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Manzoni C, Kia DA, Ferrari R, Leonenko G, Costa B, Saba V, Jabbari E, Tan MM, Albani D, Alvarez V, Alvarez I, Andreassen OA, Angiolillo A, Arighi A, Baker M, Benussi L, Bessi V, Binetti G, Blackburn DJ, Boada M, Boeve BF, Borrego-Ecija S, Borroni B, Bråthen G, Brooks WS, Bruni AC, Caroppo P, Bandres-Ciga S, Clarimon J, Colao R, Cruchaga C, Danek A, de Boer SC, de Rojas I, di Costanzo A, Dickson DW, Diehl-Schmid J, Dobson-Stone C, Dols-Icardo O, Donizetti A, Dopper E, Durante E, Ferrari C, Forloni G, Frangipane F, Fratiglioni L, Kramberger MG, Galimberti D, Gallucci M, García-González P, Ghidoni R, Giaccone G, Graff C, Graff-Radford NR, Grafman J, Halliday GM, Hernandez DG, Hjermind LE, Hodges JR, Holloway G, Huey ED, Illán-Gala I, Josephs KA, Knopman DS, Kristiansen M, Kwok JB, Leber I, Leonard HL, Libri I, Lleo A, Mackenzie IR, Madhan GK, Maletta R, Marquié M, Maver A, Menendez-Gonzalez M, Milan G, Miller BL, Morris CM, Morris HR, Nacmias B, Newton J, Nielsen JE, Nilsson C, Novelli V, Padovani A, Pal S, Pasquier F, Pastor P, Perneczky R, Peterlin B, Petersen RC, Piguet O, Pijnenburg YA, Puca AA, Rademakers R, Rainero I, Reus LM, Richardson AM, Riemenschneider M, Rogaeva E, Rogelj B, Rollinson S, Rosen H, Rossi G, Rowe JB, Rubino E, Ruiz A, Salvi E, Sanchez-Valle R, Sando SB, Santillo AF, Saxon JA, Schlachetzki JC, Scholz SW, Seelaar H, Seeley WW, Serpente M, Sorbi S, Sordon S, St George-Hyslop P, Thompson JC, Van Broeckhoven C, Van Deerlin VM, Van der Lee SJ, Van Swieten J, Tagliavini F, van der Zee J, Veronesi A, Vitale E, Waldo ML, Yokoyama JS, Nalls MA, Momeni P, Singleton AB, Hardy J, Escott-Price V. Genome-wide analyses reveal a potential role for the MAPT, MOBP, and APOE loci in sporadic frontotemporal dementia. Am J Hum Genet 2024; 111:1316-1329. [PMID: 38889728 PMCID: PMC11267522 DOI: 10.1016/j.ajhg.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Frontotemporal dementia (FTD) is the second most common cause of early-onset dementia after Alzheimer disease (AD). Efforts in the field mainly focus on familial forms of disease (fFTDs), while studies of the genetic etiology of sporadic FTD (sFTD) have been less common. In the current work, we analyzed 4,685 sFTD cases and 15,308 controls looking for common genetic determinants for sFTD. We found a cluster of variants at the MAPT (rs199443; p = 2.5 × 10-12, OR = 1.27) and APOE (rs6857; p = 1.31 × 10-12, OR = 1.27) loci and a candidate locus on chromosome 3 (rs1009966; p = 2.41 × 10-8, OR = 1.16) in the intergenic region between RPSA and MOBP, contributing to increased risk for sFTD through effects on expression and/or splicing in brain cortex of functionally relevant in-cis genes at the MAPT and RPSA-MOBP loci. The association with the MAPT (H1c clade) and RPSA-MOBP loci may suggest common genetic pleiotropy across FTD and progressive supranuclear palsy (PSP) (MAPT and RPSA-MOBP loci) and across FTD, AD, Parkinson disease (PD), and cortico-basal degeneration (CBD) (MAPT locus). Our data also suggest population specificity of the risk signals, with MAPT and APOE loci associations mainly driven by Central/Nordic and Mediterranean Europeans, respectively. This study lays the foundations for future work aimed at further characterizing population-specific features of potential FTD-discriminant APOE haplotype(s) and the functional involvement and contribution of the MAPT H1c haplotype and RPSA-MOBP loci to pathogenesis of sporadic forms of FTD in brain cortex.
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Affiliation(s)
| | - Demis A Kia
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Raffaele Ferrari
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Ganna Leonenko
- Division of Psychological Medicine and Clinical Neurosciences, UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Beatrice Costa
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Valentina Saba
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Edwin Jabbari
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Manuela Mx Tan
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK; Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Diego Albani
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Victoria Alvarez
- Hospital Universitario Central de Asturias, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
| | - Ignacio Alvarez
- Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Barcelona, Spain; Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Antonella Angiolillo
- Centre for Research and Training in Medicine of Aging, Department of Medicine and Health Science "V. Tiberio," University of Molise, Campobasso, Italy
| | - Andrea Arighi
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Matt Baker
- Department of Neuroscience, Mayo Clinic Jacksonville, Jacksonville, FL, USA
| | - Luisa Benussi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Giuliano Binetti
- MAC-Memory Clinic and Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Merce Boada
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Sergi Borrego-Ecija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Service of Neurology. Hospital Clínic de Barcelona, Fundació Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Barbara Borroni
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Geir Bråthen
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway; Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - William S Brooks
- Neuroscience Research Australia, and Randwick Clinical Campus, UNSW Medicine and Health, University of New South Wales, Sydney, Australia
| | - Amalia C Bruni
- Regional Neurogenetic Centre, ASPCZ, Lamezia Terme, Italy
| | - Paola Caroppo
- Unit of Neurology (V) and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Jordi Clarimon
- Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosanna Colao
- Regional Neurogenetic Centre, ASPCZ, Lamezia Terme, Italy
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian Danek
- Neurologische Klinik, LMU Klinikum, Munich, Germany
| | - Sterre Cm de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Itziar de Rojas
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Alfonso di Costanzo
- Centre for Research and Training in Medicine of Aging, Department of Medicine and Health Science "V. Tiberio," University of Molise, Campobasso, Italy
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic Jacksonville, Jacksonville, FL, USA
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, School of Medicine, Munich, Germany; kbo-Inn-Salzach-Klinikum, Wasserburg, Germany
| | - Carol Dobson-Stone
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia; School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
| | - Oriol Dols-Icardo
- Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Aldo Donizetti
- Department of Biology, University of Naples Federico II, Naples, Italy
| | - Elise Dopper
- Department of Neurology & Alzheimer Center, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Elisabetta Durante
- Immunohematology and Transfusional Medicine Service, Local Health Authority n.2 Marca Trevigiana, Treviso, Italy
| | - Camilla Ferrari
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Gianluigi Forloni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | | | - Laura Fratiglioni
- Karolinska Institutet, Department NVS, KI-Alzheimer Disease Research Center, Stockholm, Sweden; Theme Inflammation and Aging, Karolinska Universtiy Hospital, Stockholm, Sweden
| | - Milica G Kramberger
- Department of Neurology, University Medical Center, Medical faculty, Ljubljana University of Ljubljana, Ljubljana, Slovenia; Karolinska Institutet, Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Huddinge, Sweden
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Maurizio Gallucci
- Cognitive Impairment Center, Local Health Authority n.2 Marca Trevigiana, Treviso, Italy
| | - Pablo García-González
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giorgio Giaccone
- Unit of Neurology (V) and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Caroline Graff
- Karolinska Institutet, Department NVS, KI-Alzheimer Disease Research Center, Stockholm, Sweden; Unit for hereditary dementia, Karolinska Universtiy Hospital-Solna, Stockholm, Sweden
| | | | | | - Glenda M Halliday
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia; School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lena E Hjermind
- Neurogenetics Clinic & Research Lab, Danish Dementia Research Centre, Copenhagen University Hospital, Copenhagen, Denmark
| | - John R Hodges
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Guy Holloway
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Edward D Huey
- Bio Med Psychiatry & Human Behavior, Brown University, Providence, RI, USA
| | - Ignacio Illán-Gala
- Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic Rochester, Rochester, MN, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic Rochester, Rochester, MN, USA
| | - Mark Kristiansen
- UCL Genomics, London, UK; UCL Great Ormond Street Institute of Child Health, London, UK; Zayed Centre for Research into Rare Disease in Children, London, UK
| | - John B Kwok
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia; School of Medical Sciences, University of Sydney, Sydney, NSW, Australia
| | - Isabelle Leber
- Sorbonne Université, INSERM U1127, CNRS 7225, Institut du Cerveau - ICM, Paris, France; AP-HP Sorbonne Université, Pitié-Salpêtrière Hospital, Department of Neurology, Institute of Memory and Alzheimer's Disease, Paris, France
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington, DC, USA; DZNE Tübingen, Tübingen, Germany
| | - Ilenia Libri
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alberto Lleo
- Memory Unit, Neurology Department and Sant Pau Biomedical Research Institute, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Ian R Mackenzie
- Department of Pathology, University of British Columbia, Vancouver, Canada; Department of Pathology, Vancouver Coastal Health, Vancouver, Canada
| | - Gaganjit K Madhan
- UCL Genomics, London, UK; UCL Great Ormond Street Institute of Child Health, London, UK; Zayed Centre for Research into Rare Disease in Children, London, UK
| | | | - Marta Marquié
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Ales Maver
- Clinical institute of Genomic Medicine, University Medical Center Ljubljana, Ljubljana, Slovenija
| | - Manuel Menendez-Gonzalez
- Hospital Universitario Central de Asturias, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain; Universidad de Oviedo, Medicine Department, Oviedo, Spain
| | | | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, USA; Trinity College Dublin, Dublin, Ireland
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Newcastle University, Edwardson Building, Nuns Moor Road, Newcastle upon Tyne, UK
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Judith Newton
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Jørgen E Nielsen
- Neurogenetics Clinic & Research Lab, Danish Dementia Research Centre, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christer Nilsson
- Department of Clinical Sciences, Neurology, Lund University, Lund/Malmö, Sweden
| | | | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Suvankar Pal
- Anne Rowling Regenerative Neurology Clinic, University of Edinburgh, Edinburgh, UK
| | - Florence Pasquier
- University of Lille, Lille, France; CHU Lille, Lille, France; Inserm, Labex DISTALZ, LiCEND, Lille, France
| | - Pau Pastor
- Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol, Badalona, Barcelona, Spain; The Germans Trias i Pujol Research Institute (IGTP) Badalona, Barcelona, Spain
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, LMU Hospital, Ludwig-Maximilians-Universität Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; Ageing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK; Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Borut Peterlin
- Clinical institute of Genomic Medicine, University Medical Center Ljubljana, Ljubljana, Slovenija
| | | | - Olivier Piguet
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia; School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - Yolande Al Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Annibale A Puca
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana," University of Salerno, Fisciano, Italy; Cardiovascular Research Unit, IRCCS MultiMedica, Milan, Italy
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic Jacksonville, Jacksonville, FL, USA; VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Innocenzo Rainero
- Department of Neuroscience, "Rita Levi Montalcini," University of Torino, Torino, Italy; Center for Alzheimer's Disease and Related Dementias, Department of Neuroscience and Mental Health, A.O.UCittà della Salute e della Scienza di Torino, Torino, Italy
| | - Lianne M Reus
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
| | - Anna Mt Richardson
- Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Trust, Manchester Academic Health Sciences Unit, University of Manchester, Manchester, UK
| | | | - Ekaterina Rogaeva
- Tanz Centre for Research in Neurodegenerative Diseases and Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Boris Rogelj
- Department of Biotechnology, Jožef Stefan Institute, Ljubljana, Slovenia; Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Sara Rollinson
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Howard Rosen
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Giacomina Rossi
- Unit of Neurology (V) and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - James B Rowe
- University of Cambridge Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Elisa Rubino
- Department of Neuroscience, "Rita Levi Montalcini," University of Torino, Torino, Italy; Center for Alzheimer's Disease and Related Dementias, Department of Neuroscience and Mental Health, A.O.UCittà della Salute e della Scienza di Torino, Torino, Italy
| | - Agustin Ruiz
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Spain; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Erika Salvi
- Unit of Neuroalgologia (III), Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy; Data science center, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Service of Neurology. Hospital Clínic de Barcelona, Fundació Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Sigrid Botne Sando
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway; Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Jennifer A Saxon
- Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Trust, Manchester Academic Health Sciences Unit, University of Manchester, Manchester, UK
| | - Johannes Cm Schlachetzki
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Harro Seelaar
- Department of Neurology & Alzheimer Center, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - William W Seeley
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Maria Serpente
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Sabrina Sordon
- Department of Psychiatry, Saarland University, Homburg, Germany
| | - Peter St George-Hyslop
- Tanz Centre for Research in Neurodegenerative Diseases and Department of Medicine, University of Toronto, Toronto, ON, Canada; Department of Neurology, Columbia University, New York, NY, USA
| | - Jennifer C Thompson
- Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Trust, Manchester Academic Health Sciences Unit, University of Manchester, Manchester, UK; Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Vivianna M Van Deerlin
- Perelman School of Medicine at the University of Pennsylvania, Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, Philadelphia, PA, USA
| | - Sven J Van der Lee
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands; Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - John Van Swieten
- Department of Neurology & Alzheimer Center, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Fabrizio Tagliavini
- Unit of Neurology (V) and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Julie van der Zee
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Arianna Veronesi
- Immunohematology and Transfusional Medicine Service, Local Health Authority n.2 Marca Trevigiana, Treviso, Italy
| | - Emilia Vitale
- Institute of Biochemistry and Cell Biology, National Research Council (CNR), Naples, Italy; School of Integrative Science and Technology Department of Biology Kean University, Union, NJ, USA
| | - Maria Landqvist Waldo
- Clinical Sciences Helsingborg, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA; Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, USA; Trinity College Dublin, Dublin, Ireland
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington, DC, USA
| | | | - Andrew B Singleton
- Center for Alzheimer's and Related Dementias, National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - John Hardy
- UK Dementia Research Institute at UCL and Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK; Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, London, UK; NIHR University College London Hospitals Biomedical Research Centre, London, UK; Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, UK Dementia Research Institute, School of Medicine, Cardiff University, Cardiff, UK.
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Trastulla L, Dolgalev G, Moser S, Jiménez-Barrón LT, Andlauer TFM, von Scheidt M, Budde M, Heilbronner U, Papiol S, Teumer A, Homuth G, Völzke H, Dörr M, Falkai P, Schulze TG, Gagneur J, Iorio F, Müller-Myhsok B, Schunkert H, Ziller MJ. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. Nat Commun 2024; 15:5534. [PMID: 38951512 PMCID: PMC11217418 DOI: 10.1038/s41467-024-49338-2] [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: 02/20/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Stratified medicine holds great promise to tailor treatment to the needs of individual patients. While genetics holds great potential to aid patient stratification, it remains a major challenge to operationalize complex genetic risk factor profiles to deconstruct clinical heterogeneity. Contemporary approaches to this problem rely on polygenic risk scores (PRS), which provide only limited clinical utility and lack a clear biological foundation. To overcome these limitations, we develop the CASTom-iGEx approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue specific gene expression levels. The paradigmatic application of this approach to coronary artery disease or schizophrenia patient cohorts identified diverse strata or biotypes. These biotypes are characterized by distinct endophenotype profiles as well as clinical parameters and are fundamentally distinct from PRS based groupings. In stark contrast to the latter, the CASTom-iGEx strategy discovers biologically meaningful and clinically actionable patient subgroups, where complex genetic liabilities are not randomly distributed across individuals but rather converge onto distinct disease relevant biological processes. These results support the notion of different patient biotypes characterized by partially distinct pathomechanisms. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine paradigms.
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Affiliation(s)
- Lucia Trastulla
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- Human Technopole, Milan, Italy
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Georgii Dolgalev
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Sylvain Moser
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Laura T Jiménez-Barrón
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Moritz von Scheidt
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Sergi Papiol
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
| | - Alexander Teumer
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Peter Falkai
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Institute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Heribert Schunkert
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael J Ziller
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Psychiatry, University of Münster, Münster, Germany.
- Center for Soft Nanoscience, University of Münster, Münster, Germany.
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3
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Sánchez-Valle J, Valencia A. Molecular bases of comorbidities: present and future perspectives. Trends Genet 2023; 39:773-786. [PMID: 37482451 DOI: 10.1016/j.tig.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023]
Abstract
Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.
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Affiliation(s)
- Jon Sánchez-Valle
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain.
| | - Alfonso Valencia
- Life Sciences Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain; ICREA, Barcelona, 08010, Spain.
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4
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Trastulla L, Moser S, Jiménez-Barrón LT, Andlauer TF, von Scheidt M, Budde M, Heilbronner U, Papiol S, Teumer A, Homuth G, Falkai P, Völzke H, Dörr M, Schulze TG, Gagneur J, Iorio F, Müller-Myhsok B, Schunkert H, Ziller MJ. Distinct genetic liability profiles define clinically relevant patient strata across common diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.10.23289788. [PMID: 37214898 PMCID: PMC10197798 DOI: 10.1101/2023.05.10.23289788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.
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Affiliation(s)
- Lucia Trastulla
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- Human Technopole, Milan, Italy
| | - Sylvain Moser
- Max Planck Institute of Psychiatry, Munich, Germany
- Technische Universität München Medical Graduate Center Experimental Medicine, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Laura T. Jiménez-Barrón
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | | | - Moritz von Scheidt
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | | | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Alexander Teumer
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich 80336, Germany
| | - Henry Völzke
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich 80336, Germany
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Heribert Schunkert
- Klinik für Herz-und Kreislauferkrankungen, Deutsches Herzzentrum München, Technical University Munich, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Michael J. Ziller
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry, University of Münster, Münster, Germany
- Center for Soft Nanoscience, University of Münster, Münster, Germany
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5
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Dubey S, Ghosh R, Dubey MJ, Das S, Chakraborty AP, Santra A, Dutta A, Roy D, Pandit A, Roy BK, Das G, Benito-León J. Psychosocial Basis of Human Sufferings and Poverty in Patients with Neurological and Psychiatric Disorders. MEDICAL RESEARCH ARCHIVES 2023; 11:3919. [PMID: 37641666 PMCID: PMC10461571 DOI: 10.18103/mra.v11i5.3919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Neurological disorders and psychiatric ailments often lead to cognitive disabilities and low attainment of education, pivoting misconceptions, myths, and misbeliefs. Poverty and low educational attainment are intriguingly associated with poor awareness and perception of these diseases that add to the suffering. Poverty goes parallel with a low level of education and is intricately associated with neuropsychiatric ailments, which have the potential to spread transgenerationally. Robust education policies, proper government rules and regulations against the spread of disease-related myths and misconceptions, uplifting medical education in its true sense, voices against consanguinity, and programs to raise scientific perception about diseases can help to throw light at the end of this dark tunnel. In this article, the authors intend to 1) decipher the potential psychosocial basis of human suffering and poverty in patients with neurological and psychiatric disorders, and 2) discuss the apropos way-outs that would potentially mitigate suffering, and alleviate the economic burden and cognitive disabilities of families with neuropsychiatric diseases.
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Affiliation(s)
- Souvik Dubey
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Ritwik Ghosh
- Department of General Medicine, Burdwan Medical College, and Hospital, Burdwan, West Bengal, India
| | - Mahua Jana Dubey
- Department of Psychiatry, Berhampur Mental Hospital, Berhampur, West Bengal, India
| | - Shambaditya Das
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Arka Prava Chakraborty
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Arindam Santra
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Ajitava Dutta
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Dipayan Roy
- Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India
- School of Humanities, Indira Gandhi National Open University (IGNOU), New Delhi, India
| | - Alak Pandit
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Biman Kanti Roy
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Gautam Das
- Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, West Bengal, India
| | - Julián Benito-León
- Department of Neurology, University Hospital “12 de Octubre”, Madrid, Spain
- Research Institute (i+12), University Hospital “12 de Octubre”, Madrid, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
- Department of Medicine, Complutense University, Madrid, Spain
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6
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An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping. Nat Commun 2023; 14:1208. [PMID: 36869052 PMCID: PMC9984425 DOI: 10.1038/s41467-023-36897-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/22/2023] [Indexed: 03/05/2023] Open
Abstract
Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity.
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7
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Zhang L, Qiu L, Wu J, Qi Y, Gao X, He C, Qi R, Wang H, Yao X, Zhu H, Li Y, Hao S, Lu Q, Long H, Lian S, Zhu W, Zhang H, Lai W, Su X, Lu R, Guo Z, Li J, Li M, Liu Q, Wang H, He L, Nong X, Li F, Li Y, Yao C, Xu J, Tang H, Wang D, Li Z, Yu H, Xiao S, An J, Pu X, Yu S, Zhang J, Chen X, Wang H, Huang W, Chen H, Xiao T. GWAS of Chronic Spontaneous Urticaria Reveals Genetic Overlap with Autoimmune Diseases, Not Atopic Diseases. J Invest Dermatol 2023; 143:67-77.e15. [PMID: 35933036 DOI: 10.1016/j.jid.2022.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 07/03/2022] [Accepted: 07/09/2022] [Indexed: 02/08/2023]
Abstract
Although chronic spontaneous urticaria (CSU) is a common disease, GWASs of CSU are lacking. We aimed to identify susceptibility SNPs by performing a GWAS in Chinese Han adults with CSU. The discovery cohort included 430 CSU cases and 482 healthy controls. The GWAS findings were validated in 800 CSU cases and 900 healthy controls. Genetic, functional enrichment, and bioinformatic analyses of genome-wide significant SNPs were performed to assess the association between CSU and autoimmunity or atopy. Five genome-wide significant SNPs were identified: rs434124/LILRA3, rs61986182/IGHG1/2, rs73075571/TDGF1, rs9378141/HLA-G, and rs3789612/PTPN22. The first four SNPs were in linkage disequilibrium with autoimmune-related diseases‒associated SNPs and were cis-expression quantitative trait loci in immune cells. The five SNPs-annotated genes were significantly enriched in immune processes. Higher polygenic risk scores and allele frequencies of rs3789612∗T, rs9378141∗C, and rs73075571∗G were significantly associated with autoimmune-related CSU phenotypes, including positive antithyroglobulin IgG, positive anti-FcεRIα IgG, total IgE <40 IU/ml, and positive antithyroid peroxidase IgG but not with atopic or allergic sensitized CSU phenotypes. This GWAS of CSU identifies five risk loci and reveals that CSU shares genetic overlap with autoimmune diseases and that genetic factors predisposing to CSU mainly manifest through associations with autoimmune traits.
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Affiliation(s)
- Liming Zhang
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Li Qiu
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Jian Wu
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Yumeng Qi
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Xinghua Gao
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Chundi He
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Ruiqun Qi
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Hexiao Wang
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Xu Yao
- Department of Allergy and Rheumatology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Hong Zhu
- Department of Allergy and Rheumatology, Hospital for Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Yuzhen Li
- Department of Dermatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Siyu Hao
- Department of Dermatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qianjin Lu
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hai Long
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shi Lian
- Department of Dermatology, Xuan Wu Hospital Capital Medical Univerisity, Beijing, China
| | - Wei Zhu
- Department of Dermatology, Xuan Wu Hospital Capital Medical Univerisity, Beijing, China
| | - Haiping Zhang
- Department of Dermatology, Xuan Wu Hospital Capital Medical Univerisity, Beijing, China
| | - Wei Lai
- Department of Dermatology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiangyang Su
- Department of Dermatology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Rongbiao Lu
- Department of Dermatology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zaipei Guo
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingyi Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
| | - Mengmeng Li
- Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China
| | - Quanzhong Liu
- Department of Dermatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Huiping Wang
- Department of Dermatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Li He
- Department of Dermatology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiang Nong
- Department of Dermatology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Fuqiu Li
- Department of Dermatology, The Second Hospital of Jilin University, Changchun, China
| | - Yang Li
- Department of Dermatology, The Second Hospital of Jilin University, Changchun, China
| | - Chunli Yao
- Department of Dermatology, The Second Hospital of Jilin University, Changchun, China
| | - Jinhua Xu
- Department of Dermatology, Huashan Hospital, Fudan Univeristy, Shanghai, China
| | - Hui Tang
- Department of Dermatology, Huashan Hospital, Fudan Univeristy, Shanghai, China
| | - Duoqin Wang
- Department of Dermatology, Huashan Hospital, Fudan Univeristy, Shanghai, China
| | - Zhenlu Li
- Department of Dermatology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Huiqian Yu
- Department of Dermatology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Shengxiang Xiao
- Department of Dermatology, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Jingang An
- Department of Dermatology, The Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Xiongming Pu
- Department of Dermatology, People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Key Laboratory of Dermatology Research (XJYS1707), Xinjiang, China
| | - Shirong Yu
- Department of Dermatology, People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Key Laboratory of Dermatology Research (XJYS1707), Xinjiang, China
| | - Jianzhong Zhang
- Department of Dermatology, Peking University People's Hospital, Beijing, China
| | - Xue Chen
- Department of Dermatology, Peking University People's Hospital, Beijing, China
| | - Haifeng Wang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC), Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC), Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, China
| | - Hongduo Chen
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China
| | - Ting Xiao
- Department of Dermatology, The First Hospital of China Medical University, National Health Commission Key Laboratory of Immunodermatology, Key Laboratory of Immunodermatology of Ministry of Education, Shenyang, China.
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8
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Derks EM, Thorp JG, Gerring ZF. Ten challenges for clinical translation in psychiatric genetics. Nat Genet 2022; 54:1457-1465. [PMID: 36138228 DOI: 10.1038/s41588-022-01174-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/27/2022] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.
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Affiliation(s)
- Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Zachary F Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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9
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mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights. Metabolites 2022; 12:metabo12060526. [PMID: 35736459 PMCID: PMC9230867 DOI: 10.3390/metabo12060526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.
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10
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Abstract
Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.
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Affiliation(s)
- Andrew D Grotzinger
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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11
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Abstract
Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.
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Affiliation(s)
- Andy Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA; .,Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
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12
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Yuan J, Xing H, Lamy AL, Lencz T, Pe’er I. Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts. PLoS Genet 2020; 16:e1009015. [PMID: 32956347 PMCID: PMC7529195 DOI: 10.1371/journal.pgen.1009015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 10/01/2020] [Accepted: 07/29/2020] [Indexed: 11/18/2022] Open
Abstract
Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0−4), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10−9. Scores were not significantly reduced when partitioning by known subclusters (“Depression” and “Worry”), suggesting that these factors are not the primary source of observed heterogeneity. Several traits, such as bipolar disease, are known to be heterogeneous and comprise distinct subtypes with unique genomic associations. For other traits such as schizophrenia, heterogeneity may be suspected, but specific subtypes are less well characterized. Furthermore, conventional mixture model-based methods to detect subtypes in genome-wide association data struggle with the high polygenicity of complex traits. We propose CLiP (Correlated Liability Predictors), a method that does not identify subtype-specific effects, but is very well-powered to detect heterogeneity of any kind within the very weak signals of GWAS. CLiP serves as a method to flag particular phenotypes for potential further study into the genomic factors driving heterogeneity, as well as a means to evaluate the transferability of polygenic risk scores. We also develop extensions of CLiP applicable to scoring heterogeneity in quantitative phenotypes and quantitative predictors such as gene expression. We apply CLiP to scoring heterogeneity in schizophrenia cohorts from the Psychiatric Genomics Consortium and neuroticism in individuals in the UK Biobank and find significant heterogeneity in both phenotypes, warranting further study.
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Affiliation(s)
- Jie Yuan
- Department of Computer Science, Columbia University, New York, United States of America
- * E-mail:
| | - Henry Xing
- Department of Computer Science, Columbia University, New York, United States of America
| | - Alexandre Louis Lamy
- Department of Computer Science, Columbia University, New York, United States of America
| | | | - Todd Lencz
- The Center for Psychiatric Neuroscience, Feinstein Institutes for Medical Research, New York, United States of America
| | - Itsik Pe’er
- Department of Computer Science, Columbia University, New York, United States of America
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13
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Rovira P, Demontis D, Sánchez-Mora C, Zayats T, Klein M, Mota NR, Weber H, Garcia-Martínez I, Pagerols M, Vilar-Ribó L, Arribas L, Richarte V, Corrales M, Fadeuilhe C, Bosch R, Martin GE, Almos P, Doyle AE, Grevet EH, Grimm O, Halmøy A, Hoogman M, Hutz M, Jacob CP, Kittel-Schneider S, Knappskog PM, Lundervold AJ, Rivero O, Rovaris DL, Salatino-Oliveira A, da Silva BS, Svirin E, Sprooten E, Strekalova T, Arias-Vasquez A, Sonuga-Barke EJS, Asherson P, Bau CHD, Buitelaar JK, Cormand B, Faraone SV, Haavik J, Johansson SE, Kuntsi J, Larsson H, Lesch KP, Reif A, Rohde LA, Casas M, Børglum AD, Franke B, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Shared genetic background between children and adults with attention deficit/hyperactivity disorder. Neuropsychopharmacology 2020; 45:1617-1626. [PMID: 32279069 PMCID: PMC7419307 DOI: 10.1038/s41386-020-0664-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 02/25/2020] [Accepted: 03/03/2020] [Indexed: 12/14/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate symptoms of inattention, impulsivity, and hyperactivity that persist into adulthood in the majority of the diagnosed children. Despite several risk factors during childhood predicting the persistence of ADHD symptoms into adulthood, the genetic architecture underlying the trajectory of ADHD over time is still unclear. We set out to study the contribution of common genetic variants to the risk for ADHD across the lifespan by conducting meta-analyses of genome-wide association studies on persistent ADHD in adults and ADHD in childhood separately and jointly, and by comparing the genetic background between them in a total sample of 17,149 cases and 32,411 controls. Our results show nine new independent loci and support a shared contribution of common genetic variants to ADHD in children and adults. No subgroup heterogeneity was observed among children, while this group consists of future remitting and persistent individuals. We report similar patterns of genetic correlation of ADHD with other ADHD-related datasets and different traits and disorders among adults, children, and when combining both groups. These findings confirm that persistent ADHD in adults is a neurodevelopmental disorder and extend the existing hypothesis of a shared genetic architecture underlying ADHD and different traits to a lifespan perspective.
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Affiliation(s)
- Paula Rovira
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
| | - Ditte Demontis
- Department of Biomedicine (Human Genetics), and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cristina Sánchez-Mora
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Tetyana Zayats
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT, and Harvard, Cambridge, MA, USA
| | - Marieke Klein
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- University Medical Center Utrecht, UMC Utrecht Brain Center, Department of Psychiatry, Utrecht, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Heike Weber
- Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg, Germany
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Iris Garcia-Martínez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Banc de Sang i Teixits (BST), Barcelona, Spain
- Grup de Medicina Transfusional, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona (VHIR-UAB), Barcelona, Spain
| | - Mireia Pagerols
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, 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, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
| | - Vanesa Richarte
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Montserrat Corrales
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Christian Fadeuilhe
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Rosa Bosch
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Gemma Español Martin
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain
| | - Peter Almos
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Alysa E Doyle
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Eugenio Horacio Grevet
- ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Psychiatry, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Oliver Grimm
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Anne Halmøy
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mara Hutz
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Christian P Jacob
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Per M Knappskog
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Olga Rivero
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Diego Luiz Rovaris
- ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Angelica Salatino-Oliveira
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Bruna Santos da Silva
- ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Evgeniy Svirin
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia
| | - Emma Sprooten
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Tatyana Strekalova
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Alejandro Arias-Vasquez
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Edmund J S Sonuga-Barke
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Aarhus University, Aarhus, Denmark
| | - Philip Asherson
- Social Genetic and Developmental Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Claiton Henrique Dotto Bau
- ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands
| | - Bru Cormand
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Catalonia, Spain
| | - Stephen V Faraone
- Departments of Psychiatry, of Neuroscience, and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Stefan E Johansson
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jonna Kuntsi
- Social Genetic and Developmental Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Henrik Larsson
- School of medical Sciences, Örebro University, Örebro, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Klaus-Peter Lesch
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Luis Augusto Rohde
- Division of Child Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Miquel Casas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Anders D Børglum
- Department of Biomedicine (Human Genetics), and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - 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, Catalonia, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, 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, Catalonia, Spain.
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, 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, Catalonia, Spain.
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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14
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Genetic stratification of depression in UK Biobank. Transl Psychiatry 2020; 10:163. [PMID: 32448866 PMCID: PMC7246256 DOI: 10.1038/s41398-020-0848-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 11/18/2022] Open
Abstract
Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations were used to test for evidence of subgroups using genetic data from seven other complex traits and disorders that were genetically correlated with depression and had sufficient power (>0.6) for detection. We found no evidence for subgroups within depression for schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, anorexia nervosa, inflammatory bowel disease or obesity. This suggests that for these traits, genetic correlations with depression were driven by pleiotropic genetic variants carried by everyone rather than by a specific subgroup.
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15
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Bellou E, Stevenson-Hoare J, Escott-Price V. Polygenic risk and pleiotropy in neurodegenerative diseases. Neurobiol Dis 2020; 142:104953. [PMID: 32445791 PMCID: PMC7378564 DOI: 10.1016/j.nbd.2020.104953] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.
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16
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Schijven D, Veldink JH, Luykx JJ. Genetic cross-disorder analysis in psychiatry: from methodology to clinical utility. Br J Psychiatry 2020; 216:246-249. [PMID: 30931869 DOI: 10.1192/bjp.2019.72] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-wide association studies have uncovered hundreds of loci associated with psychiatric disorders. Cross-disorder studies are among the prime ramifications of such research. Here, we discuss the methodology of the most widespread methods and their clinical utility with regard to diagnosis, prediction, disease aetiology and treatment in psychiatry.
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Affiliation(s)
- Dick Schijven
- Researcher, Department of Psychiatry, Department of Neurology and Neurosurgery and Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jan H Veldink
- Professor, Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jurjen J Luykx
- Assistant Professor, Department of Psychiatry and Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University; and GGNet Mental Health, Apeldoorn, the Netherlands
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17
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Sanchez-Roige S, Palmer AA. Emerging phenotyping strategies will advance our understanding of psychiatric genetics. Nat Neurosci 2020; 23:475-480. [DOI: 10.1038/s41593-020-0609-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 02/12/2020] [Indexed: 01/19/2023]
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18
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Abstract
The genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease.
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19
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Schwabe I, Milaneschi Y, Gerring Z, Sullivan PF, Schulte E, Suppli NP, Thorp JG, Derks EM, Middeldorp CM. Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies. Psychol Med 2019; 49:2646-2656. [PMID: 31559935 PMCID: PMC6877467 DOI: 10.1017/s0033291719002502] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/23/2019] [Accepted: 08/23/2019] [Indexed: 11/27/2022]
Abstract
To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.
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Affiliation(s)
- I. Schwabe
- Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Y. Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Z. Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - P. F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - E. Schulte
- Medical Centre of the University of Munich, Munich, Germany
| | - N. P. Suppli
- Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - J. G. Thorp
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - E. M. Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - C. M. Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Australia
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
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20
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Abstract
Leprosy is a chronic infectious disease of the skin and peripheral nerves that presents a strong link with the host genetic background. Different approaches in genetic studies have been applied to leprosy and today leprosy is among the infectious diseases with the greatest number of genetic risk variants identified. Several leprosy genes have been implicated in host immune response to pathogens and point to specific pathways that are relevant for host defense to infection. In addition, host genetic factors are also involved in the heterogeneity of leprosy clinical manifestations and in excessive inflammatory responses that occur in some leprosy patients. Finally, genetic studies in leprosy have provided strong evidence of pleiotropic effects between leprosy and other complex diseases, such as immune-mediated or neurodegenerative diseases. These findings not only impact on the field of leprosy and infectious diseases but also make leprosy a good model for the study of complex immune-mediated diseases. Here, we summarize recent genetic findings in leprosy susceptibility and discuss the overlap of the genetic control in leprosy with Parkinson's disease and inflammatory bowel disease. Moreover, some limitations, challenges, and potential new avenues for future genetics studies of leprosy are also discussed in this review.
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21
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Meng W, Adams MJ, Reel P, Rajendrakumar A, Huang Y, Deary IJ, Palmer CNA, McIntosh AM, Smith BH. Genetic correlations between pain phenotypes and depression and neuroticism. Eur J Hum Genet 2019; 28:358-366. [PMID: 31659249 PMCID: PMC7028719 DOI: 10.1038/s41431-019-0530-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/23/2019] [Accepted: 09/27/2019] [Indexed: 12/18/2022] Open
Abstract
Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. In this study, we estimated the genetic correlations of eight pain phenotypes (defined by the UK Biobank, n = 151,922–226,683) with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. We also used the LDSC software to calculate the genetic correlations among pain phenotypes. All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2 = 0.31, standard error = 0.072). Many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Our results suggest that pain, neuroticism and depression share partially overlapping genetic risk factors.
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Affiliation(s)
- Weihua Meng
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK.
| | - Mark J Adams
- Division of Psychiatry, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Parminder Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK
| | - Aravind Rajendrakumar
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK
| | - Yu Huang
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Edinburgh Medical School, University of Edinburgh, Edinburgh, EH10 5HF, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Blair H Smith
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD2 4BF, UK
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22
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Pouget JG, Han B, Wu Y, Mignot E, Ollila HM, Barker J, Spain S, Dand N, Trembath R, Martin J, Mayes MD, Bossini-Castillo L, López-Isac E, Jin Y, Santorico SA, Spritz RA, Hakonarson H, Polychronakos C, Raychaudhuri S, Knight J. Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk. Hum Mol Genet 2019; 28:3498-3513. [PMID: 31211845 PMCID: PMC6891073 DOI: 10.1093/hmg/ddz145] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 05/24/2019] [Accepted: 06/13/2019] [Indexed: 01/01/2023] Open
Abstract
Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.
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Affiliation(s)
- Jennie G Pouget
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | | | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Emmanuel Mignot
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Palo Alto, CA, USA
| | - Hanna M Ollila
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Palo Alto, CA, USA
- Finnish Institute for Molecular Medicine, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA and Broad Institute, Cambridge, MA, USA
| | - Jonathan Barker
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- St. John’s Institute of Dermatology, Faculty of Life Sciences and Medicine, King’s College London, London, UK
| | - Sarah Spain
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Nick Dand
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
| | - Richard Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- Queen Mary University of London, Barts and the London School of Medicine and Dentistry, London, UK
| | - Javier Martin
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), Granada, Spain
| | - Maureen D Mayes
- The University of Texas Health Science Center–Houston, Houston, USA
| | - Lara Bossini-Castillo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Elena López-Isac
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), Granada, Spain
| | - Ying Jin
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora 80045, CO, USA
| | - Stephanie A Santorico
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Richard A Spritz
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora 80045, CO, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Constantin Polychronakos
- Endocrine Genetics Laboratory, Department of Pediatrics and the Child Health Program of the Research Institute, McGill University Health Centre, Montreal, QC, Canada
| | - Soumya Raychaudhuri
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Partners HealthCare Center for Personalized Genetic Medicine, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK
| | - Jo Knight
- Lancaster Medical School and Data Science Institute, Lancaster University, Lancaster, UK
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23
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Trzaskowski M, Mehta D, Peyrot W, Hawkes D, Davies D, Howard D, Kemper KE, Sidorenko J, Maier R, Ripke S, Mattheisen M, Baune BT, Grabe HJ, Heath AC, Jones L, Jones I, Madden PAF, McIntosh AM, Breen G, Lewis CM, Børglum AD, Sullivan PF, Martin NG, Kendler KS, Levinson DF, Wray NR. Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder. Am J Med Genet B Neuropsychiatr Genet 2019; 180:439-447. [PMID: 30708398 PMCID: PMC6675638 DOI: 10.1002/ajmg.b.32713] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/19/2018] [Accepted: 01/04/2019] [Indexed: 01/08/2023]
Abstract
Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between-cohort and between-sex heterogeneity. First, we used genome-wide association study (GWAS) summary statistics to investigate between-cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; N cases = 16,823, N controls = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between-sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD.
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Affiliation(s)
- Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Divya Mehta
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
| | - Wouter Peyrot
- Department of Psychiatry, Vrije Universiteit Medical Center and GGZ in Geest, Amsterdam, The Netherlands
| | - David Hawkes
- AGRF, The University of Queensland, Brisbane, Queensland, Australia
| | - Daniel Davies
- Department of Psychiatry, Behavioural and Clinical Neuroscience Institute and Developmental Psychiatry, Cambridge University, Cambridge, England, United Kingdom
| | - David Howard
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Robert Maier
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Stephan Ripke
- Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany
| | - Manuel Mattheisen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Bernhard T Baune
- Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Andrew C Heath
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri
| | - Lisa Jones
- Institute of Health & Society, University of Worcester, Worcester, United Kingdom
| | - Ian Jones
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Pamela AF Madden
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Gerome Breen
- MRC Social Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
- NIHR BRC for Mental Health, King's College London, London, United Kingdom
| | - Cathryn M. Lewis
- MRC Social Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
- Department of Medical and Molecular Genetics, King's College London, London, United Kingdom
| | - Anders D. Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine and iSEQ-Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nicholas G. Martin
- Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Kenneth S. Kendler
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Douglas F. Levinson
- Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
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24
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Zhu M, Xu K, Chen Y, Gu Y, Zhang M, Luo F, Liu Y, Gu W, Hu J, Xu H, Xie Z, Sun C, Li Y, Sun M, Xu X, Hsu HT, Chen H, Fu Q, Shi Y, Xu J, Ji L, Liu J, Bian L, Zhu J, Chen S, Xiao L, Li X, Jiang H, Shen M, Huang Q, Fang C, Li X, Huang G, Fan J, Jiang Z, Jiang Y, Dai J, Ma H, Zheng S, Cai Y, Dai H, Zheng X, Zhou H, Ni S, Jin G, She JX, Yu L, Polychronakos C, Hu Z, Zhou Z, Weng J, Shen H, Yang T. Identification of Novel T1D Risk Loci and Their Association With Age and Islet Function at Diagnosis in Autoantibody-Positive T1D Individuals: Based on a Two-Stage Genome-Wide Association Study. Diabetes Care 2019; 42:1414-1421. [PMID: 31152121 DOI: 10.2337/dc18-2023] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 05/03/2019] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians. RESEARCH DESIGN AND METHODS We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis. RESULTS We observed a high genetic correlation between children/adolescents and adult T1D case subjects (r g = 0.87), as well as subgroups of autoantibody status (r g ≥ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26, P = 2.70 × 10-8) and rs3802604 in GATA3 (OR 1.24, P = 2.06 × 10-8), and two previously reported loci, rs1770 in MHC (OR 4.28, P = 2.25 × 10-232) and rs705699 in SUOX (OR 1.46, P = 7.48 × 10-20). Further fine mapping in the MHC region revealed five independent variants, including another novel locus, HLA-C position 275 (omnibus P = 9.78 × 10-12), specific to the Chinese population. Based on the identified eight variants, we achieved an area under the curve value of 0.86 (95% CI 0.85-0.88). By building a genetic risk score (GRS) with these variants, we observed that the higher GRS were associated with an earlier age of T1D diagnosis (P = 9.08 × 10-11) and lower fasting C-peptide levels (P = 7.19 × 10-3) in individuals newly diagnosed with T1D. CONCLUSIONS Our results extend current knowledge on genetic contributions to T1D risk. Further investigations in different populations are needed for genetic heterogeneity and subsequent precision medicine.
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Affiliation(s)
- Meng Zhu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Kuanfeng Xu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yang Chen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yong Gu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei Zhang
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Feihong Luo
- Department of Pediatric Endocrinology and Inherited Metabolic Diseases, Children's Hospital of Fudan University, Shanghai, China
| | - Yu Liu
- Department of Endocrinology and Metabolism, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China
| | - Wei Gu
- Department of Endocrinology, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Ji Hu
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Haixia Xu
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhiguo Xie
- Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Metabolic Diseases, Changsha, China.,Key Laboratory of Diabetes Immunology, Central South University, Ministry of Education, Changsha, China
| | - Chengjun Sun
- Department of Pediatric Endocrinology and Inherited Metabolic Diseases, Children's Hospital of Fudan University, Shanghai, China
| | - Yuxiu Li
- Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Min Sun
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Xu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hsiang-Ting Hsu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Heng Chen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qi Fu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Shi
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jingjing Xu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Ji
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jin Liu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lingling Bian
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Zhu
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shuang Chen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xiao
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin Li
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hemin Jiang
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Min Shen
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qianwen Huang
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chen Fang
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xia Li
- Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Metabolic Diseases, Changsha, China.,Key Laboratory of Diabetes Immunology, Central South University, Ministry of Education, Changsha, China
| | - Gan Huang
- Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Metabolic Diseases, Changsha, China.,Key Laboratory of Diabetes Immunology, Central South University, Ministry of Education, Changsha, China
| | - Jingyi Fan
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhu Jiang
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Yue Jiang
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Shuai Zheng
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Cai
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hao Dai
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xuqin Zheng
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hongwen Zhou
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shining Ni
- Department of Endocrinology, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jin-Xiong She
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA
| | - Liping Yu
- Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO
| | - Constantin Polychronakos
- The Endocrine Genetics Laboratory, Child Health and Human Development Program and Department of Pediatrics, McGill University Health Centre Research Institute, Montreal, Canada
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China .,Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhiguang Zhou
- Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, China .,National Clinical Research Center for Metabolic Diseases, Changsha, China.,Key Laboratory of Diabetes Immunology, Central South University, Ministry of Education, Changsha, China
| | - Jianping Weng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hongbing Shen
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China .,Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Tao Yang
- Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China .,Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
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25
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Myngbay A, Bexeitov Y, Adilbayeva A, Assylbekov Z, Yevstratenko BP, Aitzhanova RM, Matkarimov B, Adarichev VA, Kunz J. CTHRC1: A New Candidate Biomarker for Improved Rheumatoid Arthritis Diagnosis. Front Immunol 2019; 10:1353. [PMID: 31249576 PMCID: PMC6582781 DOI: 10.3389/fimmu.2019.01353] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 05/28/2019] [Indexed: 11/23/2022] Open
Abstract
Background: The purpose of this study was to determine whether plasma levels of the collagen triple helix repeat containing 1 (CTHRC1) protein can serve as a blood-based biomarker for improved diagnosis of rheumatoid arthritis (RA) patients and monitoring of RA disease activity. Methods: We measured levels of CTHRC1 in the plasma of patients diagnosed with RA, osteoarthritis (OA), reactive arthritis (ReA), as well as in healthy individuals. We then assessed the correlation between CTHRC1 protein and a range of indices including the 28-joint disease activity score (DAS28), rheumatoid factor (RF), C-reactive protein (CRP), anti-citrullinated protein antibodies (ACPA), erythrocyte sedimentation rate (ESR), as well as a panel of cytokines, including interleukin 1 beta (IL-1β), interleukin 6 (IL-6), interleukin 8 (IL-8), and interferon gamma (IFNγ). Receiver operating characteristic (ROC) analysis was further performed to assess the diagnostic value of CTHRC1. Results: CTHRC1 plasma levels were significantly elevated in RA patients compared to healthy individuals, OA and ReA patients. ROC curve and risk score analysis suggested that plasma CTHRC1 can accurately discriminate patients with RA from healthy controls and may have practical value for RA diagnosis. CTHRC1 levels were positively associated with RF, ACPA, CRP, and disease activity based on the combined index of DAS28 with CRP (DAS28-CRP), and also strongly correlated with IL-1β, IL-6, IL-8, and IFNγ. Conclusion: Our studies show that CTHRC1 is a sensitive and easy-to-measure plasma marker that differentiates between RA and healthy status and also distinguishes between RA and other forms of arthritis, such as OA and ReA. At the current level of understanding, plasma CTHRC1 levels may improve the diagnosis of RA and these findings warrant confirmation in a larger, more comprehensive patient population.
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Affiliation(s)
- Askhat Myngbay
- PhD Program in Science, Engineering and Technology, Nazarbayev University, Astana, Kazakhstan.,National Laboratory Astana, Astana, Kazakhstan
| | - Yergali Bexeitov
- National Laboratory Astana, Department of General Biology and Genomics, Faculty of Natural Sciences, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan
| | | | | | | | | | | | - Vyacheslav A Adarichev
- Department of Medicine (Division of Rheumatology), Department of Microbiology & Immunology, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jeannette Kunz
- Department of Biology, Nazarbayev University, Astana, Kazakhstan
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Lane JM, Jones SE, Dashti HS, Wood AR, Aragam KG, van Hees VT, Strand LB, Winsvold BS, Wang H, Bowden J, Song Y, Patel K, Anderson SG, Beaumont RN, Bechtold DA, Cade BE, Haas M, Kathiresan S, Little MA, Luik AI, Loudon AS, Purcell S, Richmond RC, Scheer FAJL, Schormair B, Tyrrell J, Winkelman JW, Winkelmann J, Hveem K, Zhao C, Nielsen JB, Willer CJ, Redline S, Spiegelhalder K, Kyle SD, Ray DW, Zwart JA, Brumpton B, Frayling TM, Lawlor DA, Rutter MK, Weedon MN, Saxena R. Biological and clinical insights from genetics of insomnia symptoms. Nat Genet 2019; 51:387-393. [PMID: 30804566 PMCID: PMC6415688 DOI: 10.1038/s41588-019-0361-7] [Citation(s) in RCA: 205] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 01/25/2019] [Indexed: 11/09/2022]
Abstract
Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes. The underlying pathophysiological processes and causal relationships of insomnia with disease are poorly understood. Here we identified 57 loci for self-reported insomnia symptoms in the UK Biobank (n = 453,379) and confirmed their effects on self-reported insomnia symptoms in the HUNT Study (n = 14,923 cases and 47,610 controls), physician-diagnosed insomnia in the Partners Biobank (n = 2,217 cases and 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n = 83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis and of genes expressed in multiple brain regions, skeletal muscle, and adrenal glands. Evidence of shared genetic factors was found between frequent insomnia symptoms and restless legs syndrome, aging, and cardiometabolic, behavioral, psychiatric, and reproductive traits. Evidence was found for a possible causal link between insomnia symptoms and coronary artery disease, depressive symptoms, and subjective well-being.
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Affiliation(s)
- Jacqueline M Lane
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Samuel E Jones
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Krishna G Aragam
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | | | - Linn B Strand
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bendik S Winsvold
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
- Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Heming Wang
- Broad Institute, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jack Bowden
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Yanwei Song
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- College of Science, Northeastern University, Boston, MA, USA
| | - Krunal Patel
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- College of Science, Northeastern University, Boston, MA, USA
| | - Simon G Anderson
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Farr Institute of Health Informatics Research, University College London, London, UK
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - David A Bechtold
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Brian E Cade
- Broad Institute, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mary Haas
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Sekar Kathiresan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Max A Little
- Department of Mathematics, Aston University, Birmingham, UK
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Annemarie I Luik
- Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Andrew S Loudon
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Shaun Purcell
- Department of Psychiatry, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rebecca C Richmond
- School of Social and Community Medicine, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Frank A J L Scheer
- Broad Institute, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Barbara Schormair
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - John W Winkelman
- Departments of Psychiatry and Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Neurogenetics, Technische Universität München, Munich, Germany
| | - Kristian Hveem
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jonas B Nielsen
- FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Cristen J Willer
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Susan Redline
- Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kai Spiegelhalder
- Clinic for Psychiatry and Psychotherapy, Medical Centre-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Simon D Kyle
- Sleep and Circadian Neuroscience Institute, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David W Ray
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, OX37LE/NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - John-Anker Zwart
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ben Brumpton
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Department of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Martin K Rutter
- Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Michael N Weedon
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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27
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The effect of chronic physical illnesses on psychiatric hospital admission in patients with recurrent major depression. Psychiatry Res 2019; 272:602-608. [PMID: 30616130 DOI: 10.1016/j.psychres.2018.12.178] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 11/26/2018] [Accepted: 12/31/2018] [Indexed: 12/19/2022]
Abstract
People with major depressive disorder (MDD) have an increased burden of chronic physical illnesses (CPI). However, information about the effect of CPIs on recurrent MDD treatment outcome is limited. The objective of this study was to explore whether the number of CPIs in patients with recurrent MDD was associated with higher rate of psychiatric admissions. Data were collected for a consecutive sample of 190 patients diagnosed with recurrent MDD. The key outcome was the number of psychiatric admissions following psychiatric diagnosis. The independent variable was the number of CPIs. The effects of different clinical, sociodemographic, and lifestyle confounding factors were controlled using robust regression. The patients with CPI had significantly more psychiatric admissions than the patients without CPI, and the number of CPIs was significantly associated with the number of psychiatric admissions. The results of our study largely confirmed that more than two CPIs in patients diagnosed with recurrent MDD are associated with higher rates of psychiatric admission, independent of psychiatric comorbidities and other clinical and sociodemographic factors. These findings indicate that to improve treatment outcome and to reduce recurrence, it is crucial to enhance early recognition and treatment of physical comorbidity.
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Abstract
Supplemental Digital Content is available in the text. The clinical comorbidity of alcohol dependence (AD) and major depressive disorder (MDD) is well established, whereas genetic factors influencing co-occurrence remain unclear. A recent study using polygenic risk scores (PRS) calculated based on the first-wave Psychiatric Genomics Consortium MDD meta-analysis (PGC-MDD1) suggests a modest shared genetic contribution to MDD and AD. Using a (∼10 fold) larger discovery sample, we calculated PRS based on the second wave (PGC-MDD2) of results, in a severe AD case–control target sample. We found significant associations between AD disease status and MDD-PRS derived from both PGC-MDD2 (most informative P-threshold=1.0, P=0.00063, R2=0.533%) and PGC-MDD1 (P-threshold=0.2, P=0.00014, R2=0.663%) meta-analyses; the larger discovery sample did not yield additional predictive power. In contrast, calculating PRS in a MDD target sample yielded increased power when using PGC-MDD2 (P-threshold=1.0, P=0.000038, R2=1.34%) versus PGC-MDD1 (P-threshold=1.0, P=0.0013, R2=0.81%). Furthermore, when calculating PGC-MDD2 PRS in a subsample of patients with AD recruited explicitly excluding comorbid MDD, significant associations were still found (n=331; P-threshold=1.0, P=0.042, R2=0.398%). Meanwhile, in the subset of patients in which MDD was not the explicit exclusion criteria, PRS predicted more variance (n=999; P-threshold=1.0, P=0.0003, R2=0.693%). Our findings replicate the reported genetic overlap between AD and MDD and also suggest the need for improved, rigorous phenotyping to identify true shared cross-disorder genetic factors. Larger target samples are needed to reduce noise and take advantage of increasing discovery sample size.
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29
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Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree. Transl Psychiatry 2018; 8:218. [PMID: 30315151 PMCID: PMC6185949 DOI: 10.1038/s41398-018-0226-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 05/10/2018] [Accepted: 07/14/2018] [Indexed: 12/15/2022] Open
Abstract
Bipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this is due to increased risk for comorbid diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole-genome sequence data using a population-specific reference panel for an extended multigenerational Old Order Amish pedigree (n = 394), segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores (PRS) to establish each individual's risk for several complex diseases. We identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation in APOB associated with hypercholesterolemia. Using PRS, we demonstrated that BD individuals from this pedigree were enriched for the same common risk alleles for BD as the general population (β = 0.416, p = 6 × 10-4). Furthermore, we find evidence for a common genetic etiology between BD risk and polygenic risk for clinical autoimmune thyroid disease (p = 1 × 10-4), diabetes (p = 1 × 10-3), and lipid traits such as triglyceride levels (p = 3 × 10-4) in the pedigree. We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits.
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Zhang Z, Ma P, Li Q, Xiao Q, Sun H, Olasege BS, Wang Q, Pan Y. Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies. Front Genet 2018; 9:393. [PMID: 30271426 PMCID: PMC6149433 DOI: 10.3389/fgene.2018.00393] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 08/29/2018] [Indexed: 01/09/2023] Open
Abstract
The relationship between growth and immune phenotypes has been presented in the context of physiology and energy allocation theory, but has rarely been explained genetically in humans. As more summary statistics of genome-wide association studies (GWAS) become available, it is increasingly possible to explore the genetic relationship between traits at the level of genome-wide summary statistics. In this study, publicly available summary statistics of growth and immune related traits were used to evaluate the genetic correlation coefficients between immune and growth traits, as well as the cause and effect relationship between them. In addition, pleiotropic variants and KEGG pathways were identified. As a result, we found negative correlations between birthweight and immune cell count phenotypes, a positive correlation between childhood head circumference and eosinophil counts (EO), and positive or negative correlations between childhood body mass index and immune phenotypes. Statistically significant negative effects of immune cell count phenotypes on human height, and a slight but significant negative influence of human height on allergic disease were also observed. A total of 98 genomic regions were identified as containing variants potentially related to both immunity and growth. Some variants, such as rs3184504 located in SH2B3, rs13107325 in SLC39A8, and rs1260326 located in GCKR, which have been identified to be pleiotropic SNPs among other traits, were found to also be related to growth and immune traits in this study. Meanwhile, the most frequent overlapping KEGG pathways between growth and immune phenotypes were autoimmune related pathways. Pleiotropic pathways such as the adipocytokine signaling pathway and JAK-STAT signaling pathway were also identified to be significant. The results of this study indicate the complex genetic relationship between growth and immune phenotypes, and reveal the genetic background of their correlation in the context of pleiotropy.
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Affiliation(s)
- Zhe Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qiumeng Li
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qian Xiao
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Hao Sun
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Babatunde Shittu Olasege
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
| | - Yuchun Pan
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
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Abstract
Schizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene-environment interactions. Only a few specific gene-environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene-environment interaction is a common polymorphism in the AKT1 gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.
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Affiliation(s)
- Alyson Zwicker
- Department of Pathology,Dalhousie University,Halifax,NS,Canada
| | | | - Rudolf Uher
- Department of Pathology,Dalhousie University,Halifax,NS,Canada
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Franks PW, Timpson NJ. Genotype-Based Recall Studies in Complex Cardiometabolic Traits. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2018; 11:e001947. [PMID: 30354344 PMCID: PMC6813040 DOI: 10.1161/circgen.118.001947] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.
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Affiliation(s)
- Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, SE-21741, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Avon Longitudinal Study of Parents and Children, Population Health Science, Bristol Medical School, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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Martin J, Taylor MJ, Lichtenstein P. Assessing the evidence for shared genetic risks across psychiatric disorders and traits. Psychol Med 2018; 48:1759-1774. [PMID: 29198204 PMCID: PMC6088770 DOI: 10.1017/s0033291717003440] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/26/2017] [Accepted: 10/27/2017] [Indexed: 12/21/2022]
Abstract
Genetic influences play a significant role in risk for psychiatric disorders, prompting numerous endeavors to further understand their underlying genetic architecture. In this paper, we summarize and review evidence from traditional twin studies and more recent genome-wide molecular genetic analyses regarding two important issues that have proven particularly informative for psychiatric genetic research. First, emerging results are beginning to suggest that genetic risk factors for some (but not all) clinically diagnosed psychiatric disorders or extreme manifestations of psychiatric traits in the population share genetic risks with quantitative variation in milder traits of the same disorder throughout the general population. Second, there is now evidence for substantial sharing of genetic risks across different psychiatric disorders. This extends to the level of characteristic traits throughout the population, with which some clinical disorders also share genetic risks. In this review, we summarize and evaluate the evidence for these two issues, for a range of psychiatric disorders. We then critically appraise putative interpretations regarding the potential meaning of genetic correlation across psychiatric phenotypes. We highlight several new methods and studies which are already using these insights into the genetic architecture of psychiatric disorders to gain additional understanding regarding the underlying biology of these disorders. We conclude by outlining opportunities for future research in this area.
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Affiliation(s)
- Joanna Martin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Mark J. Taylor
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Hackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol 2018; 7:rsob.170125. [PMID: 29093210 PMCID: PMC5717338 DOI: 10.1098/rsob.170125] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 09/29/2017] [Indexed: 12/13/2022] Open
Abstract
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.
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35
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Maier RM, Visscher PM, Robinson MR, Wray NR. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med 2018; 48:1055-1067. [PMID: 28847336 PMCID: PMC6088780 DOI: 10.1017/s0033291717002318] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 07/16/2017] [Accepted: 07/18/2017] [Indexed: 01/09/2023]
Abstract
The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.
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Affiliation(s)
- R. M. Maier
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - P. M. Visscher
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - M. R. Robinson
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - N. R. Wray
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
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36
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Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, Iwata N, Ikegawa S, Hirata M, Matsuda K, Kubo M, Okada Y, Kamatani Y. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet 2018; 50:390-400. [PMID: 29403010 DOI: 10.1038/s41588-018-0047-6] [Citation(s) in RCA: 474] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 12/21/2017] [Indexed: 12/18/2022]
Abstract
Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 × 10-8), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity, represented by the genetic links among clinical measurements, complex diseases, and relevant cell types. Our findings demonstrate that even without prior biological knowledge of cross-phenotype relationships, genetics corresponding to clinical measurements successfully recapture those measurements' relevance to diseases, and thus can contribute to the elucidation of unknown etiology and pathogenesis.
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Affiliation(s)
- Masahiro Kanai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Atsushi Takahashi
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Genomic Medicine, Research Institute, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Nana Matoba
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masashi Ikeda
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Aichi, Japan
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Makoto Hirata
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan. .,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan.
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
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37
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Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. Am J Hum Genet 2017; 101:939-964. [PMID: 29220677 PMCID: PMC5812911 DOI: 10.1016/j.ajhg.2017.11.001] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 10/25/2017] [Indexed: 02/08/2023] Open
Abstract
Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses, we demonstrate that our method provides accurate covariance estimates, thereby enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD's correlation with cognitive traits and hints at an autoimmune component for ALS.
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Affiliation(s)
- Qiongshi Lu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Derek Ou
- Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Ryan L Powles
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA
| | | | - Yiming Hu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - David Chang
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA
| | | | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA
| | - Qidu He
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zefeng Liu
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shubhabrata Mukherjee
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Paul K Crane
- Division of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA; Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA; VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, USA.
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38
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Zhang J, Peng S, Cheng H, Nomura Y, Di Narzo AF, Hao K. Genetic Pleiotropy between Nicotine Dependence and Respiratory Outcomes. Sci Rep 2017; 7:16907. [PMID: 29203782 PMCID: PMC5715160 DOI: 10.1038/s41598-017-16964-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 11/20/2017] [Indexed: 01/04/2023] Open
Abstract
Smoking is a major cause of respiratory conditions. To date, the genetic pleiotropy between smoking behavior and lung function/chronic obstructive pulmonary disease (COPD) have not been systematically explored. We leverage large data sets of smoking behavior, lung function and COPD, and addressed two questions, (1) whether the genetic predisposition of nicotine dependence influence COPD risk and lung function; and (2) the genetic pleiotropy follow causal or independent model. We found the genetic predisposition of nicotine dependence was associated with COPD risk, even after adjusting for smoking behavior, indicating genetic pleiotropy and independent model. Two known nicotine dependent loci (15q25.1 and 19q13.2) were associated with smoking adjusted lung function, and 15q25.1 reached genome-wide significance. At various suggestive p-value thresholds, the smoking adjusted lung function traits share association signals with cigarettes per day and former smoking, substantially greater than random chance. Empirical data showed the genetic pleiotropy between nicotine dependence and COPD or lung function. The basis of pleiotropic effect is rather complex, attributable to a large number of genetic variants, and many variants functions through independent model, where the pleiotropic variants directly affect lung function, not mediated by influencing subjects' smoking behavior.
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Affiliation(s)
- Jushan Zhang
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China.,School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Shouneng Peng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yoko Nomura
- Department of Psychology, Queens College & Graduate Center, the City University of New York, New York, NY 10016, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Antonio Fabio Di Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Ke Hao
- Department of Respiratory Medicine, Shanghai Tenth People's Hospital, Tongji University, Shanghai 200072, China. .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. .,Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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39
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Nigrovic PA, Raychaudhuri S, Thompson SD. Review: Genetics and the Classification of Arthritis in Adults and Children. Arthritis Rheumatol 2017; 70:7-17. [PMID: 29024575 DOI: 10.1002/art.40350] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/05/2017] [Indexed: 12/16/2022]
Abstract
Current classification of primary inflammatory arthritis begins from the assumption that adults and children are different. No form of juvenile idiopathic arthritis bears the same name as an adult arthritis, a nomenclature gap with implications for both clinical care and research. Recent genetic data have raised questions regarding this adult/pediatric divide, revealing instead broad patterns that span the age spectrum. Combining these genetic patterns with demographic and clinical data, we propose that inflammatory arthritis can be segregated into 4 main clusters, largely irrespective of pediatric or adult onset: seropositive, seronegative (likely including a distinct group that usually begins in early childhood), spondyloarthritis, and systemic. Each of these broad clusters is internally heterogeneous, highlighting the need for further study to resolve etiologically discrete entities. Eliminating divisions based on arbitrary age cutoffs will enhance opportunities for collaboration between adult and pediatric rheumatologists, thereby helping to promote the understanding and treatment of arthritis.
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Affiliation(s)
- Peter A Nigrovic
- Brigham and Women's Hospital and Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Soumya Raychaudhuri
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, and Broad Institute of MIT and Harvard, Cambridge, Massachusetts
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40
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Hammerschlag AR, Stringer S, de Leeuw CA, Sniekers S, Taskesen E, Watanabe K, Blanken TF, Dekker K, te Lindert BHW, Wassing R, Jonsdottir I, Thorleifsson G, Stefansson H, Gislason T, Berger K, Schormair B, Wellmann J, Winkelmann J, Stefansson K, Oexle K, Van Someren EJW, Posthuma D. Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits. Nat Genet 2017; 49:1584-1592. [PMID: 28604731 PMCID: PMC5600256 DOI: 10.1038/ng.3888] [Citation(s) in RCA: 182] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2016] [Accepted: 05/03/2017] [Indexed: 12/31/2022]
Abstract
Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10-8) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.
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Affiliation(s)
- Anke R Hammerschlag
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
| | - Sven Stringer
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
| | - Suzanne Sniekers
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
| | - Erdogan Taskesen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
- Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
| | - Tessa F Blanken
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Departments of Integrative Neurophysiology and Psychiatry, Amsterdam Neuroscience, VU University and Medical Center, Amsterdam, The Netherlands
| | - Kim Dekker
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Bart HW te Lindert
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Rick Wassing
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Ingileif Jonsdottir
- deCODE genetics / Amgen Inc., Reykjavík, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | - Thorarinn Gislason
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Respiratory Medicine and Sleep, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Barbara Schormair
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Juergen Wellmann
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
- Neurologische Klinik und Poliklinik, Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Kari Stefansson
- deCODE genetics / Amgen Inc., Reykjavík, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany
| | - Eus JW Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Departments of Integrative Neurophysiology and Psychiatry, Amsterdam Neuroscience, VU University and Medical Center, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
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41
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Knevel R, Huizinga TW, Kurreeman F. Genomic Influences on Susceptibility and Severity of Rheumatoid Arthritis. Rheum Dis Clin North Am 2017; 43:347-361. [DOI: 10.1016/j.rdc.2017.04.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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42
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Wigmore EM, Clarke TK, Howard DM, Adams MJ, Hall LS, Zeng Y, Gibson J, Davies G, Fernandez-Pujals AM, Thomson PA, Hayward C, Smith BH, Hocking LJ, Padmanabhan S, Deary IJ, Porteous DJ, Nicodemus KK, McIntosh AM. Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766). Transl Psychiatry 2017; 7:e1205. [PMID: 28809859 PMCID: PMC5611720 DOI: 10.1038/tp.2017.148] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 05/08/2017] [Accepted: 06/07/2017] [Indexed: 12/23/2022] Open
Abstract
Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV.
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Affiliation(s)
- E M Wigmore
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK,Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh EH10 5HF, UK. E-mail:
| | - T-K Clarke
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - D M Howard
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - M J Adams
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - L S Hall
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Y Zeng
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - J Gibson
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - G Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - A M Fernandez-Pujals
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - P A Thomson
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - C Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - B H Smith
- Division of Population Health Sciences, University of Dundee, Dundee, UK
| | - L J Hocking
- Division of Applied Medicine, University of Aberdeen, Aberdeen, UK
| | - S Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - D J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - K K Nicodemus
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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43
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Uher R, Zwicker A. Etiology in psychiatry: embracing the reality of poly-gene-environmental causation of mental illness. World Psychiatry 2017; 16:121-129. [PMID: 28498595 PMCID: PMC5428165 DOI: 10.1002/wps.20436] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Intriguing findings on genetic and environmental causation suggest a need to reframe the etiology of mental disorders. Molecular genetics shows that thousands of common and rare genetic variants contribute to mental illness. Epidemiological studies have identified dozens of environmental exposures that are associated with psychopathology. The effect of environment is likely conditional on genetic factors, resulting in gene-environment interactions. The impact of environmental factors also depends on previous exposures, resulting in environment-environment interactions. Most known genetic and environmental factors are shared across multiple mental disorders. Schizophrenia, bipolar disorder and major depressive disorder, in particular, are closely causally linked. Synthesis of findings from twin studies, molecular genetics and epidemiological research suggests that joint consideration of multiple genetic and environmental factors has much greater explanatory power than separate studies of genetic or environmental causation. Multi-factorial gene-environment interactions are likely to be a generic mechanism involved in the majority of cases of mental illness, which is only partially tapped by existing gene-environment studies. Future research may cut across psychiatric disorders and address poly-causation by considering multiple genetic and environmental measures across the life course with a specific focus on the first two decades of life. Integrative analyses of poly-causation including gene-environment and environment-environment interactions can realize the potential for discovering causal types and mechanisms that are likely to generate new preventive and therapeutic tools.
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Affiliation(s)
- Rudolf Uher
- Departments of Psychiatry and PathologyDalhousie UniversityHalifaxB3H 2E2Nova ScotiaCanada
| | - Alyson Zwicker
- Departments of Psychiatry and PathologyDalhousie UniversityHalifaxB3H 2E2Nova ScotiaCanada
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44
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Abstract
Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.
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45
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McLaughlin RL, Schijven D, van Rheenen W, van Eijk KR, O'Brien M, Kahn RS, Ophoff RA, Goris A, Bradley DG, Al-Chalabi A, van den Berg LH, Luykx JJ, Hardiman O, Veldink JH. Genetic correlation between amyotrophic lateral sclerosis and schizophrenia. Nat Commun 2017; 8:14774. [PMID: 28322246 PMCID: PMC5364411 DOI: 10.1038/ncomms14774] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 02/03/2017] [Indexed: 12/11/2022] Open
Abstract
We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10-4) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10-7). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.
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Affiliation(s)
- Russell L. McLaughlin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin DO2 DK07, Republic of Ireland
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 DK07, Republic of Ireland
| | - Dick Schijven
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Wouter van Rheenen
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Kristel R. van Eijk
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Margaret O'Brien
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin DO2 DK07, Republic of Ireland
| | - René S. Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Roel A. Ophoff
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California 90095, USA
| | - An Goris
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven—University of Leuven, Leuven B-3000, Belgium
| | - Daniel G. Bradley
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 DK07, Republic of Ireland
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London WC2R 2LS, UK
| | - Leonard H. van den Berg
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Jurjen J. Luykx
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
- Department of Psychiatry, Hospital Network Antwerp (ZNA) Stuivenberg and Sint Erasmus, Antwerp 2020, Belgium
| | - Orla Hardiman
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 DK07, Republic of Ireland
| | - Jan H. Veldink
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
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Hu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet 2016; 17:615-29. [PMID: 27498692 DOI: 10.1038/nrg.2016.87] [Citation(s) in RCA: 201] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.
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Affiliation(s)
- Jessica Xin Hu
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Cecilia Engel Thomas
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen DK-2200, Denmark.,Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, Copenhagen DK-2100, Denmark
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Gratten J, Visscher PM. Genetic pleiotropy in complex traits and diseases: implications for genomic medicine. Genome Med 2016; 8:78. [PMID: 27435222 PMCID: PMC4952057 DOI: 10.1186/s13073-016-0332-x] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Jacob Gratten
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Peter M Visscher
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
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