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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

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NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Whole-genome sequencing of 1,171 elderly admixed individuals from the largest Latin American metropolis (São Paulo, Brazil)

Research Square

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Research Square 2020年

作者： Naslavsky, Michel Scliar, Marilia Yamamoto, Guilherme Wang, Jaqueline Zverinova, Stepanka Karp, Tatiana Nunes, Kelly Ceroni, José Ricardo Carvalho, Diego Simões, Cerlos Eduardo Bozoklian, Daniel Nonaka, Ricardo Silva, Nayane Souza, Andreia Andrade, Heloísa Passos, Marília Castro, Camila Mendes-Junior, Celso Mercuri, Rafael Miller, Thiago Buzzo, José Rego, Fernanda Araújo, Nathalia Magalhaes, Wagner Mingroni-Netto, Regina Borda, Vctor Guio, Heinner Barreto, Maurício Lima-Costa, Maria Horta, Bernardo Tarazona-Santos, Eduardo Meyer, Diogo Galante, Pedro Castelli, Erick Duarte, Yeda Passos-Bueno, Maria Rita Zatz, Mayana Human Genome and Stem Cell Research Center University of São Paulo Department of Genetics and Evolutionary Biology Biosciences Institute University of São Paulo Brazil Universidade Federal de Minas Gerais Brazil University of Sao Paulo Brazil Human Genome and Stem Cell Research Center University of São Paulo Brazil Laboratory of Genome Structure and Ageing European Research Institute for the Biology of Ageing University Medical Center Groningen Netherlands Department of Genetics and Evolutionary Biology University of São Paulo Brazil Molecular Genetics and Bioinformatics Laboratory School of Medicine Sao Paulo State University Brazil Molecular Genetics and Bioinformatics Laboratory School of Medicine Sao Paulo State University Department of Pathology School of Medicine Sao Paulo State University Brazil Faculdade de Filosofia Ciências e Letras de Ribeirão Preto University of São Paulo Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Department of Biochemistry Institute of Chemistry University of São Paulo Bioinformatics Graduate program University of S Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Department of Biochemistry Institute of Chemistry University of São Paulo Brazil Centro de Oncologia Molecular Hospital Sírio-Libanes Bolivia Instituto Nacional de Salud Instituto de Saúde Coletiva Universidade Federal da Bahia Center for Data and Knowledge Integration for Health Institute Gonçalo Muniz Fundação Oswaldo Cruz Brazil Instituto de Pesquisas René Rachou Fundação Oswaldo Cruz Programa De Pós-Graduação em Saúde Pública Universidade Federal de Minas Gerais Brazil Universidade Federal de Pelotas Brazil Departamento de Genética Ecologia e Evolução Instituto de Ciências Biológicas Universidade Federal de Minas Gerais Universidad Peruana Cayetano Heredia Lima Peru Medical-Surgical Nursing Department School of Nursing University of São Paulo Epidemiology Department Public Health School University of São Paulo Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~ 2 million are absent from large public databases. WGS enabled identifying ~ 2,000 novel mobile element insertions, nearly 5 Mb of genomic segments absent from human genome reference, and over 140 novel alleles from HLA genes. We reclassified and curated nearly four hundred variant's pathogenicity assertions in genes associated with dominantly inherited Mendelian disorders and calculated the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observed that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS. © 2020, CC BY.

关键词： Genome

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

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Procedia Computer Science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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The Effects of Trajectory and Endpoint Errors in a Reaching Movement on the Sense of Agency

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Psychology 2017年 8卷 2321-2332页

作者： Takuro Zama Yoshiyuki Takahashi Sotaro Shimada Electrical Engineering Program Graduate School of Science and Technology Meiji University Kawasaki-shi Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kawasaki-shi Japan

The sense of agency (SoA) refers to the subjective feeling that an individual can control their own action with their own will. However, it is still unclear which aspects of the motor control process precisely affect the sense of agency. In this study, we investigated how the SoA is modulated by the online motor performance (trajectory error) and the outcome of the reaching movement (endpoint error). The results showed that the invalid priming and the visual feedback delay significantly increased both the trajectory and endpoint errors and that these errors significantly attenuated the SoA. The further correlation analyses showed that the decrease in SoA was significantly correlated with the trajectory error, but not with the endpoint error, when the error was explicitly noticed. We suggest that the deterioration in online motor performance, at least in a reaching movement, is the main cause of the attenuation in the SoA.

关键词： Sense of Agency Online Motor Control Error Detection

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Hybrid support vector regression in electric load during national holiday season

Hybrid support vector regression in electric load during nat...

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2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017

作者： Caraka, Rezzy Eko Bakar, Sakhinah Abu Pardamean, Bens Budiarto, Arif School of Mathematical Sciences Faculty of Science and Technology National University of Malaysia Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538640456

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation. © 2017 IEEE.

关键词： Time series

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Ten simple rules for reproducible research in jupyter notebooks

arXiv

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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Correction: Inhibition of IRP2-dependent reprogramming of iron metabolism suppresses tumor growth in colorectal cancer

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Cell communication and signaling : CCS 2024年第1期22卷 480页

作者： Jieon Hwang Areum Park Chinwoo Kim Chang Gon Kim Jaesung Kwak Byungil Kim Hyunjin Shin Minhee Ku Jaemoon Yang Ayoung Baek Jiwon Choi Hocheol Lim Kyoung Tai No Xianghua Zhao Uyeong Choi Tae Il Kim Kyu-Sung Jeong Hyuk Lee Sang Joon Shin Department of Medicine Yonsei University College of Medicine Seoul 03722 Korea. Songdang Institute for Cancer Research Yonsei University College of Medicine Seoul 03722 Korea. Infectious Diseases Therapeutic Research Center Korea Research Institute of Chemical Technology Daejeon 34114 Korea. Department of Chemistry Yonsei University Seoul 03722 Korea. Division of Medical Oncology Department of Internal Medicine Yonsei Cancer Center Yonsei University College of Medicine Seoul 03722 Korea. Department of Radiology Yonsei University College of Medicine Seoul 03722 Korea. Convergence Research Center for Systems Molecular Radiological Science Yonsei University Seoul 03722 Korea. Bioinformatics and Molecular Design Research Center Incheon 21983 Korea. College of Pharmacy Dongduk Women's University Seoul 02748 Korea. The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon 21983 Korea. Division of Gastroenterology Department of Internal Medicine Yonsei University College of Medicine Seoul 03722 Korea. Infectious Diseases Therapeutic Research Center Korea Research Institute of Chemical Technology Daejeon 34114 Korea. leeh@krict.re.kr. Songdang Institute for Cancer Research Yonsei University College of Medicine Seoul 03722 Korea. ssj338@yuhs.ac. Division of Medical Oncology Department of Internal Medicine Yonsei Cancer Center Yonsei University College of Medicine Seoul 03722 Korea. ssj338@yuhs.ac. Convergence Research Center for Systems Molecular Radiological Science Yonsei University Seoul 03722 Korea. ssj338@yuhs.ac.

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AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens

Research Square

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Research Square 2020年

作者： Li, Chenkai Sutherland, Darcy Hammond, S. Austin Yang, Chen Taho, Figali Bergman, Lauren Houston, Simon Warren, René L. Wong, Titus Hoang, Linda M.N. Cameron, Caroline E. Helbing, Caren C. Birol, Inanc BC Cancer Genome Sciences Centre Canada Canada’s Michael Smith Genome Sciences Centre BC Cancer Agency VancouverBCV5Z 4S6 Canada Bioinformatics Graduate Program University of British Columbia VancouverBCV6T 1Z4 Canada Public Health Laboratory British Columbia Centre for Disease Control VancouverBCV5Z 4R4 Canada Department of Pathology and Laboratory Medicine University of British Columbia VancouverBCV6T 1Z4 Canada Department of Biochemistry and Microbiology University of Victoria VictoriaBCV8P 5C3 Canada Medical Microbiology Laboratory Vancouver General Hospital VancouverBCV5Z 1M9 Canada Division of Infectious Diseases Department of Medicine University of Washington SeattleWA98195 United States Department of Medical Genetics University of British Columbia VancouverBCV6H 3N1 Canada

Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging therapeutic agents with promising utility in this domain and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can filter through large volumes of candidate sequences and reduce lab screening costs. Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s "priority pathogens" list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli, demonstrating the utility of tools like AMPlify in our fight against antibiotic resistance. © 2020, CC BY.

关键词： Peptides

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The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

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四川生理科学杂志 2022年第7期44卷 1246-1246页

作者： Michael Worobey Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics University of Utah School of Medicine Salt Lake City UT 84112 USA Vaccine and Infectious Disease Organization University of Saskatchewan Saskatoon SK S7N 5E3 Canada Center for Global Health Science and Security Georgetown University Washington DC 20057 USA Department of Zoology University of Oxford Oxford OX1 3SZ UK Wildlife Conservation Research Unit Department of Zoology The Recanati-Kaplan Centre University of Oxford Oxford OX13 5QL UK Pandemic and Disaster Preparedness Centre Erasmus University Medical Center 3015 CE Rotterdam Netherlands Department of Viroscience Erasmus University Medical Center 3015 CE Rotterdam Netherlands Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA 90095 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology Immunology and Transplantation Rega Institute for Medical Research KU Leuven 3000 Leuven Belgium Global Virus Network (GVN) Baltimore MD 21201 USA MRC-University of Glasgow Center for Virus Research Glasgow G61 1QH UK Tulane University School of Medicine Department of Microbiology and Immu

Understanding how severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)emerged in 2019 is critical to preventing zoonotic outbreaks before they become the next *** Huanan Seafood Wholesale Market in Wuhan,China,was identified as a likely source of cases in early reports but later this conclusion became *** show the earliest known COVID-19 cases from December 2019,including those without reported direct links,were geographically centered on this *** report that live SARS-CoV-2 susceptible mammals were sold at the market in late 2019 and,within the market,SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals.

关键词： Market likely Wuhan

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