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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and markov random field

arXiv

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arXiv 2020年

作者： Bi, Haixia Xu, Lin Cao, Xiangyong Xue, Yong Xu, Zongben Faculty of Engineering University of Bristol BristolBS8 1UB United Kingdom Institute of Artificial Intelligence Shanghai Em-Data Technology Co. Ltd. Shanghai200000 China School of Environment Science and Spatial Informatics China University of Mining and Technology XuzhouJiangsu221116 China School of Electronics Computing and Mathematics University of Derby DerbyDE22 1GB United Kingdom Institute for Information and System Sciences Xi’an Jiaotong University Xi’an China

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Correction: COVID-19 outbreaks surveillance through text mining applied to electronic health records

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BMC infectious diseases 2024年第1期24卷 394页

作者： Hermano Alexandre Lima Rocha Erik Zarko Macêdo Solha Vasco Furtado Francion Linhares Justino Lucas Arêa Leão Barreto Ronaldo Guedes da Silva Ítalo Martins de Oliveira David Westfall Bates Luciano Pamplona de Góes Cavalcanti Antônio Silva Lima Neto Erneson Alves de Oliveira Department of Community Health Federal University of Ceará Street Papi Júnior 1223 5Th. Floor Fortaleza CE Brazil. hermano@ufc.br. Postgraduate Program in Applied Informatics University of Fortaleza Fortaleza CE 60811-905 Brazil. Department of Community Health Federal University of Ceará Street Papi Júnior 1223 5Th. Floor Fortaleza CE Brazil. Health Secretariat Ceará State Government Fortaleza CE Brazil. Harvard Medical School Boston MA USA. School of Public Health of Ceará Fortaleza CE Brazil. Faculty of Medicine Christus University Center Fortaleza CE Brazil. Laboratory of Data Science and Artifcial Intelligence University of Fortaleza Fortaleza Ceará 60811-905 Brazil. Professional Masters in City Science University of Fortaleza Fortaleza Ceará 60811-905 Brazil.

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Generalized Space Time Autoregressive of Chili Prices 3

Generalized Space Time Autoregressive of Chili Prices

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Jamilatuzzahro Caraka, Rezzy Eko Herliansyah, Riki Asmawati, S. Sari, Dian Megah Pardamean, Bens Bandung Institute of Technology Actuarial Science Bandung Indonesia Bina Nusantara University Department of Statistics Bioinformatics and Data Science Research Center Bandung Jakarta11480 Indonesia Department of Mathematics Kalimantan Institute of Technology Kalimantan Timur Indonesia Informatics Faculty of Engineering West Sulawesi University Sulawesi Barat Indonesia

ISBN: (纸本)9781538658215

Indonesian people are mostly like spicy. Therefore, Chili becomes an ingredient in cooking that cannot be separated from Indonesian people. In certain months there is too much demand from the public and inversely proportional to the stock of chili. So, appreciate the chili prices to rise. In the heart of statistics, several methods can be used as forecasting as well as data series from univariate, bivariate and multivariate cases. The reason for choosing the method is following the analysis needs and available data. One of the popular methods in the multivariate case is Generalized Spacetime Autoregressive (GSTAR) the advantage of this method is that it can capture the characteristics of location-based data as well as time. For this reason, in this paper, we used chili prices data in 4 major cities in Java Province, Indonesia. Such as Jakarta, Bandung, Semarang, and Yogyakarta. As a result, GSTAR (21) I (1with inverse weighted be the best models because it fulfilled white noise and normal multivariate assumption with the lowest RMSE and MAPE. © 2018 IEEE.

关键词： Costs

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OUTPOST: A comprehensive analysis software for whole-metagenome shotgun sequencing incorporating group stratification

iMetaOmics

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iMetaOmics 2024年第2期1卷

作者： Yihang Zhou Jihong Zheng Wenqi Song Xinyi Yan Li Du Zhonglin Ma Yanbin Fu Zhaohui Ouyang Yuchen Xiao Zhuoqun Liu Feng Tian Jason Wing Hon Wong Jen Hao David Shih Shikang Liang Honglei Tian Liu Liu Ke Wei Chao Zhang Jiangtao Li Xiaozhu Wang Fundamental Research Center Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center) School of Life Sciences and Technology Tongji University Shanghai China State Key Laboratory of Marine Geology School of Ocean and Earth Science Tongji University Shanghai China Department of Biliary-Pancreatic Surgery Shanghai Cancer Institute Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China State Key Laboratory of Cardiology and Medical Innovation Center Shanghai East Hospital Shanghai Key Laboratory of Signaling and Disease Research Frontier Science Center for Stem Cell Research School of Life Sciences and Technology Tongji University Shanghai China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Handan China School of Biomedical Sciences Li Ka Shing Faculty of Medicine The University of Hong Kong Pokfulam Hong Kong SAR China Division of Life Science The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China Shanghai Yuhui Pharmaceutical Technology (Group) Co. Ltd Shanghai China Shanghai Institute of Precision Medicine Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

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Riau Forest Fire Prediction using Supervised Machine Learning

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： B S Negara R Kurniawan M Z A Nazri S N H S Abdullah R W Saputra A Ismanto Department of Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Jl. HR. Subrantas Km.15 Pekanbaru 28293 Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia Riau Data Camp (RDC) Riau Indonesia

Forest fire is one of the environmental problems in terms of economically and ecologically detrimental. The number hotspot of forest fires in Indonesia has become increasing dramatically in September 2019 with 16178 hotspots that caused hazardous haze. The hazardous haze has disturbed about 1.04 million and 6.5 million people in Pekanbaru City and Riau Province subsequently. Hence, the development of early warning systems may provide effective strategic information plus accurate prediction results tailored for forest fire prevention and control. Weather data can be used as the main source for analyzing forest fires. This study aims to predict forest fire in Riau, Indonesia. This study has used 1733 weather data in five years (2015-2019). The forest fire prediction models were developed by using two supervised machine-learning techniques namely Decision Tree (DT) and Bayesian Network (BN). The experimental result shows that BN outperforms DT with accuracy rate and RMSE value in pairs of 99.62% and 0.076, and 93.18% and 0.244 subsequently. Despite its low performance, DT able to extract the main factors that caused the forest and land fires efficiently. Thus, it can be concluded that the prediction model using BN has the potential to be used effectively but still has plenty of room for improvement.

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JS08.6.A A connectivity signature for glioblastoma

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii8-ii9页

作者： Hoffmann, D C Hai, L Wagener, R Mandelbaum, H Xie, R Hausmann, D Dominguez Azorín, D Weil, S Sievers, P Cebulla, G Ito, J Warnken, U Venkataramani, V Ernst, K Reibold, D Will, R Suvà, M L Herold-Mende, C Sahm, F Winkler, F Schlesner, M Wick, W Kessler, T Clinical Cooperation Unit Neurooncology German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Bioinformatics and Omics Data Analytics DKFZ Heidelberg Germany Department of Neuropathology Institute of Pathology University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neuropathology DKTK DKFZ Heidelberg Germany Department of Neuroanatomy Institute for Anatomy and Cell Biology Heidelberg University Heidelberg Germany Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology DKTK DKFZ Heidelberg Germany Genomics and Proteomics Core Facility DKTK DKFZ Heidelberg Germany Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology and Center for Cancer Research Massachusetts General Hospital and Harvard Medical School Boston MA United States Department of Neurosurgery Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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Pruritus and Its Association With Cancer and Mortality in Dermatomyositis and Polymyositis: A Nationwide Cohort Study in Taiwan From 2005 to 2022

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Dermatologic Therapy 2025年第1期2025卷

作者： Der-Jr Huang Yu-Hsuan Joni Shao Yi-Hsien Shih Woan-Ruoh Lee Ling-Ya Huang Yu-Min Kuo Quoc Thao Trang Pham Hao-Jui Weng Department of Dermatology Taipei Medical University-Shuang Ho Hospital New Taipei City Taiwan shh.org.tw Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan tmu.edu.tw Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan tmuh.org.tw Health Data Analytics and Statistics Center Office of Data Science Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Division of Allergy Immunology and Rheumatology Department of Internal Medicine National Taiwan University Hospital and National Taiwan University Taipei Taiwan ntu.edu.tw Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan ntu.edu.tw International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Vietnam hcmut.edu.vn Graduate Institute of Clinical Medicine College of Medicine Taipei Medical University Taipei Taiwan medcol.mw

Background: Pruritus is the most common initial symptom reported by patients with dermatomyositis (DM) and polymyositis (PM). However, there is limited data regarding the impact of pruritus on cancer and mortality in patients with DM and PM. In this study, we aimed to investigate how pruritus is associated with cancer and mortality in patients with DM and PM. Methods: This nationwide, population-based retrospective cohort study included adult DM and PM patients from Taiwan’s National Health Insurance Research database between 2005 and 2022. Sex- and age-matched pruritic patients, identified by over 6 weeks of antipruritic medication use, and nonpruritic patients were analyzed. The primary outcome was cancer occurrence or all-cause mortality. The association between pruritus and these outcomes was estimated using Cox proportional hazards models. Results: Among 919 matched pairs of pruritic and nonpruritic patients, cancer was observed in 19.96% in the long-term pruritic group (LPG), 14.63% in the short-term pruritic group (SPG), and 10.34% in the non-pruritic group (NPG) ( p < 0.0001). All-cause mortality was documented as 30.37% in the LPG, 29.69% in the SPG, and 37.76% in the NPG ( p < 0.0001). After adjusting for sex, age, and other comorbidities, pruritus was associated with an increased risk of cancer (hazard ratio (HR) 1.708, 95% confidence interval (CI) 1.229–2.374) and a lower risk of all-cause mortality (HR 0.483, 95% CI 0.409–0.569). Conclusion: This population-based study revealed that pruritus appeared to be associated with increased risks of cancer and decreased all-cause mortality. Thus, pruritus may serve as a pragmatic factor for risk stratification and tailored treatment strategies in DM and PM. Cancer screening, particularly for nasopharyngeal and breast cancers in East Asian populations, is recommended for patients with DM or PM, especially those presenting with pruritus. Meanwhile, patients without pruritus may require vigilant management for pote

关键词： cancer dermatomyositis idiopathic inflammatory myopathies mortality polymyositis pruritus

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Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

arXiv

引用

arXiv 2020年

作者： Gysi, Deisy Morselli Valle, Ítalo Do Zitnik, Marinka Ameli, Asher Gan, Xiao Varol, Onur Ghiassian, Susan Dina Patten, J.J. Davey, Robert Loscalzo, Joseph Barabási, Albert-László Network Science Institute Department of Physics Northeastern University BostonMA02115 United States Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA02138 United States Scipher Medicine 221 Crescent St Suite 103A WalthamMA02453 United States Department of Physics Northeastern University BostonMA02115 United States Faculty of Engineering and Natural Sciences Sabanci University Istanbul34956 Turkey Department of Microbiology NEIDL Boston University BostonMA United States Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States Department of Network and Data Science Central European University Budapest1051 Hungary

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug’s targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development. © 2020, CC BY-NC-SA.

关键词： SARS

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Brugada syndrome in Japan and Europe: a genome-wide association study reveals shared genetic architecture and new risk loci

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European heart journal 2024年第26期45卷 2320-2332页

作者： Taisuke Ishikawa Tatsuo Masuda Tsuyoshi Hachiya Christian Dina Floriane Simonet Yuki Nagata Michael W T Tanck Kyuto Sonehara Charlotte Glinge Rafik Tadros Apichai Khongphatthanayothin Tzu-Pin Lu Chihiro Higuchi Tadashi Nakajima Kenshi Hayashi Yoshiyasu Aizawa Yukiko Nakano Akihiko Nogami Hiroshi Morita Seiko Ohno Takeshi Aiba Christian Krijger Juárez John Mauleekoonphairoj Yong Poovorawan Jean-Baptiste Gourraud Wataru Shimizu Vincent Probst Minoru Horie Arthur A M Wilde Richard Redon Jyh-Ming Jimmy Juang Koonlawee Nademanee Connie R Bezzina Julien Barc Toshihiro Tanaka Yukinori Okada Jean-Jacques Schott Naomasa Makita Omics Research Center National Cerebral and Cardiovascular Center Suita Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. StemRIM Institute of Regeneration-Inducing Medicine Osaka University Graduate School of Medicine Suita Japan. Department of Obstetrics and Gynecology Osaka University Graduate School of Medicine Suita Japan. Institute for Biomedical Sciences Iwate Medical University Iwate Japan. L'institut du thorax Nantes Université CHU Nantes CNRS INSERM Nantes France. Bioresource Research Center Tokyo Medical and Dental University Tokyo Japan. Department of Human Genetics and Disease Diversity Tokyo Medical and Dental University Tokyo Japan. Epidemiology and Data Science Amsterdam UMC location University of Amsterdam Amsterdam The Netherlands. Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan. Department of Experimental Cardiology Amsterdam UMC location University of Amsterdam Heart Centre Amsterdam The Netherlands. Heart Failure & Arrhythmias Amsterdam Cardiovascular Sciences Amsterdam The Netherlands. Department of Cardiology The Heart Centre Copenhagen University Hospital Rigshospitalet Copenhagen Denmark. European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART https://guardheart.ern-net.eu). Montreal Heart Institute Universite de Montreal Cardiovascular Genetics Centre Montreal Quebec Canada. Department of Medicine Center of Excellence in Arrhythmia Research Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Pediatrics Division of Cardiology Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Cardiology Bangkok Hospital Bangkok Thailand. Department of Public Health Institute of Health Data Analytics and Statistics National Taiwan University Taipei Taiwan. Artificial Intelligence Center for Health and Biomedical Research National Institutes of Bio

BACKGROUND AND AIMS:Brugada syndrome (BrS) is an inherited arrhythmia with a higher disease prevalence and more lethal arrhythmic events in Asians than in Europeans. Genome-wide association studies (GWAS) have revealed its polygenic architecture mainly in European populations. The aim of this study was to identify novel BrS-associated loci and to compare allelic effects across ancestries. METHODS:A GWAS was conducted in Japanese participants, involving 940 cases and 1634 controls, followed by a cross-ancestry meta-analysis of Japanese and European GWAS (total of 3760 cases and 11 635 controls). The novel loci were characterized by fine-mapping, gene expression, and splicing quantitative trait associations in the human heart. RESULTS:The Japanese-specific GWAS identified one novel locus near ZSCAN20 (P = 1.0 × 10-8), and the cross-ancestry meta-analysis identified 17 association signals, including six novel loci. The effect directions of the 17 lead variants were consistent (94.1%; P for sign test = 2.7 × 10-4), and their allelic effects were highly correlated across ancestries (Pearson's R = .91; P = 2.9 × 10-7). The genetic risk score derived from the BrS GWAS of European ancestry was significantly associated with the risk of BrS in the Japanese population [odds ratio 2.12 (95% confidence interval 1.94-2.31); P = 1.2 × 10-61], suggesting a shared genetic architecture across ancestries. Functional characterization revealed that a lead variant in CAMK2D promotes alternative splicing, resulting in an isoform switch of calmodulin kinase II-δ, favouring a pro-inflammatory/pro-death pathway. CONCLUSIONS:This study demonstrates novel susceptibility loci implicating potentially novel pathogenesis underlying BrS. Despite differences in clinical expressivity and epidemiology, the polygenic architecture of BrS was substantially shared across ancestries.

关键词： SCN5A Brugada syndrome Cross-ancestry meta-analysis Genetic risk score Genome-wide association studies

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