, Bristol, BS82BN, UK
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, 21215, USA
| | - Ioanna Ntalla
- Clinical Pharmacology and Precision Medicine, William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Emil V Appel
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine Science, University of Copenhagen, Copenhagen Ø, 2100, Denmark
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, 10029, USA
| | | | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Ozren Polasek
- Department of Public Health, University of Split School of Medicine, Split, 21000, Croatia
- Algebra LAB, Algebra University College, Zagreb, 10000, Croatia
| | | | - Cecile Lecoeur
- UMR 8199, University of Lille Nord de France, Lille, 59000, France
| | - Claes Ladenvall
- Clinial Genomics Uppsala, Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, 75185, Sweden
- Lund University Diabetes Center, Department of Clinical Sciences, Lund University, Malmö, 20502, Sweden
| | - Jing Hua Zhao
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, Victor Phillip Dahdaleh Heart & Lung Research Institute, University of Cambridge, Cambridge, CB2 0BB, UK
| | - Aaron Isaacs
- CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), Department of Physiology, Maastricht University, Maastricht, 6229ER, Netherlands
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108-2212, Campus Box 8506, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Shih-Jen Hwang
- Division of Intramural Research, National Heart Lung and Blood Institute, NIH, USA, Framingham, 1702, USA
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, FI-33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FI-33014, Finland
| | - Kirsi Auro
- Department of Health, unit of genetics and biomarkers, , National Institute for Health and Welfare, Finland, Helsinki, FI-00290, Finland
- Department of molecular medicine, University of Helsinki, Helsinki, FI-00290, Finland
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Linyao Zeng
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, 80636, Germany
| | - Nabi Shah
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
- Pharmacogenetics Research Lab, Department of Pharmacy, COMSATS University Islamabad, Abbottabad, 22060, Pakistan
| | - Maria Nethander
- Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 40530, Sweden
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA
| | - Sonali Pechlivanis
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, 45122, Germany
| | - Lu Qi
- Department of Epidemiology, Tulane University, New Orleans, LA, 70112, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Federica Rizzi
- Unit of Biomedicine, Bio4Dreams-Business Nursery for Life Sciences, Milano, 20121, Italy
| | - Toshiko Tanaka
- Longitudinal Study Section, National Institute on Aging, Baltimore, 21224, USA
| | - Antonietta Robino
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - Massimiliano Cocca
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - Leslie Lange
- Medicine, University of Colorado Anschutz Medical Campus, Aurora, 80045, USA
| | - Martina Müller-Nurasyid
- IBE, Ludwig-Maximilians-University Munich, LMU Munich, Munich, 81377, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, 55101, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Carolina Roselli
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, 02142, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68167, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, 68163, Germany
| | - Xiuqing Guo
- Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA, Torrance, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Henry J Lin
- Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA, Torrance, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Francesca Pavani
- Institute for Biomedicine, Eurac Research, Bolzano, 39100, Italy
| | | | - Raymond Noordam
- Department of Internal Medicine, section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC, the Netherlands
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Public Health, Amsterdam UMC, Amsterdam UMC, Vrije Universiteit, Amsterdam, Amsterdam, 1081 HL, the Netherlands
| | - Katharina E Schraut
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, Scotland, UK
| | - Marcel den Hoed
- The Beijer laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and Science for Life Laboratory, Uppsala, 75237, Sweden
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, 24105, Germany
| | - Stella Trompet
- Department of Internal Medicine, section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, ZA, 2333, the Netherlands
| | - Marten E van den Berg
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015GD, the Netherlands
| | - Giorgio Pistis
- Institute of Genetics and Biomedic Research (IRGB), Italian National Research Council (CNR), Monserrato, (CA), 9042, Italy
- Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, USA
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, 17475, Germany
| | - Xueling S Sim
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
| | - Hengtong L Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, FI-33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FI-33014, Finland
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 0SL, UK
| | - M Abdullah Said
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus C, 8000, Denmark
| | - Carlos Iribarren
- Division of Research, Kaiser Permenente of Northern California, Oakland, 94612, USA
- The Scripps Research Institute, La Jolla, 10550, USA
| | | | - Susan M Ring
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, BS82BN, UK
- MRC Integrative Epidemiology, University of Bristol, Bristol, BS82BN, UK
| | - Paul S de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, School of Public Health, Houston, 77030, USA
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, 2400, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, 10029, USA
- Department of Preventive Medicine, The Icahn School of Medicine at Mount Sinai, New York, 10029, USA
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8TA, UK
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology and Health Systems and Population Health, University of Washington, Seattle, 98195, USA
| | - Nona Sotoodehnia
- Medicine and Epidemiology, University of Washington, Seattle, 98195, USA
| | - Ivana Kolcic
- Department of Public Health, University of Split School of Medicine, Split, 21000, Croatia
- Algebra LAB, Algebra University College, Zagreb, 10000, Croatia
| | - David O Arnar
- deCODE genetics / Amgen Inc., Reykjavik, 102, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, 101, Iceland
- Department of Medicine, Landspitali-The National University Hospital of Iceland, Reykjavik, 101, Iceland
| | - Daniel F Gudbjartsson
- deCODE genetics / Amgen Inc., Reykjavik, 102, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, 101, Iceland
| | - Hilma Holm
- deCODE genetics / Amgen Inc., Reykjavik, 102, Iceland
| | - Beverley Balkau
- Centre for Research in Epidemiology and Population Health, Institut national de la santé et de la recherche médicale, Villejuif, 94800, France
- UMRS 1018, University Versailles Saint-Quentin-en-Yvelines, Versailles, 78035, France
- UMRS 1018, University Paris Sud, Villejuif, 94807, France
| | - Claudia T Silva
- Genetic Epidemiology Unit, Dept. of Epidemiology, Erasmus University Medical Center, Rotterdam, 3000CA, Netherlands
| | | | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, FI-33521, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FI-33014, Finland
| | - Perttu Salo
- Department of Health, unit of genetics and biomarkers, , National Institute for Health and Welfare, Finland, Helsinki, FI-00290, Finland
- Department of molecular medicine, University of Helsinki, Helsinki, FI-00290, Finland
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, 80636, Germany
| | - Mattias Lorentzon
- Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
- Region Västra Götaland, Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Mölndal, 43180, Sweden
- Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, 3000, Australia
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, 00014, Finland
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA
| | | | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Katarzyna Stolarz-Skrzypek
- Department of Cardiology, Interventional Electrocardiology and Hypertension, Jagiellonian University Medical College, Kraków, 31-008, Poland
| | - Stefania Bandinelli
- Geriatric Unit, Unità sanitaria locale Toscana Centro, Florence, 50142, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - Gianfranco Sinagra
- Cardiovascular Department, "Ospedali Riuniti and University of Trieste", Trieste, 34149, Italy
| | - Thomas Meitinger
- Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, München, 81675, Germany
- Institute of Human Genetics, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, 80802, Germany
| | - Moritz F Sinner
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, 80802, Germany
- Department of Cardiology, University Hospital, LMU Munich, Munich, 81377, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, 55101, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, 81377, Germany
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68167, Germany
| | - Kent D Taylor
- Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA, Torrance, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Jie Yao
- Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA, Torrance, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Luisa Foco
- Institute for Biomedicine, Eurac Research, Bolzano, 39100, Italy
| | - Olle Melander
- Department of Internal Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Malmo, 221 85, Sweden
- Lund University Diabetes Center, Lund University, Malmö, 221 85, Sweden
| | | | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, the Netherlands
| | - Eco J C de Geus
- Biological Psychology, EMGO+ Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University, Amsterdam, 1081 BT, the Netherlands
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, 75108, Sweden
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University Hospital, Uppsala, 75237, Sweden
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, 24105, Germany
| | - Peter W Macfarlane
- Institute of Health and Wellbeing, Faculty of Medicine, University of Glasgow, Glasgow, G12 0XH, UK
| | - Kirill V Tarasov
- Laboratory of Cardiovascular Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Nicholas Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
| | - Stephan B Felix
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, 17475, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475, Germany
| | - E-Shyong Tai
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Debra Q Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Johan Ormel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Molecular Geriatrics, Uppsala University, Uppsala, 75237, Sweden
| | - Cecilia Lindgren
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Andrew P Morris
- Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, FI-20521, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, FI-20521, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, FI-20521, Finland
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine Science, University of Copenhagen, Copenhagen Ø, 2100, Denmark
| | - Themistocles Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, 94305, USA
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
- Icelandic Heart Association, Kopavogur, 201, Iceland
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Population Health Sciences, Bristol Medical School,, University of Bristol, Bristol, BS8 2BN, UK
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, School of Public Health, Houston, 77030, USA
| | - Patricia B Munroe
- Clinical Pharmacology and Precision Medicine, William Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Biomedical Research Centre, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, London, SW17 0RE, UK
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine Science, University of Copenhagen, Copenhagen Ø, 2100, Denmark
| | - Ruth J F Loos
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine Science, University of Copenhagen, Copenhagen Ø, 2100, Denmark
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, 10029, USA
- The Mindich Child Health and Development Institute, The Icahn School of Medicine at Mount Sinai, New York, 10029, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, 98195, USA
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University hospital, Lausanne, 1015, Switzerland
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, Scotland, UK
| | - Kari Stefansson
- deCODE genetics / Amgen Inc., Reykjavik, 102, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, 101, Iceland
| | - Philippe Froguel
- Department of Metabolism, Imperial College London, London, W12 0HS, UK
- Inserm/CNRS UMR 1283/8199, Pasteur Institute of Lille, Lille University Hospital, EGID, Lille, 59000, France
- University of Lille, Lille, 59000, France
| | - Leif Groop
- Lund University Diabetes Center, Department of Clinical Sciences, Lund University, Malmö, 20502, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 00290, Finland
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Dept. of Epidemiology, Erasmus University Medical Center, Rotterdam, 3000CA, Netherlands
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108-2212, Campus Box 8506, USA
| | - Christopher J O'Donnell
- Cardiology Section, VA Boston Healthcare System, Harvard Medical School, Boston, MA, 02132, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, FI-33521, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FI-33521, Finland
| | - Markus Perola
- Department of Health, unit of genetics and biomarkers, , National Institute for Health and Welfare, Finland, Helsinki, FI-00290, Finland
- Department of molecular medicine, University of Helsinki, Helsinki, FI-00290, Finland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jeanette Erdmann
- Institute for Cardiogenetics, University of Lübeck, Lübeck, 23562, Germany
| | - Colin N A Palmer
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 41345, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, 41345, Sweden
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Johan G Eriksson
- Department of General practice and primary care, University of Helsinki, Helsinki, 00014, Finland
- Department of Obstetrics and Gynecology, National University of Singapore, Singapore, 119228, Singapore
- Public health Research Program, Folkhalsan Research Center, Helsinki, 000250, Finland
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA
| | - Susanne Moebus
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, 45122, Germany
- Centre for Urban Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, 45122, Germany
| | - Peter Kraft
- Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02112, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Daniele Cusi
- Unit of Biomedicine, Bio4Dreams-Business Nursery for Life Sciences, Milano, 20121, Italy
- Institute of Biomedical Technologies, National Research Council of Italy, Segrate, (MI), 20090, Italy
| | - Luigi Ferrucci
- Longitudinal Study Section, National Institute on Aging, Baltimore, 21224, USA
| | - Sheila Ulivi
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - Giorgia Girotto
- Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, 34137, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, 34149, Italy
| | - Adolfo Correa
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, 39216, USA
| | - Stefan Kääb
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, 80802, Germany
- Department of Cardiology, University Hospital, LMU Munich, Munich, 81377, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, 85764, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, 80802, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, 81377, Germany
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68167, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, 68161, Germany
| | - Jerome I Rotter
- Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA, Torrance, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90502, USA
| | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Bolzano, 39100, Italy
| | - J Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, 221 85, Sweden
- Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, 221 84, Sweden
- The Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and the Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, 413 45, Sweden
| | | | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300 RC, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health, Amsterdam UMC, Amsterdam UMC, Vrije Universiteit, Amsterdam, Amsterdam, 1081 HL, the Netherlands
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, 75108, Sweden
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, Scotland, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, Scotland, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Johan Sundström
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University Hospital, Uppsala, 75237, Sweden
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank PopGen, Kiel University, Kiel, 24105, Germany
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, ZA, 2333, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, ZA, 2333, the Netherlands
- Netherlands Heart Institute, Utrecht, 3511 EP, the Netherlands
| | - Mark Eijgelsheim
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015GD, the Netherlands
- Department of Nephrology, University Medical Center Groningen, Groningen, 9700RB, the Netherlands
| | - Edward L M Lakatta
- Laboratory of Cardiovascular Sciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, 169857, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, 17475, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, 17475, Germany
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, 169857, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, 100084, China
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, 169856, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Harriette Riese
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, FI-33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, FI-33014, Finland
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands.
- Department of Cardiology, University medical Center Utrecht, Utrecht, 3584 Cx, the Netherlands.
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, 9700RB, the Netherlands.
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37
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Cabana-Domínguez J, Llonga N, Arribas L, Alemany S, Vilar-Ribó L, Demontis D, Fadeuilhe C, Corrales M, Richarte V, Børglum AD, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 2023; 28:3493-3502. [PMID: 37537283 PMCID: PMC10618083 DOI: 10.1038/s41380-023-02200-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.
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Affiliation(s)
- Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Fadeuilhe
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montse Corrales
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vanesa Richarte
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anders D Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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38
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Wu Y, Goleva SB, Breidenbach LB, Kim M, MacGregor S, Gandal MJ, Davis LK, Wray NR. 150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility. CELL GENOMICS 2023; 3:100326. [PMID: 37492107 PMCID: PMC10363821 DOI: 10.1016/j.xgen.2023.100326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/11/2023] [Accepted: 04/20/2023] [Indexed: 07/27/2023]
Abstract
We conducted a genome-wide association study (GWAS) analysis of diverticular disease (DivD) of intestine within 724,372 individuals and identified 150 independent genome-wide significant DNA variants. Integration of the GWAS results with human gut single-cell RNA sequencing data implicated gut myocyte, mesothelial and stromal cells, and enteric neurons and glia in DivD development. Ninety-five genes were prioritized based on multiple lines of evidence, including SLC9A3, a drug target gene of tenapanor used for the treatment of the constipation subtype of irritable bowel syndrome. A DivD polygenic score (PGS) enables effective risk prediction (area under the curve [AUC], 0.688; 95% confidence interval [CI], 0.645-0.732) and the top 20% PGS was associated with ∼3.6-fold increased DivD risk relative to the remaining population. Our statistical and bioinformatic analyses suggest that the mechanism of DivD is through colon structure, gut motility, gastrointestinal mucus, and ionic homeostasis. Our analyses reinforce the link between gastrointestinal disorders and the enteric nervous system through genetics.
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Affiliation(s)
- Yeda Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Slavina B. Goleva
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lindsay B. Breidenbach
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Minsoo Kim
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Lea K. Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioural Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University, 511-A Light Hall, 2215 Garland Avenue, Nashville, TN 37232, USA
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
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Patel AP, Wang M, Ruan Y, Koyama S, Clarke SL, Yang X, Tcheandjieu C, Agrawal S, Fahed AC, Ellinor PT, Tsao PS, Sun YV, Cho K, Wilson PWF, Assimes TL, van Heel DA, Butterworth AS, Aragam KG, Natarajan P, Khera AV. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med 2023; 29:1793-1803. [PMID: 37414900 PMCID: PMC10353935 DOI: 10.1038/s41591-023-02429-x] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
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Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
| | - Yunfeng Ruan
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satoshi Koyama
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | - Shoa L Clarke
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Xiong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | | | - Saaket Agrawal
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Akl C Fahed
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Philip S Tsao
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Yan V Sun
- Veteran Affairs Atlanta Healthcare System, Decatur, GA, USA
| | - Kelly Cho
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | | | - Themistocles L Assimes
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Krishna G Aragam
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Boston, MA, USA.
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Morita Y, Kamatani Y, Ito H, Ikegawa S, Kawaguchi T, Kawaguchi S, Takahashi M, Terao C, Ito S, Nishitani K, Nakamura S, Kuriyama S, Tabara Y, Matsuda F, Matsuda S. Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score. Arthritis Res Ther 2023; 25:103. [PMID: 37309008 PMCID: PMC10258963 DOI: 10.1186/s13075-023-03082-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Polygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population. METHODS We calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees (n = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors. RESULTS A total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA (p = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA (p = 6.7 × 10-5, odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA (p = 5.4 × 10-7, OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p = 0.029). CONCLUSIONS This study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population. TRIAL REGISTRATION No. C278.
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Affiliation(s)
- Yugo Morita
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yoichiro Kamatani
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hiromu Ito
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
- Department of Orthopedic Surgery, Kurashiki Central Hospital, Kurashiki, Japan.
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, Center for Genomic Medicine, RIKEN, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuji Ito
- Laboratory for Bone and Joint Diseases, Center for Genomic Medicine, RIKEN, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Orthopedic Surgery, Shimane University Faculty of Medicine, Izumo, Japan
| | - Kohei Nishitani
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shinichiro Nakamura
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shinichi Kuriyama
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Aoi-Ku, Shizuoka, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuichi Matsuda
- Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Alemany-Navarro M, Diz-de Almeida S, Cruz R, Riancho JA, Rojas-Martínez A, Lapunzina P, Flores C, Carracedo A. Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19. Transl Psychiatry 2023; 13:189. [PMID: 37280221 DOI: 10.1038/s41398-023-02482-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/24/2023] [Accepted: 05/23/2023] [Indexed: 06/08/2023] Open
Abstract
Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.
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Affiliation(s)
- M Alemany-Navarro
- IBIS (Universidad de Sevilla, HUVR, Junta de Andalucia, CSIC), Sevilla, Spain.
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS) Santiago de Compostela, Santiago de Compostela, Spain.
- Grupo de Genética. Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain.
| | - S Diz-de Almeida
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
| | - R Cruz
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
| | - J A Riancho
- IDIVAL, Cantabria, Spain
- Universidad de Cantabria, Cantabria, Spain
- Hospital U M Valdecilla, Cantabria, Spain
| | - A Rojas-Martínez
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
| | - P Lapunzina
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Genética Médica y Molecular (INGEMM) del Hospital Universitario La Paz, Madrid, Spain
- ERN-ITHACA-European Reference Network, Santa Cruz de Tenerife, Canarias, Spain
| | - C Flores
- Research Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain
- Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
| | - A Carracedo
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS) Santiago de Compostela, Santiago de Compostela, Spain
- Grupo de Genética. Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
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Odintsova VV, van Dongen J, van Beijsterveldt CEM, Ligthart L, Willemsen G, de Geus EJC, Dolan CV, Boomsma DI. Handedness and 23 Early Life Characteristics in 37,495 Dutch Twins. Twin Res Hum Genet 2023; 26:199-208. [PMID: 37448258 DOI: 10.1017/thg.2023.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
In studies of singletons, a range of early-life characteristics have been reported to be associated with handedness, but some of these associations have failed to replicate. We examined associations between 23 early life characteristics with handedness in a large sample of 37,495 5-year-old twins. We considered three definitions of handedness: left-handedness (LH), mixed-handedness (MH), and non-right-handedness (NRH). Our main aim was to test whether the associations with sex, birth weight, gestational age, and season of birth - as reported in singletons - replicate in twins, and to examine twin-specific variables, including zygosity, chorionicity, birth order, and intertwin delivery time. Compared to previously published data from adults born as singletons (7.23%), the prevalence of NRH was higher in both twins (16.19%) and their parents (15.09%). In the twins, LH and NRH were associated with parents' LH. Male sex and lower gestational age were associated with NRH, and LH was associated with not being breastfed. MH was related to neurodevelopmental delays and higher externalizing problems later in childhood. Other previously reported associations were not replicated, and no twin-specific characteristics were related to handedness. These results emphasize the importance of considering multiple definitions of handedness and indicate a small number of replicated associations across studies.
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Affiliation(s)
- Veronika V Odintsova
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research institute, Amsterdam, The Netherlands
| | | | - Lannie Ligthart
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Conor V Dolan
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research institute, Amsterdam, The Netherlands
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Cornish N, Haycock P, Brenner H, Figueiredo JC, Galesloot T, Grant RC, Johansson M, Mariosa D, McKay J, Pai R, Pellatt AJ, Samadder NJ, Shi J, Thibord F, Trégouët DA, Voegele C, Thirlwell C, Mumford A, Langdon R. Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomisation analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.16.23289792. [PMID: 37292802 PMCID: PMC10246038 DOI: 10.1101/2023.05.16.23289792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Background People with cancer experience high rates of venous thromboembolism (VTE). Additionally, risk of subsequent cancer is increased in people experiencing their first VTE. The causal mechanisms underlying this association are not completely understood, and it is unknown whether VTE is itself a risk factor for cancer. Methods We used data from large genome-wide association study meta-analyses to perform bi-directional Mendelian randomisation analyses to estimate causal associations between genetically-proxied lifetime risk of VTE and risk of 18 different cancers. Results We found no conclusive evidence that genetically-proxied lifetime risk of VTE was causally associated with an increased incidence of cancer, or vice-versa. We observed an association between VTE and pancreatic cancer risk (odds ratio for pancreatic cancer 1.23 (95% confidence interval 1.08 - 1.40) per log-odds increase in risk of VTE, P = 0.002). However, sensitivity analyses revealed this association was predominantly driven by a variant proxying non-O blood group, with inadequate evidence from Mendelian randomisation to suggest a causal relationship. Conclusions These findings do not support the hypothesis that genetically-proxied lifetime risk of VTE is a cause of cancer. Existing observational epidemiological associations between VTE and cancer are therefore more likely to be driven by pathophysiological changes which occur in the setting of active cancer and anti-cancer treatments. Further work is required to explore and synthesise evidence for these mechanisms.
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Affiliation(s)
- Naomi Cornish
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Philip Haycock
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jane C. Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles CA
| | - Tessel Galesloot
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert C Grant
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | | | | | - Mattias Johansson
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Daniela Mariosa
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - James McKay
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rish Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Arizona, Scottsdale, USA
| | - Andrew J Pellatt
- Division of Cancer Medicine, MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Florian Thibord
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA
| | | | - Catherine Voegele
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Andrew Mumford
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Ryan Langdon
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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Blostein F, Zou T, Bhaumik D, Salzman E, Bakulski KM, Shaffer JR, Marazita ML, Foxman B. Bacterial community modifies host genetics effect on early childhood caries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284235. [PMID: 37090669 PMCID: PMC10120800 DOI: 10.1101/2023.01.11.23284235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Background By age five approximately one-fifth of children have early childhood caries (ECC). Both the oral microbiome and host genetics are thought to influence susceptibility. Whether the oral microbiome modifies genetic susceptibility to ECC has not been tested. We test whether the salivary bacteriome modifies the association of a polygenic score (PGS, a score derived from genomic data that summarizes genetic susceptibility to disease) for primary tooth decay on ECC in the Center for Oral Health Research in Appalachia 2 longitudinal birth cohort. Methods Children were genotyped using the Illumina Multi-Ethnic Genotyping Array and underwent annual dental examinations. We constructed a PGS for primary tooth decay using weights from an independent, genome-wide association meta-analysis. Using Poisson regression, we tested for associations between the PGS (high versus low) and ECC incidence, adjusting for demographic characteristics (n=783). An incidence-density sampled subset of the cohort (n=138) had salivary bacteriome data at 24- months of age. We tested for effect modification of the PGS on ECC case status by salivary bacterial community state type (CST). Results By 60-months, 20.69% of children had ECC. High PGS was not associated with an increased rate of ECC (incidence-rate ratio:1.09 (95% confidence interval (CI): 0.83, 1.42)). However, having a cariogenic salivary bacterial CST at 24-months was associated with ECC (odds ratio (OR): 7.48 (95%CI: 3.06, 18.26)), which was robust to PGS adjustment. An interaction existed between the salivary bacterial CST and the PGS on the multiplicative scale (P= 0.04). The PGS was associated with ECC (OR: 4.83 (95% CI: 1.29, 18.17)) only among individuals with a noncariogenic salivary bacterial CST (n=70). Conclusions Genetic causes of caries may be harder to detect when not accounting for cariogenic oral microbiomes. As certain salivary bacterial CSTs increased ECC-risk across genetic-risk strata, preventing colonization of cariogenic microbiomes would be universally beneficial.
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Affiliation(s)
- Freida Blostein
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Tianyu Zou
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Deesha Bhaumik
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Elizabeth Salzman
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kelly M Bakulski
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - John R Shaffer
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Clinical and Translational Sciences Institute, and Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Betsy Foxman
- Department of Epidemiology, University of Michigan School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
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Otsuka I, Galfalvy H, Guo J, Akiyama M, Rujescu D, Turecki G, Hishimoto A, Mann JJ. Mapping the genetic architecture of suicide attempt and suicide death using polygenic risk scores for clinically-related psychiatric disorders and traits. Psychol Med 2023; 53:2689-2697. [PMID: 37310312 DOI: 10.1017/s0033291721004700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Suicidal behavior is moderately heritable and a consequence of a combination of the diathesis traits for suicidal behavior and suicide-related major psychiatric disorders. Here, we sought to examine shared polygenic effects between various psychiatric disorders/traits and suicidal behavior and to compare the shared polygenic effects of various psychiatric disorders/traits on non-fatal suicide attempt and suicide death. METHODS We used our genotyped European ancestry sample of 260 non-fatal suicide attempters, 317 suicide decedents and 874 non-psychiatric controls to test whether polygenic risk scores (PRSs) obtained from large GWASs for 22 suicide-related psychiatric disorders/traits were associated with suicidal behavior. Results were compared between non-fatal suicide attempt and suicide death in a sensitivity analysis. RESULTS PRSs for major depressive disorder, bipolar disorder, schizophrenia, ADHD, alcohol dependence, sensitivity to environmental stress and adversity, educational attainment, cognitive performance, and IQ were associated with suicidal behavior (Bonferroni-corrected p < 2.5 × 10-4). The polygenic effects of all 22 psychiatric disorders/traits had the same direction (p for binomial tests = 4.8 × 10-7) and were correlated (Spearman's ρ = 0.85) between non-fatal suicide attempters and suicide decedents. CONCLUSIONS We found that polygenic effects for major psychiatric disorders and diathesis-related traits including stress responsiveness and intellect/cognitive function contributed to suicidal behavior. While we found comparable polygenic architecture between non-fatal suicide attempters and suicide decedents based on correlations with PRSs of suicide-related psychiatric disorders/traits, our analyses are limited by small sample size resulting in low statistical power to detect difference between non-fatal suicide attempt and suicide death.
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Affiliation(s)
- Ikuo Otsuka
- Division of Molecular Imaging and Neuropathology, Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Hanga Galfalvy
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Jia Guo
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Masato Akiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Verdun, QC, Canada
| | - Akitoyo Hishimoto
- Department of Psychiatry, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - J John Mann
- Division of Molecular Imaging and Neuropathology, Department of Psychiatry, Columbia University, New York State Psychiatric Institute, New York, NY, USA
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Ghouse J, Tragante V, Ahlberg G, Rand SA, Jespersen JB, Leinøe EB, Vissing CR, Trudsø L, Jonsdottir I, Banasik K, Brunak S, Ostrowski SR, Pedersen OB, Sørensen E, Erikstrup C, Bruun MT, Nielsen KR, Køber L, Christensen AH, Iversen K, Jones D, Knowlton KU, Nadauld L, Halldorsson GH, Ferkingstad E, Olafsson I, Gretarsdottir S, Onundarson PT, Sulem P, Thorsteinsdottir U, Thorgeirsson G, Gudbjartsson DF, Stefansson K, Holm H, Olesen MS, Bundgaard H. Genome-wide meta-analysis identifies 93 risk loci and enables risk prediction equivalent to monogenic forms of venous thromboembolism. Nat Genet 2023; 55:399-409. [PMID: 36658437 DOI: 10.1038/s41588-022-01286-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/13/2022] [Indexed: 01/21/2023]
Abstract
We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5. We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels).
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Affiliation(s)
- Jonas Ghouse
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Gustav Ahlberg
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren A Rand
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob B Jespersen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Eva Birgitte Leinøe
- Department of Hematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Linea Trudsø
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ingileif Jonsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Iceland Department of Immunology, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Karina Banasik
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Næstved Hospital, Næstved, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Kaspar Rene Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Lars Køber
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Alex H Christensen
- Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev, Denmark
| | - Kasper Iversen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Herlev, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | | | | | | | - Pall T Onundarson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Laboratory Hematology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Gudmundur Thorgeirsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Department of Medicine, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Inc., Reykjavik, Iceland
| | - Morten Salling Olesen
- Laboratory for Molecular Cardiology, Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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47
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Shams H, Shao X, Santaniello A, Kirkish G, Harroud A, Ma Q, Isobe N, Schaefer CA, McCauley JL, Cree BAC, Didonna A, Baranzini SE, Patsopoulos NA, Hauser SL, Barcellos LF, Henry RG, Oksenberg JR. Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain 2023; 146:645-656. [PMID: 35253861 PMCID: PMC10169285 DOI: 10.1093/brain/awac092] [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] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.
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Affiliation(s)
- Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Xiaorong Shao
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adil Harroud
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Noriko Isobe
- Department of Neurology, Graduate School of medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | | | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Dr. John T. Macdonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Anatomy and Cell Biology, East Carolina University, Greenville, NC 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, 02115 MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lisa F Barcellos
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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48
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Khunsriraksakul C, Li Q, Markus H, Patrick MT, Sauteraud R, McGuire D, Wang X, Wang C, Wang L, Chen S, Shenoy G, Li B, Zhong X, Olsen NJ, Carrel L, Tsoi LC, Jiang B, Liu DJ. Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus. Nat Commun 2023; 14:668. [PMID: 36750564 PMCID: PMC9905560 DOI: 10.1038/s41467-023-36306-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/25/2023] [Indexed: 02/09/2023] Open
Abstract
Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qinmengge Li
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Havell Markus
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Renan Sauteraud
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Daniel McGuire
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Xingyan Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Chen Wang
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lida Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Siyuan Chen
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Ganesh Shenoy
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37235, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Nancy J Olsen
- Department of Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Dajiang J Liu
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
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49
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Significance tests for R 2 of out-of-sample prediction using polygenic scores. Am J Hum Genet 2023; 110:349-358. [PMID: 36702127 PMCID: PMC9943721 DOI: 10.1016/j.ajhg.2023.01.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R2 is required to test the difference between them. Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p value of the R2 difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.
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50
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Wang Y, Namba S, Lopera E, Kerminen S, Tsuo K, Läll K, Kanai M, Zhou W, Wu KH, Favé MJ, Bhatta L, Awadalla P, Brumpton B, Deelen P, Hveem K, Lo Faro V, Mägi R, Murakami Y, Sanna S, Smoller JW, Uzunovic J, Wolford BN, Willer C, Gamazon ER, Cox NJ, Surakka I, Okada Y, Martin AR, Hirbo J. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. CELL GENOMICS 2023; 3:100241. [PMID: 36777179 PMCID: PMC9903818 DOI: 10.1016/j.xgen.2022.100241] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 08/28/2022] [Accepted: 12/03/2022] [Indexed: 01/06/2023]
Abstract
Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Esteban Lopera
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kuan-Han Wu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA
| | | | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
- Clinic of Medicine, St. Olav’s Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Patrick Deelen
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Valeria Lo Faro
- Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Serena Sanna
- Department of Genetics, UMCG, University of Groningen, Groningen, the Netherlands
- Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), 09100 Cagliari, Italy
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Brooke N. Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48103, USA
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
| | - Cristen Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, 7030 Trondheim, Norway
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biostatistics and Center for Statistical Genetics, and Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eric R. Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC) and Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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