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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber

IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Harsawardana Reza Rahutomo Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Teddy Suparyanto Don Bosco Surya Atmaja Bayu Samoedro Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

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Procedia Computer Science 2018年 135卷 579-586页

作者： Kahlil Muchtar Faris Rahman Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

关键词： change detection foreground segmentation texture features

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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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Mechanistic Insights into CLNS1A-Mediated Chemoresistance and Tumor Progression in Non-small Cell Lung Cancer

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Cancer letters 2025年 626卷 217783页

作者： Tong-You Wade Wei Jiun-Yi Hsia Tsung-Ying Yang Yu-Xu Huang Guo-Zhi Wang Yi-Chung Liu Yi-Wun Wang Fangzhou He Jo-Mei Maureen Chen Guo-Jun Zhao Ping-Chiang Lyu Li-Jen Su Shao-Chih Chiu Chang-Tze Ricky Yu Department of Applied Chemistry National Chi Nan University Nantou Taiwan Division of Cardiology Department of Medicine University of California San Diego La Jolla California USA. Electronic address: t1wei@health.ucsd.edu. Division of Thoracic Surgery Chung Shan Medical University Hospital Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan. Department of Chest Medicine Taichung Veterans General Hospital Taichung Taiwan Doctoral Program in Translational Medicine National Chung Hsing University Taichung Taiwan. Department of Medicine JingGangShan University Ji'an China. Division of Thoracic Surgery Chung Shan Medical University Hospital Taichung Taiwan. Department of Life Sciences National Tsing Hua University Hsinchu Taiwan. Cardiovascular Research Center School of Basic Medical Sciences Xi'an Jiaotong University Health Science Center Xi'an China. Department of Applied Chemistry National Chi Nan University Nantou Taiwan. Department of Cardiology The First Affiliated Hospital of Zhengzhou University Zhengzhou China. Institute of Bioinformatics and Structural Biology Department of Life Science National Tsing Hua University Hsinchu Taiwan. Department of Biomedical Sciences and Engineering Education and Research Center for Technology Assisted Substance Abuse Prevention and Management and Core Facilities for High Throughput Experimental Analysis National Central University Taoyuan Taiwan. Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan Translational Cell Therapy Center Department of Medical Research China Medical University Hospital Taichung Taiwan. Electronic address: scchiu@mail.cmu.edu.tw. Department of Applied Chemistry National Chi Nan University Nantou Taiwan. Electronic address: ctyu@ncnu.edu.tw.

CLNS1A is a chloride channel protein and an essential component of the methylosome complex, which additionally comprises PRMT5 and MEP50. In this study, we investigated its contribution to lung cancer and its potential as a therapeutic target. Analysis of transcriptomic datasets and western blotting revealed that CLNS1A, PRMT5, and MEP50 were overexpressed in lung cancer tissues, with elevated CLNS1A expression correlating with poor patient survival. CLNS1A overexpression enhanced platinum clearance from cells, increased the IC values for chemotherapy, and improved cell survival. Conversely, the knockdown of CLNS1A increased drug accumulation, reduced survival, and increased sensitivity to chemotherapy. The 3W mutant, a chloride channel-defective variant with steric hindrance at key bottleneck residues, impaired chloride ion transport, thereby reducing drug resistance, migration, and anchorage-independent growth. Mechanistically, CLNS1A promotes drug efflux through its chloride channel activity and activates the FAK-SRC-RAC1 pathway to enhance motility and clonogenicity. It also facilitates PRMT5-mediated RUVBL1 methylation to support anti-apoptotic DNA damage response signaling. In vivo, CLNS1A overexpression accelerated tumor growth and reduced survival, whereas CLNS1A knockdown sensitized tumors to cisplatin, enhancing therapeutic efficacy. These findings suggest that CLNS1A is a potential biomarker and therapeutic target, and its inhibition offers a strategy to overcome drug resistance and limit the metastatic progression of lung cancer.

关键词： Chemoresistance Chloride channel DNA damage response Lung cancer Metastasis

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Correction to: Identification and characterization of conserved lncRNAs in human and rat brain

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BMC bioinformatics 2018年第1期19卷 181页

作者： Dan Li Mary Qu Yang MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock and University of Arkansas for Medical Sciences 2801 S. University Avenue Little Rock AR 72204 USA. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock and University of Arkansas for Medical Sciences 2801 S. University Avenue Little Rock AR 72204 USA. mqyang@ualr.edu.

After publication of the original article [1], it was noticed that the Acknowledgement statement was incorrect. The original statement reads.

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Generalized Spatio Temporal Autoregressive Rainfall-Enso Pattern in East Java Indonesia 1

Generalized Spatio Temporal Autoregressive Rainfall-Enso Pat...

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1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Caraka, Rezzy Eko Ulhusna, Mishbah Darmawan Supatmanto, Budi Goldameir, Noor Ell Hutapea, Bonar Darmawan, Gumgum Novitasari, Dian Candra Rini Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia School of Mathematical Sciences National University of Malaysia Malaysia Department of Statistics Padjadjaran University Indonesia Modifikasi Cuaca - BPPT Balai Besar Teknologi Indonesia Faculty of Psychology Tarumanagara University Indonesia Department of Mathematics UIN Sunan Ampel Surabaya Indonesia Riau University Indonesia 'Yptk' Padang Universitas Putra Indonesia Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538694220

Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January - March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out samp;e amount 2.95% dan RMSE out sample amount 4.77. © 2018 IEEE.

关键词： Rain

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Science (New York, N.Y.) 2025年第6734期387卷 eadp4753页

作者： Liu, Hongbo Abedini, Amin Ha, Eunji Ma, Ziyuan Sheng, Xin Dumoulin, Bernhard Qiu, Chengxiang Aranyi, Tamas Li, Shen Dittrich, Nicole Chen, Hua-Chang Tao, Ran Tarng, Der-Cherng Hsieh, Feng-Jen Chen, Shih-Ann Yang, Shun-Fa Lee, Mei-Yueh Kwok, Pui-Yan Wu, Jer-Yuarn Chen, Chien-Hsiun Khan, Atlas Limdi, Nita A. Wei, Wei-Qi Walunas, Theresa L. Karlson, Elizabeth W. Kenny, Eimear E. Luo, Yuan Kottyan, Leah Connolly, John J. Jarvik, Gail P. Weng, Chunhua Shang, Ning Cole, Joanne B. Mercader, Josep M. Mandla, Ravi Majarian, Timothy D. Florez, Jose C. Haas, Mary E. Lotta, Luca A. Drivas, Theodore G. Vy, Ha My T Nadkarni, Girish N. Wiley, Laura K. Wilson, Melissa P. Gignoux, Christopher R. Rasheed, Humaira Thomas, Laurent F. Åsvold, Bjørn Olav Brumpton, Ben M. Hallan, Stein I. Hveem, Kristian Zheng, Jie Hellwege, Jacklyn N. Zawistowski, Matthew Zöllner, Sebastian Franceschini, Nora Hu, Hailong Zhou, Jianfu Kiryluk, Krzysztof Ritchie, Marylyn D. Palmer, Matthew Edwards, Todd L. Voight, Benjamin F. Hung, Adriana M. Susztak, Katalin Department of Medicine Renal Electrolyte and Hypertension Division University of Pennsylvania Philadelphia PA United States Institute of Diabetes Obesity and Metabolism University of Pennsylvania Philadelphia PA United States Department of Genetics University of Pennsylvania Philadelphia PA United States Penn-CHOP Kidney Innovation Center University of Pennsylvania Philadelphia PA United States Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou Zhejiang China Department of Nephrology Children' Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou Zhejiang China Institute of Molecular Life Sciences HUN-REN Research Center for Natural Sciences Budapest Hungary Department of Molecular Biology Semmelweis University Budapest Hungary Department of Medicine Federal University of São Paulo São Paulo Brazil Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN United States Institute of Clinical Medicine School of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Division of Nephrology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Institute of Biomedical Sciences Academia Sinica ROC Taipei Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung Taiwan ROC National Chung Hsing University Taichung Taiwan ROC Heart Rhythm Center Division of Cardiology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Department of Internal Medicine College of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan ROC Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan ROC Division of Endocrinology and Metabolism Department of Internal Medicine Kaohsiung Medical Universit

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

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GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

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Nature genetics 2023年第9期55卷 1471-1482页

作者： Remi Stevelink Dorothee Kasteleijn-Nolst Trenite Bobby P. C. Koeleman Dick Lindhout Anja C. M. Sonsma Ciarán Campbell Gianpiero L. Cavalleri Norman Delanty Hany El-Naggar Austin Lacey Peter Widdess-Walsh Colin P. Doherty Siwei Chen Claire Churchhouse Mark J. Daly Yen-Chen A. Feng Daniel P. Howrigan Mitja I. Kurki Benjamin M. Neale Aarno Palotie Wei Zhou Namrata Gupta Henrike O. Heyne Bassel Abou-Khalil Kevin F. Haas R. S. Taneja Oluyomi M. Adesoji Michael Nothnagel Peter Nürnberg Thomas Sander Zaid Afawi Ilan Blatt Amos D. Korczyn Elisabetta Amadori Ganna Balagura Antonella Riva Marcello Scala Paolo Scudieri Pasquale Striano Federico Zara Michele Iacomino Francesca Madia Alison Anderson Patrick Kwan Terence J. O’Brien Slavé Petrovski K. Meng Tan Marian Todaro Joseph Anderson Danielle M. Andrade Quratulain Zulfiqar Ali Grazia Annesi Pauls Auce Andreja Avbersek Simona Balestrini Vera Braatz Claudia B. Catarino Krishna Chinthapalli Jennifer Jamnadas-Khoda Dalia Kasperavičiūte Costin Leu Josemir W. Sander Sanjay M. Sisodiya Sara Zagaglia Melanie Bahlo Karen L. Oliver Mark D. Baker Beata Fonferko-Shadrach William O. Pickrell Carmen Barba Claudia Bianchini Viola Doccini Renzo Guerrini Davide Mei Martino Montomoli Elena Parrini Annalisa Vetro Karen Barboza Fabrice Bartolomei Thomas Bast Bernhard J. Steinhoff Larry Baum Stacey S. Cherny Pak Sham Tobias Baumgartner Christian E. Elger Kerstin Hallmann Wolfram S. Kunz Rainer Surges Randi von Wrede Gábor Zsurka Betül Baykan Nerses Bebek Yeşim Kesim Albert J. Becker Susanne Schoch Felicitas Becker Caitlin A. Bennett Samuel F. Berkovic Stephanie L. Leech Brigid M. Regan Ingrid E. Scheffer Bianca Berghuis Ahmad Beydoun Wassim Nasreddine Francesca Bisulli Lidia Di Vito Lorenzo Ferri Tania Giangregorio Laura Licchetta Raffaella Minardi Barbara Mostacci Carlotta Stipa Paolo Tinuper Lorenzo Muccioli Federica Pondrelli Dheeraj R. Bobbili Emadeldin Hassanin Roland Krause Zied Landoulsi Patrick May Ingo Borggraefe Christian Bosselmann Christian Hengsbach Josua Kegele Stephen Lauxmann Department of Genetics University Medical Center Utrecht Utrecht The Netherlands Stichting Epilepsie Instellingen Nederland (SEIN) Heemstede The Netherlands School of Pharmacy and Biomolecular Sciences The Royal College of Surgeons in Ireland Dublin Ireland The FutureNeuro Research Centre Dublin Ireland Department of Neurology Beaumont Hospital Dublin Ireland Department of Neurology St. James’s Hospital Dublin Ireland Analytic and Translational Genetics Unit Department of Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and M.I.T Cambridge MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Psychiatric & Neurodevelopmental Genetics Unit Department of Psychiatry Massachusetts General Hospital and Harvard Medical School Boston MA USA Division of Biostatistics Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Hasso Plattner Institute Digital Health Center University of Potsdam Potsdam Germany Hasso Plattner Institute Mount Sinai School of Medicine New York City NY USA Department of Neurology Vanderbilt University Medical Center Nashville TN USA Cologne Center for Genomics (CCG) University of Cologne Faculty of Medicine and University Hospital Cologne Cologne Germany University Hospital Cologne Cologne Germany Department of Neurology Charité Universitaetsmedizin Berlin Campus Virchow-Clinic Berlin Germany Tel-Aviv University Sackler Faculty of Medicine Ramat Aviv Israel Department of Neurology Sheba Medical Center Ramat Gan Israel Department of Neurosciences Rehabilitation Ophthalmology Genetics Maternal and Child Health University of Genova Genova Italy IRCCS Istituto Giannina Gaslini Genova Italy Department of Medicine University of Melbour

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

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