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Pro?ling and Validation of the Circular RNA Repertoire in Adult Murine Hearts

Genomics, Proteomics & bioinformatics 2016年第4期14卷 216-223页

作者： Tobias Jakobi Lisa F. Czaja-Hasse Richard Reinhardt Christoph Dieterich Section of Bioinformatics and Systems Cardiology Department of Internal Medicine IIIUniversity Hospital Heidelberg 69120 HeidelbergGermany German Centre for Cardiovascular Research(DZHK) Heidelberg/Mannheim.Germany Max Planck-Genome-Centre Cologne 50829 CologneGermany

For several decades, cardiovascular disease has been the leading cause of death throughout all countries. There is a strong genetic component to many disease subtypes （e.g., cardiomyopa- thy） and we are just beginning to understand the relevant genetic factors. Several studies have related RNA splicing to cardiovascular disease and circular RNAs （circRNAs） are an emerging player, circRNAs, which originate through back-splicing events from primary transcripts, are resis- tant to exonucleases and typically not polyadenylated. Initial functional studies show clear phenotypic outcomes for selected circRNAs. We provide, for the first time, a comprehensive catalogue of RNase R-resistant circRNA species for the adult murine heart. This work combines state-of-the-art circle sequencing with our novel DCC software to explore the circRNA landscape of heart tissue. Overall, we identified 575 circRNA species that pass a beta-binomial test for enrichment （false discovery rate of 1%） in the exonuclease-treated sequencing sample. Several circRNAs can be directly attributed to host genes that have been previously described as associated with cardiovascular disease. Further studies of these candidate circRNAs may reveal disease-relevant properties or functions of specific circRNAs.

关键词： Circular RNA circSeq Cardiovascular disease Cardiomyopathy Computational cardiology

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Profiling and Validation of the Circular RNA Repertoire in Adult Murine Hearts

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Genomics,Proteomics & bioinformatics 2016年第4期14卷 216-223页

作者： Tobias Jakobi Lisa F Czaja-Hasse Richard Reinhardt Christoph Dieterich Section of Bioinformatics and Systems Cardiology Department of Internal Medicine III University Hospital Heidelberg German Centre for Cardiovascular Research (DZHK) Max Planck-Genome-Centre Cologne

For several decades, cardiovascular disease has been the leading cause of death throughout all countries. There is a strong genetic component to many disease subtypes(e.g., cardiomyopathy) and we are just beginning to understand the relevant genetic factors. Several studies have related RNA splicing to cardiovascular disease and circular RNAs(circ RNAs) are an emerging player. circ RNAs, which originate through back-splicing events from primary transcripts, are resistant to exonucleases and typically not polyadenylated. Initial functional studies show clear phenotypic outcomes for selected circ RNAs. We provide, for the ?rst time, a comprehensive catalogue of RNase R-resistant circ RNA species for the adult murine heart. This work combines state-of-the-art circle sequencing with our novel DCC software to explore the circ RNA landscape of heart ***, we identi?ed 575 circ RNA species that pass a beta-binomial test for enrichment(false discovery rate of 1%) in the exonuclease-treated sequencing sample. Several circ RNAs can be directly attributed to host genes that have been previously described as associated with cardiovascular disease. Further studies of these candidate circ RNAs may reveal disease-relevant properties or functions of speci?c circ RNAs.

关键词： RNA splicing sequencing transcripts cardiovascular RNase cardiomyopathy catalogue candidate attributed

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TNF-α induces CXCL1 chemokine expression and release in human vascular endothelial cells in vitro via two distinct signaling pathways

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中国药理学报（英文版） 2014年第3期35卷 339-350页

作者： Huey-ming LO Tsung-hsuan LAI Chih-hung LI Wen-bin WU School of Medicine Fu-Jen Catholic University New Taipei City Taiwan China Section of Cardiology Department of Internal Medicine Shin Kong Wu Ho-Su Memorial Hospital Taipei Taiwan China School of Medicine Fu-Jen Catholic University New Taipei City Taiwan China Department of Obstetrics and Gynecology Cathay General Hospital Taipei Taiwan China Institute of Systems Biology and Bioinformatics National Central University Jhongli City Taoyuan TaiwanChina School of Medicine Fu-Jen Catholic University New Taipei City Taiwan China

Aim:Chemokines usually direct the movement of circulating leukocytes to sites of inflammation or injury. CXCL1/GRO-a has been shown to be upregulated in atherosclerotic lesions and various cancers. The aim of this study was to investigate the mechanisms underlying the TNF-α-induced release of CXCL1 from human vascular endothelial cells in vitro. Methods:Human umbilical vein endothelial cells (HUVECs) were treated with different proinflam-matory mediators and growth factors. CXCL1 expression and secretion were determined using RT-PCR and ELISA, respectively. TNF-a-induced cell signaling was assayed with Western blotting. Cell viability/growth was determined using MTTassay. Monocyte migration was measured with transwell migration assay. Results:Among the 17mediators and growth factors tested, TNF-α, LPS and thrombin induced marked increase in CXCL1 release from HUVEC cells. TNF-α(2, 5 ng/mL) induced CXCL1 release and mRNA expression in the cells in concentration- and time-dependent manners. TNF-α(5 ng/mL) caused activation of JNK, p38 MAPK, PI3K and Akt, whereas pretreatment with JNK inhibitor (SP600125), p38 MAPK inhibitor (SB202190) or PI-3K inhibitor (LY294002) significantly suppressed TNF-a-induced CXCL1 release from the cells. But only SP600125 significantly reduced TNF-a-induced CXCL1mRNA expression in the cells. Moreover, dexamethasone (up to 500 nmol/L) failed to affect TNF-a-induced CXCL1 release from the cells. In functional studies, recombinant CXCL1 enhanced HUVEC proliferation, and both recombinant CXCL1 and TNF-a-induced CXCL1 from HUVECs attracted human monocyte migration. Conclusion:TNF-a stimulates CXCL1 release from human ECs through JNK-mediated CXCL1mRNA expression and p38 MAPK- and PI-3K-mediated CXCL1 secretory processes.

关键词： chemokine CXCL1 GRO-α TNF vascular endothelial cell JNK p38 MAPK PI3K monocyte dexamethasone

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA 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 Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes

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Cell Reports 2023年第2期42卷 112046页

作者： Gainullina, Anastasiia Mogilenko, Denis A. Huang, Li-Hao Todorov, Helena Narang, Vipin Kim, Ki-Wook Yng, Lim Sheau Kent, Andrew Jia, Baosen Seddu, Kumba Krchma, Karen Wu, Jun Crozat, Karine Tomasello, Elena Dress, Regine See, Peter Scott, Charlotte Gibbings, Sophie Bajpai, Geetika Desai, Jigar V. Maier, Barbara This, Sébastien Wang, Peter Aguilar, Stephanie Vargas Poupel, Lucie Dussaud, Sébastien Zhou, Tyng-An Angeli, Veronique Blander, J. Magarian Choi, Kyunghee Dalod, Marc Dzhagalov, Ivan Gautier, Emmanuel L. Jakubzick, Claudia Lavine, Kory Lionakis, Michail S. Paidassi, Helena Sieweke, Michael H. Ginhoux, Florent Guilliams, Martin Benoist, Christophe Merad, Miriam Randolph, Gwendalyn J. Sergushichev, Alexey Artyomov, Maxim N. Department of Pathology and Immunology Washington University School of Medicine St. Louis 63110 MO United States Computer Technologies Department ITMO University St. Petersburg 197101 Russian Federation Laboratory of Bioinformatics and Molecular Genetics Koltzov Institute of Developmental Biology of the Russian Academy of Sciences Moscow 119334 Russian Federation Department of Medicine Department of Pathology Microbiology and Immunology Vanderbilt Center for Immunobiology Vanderbilt University Medical Center Nashville 37232 TN United States Laboratory of Immunoregulation Inflammation Research Centre VIB Ghent University Ghent 9052 Belgium Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A∗STAR) Singapore 138648 Singapore Immunology Translational Research Programme Yong Loo Lin School of Medicine Department of Microbiology and Immunology National University of Singapore Singapore 117545 Singapore Immunology Programme Life Sciences Institute National University of Singapore Singapore 117545 Singapore The Jill Roberts Institute for Research in Inflammatory Bowel Disease Joan and Sanford I. Weill Department of Medicine Department of Microbiology and Immunology Weill Cornell Medicine Cornell University New York 10021 NY United States Department of Immunology Harvard Medical School Boston 02115 MA United States Aix Marseille University CNRS INSERM CIML Centre d'Immunologie de Marseille-Luminy Turing Center for Living Systems Marseille 13288 France Department of Pediatrics National Jewish Health Denver 80206 CO United States Center for Cardiovascular Research Division of Cardiology Department of Medicine Washington University School of Medicine Saint Louis 63110 MO United States Fungal Pathogenesis Section Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda 20892 MD United States Immunology Institute and Tisch Cancer

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues;(2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples);and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages. © 2023 The Author(s)

关键词： CP: Immunology CP: Metabolism ImmGen immunometabolism mononuclear phagocytes myeloid cells network analysis single-cell RNA-seq

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Corrigendum to "European contribution to the study of ROS: A summary of the findings and prospects for the future from the COST action BM1203 (EU-ROS)" [Redox Biol. 13 (2017) 94-162]

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Redox biology 2018年 14卷 694-696页

作者： J Egea I Fabregat Y M Frapart P Ghezzi A Görlach T Kietzmann K Kubaichuk U G Knaus M G Lopez G Olaso-Gonzalez A Petry R Schulz J Vina P Winyard K Abbas O S Ademowo C B Afonso I Andreadou H Antelmann F Antunes M Aslan M M Bachschmid R M Barbosa V Belousov C Berndt D Bernlohr E Bertrán A Bindoli S P Bottari P M Brito G Carrara A I Casas A Chatzi N Chondrogianni M Conrad M S Cooke J G Costa A Cuadrado P My-Chan Dang B De Smet B Debelec-Butuner I H K Dias J D Dunn A J Edson M El Assar J El-Benna P Ferdinandy A S Fernandes K E Fladmark U Förstermann R Giniatullin Z Giricz A Görbe H Griffiths V Hampl A Hanf J Herget P Hernansanz-Agustín M Hillion J Huang S Ilikay P Jansen-Dürr V Jaquet J A Joles B Kalyanaraman D Kaminskyy M Karbaschi M Kleanthous L O Klotz B Korac K S Korkmaz R Koziel D Kračun K H Krause V Křen T Krieg J Laranjinha A Lazou H Li A Martínez-Ruiz R Matsui G J McBean S P Meredith J Messens V Miguel Y Mikhed I Milisav L Milković A Miranda-Vizuete M Mojović M Monsalve P A Mouthuy J Mulvey T Münzel V Muzykantov I T N Nguyen M Oelze N G Oliveira C M Palmeira N Papaevgeniou A Pavićević B Pedre F Peyrot M Phylactides G G Pircalabioru A R Pitt H E Poulsen I Prieto M P Rigobello N Robledinos-Antón L Rodríguez-Mañas A P Rolo F Rousset T Ruskovska N Saraiva S Sasson K Schröder K Semen T Seredenina A Shakirzyanova G L Smith T Soldati B C Sousa C M Spickett A Stancic M J Stasia H Steinbrenner V Stepanić S Steven K Tokatlidis E Tuncay B Turan F Ursini J Vacek O Vajnerova K Valentová F Van Breusegem L Varisli E A Veal A S Yalçın O Yelisyeyeva N Žarković M Zatloukalová J Zielonka R M Touyz A Papapetropoulos T Grune S Lamas H H H W Schmidt F Di Lisa A Daiber Institute Teofilo Hernando Department of Pharmacology School of Medicine Univerisdad Autonoma de Madrid Spain. Bellvitge Biomedical Research Institute (IDIBELL) and University of Barcelona (UB) L'Hospitalet Barcelona Spain. LCBPT UMR 8601 CNRS - Paris Descartes University Sorbonne Paris Cité Paris France. Brighton & Sussex Medical School Brighton UK. Experimental and Molecular Pediatric Cardiology German Heart Center Munich at the Technical University Munich Munich Germany DZHK (German Centre for Cardiovascular Research) partner site Munich Heart Alliance Munich Germany. Faculty of Biochemistry and Molecular Medicine and Biocenter Oulu University of Oulu Oulu Finland. Conway Institute School of Medicine University College Dublin Dublin Ireland. Department of Physiology University of Valencia Spain. Experimental and Molecular Pediatric Cardiology German Heart Center Munich at the Technical University Munich Munich Germany. Institute of Physiology JLU Giessen Giessen Germany. University of Exeter Medical School St Luke's Campus Exeter EX1 2LU UK. Life & Health Sciences and Aston Research Centre for Healthy Ageing Aston University Aston Triangle Birmingham B4 7ET UK. School of Life & Health Sciences Aston University Aston Triangle Birmingham B47ET UK. Laboratory of Pharmacology Faculty of Pharmacy National and Kapodistrian University of Athens Greece. Institute for Biology-Microbiology Freie Universität Berlin Berlin Germany. Departamento de Química e Bioquímica and Centro de Química e Bioquímica Faculdade de Ciências Portugal. Department of Medical Biochemistry Faculty of Medicine Akdeniz University Antalya Turkey. Vascular Biology Section & Whitaker Cardiovascular Institute Boston University School of Medicine Boston MA USA. Center for Neurosciences and Cell Biology University of Coimbra and Faculty of Pharmacy University of Coimbra Coimbra Portugal. Molecular technologies laboratory Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry Miklukho

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar 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 Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

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Nature genetics 2019年第7期51卷 1192-1193页

作者： Rainer Malik Ganesh Chauhan Matthew Traylor Muralidharan Sargurupremraj Yukinori Okada Aniket Mishra Loes Rutten-Jacobs Anne-Katrin Giese Sander W van der Laan Solveig Gretarsdottir Christopher D Anderson Michael Chong Hieab H H Adams Tetsuro Ago Peter Almgren Philippe Amouyel Hakan Ay Traci M Bartz Oscar R Benavente Steve Bevan Giorgio B Boncoraglio Robert D Brown Adam S Butterworth Caty Carrera Cara L Carty Daniel I Chasman Wei-Min Chen John W Cole Adolfo Correa Ioana Cotlarciuc Carlos Cruchaga John Danesh Paul I W de Bakker Anita L DeStefano Marcel den Hoed Qing Duan Stefan T Engelter Guido J Falcone Rebecca F Gottesman Raji P Grewal Vilmundur Gudnason Stefan Gustafsson Jeffrey Haessler Tamara B Harris Ahamad Hassan Aki S Havulinna Susan R Heckbert Elizabeth G Holliday George Howard Fang-Chi Hsu Hyacinth I Hyacinth M Arfan Ikram Erik Ingelsson Marguerite R Irvin Xueqiu Jian Jordi Jiménez-Conde Julie A Johnson J Wouter Jukema Masahiro Kanai Keith L Keene Brett M Kissela Dawn O Kleindorfer Charles Kooperberg Michiaki Kubo Leslie A Lange Carl D Langefeld Claudia Langenberg Lenore J Launer Jin-Moo Lee Robin Lemmens Didier Leys Cathryn M Lewis Wei-Yu Lin Arne G Lindgren Erik Lorentzen Patrik K Magnusson Jane Maguire Ani Manichaikul Patrick F McArdle James F Meschia Braxton D Mitchell Thomas H Mosley Michael A Nalls Toshiharu Ninomiya Martin J O'Donnell Bruce M Psaty Sara L Pulit Kristiina Rannikmäe Alexander P Reiner Kathryn M Rexrode Kenneth Rice Stephen S Rich Paul M Ridker Natalia S Rost Peter M Rothwell Jerome I Rotter Tatjana Rundek Ralph L Sacco Saori Sakaue Michele M Sale Veikko Salomaa Bishwa R Sapkota Reinhold Schmidt Carsten O Schmidt Ulf Schminke Pankaj Sharma Agnieszka Slowik Cathie L M Sudlow Christian Tanislav Turgut Tatlisumak Kent D Taylor Vincent N S Thijs Gudmar Thorleifsson Unnur Thorsteinsdottir Steffen Tiedt Stella Trompet Christophe Tzourio Cornelia M van Duijn Matthew Walters Nicholas J Wareham Sylvia Wassertheil-Smoller James G Wilson Kerri L Wiggins Qiong Yang Salim Yusuf Joshua C Bis Tomi Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany. Centre for Brain Research Indian Institute of Science Bangalore India. INSERM U1219 Bordeaux Population Health Research Center University of Bordeaux Bordeaux France. Stroke Research Group Division of Clinical Neurosciences University of Cambridge Cambridge UK. Department of Neurology Institute for Neurodegenerative Disease Bordeaux University Hospital Bordeaux France. Laboratory for Statistical Analysis RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Osaka Japan. Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan. Department of Neurology Massachusetts General Hospital (MGH) Harvard Medical School Boston MA USA. Laboratory of Experimental Cardiology Department of Cardiology Division of Heart and Lungs University Medical Center Utrecht University of Utrecht Utrecht the Netherlands. deCODE genetics/AMGEN Inc. Reykjavik Iceland. Center for Genomic Medicine MGH Boston MA USA. J. Philip Kistler Stroke Research Center Department of Neurology MGH Boston MA USA. Program in Medical and Population Genetics Broad Institute Cambridge MA USA. Population Health Research Institute McMaster University Hamilton Ontario Canada. Department of Epidemiology Erasmus University Medical Center Rotterdam the Netherlands. Department of Radiology and Nuclear Medicine Erasmus University Medical Center Rotterdam the Netherlands. Department of Medicine and Clinical Science Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Clinical Sciences Lund University Malmö Sweden. INSERM Institut Pasteur de Lille LabEx DISTALZ-UMR1167 Risk Factors and Molecular Determinants of Aging-Related Diseases Université Lille Lille France. Centre Hospitalier Université Lille Epidemiology and Public Health D

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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