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Journal of Anesthesia and Translational Medicine 2024年第2期3卷 36-44页

作者： Furmanek, Stephen Salunkhe, Vidyulata Pahwa, Siddharth Samanapally, Harideep Nathala, Pavani Xu, Qian Han, Yuchen Huang, Emma C. Ali, T'shura Deepti, Fnu Glynn, Alex McGuffin, Trevor Huang, Justin J. Farah, Ian Jones, Christopher M. Ramirez, Julio A. Clifford, Sean P. Arnold, Forest W. Kong, Maiying Roser, Lynn Huang, Jiapeng Division of Infectious Diseases Center of Excellence for Research in Infectious Diseases (CERID) Department of Medicine University of Louisville 501 E Broadway Louisville 40202 KY United States Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville 485 E Gray St Louisville 40202 KY United States School of Nursing University of Louisville 555 S Floyd St Louisville 40202 KY United States Department of Anesthesiology & Perioperative Medicine University of Louisville 530 S Jackson St Louisville 40202 KY United States Department of Pharmacology & Toxicology University of Louisville 505 S Hancock St Louisville 40202 KY United States Department of Cardiovascular & Thoracic Surgery University of Louisville 201 Abraham Flexner Way Louisville 40202 KY United States Center for Integrative Environmental Health Sciences University of Louisville 500 S Preston St Louisville 40202 KY United States Division of Transplantation Department of Surgery University of Louisville 323 E Chestnut St Louisville 40202 KY United States Norton Infectious Diseases Institute Norton Healthcare 234 E Gray St Louisville 40202 KY United States Department of Anesthesiology Duke University Durham 27710 NC United States Biometrics and Data Science Fosun Pharma Beijing 100026 China

Introduction: This study aims to explore the predictive roles of echocardiographic parameters and biomarkers in determining outcomes among hospitalized COVID-19 patients experiencing cardiovascular events. Methods: A retrospective cohort study was conducted involving 49 COVID-19 patients who encountered cardiovascular events during hospitalization and underwent echocardiography. Our findings revealed notable associations between echocardiographic parameters and survival time. Results: A decrease in left ventricular ejection fraction (LVEF) of 10% was linked to a 20% reduction in survival time (TR: 0.80, 95% CI: 0.67 – 0.96, p = 0.017). Similarly, an increase in left ventricular (LV) volume by 10 mL was associated with a 9% decrease in survival time (TR: 0.91, 95% CI: 0.84 – 0.98, p = 0.011). Moreover, an increase in left atrial (LA) volume by 10 mL corresponded to an 8% decrease in survival time (TR: 0.92, 95% CI: 0.86 – 0.99, p = 0.026). Additionally, each 1 cm increase in right ventricular (RV) diameter was linked to a 22% reduction in survival time (TR: 0.78, 95% CI: 0.61 – 0.99, p = 0.043). Furthermore, a 10 mL increase in right atrial (RA) volume was associated with a 12% decrease in survival time (TR: 0.88, 95% CI: 0.78 – 0.98, p = 0.017). Notably, a tenfold rise in troponin levels was linked to a 33% decrease in survival time (TR: 0.67, 95% CI: 0.48 – 0.93, p = 0.014). Conclusions: Our study emphasizes the significant associations between various echocardiographic parameters and troponin levels with reduced survival time among COVID-19 patients experiencing cardiovascular events. These findings highlight the potential utility of echocardiography and troponin assessment in predicting outcomes and guiding management strategies in this patient population. © 2024 Wuhan Institute of Intelligent Perioperative Medicine (WIIPM)

关键词： Brain natriuretic peptide Cardiovascular COVID-19 Echocardiography SARS-CoV-2 Troponin

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Cavity approach for the approximation of spectral density of graphs with heterogeneous structures

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Physical Review E 2024年第3期109卷 034303-034303页

作者： Grover E. C. Guzman Peter F. Stadler Andre Fujita Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics School of Excellence in Embedded Composite AI Dresden/Leipzig (SECAI) Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Puschstraße 4 04103 Leipzig Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Humboldtstraße 25 04105 Leipzig Germany Leipzig University Härtelstraße 16-18 D-04107 Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 D-04103 Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá 111321 Colombia and The Santa Fe Institute 1399 Hyde Park Road Santa Fe New Mexico 87501 USA Department of Computer Science Institute of Mathematics and Statistics University of São Paulo Rua do Matão 1010 São Paulo - SP 05508-090 Brazil and Division of Network AI Statistics Medical Institute of Bioregulation Kyushu University Fukuoka 812-8582 Japan

Graphs have become widely used to represent and study social, biological, and technological systems. Statistical methods to analyze empirical graphs were proposed based on the graph's spectral density. However, their running time is cubic in the number of vertices, precluding direct application to large instances. Thus, efficient algorithms to calculate the spectral density become necessary. For sparse graphs, the cavity method can efficiently approximate the spectral density of locally treelike undirected and directed graphs. However, it does not apply to most empirical graphs because they have heterogeneous structures. Thus, we propose methods for undirected and directed graphs with heterogeneous structures using a new vertex's neighborhood definition and the cavity approach. Our methods' time and space complexities are O(|E|hmax3t) and O(|E|hmax2t), respectively, where |E| is the number of edges, hmax is the size of the largest local neighborhood of a vertex, and t is the number of iterations required for convergence. We demonstrate the practical efficacy by estimating the spectral density of simulated and real-world undirected and directed graphs.

关键词： Network structure Directed networks Monte Carlo methods Network Models Topological tools

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Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (vol 1, pg 225, 2019)

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NATURE MACHINE INTELLIGENCE 2019年第6期1卷 290-290页

作者： zu Belzen, Julius Upmeier Buergel, Thore Holderbach, Stefan Bubeck, Felix Adam, Lukas Gandor, Catharina Klein, Marita Mathony, Jan Pfuderer, Pauline Platz, Lukas Przybilla, Moritz Schwendemann, Max Heid, Daniel Hoffmann, Mareike Daniela Jendrusch, Michael Schmelas, Carolin Waldhauer, Max Lehmann, Irina Niopek, Dominik Eils, Roland University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) Digital Health Center Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) German Cancer Research Center (DKFZ) Department of Theoretical Bioinformatics Heidelberg Germany (GRID:grid.7497.d) (ISNI:0000 0004 0492 0584) University Hospital Heidelberg Department of Infectious Diseases Virology Heidelberg Germany (GRID:grid.5253.1) (ISNI:0000 0001 0328 4908) BioQuant Center and Cluster of Excellence CellNetworks at Heidelberg University Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) Berlin Institute of Health (BIH) and Charité University Medicine Molecular Epidemiology Unit Berlin Germany (GRID:grid.484013.a) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) University Hospital Heidelberg Health Data Science Unit Heidelberg Germany (GRID:grid.5253.1) (ISNI:0000 0001 0328 4908) Digital Health Center Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662) University Hospital Heidelberg Health Data Science Unit Heidelberg Germany (GRI

Proteins are nature’s most versatile molecular machines. Deep neural networks trained on large protein datasets have recently been used to tackle the unmet complexity of protein sequence–function relationships. The implicit knowledge contained in these networks represents a powerful, but thus far inaccessible, resource for understanding protein biology. Here, we show that occlusion-based sensitivity analysis can leverage the knowledge present in deep-neural-network-based protein sequence classifiers to identify functionally relevant parts of proteins. We first validated our approach by successfully predicting positions that mediate small molecule binding or catalytic activity across different protein classes. Next, we inferred the impact of point mutations on the activity of ERK and HRas, signalling factors frequently deregulated in cancer. Finally, we used our approach to identify engineering hotspots in CRISPR–Cas9 and anti-CRISPR protein AcrIIA4. Our work demonstrates how implicit knowledge in neural networks can be harnessed for protein functional dissection and protein *** neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

关键词： Proteins

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Workshop proceedings: GWAS summary statistics standards and sharing

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Cell Genomics 2021年第1期1卷 100004-100004页

作者： MacArthur, Jacqueline A.L. Buniello, Annalisa Harris, Laura W. Hayhurst, James McMahon, Aoife Sollis, Elliot Cerezo, Maria Hall, Peggy Lewis, Elizabeth Whetzel, Patricia L. Bahcall, Orli G. Barroso, Inês Carroll, Robert J. Inouye, Michael Manolio, Teri A. Rich, Stephen S. Hindorff, Lucia A. Wiley, Ken Parkinson, Helen European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton United Kingdom BHF Data Science Centre Health Data Research UK London United Kingdom Division of Genomic Medicine National Human Genome Research Institute National Institutes of Health Bethesda 20892 MD United States Cell Genomics Cell Press 50 Hampshire St. 5th Floor Cambridge 02139 MA United States Exeter Centre of Excellence in Diabetes (EXCEED) University of Exeter Medical School Exeter United Kingdom Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge CB1 8RN United Kingdom Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute 75 Commercial Rd. Melbourne 3004 VIC Australia The Alan Turing Institute London United Kingdom Center for Public Health Genomics University of Virginia Charlottesville 22908 VA United States

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine. © 2022 The Authors

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Remote health monitoring in the time of COVID-19

arXiv

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arXiv 2020年

作者： Behar, Joachim A. Liu, Chengyu Tsutsui, Kenta Corino, Valentina D.A. Singh, Janmajay Pimentel, Marco A.F. Karlen, Walter Warrick, Philip Zaunseder, Sebastian Andreotti, Fernando Osipov1, Maxim McSharry, Patrick E. Kotzen, Kevin Karmakar, Chandan Clifford, Gari D. Faculty of Biomedical Engineering Technion-IIT Haifa Israel State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing Department of Cardiovascular Medicine Saitama International Medical Center Saitama Department of Electronics Information and Bioengineering Milano Italy Independent Researcher Department of Engineering Science University of Oxford Oxford United Kingdom Mobile Health Systems Lab Department of Health Sciences and Technology ETH Zurich Zurich Switzerland PeriGen CaryNC United States Faculty of Information Technology FH Dortmund Dortmund Germany Sensyne Health Oxford United Kingdom Babylon Health London United Kingdom Carnegie Mellon University Africa Kigali Rwanda African Centre of Excellence in Data Science University of Rwanda Kigali Rwanda Oxford Man Institute of Quantitative Finance Oxford University Oxford United Kingdom School of Information Technology Deakin University Geelong Australia Department of Electrical and Electronic Engineering University of Melbourne Melbourne Australia Department of Biomedical Informatics Emory University AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Enhancing Discovery of Genetic Variants for Posttraumatic Stress Disorder Through Integration of Quantitative Phenotypes and Trauma Exposure Information (vol 91, pg 626, 2022)

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BIOLOGICAL PSYCHIATRY 2022年第7期91卷 690-690页

作者： Maihofer, Adam X. Vinkers, Christiaan H. Department of Psychiatry University of California San Diego La Jolla California Department of Family Medicine and Public Health University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California. Electronic address: amaihofer@health.ucsd.edu. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Psychiatry Massachusetts General Hospital Boston Massachusetts. Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom National Institute of Health Research Maudsley Biomedical Research Centre King's College London London United Kingdom. Department of Psychiatry Harvard Medical School Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts Center of Excellence in Depression and Anxiety Disorders Belmont Massachusetts. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge Massachusetts. Department of Psychiatry University of California San Diego La Jolla California Center of Excellence for Stress and Mental Health Veterans Affairs Healthcare System San Diego California Research Service Veterans Affairs Healthcare System San Diego California. Duke Molecular Physiology Institute Duke University School of Medicine Durham North Carolina. Department of Psychiatry Veterans Affairs Connecticut Healthcare System West Haven Connecticut Department of Psychiatry Yale University School of Medicine New Haven Connecticut. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts Department of Epidemiology Columbia University Mailman School of Public Health New York. Division of Mental Health Atlanta Veterans Affairs Medical Center

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.

关键词： GWAS Genetics Heritability PTSD PheWAS Trauma

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Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (vol 50, pg 765, 2017)

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NATURE GENETICS 2018年第5期50卷 765-766页

作者： Turcot, Valerie Lu, Yingchang Highland, Heather M. Schurmann, Claudia Justice, Anne E. Fine, Rebecca S. Bradfield, Jonathan P. Esko, Tonu Giri, Ayush Graff, Mariaelisa Guo, Xiuqing Hendricks, Audrey E. Karaderi, Tugce Lempradl, Adelheid Locke, Adam E. Mahajan, Anubha Marouli, Eirini Sivapalaratnam, Suthesh Young, Kristin L. Alfred, Tamuno Feitosa, Mary F. Masca, Nicholas G. D. Manning, Alisa K. Medina-Gomez, Carolina Mudgal, Poorva Ng, Maggie C. Y. Reiner, Alex P. Vedantam, Sailaja Willems, Sara M. Winkler, Thomas W. Abecasis, Goncalo Aben, Katja K. Alam, Dewan S. Alharthi, Sameer E. Allison, Matthew Amouyel, Philippe Asselbergs, Folkert W. Auer, Paul L. Balkau, Beverley Bang, Lia E. Barroso, Ines Bastarache, Lisa Benn, Marianne Bergmann, Sven Bielak, Lawrence F. Bluher, Matthias Boehnke, Michael Boeing, Heiner Boerwinkle, Eric Boger, Carsten A. Bork-Jensen, Jette Bots, Michiel L. Bottinger, Erwin P. Bowden, Donald W. Brandslund, Ivan Breen, Gerome Brilliant, Murray H. Broer, Linda Brumat, Marco Burt, Amber A. Butterworth, Adam S. Campbell, Peter T. Cappellani, Stefania Carey, David J. Catamo, Eulalia Caulfield, Mark J. Chambers, John C. Chasman, Daniel I. Chen, Yii-Der I. Chowdhury, Rajiv Christensen, Cramer Chu, Audrey Y. Cocca, Massimiliano Collins, Francis S. Cook, James P. Corley, Janie Galbany, Jordi Corominas Cox, Amanda J. Crosslin, David S. Cuellar-Partida, Gabriel D'Eustacchio, Angela Danesh, John Davies, Gail Bakker, Paul I. W. Groot, Mark C. H. Mutsert, Renee Deary, Ian J. Dedoussis, George Demerath, Ellen W. Heijer, Martin Hollander, Anneke I. Ruijter, Hester M. Dennis, Joe G. Denny, Josh C. Angelantonio, Emanuele Drenos, Fotios Du, Mengmeng Dube, Marie-Pierre Dunning, Alison M. Easton, Douglas F. Edwards, Todd L. Ellinghaus, David Ellinor, Patrick T. Elliott, Paul Evangelou, Evangelos Farmaki, Aliki-Eleni Farooqi, I. Sadaf Faul, Jessica D. Fauser, Sascha Feng, Shuang Ferrannini, Ele Ferrieres, Jean Florez, Jose C. Ford, Ian Fornage, Myriam Franco, Oscar H. Franke, Andre Franks, Paul W. Friedrich, Ne Montreal Heart Institute Université de Montréal Montreal Quebec Canada Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA Department of Epidemiology University of North Carolina Chapel Hill NC USA Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Genetics Harvard Medical School Boston MA USA Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children’s Hospital Boston MA USA Center for Applied Genomics Division of Human Genetics Children’s Hospital of Philadelphia Philadelphia PA USA Quantinuum Research LLC San Diego CA USA Estonian Genome Center University of Tartu Tartu Estonia Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA Wellcome Trust Sanger Institute Hinxton UK Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (***35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

关键词： Genome-wide association studies Obesity

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Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder

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Nature genetics 2025年 2025 May 13页

作者： Nora I Strom Zachary F Gerring Marco Galimberti Dongmei Yu Matthew W Halvorsen Abdel Abdellaoui Cristina Rodriguez-Fontenla Julia M Sealock Tim Bigdeli Jonathan R Coleman Behrang Mahjani Jackson G Thorp Katharina Bey Christie L Burton Jurjen J Luykx Gwyneth Zai Silvia Alemany Christine Andre Kathleen D Askland Julia Bäckman Nerisa Banaj Cristina Barlassina Judith Becker Nissen O Joseph Bienvenu Donald Black Michael H Bloch Sigrid Børte Rosa Bosch Michael Breen Brian P Brennan Helena Brentani Joseph D Buxbaum Jonas Bybjerg-Grauholm Enda M Byrne Judit Cabana-Dominguez Beatriz Camarena Adrian Camarena Carolina Cappi Angel Carracedo Miguel Casas Maria Cristina Cavallini Valentina Ciullo Edwin H Cook Jesse Crosby Bernadette A Cullen Elles J De Schipper Richard Delorme Srdjan Djurovic Jason A Elias Xavier Estivill Martha J Falkenstein Bengt T Fundin Lauryn Garner Christina Gironda Fernando S Goes Marco A Grados Jakob Grove Wei Guo Jan Haavik Kristen Hagen Kelly Harrington Alexandra Havdahl Kira D Höffler Ana G Hounie Donald Hucks Christina Hultman Magdalena Janecka Eric Jenike Elinor K Karlsson Kara Kelley Julia Klawohn Janice E Krasnow Kristi Krebs Christoph Lange Nuria Lanzagorta Daniel Levey Kerstin Lindblad-Toh Fabio Macciardi Brion Maher Brittany Mathes Evonne McArthur Nathaniel McGregor Nicole C McLaughlin Sandra Meier Euripedes C Miguel Maureen Mulhern Paul S Nestadt Erika L Nurmi Kevin S O'Connell Lisa Osiecki Olga Therese Ousdal Teemu Palviainen Nancy L Pedersen Fabrizio Piras Federica Piras Sriramya Potluri Raquel Rabionet Alfredo Ramirez Scott Rauch Abraham Reichenberg Mark A Riddle Stephan Ripke Maria C Rosário Aline S Sampaio Miriam A Schiele Anne Heidi Skogholt Laura G Sloofman Jan Smit María Soler Artigas Laurent F Thomas Eric Tifft Homero Vallada Nathanial van Kirk Jeremy Veenstra-VanderWeele Nienke N Vulink Christopher P Walker Ying Wang Jens R Wendland Bendik S Winsvold Yin Yao Hang Zhou Arpana Agrawal Pino Alonso Götz Berberich Kathleen K Bucholz Cynthia M Bulik Danielle Cath Damiaan Denys Valsamma E Department of Psychology Humboldt-Universität zu Berlin Berlin Germany. nora.strom@hu-berlin.de. Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany. nora.strom@hu-berlin.de. Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden. nora.strom@hu-berlin.de. Department of Biomedicine Aarhus University Aarhus Denmark. nora.strom@hu-berlin.de. University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland. nora.strom@hu-berlin.de. Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane Queensland Australia. Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne Victoria Australia. Department of Psychiatry Human Genetics Yale University New Haven CT USA. VA Connecticut Healthcare System West Haven CT USA. Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA USA. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam the Netherlands. Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain. Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain. Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA. Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY USA. VA NY Harbor Healthcare System

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D and D type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

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