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Sun YV, Liu C, Hui Q, Zhou JJ, Gaziano JM, Wilson PWF, Joseph J, Phillips LS. Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure. Am J Hum Genet 2024; 111:1481-1493. [PMID: 38897203 PMCID: PMC11267521 DOI: 10.1016/j.ajhg.2024.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.
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Affiliation(s)
- Yan V Sun
- Atlanta VA Healthcare System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
| | - Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Qin Hui
- Atlanta VA Healthcare System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Jin J Zhou
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Division of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Healthcare System, Decatur, GA, USA; Emory University School of Medicine, Atlanta, GA, USA
| | - Jacob Joseph
- VA Providence Healthcare System, Providence, RI, USA; The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Lawrence S Phillips
- Atlanta VA Healthcare System, Decatur, GA, USA; Emory University School of Medicine, Atlanta, GA, USA
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin AA, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease. NATURE CARDIOVASCULAR RESEARCH 2024; 3:754-769. [PMID: 38898929 PMCID: PMC11182748 DOI: 10.1038/s44161-024-00488-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A. Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jorien L. Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J. A. Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Joeri J. Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D. Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D. Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
- Department of Medical and Molecular Genetics, King’s College London, London, UK
| | - Andrew M. McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A. Valdimarsdóttir
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA USA
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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3
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Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. Mendelian randomization: causal inference leveraging genetic data. Psychol Med 2024; 54:1461-1474. [PMID: 38639006 DOI: 10.1017/s0033291724000321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.
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Affiliation(s)
- Lane G Chen
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Justin D Tubbs
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zipeng Liu
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Thuan-Quoc Thach
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Pak C Sham
- Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
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4
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Schormair B, Zhao C, Bell S, Didriksen M, Nawaz MS, Schandra N, Stefani A, Högl B, Dauvilliers Y, Bachmann CG, Kemlink D, Sonka K, Paulus W, Trenkwalder C, Oertel WH, Hornyak M, Teder-Laving M, Metspalu A, Hadjigeorgiou GM, Polo O, Fietze I, Ross OA, Wszolek ZK, Ibrahim A, Bergmann M, Kittke V, Harrer P, Dowsett J, Chenini S, Ostrowski SR, Sørensen E, Erikstrup C, Pedersen OB, Topholm Bruun M, Nielsen KR, Butterworth AS, Soranzo N, Ouwehand WH, Roberts DJ, Danesh J, Burchell B, Furlotte NA, Nandakumar P, Earley CJ, Ondo WG, Xiong L, Desautels A, Perola M, Vodicka P, Dina C, Stoll M, Franke A, Lieb W, Stewart AFR, Shah SH, Gieger C, Peters A, Rye DB, Rouleau GA, Berger K, Stefansson H, Ullum H, Stefansson K, Hinds DA, Di Angelantonio E, Oexle K, Winkelmann J. Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction. Nat Genet 2024; 56:1090-1099. [PMID: 38839884 PMCID: PMC11176086 DOI: 10.1038/s41588-024-01763-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/19/2024] [Indexed: 06/07/2024]
Abstract
Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.
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Affiliation(s)
- Barbara Schormair
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Steven Bell
- Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Maria Didriksen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | | | - Nathalie Schandra
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Ambra Stefani
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yves Dauvilliers
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Cornelius G Bachmann
- SomnoDiagnostics, Osnabrück, Germany
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - David Kemlink
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Karel Sonka
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Walter Paulus
- Department of Neurology, Ludwig Maximilians University Munich, Munich, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, Kassel, Germany
- Department of Neurosurgery, University Medical Center Göttingen, Göttingen, Germany
| | - Wolfgang H Oertel
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | | | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Georgios M Hadjigeorgiou
- Department of Neurology, Nicosia General Hospital Medical School, University of Cyprus, Nicosia, Cyprus
| | - Olli Polo
- Bragée ME/CFS Center, Stockholm, Sweden
| | - Ingo Fietze
- Department of Pulmonology, Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA
| | | | - Abubaker Ibrahim
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Melanie Bergmann
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Volker Kittke
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Philip Harrer
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Joseph Dowsett
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sofiene Chenini
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Kaspar R Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Nicole Soranzo
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- Department of Haematology, University College London Hospitals, London, UK
| | - David J Roberts
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Radcliffe Department of Medicine and National Health Service Blood and Transplant, Oxford, UK
- Department of Haematology and BRC Haematology Theme, Churchill Hospital, Headington, Oxford, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | | | | | | | - Christopher J Earley
- Center for Restless Legs Syndrome, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - William G Ondo
- Department of Neurology, Methodist Neurological Institute, Weill Cornell Medical School, Houston, TX, USA
| | - Lan Xiong
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Alex Desautels
- Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec, Canada
- Department of Neurosciences, Université de Montréal, Montreal, Quebec, Canada
| | - Markus Perola
- Clinical and Molecular Metabolism Research Program (CAMM), Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health and Welfare, National Institute for Health and Welfare, Helsinki, Finland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Academy of Science of Czech Republic, Prague, Czech Republic
- First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Christian Dina
- L'institut du thorax, CNRS, INSERM, Nantes Université, Nantes, France
| | - Monika Stoll
- Department of Genetic Epidemiology, Institute for Human Genetics, University of Münster, Münster, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Wolfgang Lieb
- PopGen Biobank and Institute of Epidemiology, Christian Albrechts University Kiel, Kiel, Germany
| | - Alexandre F R Stewart
- John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Svati H Shah
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - David B Rye
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Guy A Rouleau
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | | | | | | | | | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Fondazione Human Technopole, Milan, Italy
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich-Augsburg, Munich-Augsburg, Germany
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5
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Carrasquilla GD, García-Ureña M, Romero-Lado MJ, Kilpeläinen TO. Estimating causality between smoking and abdominal obesity by Mendelian randomization. Addiction 2024; 119:1024-1034. [PMID: 38509034 DOI: 10.1111/add.16454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 01/12/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND AND AIMS Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments. DESIGN MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods. SETTING Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank. PARTICIPANTS We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits. MEASUREMENTS Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI. FINDINGS Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. CONCLUSIONS Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.
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Affiliation(s)
- Germán D Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mario García-Ureña
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - María J Romero-Lado
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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6
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Moix S, Sadler MC, Kutalik Z, Auwerx C. Breaking down causes, consequences, and mediating effects of telomere length variation on human health. Genome Biol 2024; 25:125. [PMID: 38760657 PMCID: PMC11101352 DOI: 10.1186/s13059-024-03269-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/07/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry. RESULTS Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan. CONCLUSIONS Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors.
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Affiliation(s)
- Samuel Moix
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
| | - Marie C Sadler
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
| | - Chiara Auwerx
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
- Center for Integrative Genetics, UNIL, Lausanne, 1015, Switzerland.
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7
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Xue A, Zhu Z, Wang H, Jiang L, Visscher PM, Zeng J, Yang J. Unravelling the complex causal effects of substance use behaviours on common diseases. COMMUNICATIONS MEDICINE 2024; 4:43. [PMID: 38472333 DOI: 10.1038/s43856-024-00473-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Substance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake. METHODS In this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects. RESULTS We show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake ( b ̂ G W A S = - 0.091 , s . e . = 0.007 , P = 4.90 × 10 - 35 ) but positive on moderate tea intake ( b ̂ G W A S = 0.034 , s . e . = 0.006 , P = 3.40 × 10 - 8 ) , compared to the non-tea-drinkers. CONCLUSION Our study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.
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Affiliation(s)
- Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- National Centre for Register-Based Research, Aarhus University, Aarhus V, 8210, Denmark
| | - Huanwei Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, 310024, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China.
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8
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Zou J, Talluri R, Shete S. Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose. PLoS One 2024; 19:e0293510. [PMID: 38457457 PMCID: PMC10923437 DOI: 10.1371/journal.pone.0293510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/16/2023] [Indexed: 03/10/2024] Open
Abstract
Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.
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Affiliation(s)
- Jinhao Zou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Rajesh Talluri
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Data Science, The University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center Houston, Texas, United States of America
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9
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Zhang W, Zhang L, Xiao C, Wu X, Cui H, Yang C, Yan P, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Zhang L, Yang C, Yao Y, Li J, Liu Z, Jiang X, Zhang B. Bidirectional relationship between type 2 diabetes mellitus and coronary artery disease: Prospective cohort study and genetic analyses. Chin Med J (Engl) 2024; 137:577-587. [PMID: 38062574 DOI: 10.1097/cm9.0000000000002894] [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: 09/18/2023] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD. METHODS We evaluated phenotypic associations using data from the United Kingdom Biobank ( N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase / Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DM adj BMI]: Ncase / Ncontrol = 50,409/523,897) and for CAD ( Ncase / Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM ( Ncase / Ncontrol = 180,834/1,159,055). RESULTS Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01-2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63-1.81). A positive overall genetic correlation between T2DM and CAD was observed ( rg = 0.39, P = 1.43 × 10 -75 ), which was largely independent of BMI (T2DM adj BMI-CAD: rg = 0.31, P = 1.20 × 10 -36 ). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11-1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07-1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10-1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04-1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9-83.4%) and 90.4% (95% CI: 29.3-151.5%), respectively. CONCLUSION Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.
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Affiliation(s)
- Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chenghan Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Ling Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yuqin Yao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Zhenmi Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China-Peking Union Medical College C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Darrous L, Hemani G, Davey Smith G, Kutalik Z. PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment. Nat Commun 2024; 15:1420. [PMID: 38360877 PMCID: PMC10869347 DOI: 10.1038/s41467-024-45655-8] [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: 03/19/2023] [Accepted: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
Mendelian Randomisation (MR) estimates causal effects between risk factors and complex outcomes using genetic instruments. Pleiotropy, heritable confounders, and heterogeneous causal effects violate MR assumptions and can lead to biases. To alleviate these, we propose an approach employing a Phenome-Wide association Clustering of the MR instruments (PWC-MR) and apply this method to revisit the surprisingly large apparent causal effect of body mass index (BMI) on educational attainment (EDU): [Formula: see text] = -0.19 [-0.22, -0.16]. First, we cluster 324 BMI-associated genetic instruments based on their association with 407 traits in the UK Biobank, which yields six distinct groups. Subsequent cluster-specific MR reveals heterogeneous causal effect estimates on EDU. A cluster enriched for socio-economic indicators yields the largest BMI-on-EDU causal effect estimate ([Formula: see text] = -0.49 [-0.56, -0.42]) whereas a cluster enriched for body-mass specific traits provides a more likely estimate ([Formula: see text] = -0.09 [-0.13, -0.05]). Follow-up analyses confirms these findings: within-sibling MR ([Formula: see text] = -0.05 [-0.09, -0.01]); MR for childhood BMI on EDU ([Formula: see text] = -0.03 [-0.06, -0.002]); step-wise multivariable MR ([Formula: see text] = -0.05 [-0.07, -0.02]) where socio-economic indicators are jointly modelled. Here we show how the in-depth examination of the BMI-EDU causal relationship demonstrates the utility of our PWC-MR approach in revealing distinct pleiotropic pathways and confounder mechanisms.
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Affiliation(s)
- Liza Darrous
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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11
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Hou L, Geng Z, Yuan Z, Shi X, Wang C, Chen F, Li H, Xue F. MRSL: a causal network pruning algorithm based on GWAS summary data. Brief Bioinform 2024; 25:bbae086. [PMID: 38487847 PMCID: PMC10940843 DOI: 10.1093/bib/bbae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.
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Affiliation(s)
- Lei Hou
- Beijing International Center for Mathematical Research, Peking University, Beijing, People’s Republic of China, 100871
| | - Zhi Geng
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, People’s Republic of China, 100048
| | - Zhongshang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Chuan Wang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China, 250000
| | - Feng Chen
- School of Public Health, Nanjing Medical University, Nanjing, China, 211166
| | - Hongkai Li
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China, 250000
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12
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Cao R, Olawsky E, McFowland E, Marcotte E, Spector L, Yang T. Subset scanning for multi-trait analysis using GWAS summary statistics. Bioinformatics 2024; 40:btad777. [PMID: 38191683 PMCID: PMC11087659 DOI: 10.1093/bioinformatics/btad777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/10/2024] Open
Abstract
MOTIVATION Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. RESULTS To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. AVAILABILITY AND IMPLEMENTATION Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.
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Affiliation(s)
- Rui Cao
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Evan Olawsky
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
| | - Edward McFowland
- Technology and Operations Management, Harvard Business School, Harvard University, Boston, MA 02163, United States
| | - Erin Marcotte
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Logan Spector
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States
- Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
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13
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Reyes-Pérez P, García-Marín LM, Aman AM, Antar T, Flores-Ocampo V, Mitchell BL, Medina-Rivera A, Rentería ME. Investigating the Shared Genetic Etiology Between Parkinson's Disease and Depression. JOURNAL OF PARKINSON'S DISEASE 2024; 14:483-493. [PMID: 38457145 DOI: 10.3233/jpd-230176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Background Depression is a common symptom in Parkinson's disease (PD), resulting from underlying neuropathological processes and psychological factors. However, the extent to which shared genetic risk factors contribute to the relationship between depression and PD is poorly understood. Objective To examine the effects of common genetic variants influencing the etiology of PD and depression risk at the genome-wide and local genomic regional level. Methods We comprehensively investigated the genetic relationship between PD and depression using genome-wide association studies data. First, we estimated the genetic correlation at the genome-wide level using linkage-disequilibrium score regression, followed by local genetic correlation analysis using the GWAS-pairwise method and functional annotation to identify genes that may jointly influence the risk for both traits. Also, we performed Latent Causal Variable, Latent Heritable Confounder Mendelian Randomization, and traditional Mendelian Randomization analyses to investigate the potential causal relationship. Results Although the genetic correlation between PD and depression was not statistically significant at the genome-wide level, GWAS-pairwise analyses identified 16 genomic segments associated with PD and depression, implicating nine genes. Further analyses revealed distinct patterns within individual genes, suggesting an intricate pattern. These genes involve various biological processes, including neurotransmitter regulation, senescence, and nucleo-cytoplasmic transport mechanisms. We did not observe genetic evidence of causality between PD and depression. Conclusions Our findings did not support a genome-wide genetic correlation or a causal association between both conditions. However, we identified genomic segments but identified genomic segments linked to distinct biological pathways influencing their etiology.Further research is needed to understand their functional consequences.
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Affiliation(s)
- Paula Reyes-Pérez
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Luis M García-Marín
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Asma M Aman
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Tarek Antar
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Victor Flores-Ocampo
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
- Licenciatura en Ciencias Genómicas, Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, México
| | - Brittany L Mitchell
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Miguel E Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD,Australia
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14
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Wang Z, Davey Smith G, Loos RJF, den Hoed M. Distilling causality between physical activity traits and obesity via Mendelian randomization. COMMUNICATIONS MEDICINE 2023; 3:173. [PMID: 38036650 PMCID: PMC10689836 DOI: 10.1038/s43856-023-00407-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Whether obesity is a cause or consequence of low physical activity levels and more sedentary time has not yet been fully elucidated. Better instrumental variables and a more thorough consideration of potential confounding variables that may influence the causal inference between physical activity and obesity are needed. METHODS Leveraging results from our recent genome-wide association study for leisure time moderate-to-vigorous intensity (MV) physical activity and screen time, we here disentangle the causal relationships between physical activity, sedentary behavior, education-defined by years of schooling-and body mass index (BMI), using multiple univariable and multivariable Mendelian Randomization (MR) approaches. RESULTS Univariable MR analyses suggest bidirectional causal effects of physical activity and sedentary behavior with BMI. However, multivariable MR analyses that take years of schooling into account suggest that more MV physical activity causes a lower BMI, and a higher BMI causes more screen time, but not vice versa. In addition, more years of schooling causes higher levels of MV physical activity, less screen time, and lower BMI. CONCLUSIONS In conclusion, our results highlight the beneficial effect of education on improved health and suggest that a more physically active lifestyle leads to lower BMI, while sedentary behavior is a consequence of higher BMI.
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Affiliation(s)
- Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel den Hoed
- The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden.
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15
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Sbarra DA, Ramadan FA, Choi KW, Treur JL, Levey DF, Wootton RE, Stein MB, Gelernter J, Klimentidis YC. Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies. Mol Psychiatry 2023; 28:4594-4601. [PMID: 37735503 DOI: 10.1038/s41380-023-02259-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023]
Abstract
Major depression (MD) is a serious psychiatric illness afflicting nearly 5% of the world's population. A large correlational literature suggests that loneliness is a prospective risk factor for MD; correlational assocations of this nature may be confounded for a variety of reasons. This report uses Mendelian Randomization (MR) to examine potentially causal associations between loneliness and MD. We report on analyses using summary statistics from three large genome wide association studies (GWAS). MR analyses were conducted using three independent sources of GWAS summary statistics. In the first set of analyses, we used available summary statistics from an extant GWAS of loneliness to predict MD risk. We used two sources of outcome data: the Psychiatric Genomics Consortium (PGC) meta-analysis of MD (PGC-MD; N = 142,646) and the Million Veteran Program (MVP-MD; N = 250,215). Finally, we reversed analyses using data from the MVP and PGC samples to identify risk variants for MD and used loneliness outcome data from UK Biobank. We find robust evidence for a bidirectional causal relationship between loneliness and MD, including between loneliness, depression cases status, and a continuous measure of depressive symptoms. The estimates remained significant across several sensitivity analyses, including models that account for horizontal pleiotropy. This paper provides the first genetically-informed evidence that reducing loneliness may play a causal role in decreasing risk for depressive illness, and these findings support efforts to reduce loneliness in order to prevent or ameliorate MD. Discussion focuses on the public health significance of these findings, especially in light of the SARS-CoV-2 pandemic.
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Affiliation(s)
- David A Sbarra
- Department of Psychology, University of Arizona, Tucson, AZ, USA.
| | - Ferris A Ramadan
- Department of Epidemiology and Biostatistics, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Karmel W Choi
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Daniel F Levey
- VA Connecticut Healthcare System, Psychiatry Service, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Robyn E Wootton
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Murray B Stein
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- VA Connecticut Healthcare System, Psychiatry Service, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
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16
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Yu F, Yang Y, Wu D, Chang M, Han C, Wang Q, Li Y, He D. Deciphering genetic causality between inflammatory bowel disease and periodontitis through bi-directional two-sample Mendelian randomization. Sci Rep 2023; 13:18620. [PMID: 37903824 PMCID: PMC10616190 DOI: 10.1038/s41598-023-45527-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/20/2023] [Indexed: 11/01/2023] Open
Abstract
Inflammatory bowel disease (IBD) and periodontitis are reported to be closely associated; however, whether there is a causal association between them remains unclear. To explore the existence of this causality, this study applied a bidirectional two-sample Mendelian randomization (MR). The genetic variants were obtained from the summary statistics of genome-wide association studies of IBD, including its subtypes CD and UC, and periodontitis. 175, 148, 113, and six single-nucleotide polymorphisms were selected as instrumental variables for IBD, CD, UC, and periodontitis, respectively. In MR analysis, random-effects inverse-variance weighted was used as the primary method, and weighted median and MR Egger regression were applied as the complementary method. A series of sensitivity analyses were also conducted to ensure the reliability of the results. None of these analyses found a significant effect of genetically proxied IBD and its subtypes on periodontitis, and vice versa. Subsequent sensitivity analyses did not detect any horizontal pleiotropy and heterogeneity. Caution should be exerted when it comes to clinical relevance and further studies are needed to clarify the relationship between IBD and periodontitis.
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Affiliation(s)
- Feiyan Yu
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Yang Yang
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Dongchao Wu
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Minjing Chang
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Chong Han
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Qianqian Wang
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Yi Li
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China
| | - Dongning He
- Shanxi Medical University School and Hospital of Stomatology, Taiyuan, China.
- Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Taiyuan, China.
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17
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Sun YV, Liu C, Hui Q, Zhou JJ, Gaziano JM, Wilson PW, Joseph J, Phillips LS. Correction for Collider Bias in the Genome-wide Association Study of Diabetes-Related Heart Failure due to Bidirectional Relationship between Heart Failure and Type 2 Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.22.23295915. [PMID: 37808641 PMCID: PMC10557768 DOI: 10.1101/2023.09.22.23295915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Aims Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) across demographic groups. On the other hand, metabolic impairment, including elevated T2D incidence is a hallmark of HF pathophysiology. We investigated the bidirectional relationship between T2D and HF, and identified genetic associations with diabetes-related HF after correction for potential collider bias. Methods We performed a genome-wide association study (GWAS) of HF to identify genetic instrumental variables (GIVs) for HF, and to enable bidirectional Mendelian Randomization (MR) analysis between T2D and HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted GWAS of diabetes-related HF with correction for collider bias. Results We first identified 61 genomic loci, including 24 novel loci, significantly associated with all-cause HF in 114,275 HF cases and over 1.5 million controls of European ancestry. Combined with the summary statistics of a T2D GWAS, we obtained 59 and 82 GIVs for HF and T2D, respectively. Using a two-sample bidirectional MR approach, we estimated that T2D increased HF risk (OR 1.07, 95% CI 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to prevalent HF affecting incidence of T2D. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program, and replicated the associations in the UK Biobank study. Conclusion We identified novel HF-associated loci to enable bidirectional MR study of T2D and HF. Our MR findings support T2D as a HF risk factor and provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci. Evaluation of collider bias should be a critical component when conducting GWAS of complex disease phenotypes such as diabetes-related cardiovascular complications.
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18
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 150] [Impact Index Per Article: 150.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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19
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Martin E, Schoeler T, Pingault JB, Barkhuizen W. Understanding the relationship between loneliness, substance use traits and psychiatric disorders: A genetically informed approach. Psychiatry Res 2023; 325:115218. [PMID: 37146462 PMCID: PMC10636586 DOI: 10.1016/j.psychres.2023.115218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/05/2023] [Accepted: 04/21/2023] [Indexed: 05/07/2023]
Abstract
Loneliness is a common, yet distressing experience associated with adverse outcomes including substance use problems and psychiatric disorders. To what extent these associations reflect genetic correlations and causal relationships is currently unclear. We applied Genomic Structural Equation Modelling (GSEM) to dissect the genetic architecture between loneliness and psychiatric-behavioural traits. Included were summary statistics from 12 genome-wide association analyses, including loneliness and 11 psychiatric phenotypes (range N: 9,537 - 807,553). We first modelled latent genetic factors amongst the psychiatric traits to then investigate potential causal effects between loneliness and the identified latent factors, using multivariate genome-wide association analyses and bidirectional Mendelian randomization. We identified three latent genetic factors, encompassing neurodevelopmental/mood conditions, substance use traits and disorders with psychotic features. GSEM provided evidence of a unique association between loneliness and the neurodevelopmental/mood conditions latent factor. Mendelian randomization results were suggestive of bidirectional causal effects between loneliness and the neurodevelopmental/mood conditions factor. These results imply that a genetic predisposition to loneliness may elevate the risk of neurodevelopmental/mood conditions, and vice versa. However, results may reflect the difficulty of distiguishing between loneliness and neurodevelopmental/mood conditions, which present in similar ways. We suggest, overall, the importance of addressing loneliness in mental health prevention and policy.
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Affiliation(s)
- Ellen Martin
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Tabea Schoeler
- Division of Psychology and Language Sciences, University College London, London, United Kingdom; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London, United Kingdom; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London, United Kingdom
| | - Wikus Barkhuizen
- Division of Psychology and Language Sciences, University College London, London, United Kingdom.
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20
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Sjaarda J, Kutalik Z. Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity. Nat Hum Behav 2023; 7:776-789. [PMID: 36928782 PMCID: PMC10202815 DOI: 10.1038/s41562-022-01500-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 11/17/2022] [Indexed: 03/18/2023]
Abstract
Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity-partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity.
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Affiliation(s)
- Jennifer Sjaarda
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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21
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Cheval B, Darrous L, Choi KW, Klimentidis YC, Raichlen DA, Alexander GE, Cullati S, Kutalik Z, Boisgontier MP. Genetic insights into the causal relationship between physical activity and cognitive functioning. Sci Rep 2023; 13:5310. [PMID: 37002254 PMCID: PMC10066390 DOI: 10.1038/s41598-023-32150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/23/2023] [Indexed: 04/03/2023] Open
Abstract
Physical activity and cognitive functioning are strongly intertwined. However, the causal relationships underlying this association are still unclear. Physical activity can enhance brain functions, but healthy cognition may also promote engagement in physical activity. Here, we assessed the bidirectional relationships between physical activity and general cognitive functioning using Latent Heritable Confounder Mendelian Randomization (LHC-MR). Association data were drawn from two large-scale genome-wide association studies (UK Biobank and COGENT) on accelerometer-measured moderate, vigorous, and average physical activity (N = 91,084) and cognitive functioning (N = 257,841). After Bonferroni correction, we observed significant LHC-MR associations suggesting that increased fraction of both moderate (b = 0.32, CI95% = [0.17,0.47], P = 2.89e - 05) and vigorous physical activity (b = 0.22, CI95% = [0.06,0.37], P = 0.007) lead to increased cognitive functioning. In contrast, we found no evidence of a causal effect of average physical activity on cognitive functioning, and no evidence of a reverse causal effect (cognitive functioning on any physical activity measures). These findings provide new evidence supporting a beneficial role of moderate and vigorous physical activity (MVPA) on cognitive functioning.
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Affiliation(s)
- Boris Cheval
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland.
- Laboratory for the Study of Emotion Elicitation and Expression (E3Lab), Department of Psychology, University of Geneva, Geneva, Switzerland.
| | - Liza Darrous
- University for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Karmel W Choi
- Department of Psychiatry, Massachusetts General Hospital, Massachusetts, Boston, MA, USA
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
| | - David A Raichlen
- Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
- Department of Anthropology, University of Southern California, Los Angeles, CA, USA
| | - Gene E Alexander
- Department of Psychology, University of Arizona, Tucson, AZ, USA
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
- Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Stéphane Cullati
- Population Health Laboratory, Department of Community Health, University of Fribourg, Fribourg, Switzerland
| | - Zoltán Kutalik
- University for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Matthieu P Boisgontier
- School of Rehabilitation Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
- Bruyère Research Institute, Ottawa, ON, Canada.
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22
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Shared genetic architecture between attention-deficit/hyperactivity disorder and lifespan. Neuropsychopharmacology 2023; 48:981-990. [PMID: 36906694 PMCID: PMC10209393 DOI: 10.1038/s41386-023-01555-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 02/03/2023] [Accepted: 02/20/2023] [Indexed: 03/13/2023]
Abstract
There is evidence linking ADHD to a reduced life expectancy. The mortality rate in individuals with ADHD is twice that of the general population and it is associated with several factors, such as unhealthy lifestyle behaviors, social adversity, and mental health problems that may in turn increase mortality rates. Since ADHD and lifespan are heritable, we used data from genome-wide association studies (GWAS) of ADHD and parental lifespan, as proxy of individual lifespan, to estimate their genetic correlation, identify genetic loci jointly associated with both phenotypes and assess causality. We confirmed a negative genetic correlation between ADHD and parental lifespan (rg = -0.36, P = 1.41e-16). Nineteen independent loci were jointly associated with both ADHD and parental lifespan, with most of the alleles that increased the risk for ADHD being associated with shorter lifespan. Fifteen loci were novel for ADHD and two were already present in the original GWAS on parental lifespan. Mendelian randomization analyses pointed towards a negative causal effect of ADHD liability on lifespan (P = 1.54e-06; Beta = -0.07), although these results were not confirmed by all sensitivity analyses performed, and further evidence is required. The present study provides the first evidence of a common genetic background between ADHD and lifespan, which may play a role in the reported effect of ADHD on premature mortality risk. These results are consistent with previous epidemiological data describing reduced lifespan in mental disorders and support that ADHD is an important health condition that could negatively affect future life outcomes.
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23
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Gong W, Guo P, Li Y, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Yuan Z. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry 2023; 80:360-370. [PMID: 36753304 PMCID: PMC9909581 DOI: 10.1001/jamapsychiatry.2022.4974] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
IMPORTANCE Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. OBJECTIVE To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. DESIGN, SETTING, AND PARTICIPANTS This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. MAIN OUTCOMES AND MEASURES The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. RESULTS Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). CONCLUSIONS AND RELEVANCE These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor,Center for Statistical Genetics, University of Michigan, Ann Arbor
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
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24
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Hartley AE, Power GM, Sanderson E, Smith GD. A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field. JBMR Plus 2022; 6:e10675. [PMID: 36248277 PMCID: PMC9549705 DOI: 10.1002/jbm4.10675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
Mendelian randomization (MR) is an increasingly popular component of an epidemiologist's toolkit, used to provide evidence of a causal effect of one trait (an exposure, eg, body mass index [BMI]) on an outcome trait or disease (eg, osteoarthritis). Identifying these effects is important for understanding disease etiology and potentially identifying targets for therapeutic intervention. MR uses genetic variants as instrumental variables for the exposure, which should not be influenced by the outcome or confounding variables, overcoming key limitations of traditional epidemiological analyses. For MR to generate a valid estimate of effect, key assumptions must be met. In recent years, there has been a rapid rise in MR methods that aim to test, or are robust to violations of, these assumptions. In this review, we provide an overview of MR for a non-expert audience, including an explanation of these key assumptions and how they are often tested, to aid a better reading and understanding of the MR literature. We highlight some of these new methods and how they can be useful for specific methodological challenges in the musculoskeletal field, including for conditions or traits that share underlying biological pathways, such as bone and joint disease. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- April E Hartley
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - Grace M Power
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - Eleanor Sanderson
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
| | - George Davey Smith
- MRC-Integrative Epidemiology Unit Population Health Sciences, Bristol Medical School Bristol UK
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25
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Nolde M, Holtfreter B, Kocher T, Alayash Z, Reckelkamm SL, Ehmke B, Baurecht H, Baumeister SE. No bidirectional relationship between depression and periodontitis: A genetic correlation and Mendelian randomization study. Front Immunol 2022; 13:918404. [PMID: 35935963 PMCID: PMC9355660 DOI: 10.3389/fimmu.2022.918404] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/27/2022] [Indexed: 11/15/2022] Open
Abstract
Background Observational and in-vivo research suggested a bidirectional relationship between depression and periodontitis. We estimated the genetic correlation and examined directionality of causation. Methods The study used summary statistics from published genome wide association studies, with sample sizes ranging from 45,563 to 797,563 individuals of European ancestry. We performed linkage disequilibrium score regression (LDSC) to estimate global correlation and used Heritability Estimation from Summary Statistics (ρ-HESS) to further examine local genetic correlation. Latent Heritable Confounder Mendelian randomization (LHC-MR), Causal Analysis using Summary Effect estimates (CAUSE), and conventional MR approaches assessed bidirectional causation. Results LDSC observed only weak genetic correlation (rg = 0.06, P-Value = 0.619) between depression and periodontitis. Analysis of local genetic correlation using ρ-HESS did not reveal loci of significant local genetic covariance. LHC-MR, CAUSE and conventional MR models provided no support for bidirectional causation between depression and periodontitis, with odds ratios ranging from 1.00 to 1.06 in either direction. Conclusions Results do not support shared heritability or a causal connection between depression and periodontitis.
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Affiliation(s)
- Michael Nolde
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
- *Correspondence: Michael Nolde,
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, University Medicine Greifswald, Greifswald, Germany
| | - Zoheir Alayash
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
| | - Stefan Lars Reckelkamm
- Institute of Health Services Research in Dentistry, University of Münster, Münster, Germany
| | - Benjamin Ehmke
- Clinic for Periodontology and Conservative Dentistry, University of Münster, Münster, Germany
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
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26
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Cai S, Hartley A, Mahmoud O, Tilling K, Dudbridge F. Adjusting for collider bias in genetic association studies using instrumental variable methods. Genet Epidemiol 2022; 46:303-316. [PMID: 35583096 PMCID: PMC9544531 DOI: 10.1002/gepi.22455] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/12/2022] [Accepted: 04/20/2022] [Indexed: 11/16/2022]
Abstract
Genome‐wide association studies have provided many genetic markers that can be used as instrumental variables to adjust for confounding in epidemiological studies. Recently, the principle has been applied to other forms of bias in observational studies, especially collider bias that arises when conditioning or stratifying on a variable that is associated with the outcome of interest. An important case is in studies of disease progression and survival. Here, we clarify the links between the genetic instrumental variable methods proposed for this problem and the established methods of Mendelian randomisation developed to account for confounding. We highlight the critical importance of weak instrument bias in this context and describe a corrected weighted least‐squares procedure as a simple approach to reduce this bias. We illustrate the range of available methods on two data examples. The first, waist–hip ratio adjusted for body‐mass index, entails statistical adjustment for a quantitative trait. The second, smoking cessation, is a stratified analysis conditional on having initiated smoking. In both cases, we find little effect of collider bias on the primary association results, but this may propagate into more substantial effects on further analyses such as polygenic risk scoring and Mendelian randomisation.
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Affiliation(s)
- Siyang Cai
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - April Hartley
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Osama Mahmoud
- Department of Mathematical Sciences, University of Essex, Colchester, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
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27
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Xue H, Pan W. Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data. PLoS Genet 2022; 18:e1010205. [PMID: 35576237 PMCID: PMC9135345 DOI: 10.1371/journal.pgen.1010205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 05/26/2022] [Accepted: 04/15/2022] [Indexed: 11/25/2022] Open
Abstract
To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke. Inferring causal relationships between two traits based on observational data is one of the most important as well as challenging problems in scientific research. The potential impact of such an application on and beyond genetics/genomics is significant, such as in prioritizing molecular, clinical and behavioral targets for therapeutic and behavioral interventions. However, this problem, especially on a possibly bi-directional causal relationship between a pair of traits (in which the causal direction may be from either trait to the other, and both causal directions may be present at the same time), had been largely neglected until very recently. The increasing availability of large-scale GWAS summary data of various traits has popularized the development and application of Mendelian randomization (MR) methods for such a purpose. We point out some severe limitations with the current methods, mainly due to some new and unique challenges facing inference of bi-directional relationships as compared to that of uni-directional relationships that has been more commonly and exclusively considered in MR. By combining two basic ideas of bidirectional (or reciprocal) MR and Steiger’s correlation-based screening methods, we develop two new approaches based on constrained maximum likelihood (cML) and GWAS summary data to infer causal effects (as in typical MR) and SNP-trait correlations (as in Steiger’s method), called MR-cML and CD-cML respectively, demonstrating their similar effectiveness and more importantly their advantages over existing methods through extensive simulations, real data examples and statistical theory. In particular, our proposed two methods are robust to violations of all three valid IV assumptions, including presence of correlated pleiotropy.
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Affiliation(s)
- Haoran Xue
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
- * E-mail:
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