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A second update on mapping the human genetic architecture of COVID-19

Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

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Nature communications 2023年第1期14卷 3043页

作者： Jianxin Shi Kouya Shiraishi Jiyeon Choi Keitaro Matsuo Tzu-Yu Chen Juncheng Dai Rayjean J Hung Kexin Chen Xiao-Ou Shu Young Tae Kim Maria Teresa Landi Dongxin Lin Wei Zheng Zhihua Yin Baosen Zhou Bao Song Jiucun Wang Wei Jie Seow Lei Song I-Shou Chang Wei Hu Li-Hsin Chien Qiuyin Cai Yun-Chul Hong Hee Nam Kim Yi-Long Wu Maria Pik Wong Brian Douglas Richardson Karen M Funderburk Shilan Li Tongwu Zhang Charles Breeze Zhaoming Wang Batel Blechter Bryan A Bassig Jin Hee Kim Demetrius Albanes Jason Y Y Wong Min-Ho Shin Lap Ping Chung Yang Yang She-Juan An Hong Zheng Yasushi Yatabe Xu-Chao Zhang Young-Chul Kim Neil E Caporaso Jiang Chang James Chung Man Ho Michiaki Kubo Yataro Daigo Minsun Song Yukihide Momozawa Yoichiro Kamatani Masashi Kobayashi Kenichi Okubo Takayuki Honda Dean H Hosgood Hideo Kunitoh Harsh Patel Shun-Ichi Watanabe Yohei Miyagi Haruhiko Nakayama Shingo Matsumoto Hidehito Horinouchi Masahiro Tsuboi Ryuji Hamamoto Koichi Goto Yuichiro Ohe Atsushi Takahashi Akiteru Goto Yoshihiro Minamiya Megumi Hara Yuichiro Nishida Kenji Takeuchi Kenji Wakai Koichi Matsuda Yoshinori Murakami Kimihiro Shimizu Hiroyuki Suzuki Motonobu Saito Yoichi Ohtaki Kazumi Tanaka Tangchun Wu Fusheng Wei Hongji Dai Mitchell J Machiela Jian Su Yeul Hong Kim In-Jae Oh Victor Ho Fun Lee Gee-Chen Chang Ying-Huang Tsai Kuan-Yu Chen Ming-Shyan Huang Wu-Chou Su Yuh-Min Chen Adeline Seow Jae Yong Park Sun-Seog Kweon Kun-Chieh Chen Yu-Tang Gao Biyun Qian Chen Wu Daru Lu Jianjun Liu Ann G Schwartz Richard Houlston Margaret R Spitz Ivan P Gorlov Xifeng Wu Ping Yang Stephen Lam Adonina Tardon Chu Chen Stig E Bojesen Mattias Johansson Angela Risch Heike Bickeböller Bu-Tian Ji H-Erich Wichmann David C Christiani Gadi Rennert Susanne Arnold Paul Brennan James McKay John K Field Sanjay S Shete Loic Le Marchand Geoffrey Liu Angeline Andrew Lambertus A Kiemeney Shan Zienolddiny-Narui Kjell Grankvist Mikael Johansson Angela Cox Fiona Taylor Jian-Min Yuan Philip Lazarus Matthew B Schabath Melinda C Aldrich Hyo-Sung Jeon Shih Sheng Jiang Jae Sook Sung C Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. jianxin.shi@nih.gov. Division of Genome Biology National Cancer Research Institute Tokyo Japan. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute Nagoya Japan. Institute of Population Health Sciences National Health Research Institutes Zhunan Taiwan. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Jiangsu Key Lab of Cancer Biomarkers Prevention and Treatment Collaborative Innovation Center for Cancer Medicine Nanjing Medical University Nanjing China. Prosserman Centre for Population Health Research Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada. Department of Epidemiology and Biostatistics National Clinical Research Center for Cancer Key Laboratory of Molecular Cancer Epidemiology of Tianjin Tianjin Medical University Cancer Institute and Hospital Tianjin Medical University Tianjin China. Division of Epidemiology Department of Medicine Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center Nashville TN USA. Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology Cancer Institute and Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Department of Epidemiology School of Public Health China Medical University Shenyang China. Department of Clinical Epidemiology and Center of Evidence Based Medicine The First Hospital of China Medical University Shenyang China. Department of Oncology Shandong Cancer Hospital and Institute Shandong Academy of Medical Sciences Jinan China. Ministry of Education Key Laboratory of Contemporary Anthropology School of Life Sciences Fudan University Shanghai China. S

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

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Erratum to: 'Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images'

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BMC bioinformatics 2016年第1期17卷 207页

作者： Allison Chia-Yi Wu Scott A Rifkin Graduate Program in Bioinformatics and Systems Biology University of California San Diego La Jolla CA USA. Graduate Program in Bioinformatics and Systems Biology University of California San Diego La Jolla CA USA. sarifkin@ucsd.edu. Section of Ecology Behavior and Evolution Division of Biology University of California San Diego La Jolla CA USA. sarifkin@ucsd.edu.

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Source and sink nodes in absence seizures

Source and sink nodes in absence seizures

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Abner C. Rodrigues Birajara S. Machado Luís Otávio S. F. Caboclo André Fujita Luiz A. Baccala Koichi Sameshima Bioinformatics Interunits Graduate Program Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR Hospital Israelita Albert Einstein São Paulo Brazil Institute of Mathematics and Statistics Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR School of Medicine Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR

ISBN: (纸本)9781457702198

As opposed to focal epilepsy, absence seizures do not exhibit a clear seizure onset zone or focus since its ictal activity rapidly engages both brain hemispheres. Yet recent graph theoretical analysis applied to absence seizures EEG suggests the cortical focal presence, an unexpected feature for this type of epilepsy. In this study, we explore the characteristics of absence seizure by classifying the nodes as to their source/sink natures via weighted directed graph analysis based on connectivity direction and strength estimation using information partial directed coherence (iPDC). By segmenting the EEG signals into relatively short 5-sec-long time windows we studied the evolution of coupling strengths from both sink and source nodes, and the network dynamics of absence seizures in eight patients.

关键词： Electrodes Electroencephalography Epilepsy Brain models Coherence Couplings

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Dysregulated mechanisms underlying Duchenne muscular dystrophy from co-expression network preservation analysis

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BMC Research Notes 2015年第1期8卷 1-10页

作者： Mukund, Kavitha Subramaniam, Shankar Bioinformatics and System Biology Graduate Program University of California San Diego MC0412 9500 Gilman Drive San Diego 92093 CA United States Departments of Bioengineering Computer Science and Engineering Cellular and Molecular Medicine Chemistry and Biochemistry University of California San Diego 9500 Gilman Drive San Diego 92093 CA United States

Background: Duchenne Muscular Dystrophy (DMD) is an X-linked recessive disorder with its primary insult on the skeletal muscle. Severe muscle wasting, chronic inflammation and fibrosis characterize dystrophic muscle. Here we identify dysregulated pathways in DMD utilizing a co-expression network approach as described in Weighted Gene Co-expression Network Analysis (WGCNA). Specifically, we utilize WGCNA's "preservation" statistics to identify gene modules that exhibit a weak conservation of network topology within healthy and dystrophic networks. Preservation statistics rank modules based on their topological metrics such as node density, connectivity and separability between networks. Methods: Raw data for DMD was downloaded from Gene Expression Omnibus (GSE6011) and suitably preprocessed. Co-expression networks for each condition (healthy and dystrophic) were generated using the WGCNA library in R. Preservation of healthy network edges was evaluated with respect to dystrophic muscle and vice versa using WGCNA. Highly exclusive gene pairs for each of the low preserved modules within both networks were also determined using a specificity measure. Results: A total of 11 and 10 co-expressed modules were identified in the networks generated from 13 healthy and 23 dystrophic samples respectively. 5 out of the 11, and 4 out of the 10 modules were identified as exhibiting none-to-weak preservation. Functional enrichment analysis identified that these weakly preserved modules were highly relevant to the condition under study. For instance, weakly preserved dystrophic module D2 exhibited the highest fraction of genes exclusive to DMD. The highly specific gene pairs identified within these modules were enriched for genes activated in response to wounding and affect the extracellular matrix including several markers such as SPP1, MMP9 and ITGB2. Conclusion: The proposed approach allowed us to identify clusters of genes that are non-randomly associated with the disease. Furthe

关键词： Co-expression network Differential network analysis Duchenne muscular dystrophy Preservation statistics Skeletal muscle

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Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

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Nature communications 2021年第1期12卷 2463页

作者： Joris Deelen Daniel S Evans Dan E Arking Niccolò Tesi Marianne Nygaard Xiaomin Liu Mary K Wojczynski Mary L Biggs Ashley van der Spek Gil Atzmon Erin B Ware Chloé Sarnowski Albert V Smith Ilkka Seppälä Heather J Cordell Janina Dose Najaf Amin Alice M Arnold Kristin L Ayers Nir Barzilai Elizabeth J Becker Marian Beekman Hélène Blanché Kaare Christensen Lene Christiansen Joanna C Collerton Sarah Cubaynes Steven R Cummings Karen Davies Birgit Debrabant Jean-François Deleuze Rachel Duncan Jessica D Faul Claudio Franceschi Pilar Galan Vilmundur Gudnason Tamara B Harris Martijn Huisman Mikko A Hurme Carol Jagger Iris Jansen Marja Jylhä Mika Kähönen David Karasik Sharon L R Kardia Andrew Kingston Thomas B L Kirkwood Lenore J Launer Terho Lehtimäki Wolfgang Lieb Leo-Pekka Lyytikäinen Carmen Martin-Ruiz Junxia Min Almut Nebel Anne B Newman Chao Nie Ellen A Nohr Eric S Orwoll Thomas T Perls Michael A Province Bruce M Psaty Olli T Raitakari Marcel J T Reinders Jean-Marie Robine Jerome I Rotter Paola Sebastiani Jennifer Smith Thorkild I A Sørensen Kent D Taylor André G Uitterlinden Wiesje van der Flier Sven J van der Lee Cornelia M van Duijn Diana van Heemst James W Vaupel David Weir Kenny Ye Yi Zeng Wanlin Zheng Henne Holstege Douglas P Kiel Kathryn L Lunetta P Eline Slagboom Joanne M Murabito Max Planck Institute for Biology of Ageing Cologne Germany. Joris.Deelen@age.mpg.de. Molecular Epidemiology Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Joris.Deelen@age.mpg.de. California Pacific Medical Center Research Institute San Francisco CA USA. DEvans@***. McKusick-Nathans Institute of Genetic Medicine Department of Genetic Medicine Johns Hopkins University School of Medicine Baltimore MD USA. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department of Clinical Genetics Amsterdam UMC Amsterdam The Netherlands. Delft Bioinformatics Lab Delft University of Technology Delft The Netherlands. The Danish Aging Research Center Department of Public Health University of Southern Denmark Odense C Denmark. BGI-Shenzhen Shenzhen China. China National Genebank BGI-Shenzhen Shenzhen China. Division of Statistical Genomics Department of Genetics Washington University School of Medicine Saint Louis MO USA. Department of Biostatistics University of Washington Seattle WA USA. Cardiovascular Health Research Unit Department of Medicine University of Washington Seattle WA USA. Department of Epidemiology Erasmus MC Rotterdam The Netherlands. Department of Biology Faculty of Natural Science University of Haifa Haifa Israel. Departments of Medicine and Genetics Albert Einstein College of Medicine Bronx NY USA. Institute for Social Research Survey Research Center University of Michigan Ann Arbor MI USA. Department of Biostatistics Boston University School of Public Health Boston MA USA. School of Public Health Department of Biostatistics University of Michigan Ann Arbor MI USA. Icelandic Heart Association Kópavogur Iceland. Department of Clinical Chemistry Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere Faculty of Medicine and Health Technology Tampere University Tampere Finland. I

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Users' interest assessment on job portal

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International Journal of Web Portals 2014年第1期6卷 64-75页

作者： Sudiana Pardamean, Bens Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics Research Group Bina Nusantara University Jakarta Indonesia

Job portal is considerably useful for both job seekers and employers. It enables job seekers to look for an employment, to advance their careers, or to market themselves. As for the employers, they can post vacant positions to be filled with suitable employees. A certain job portal received numerous complaints regarding its features that were unable to fulfill the users' needs. These complaints led to the decrease of the number of users. A study was conducted to determine factors that influenced users' interest by referring to the 7C framework. Questionnaires were distributed to numerous job seekers and employers. The data were analyzed using multiple linear regression technique. Chi-square analysis was also performed to further analyze which elements of the 7C framework were essential for the users. The results showed that only four out of seven elements from the framework affected the users' choice towards job portal, namely the context, the content, the community, and the commerce. Copyright © 2014, IGI Global.

关键词： Commerce

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FragGeneScan-plus for scalable high-throughput short-read open reading frame prediction

FragGeneScan-plus for scalable high-throughput short-read op...

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IEEE Symposium on Computational Intelligence and bioinformatics and Computational Biology (CIBCB)

作者： Dongjae Kim Aria S. Hahn Shang-Ju Wu Niels W. Hanson Kishori M. Konwar Steven J. Hallam Department of Computer Science University of British Columbia Vancouver Canada Department of Microbiology University of British Columbia Vancouver Canada Graduate Program in Bioinformatics University of British Columbia Vancouver Canada

A fundamental step in the analysis of environmental sequence information is the prediction of potential genes or open reading frames (ORFs) encoding the metabolic potential of individual cells and entire microbial communities. FragGeneScan, a software designed to predict intact and incomplete ORFs on short sequencing reads combines codon usage bias, sequencing error models and start/stop codon patterns in a hidden Markov model to find the most likely path of hidden states from a given input sequence, provides a promising route for gene recovery in environmental datasets with incomplete assemblies. However, the current implementation of FragGeneScan does not scale efficiently with increasing input data size. Thus, FragGeneScan cannot be applied to contemporary environmental datasets that can exceed 100s of Gb. Here, we present FragGeneScan-Plus, an improved implementation of the FragGeneScan gene prediction model that leverages algorithmic thread synchronization and efficient in-memory data management to utilize multiple CPU cores without blocking I/O operations. FragGeneScan-Plus can process data approximately 5-times faster than FragGeneScan using a single core and approximately 50-times faster using eight hyper-threaded cores when benchmarked against simulated and real world environmental datasets.

关键词： Instruction sets Buffer storage Hidden Markov models Synchronization Predictive models Prediction algorithms Soil

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Assembly independent functional annotation of short-read data using SOFA: Short-ORF functional annotation

Assembly independent functional annotation of short-read dat...

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IEEE Symposium on Computational Intelligence and bioinformatics and Computational Biology (CIBCB)

作者： Aria S. Hahn Niels W. Hanson Dongjae Kim Kishori M. Konwar Steven J. Hallam Department of Microbiology University of British Columbia Vancouver Canada Graduate Program in Bioinformatics University of British Columbia Vancouver Canada Department of Computer Science University of British Columbia Vancouver Canada

Accurate description of the microbial communities driving matter and energy transformations in complex ecosystems such as soils cannot yet be effectively accomplished using assembly-based approaches despite the rise of next generation sequencing technologies. Here we present SOFA, an open source pipeline enabling comparative functional annotation of unassembled short-read data. The pipeline attempts to merge mate pairs in fastq files, predicts open reading frames (ORFs) on merged and unmerged reads as small as 70 bps, and completes an additional step, we term `deduplication'. Deduplication prevents the double counting of ORFs predicted from unmerged paired-end reads by checking for homologous annotations that span the same ORF, allowing for quantitatively accurate predictions. The effectiveness of SOFA is validated with both simulated and bone fide soil metagenomes, and empirical results are compared to existing strategies for obtaining accurate ORF counts, and an analytical model of read duplication. SOFA enables downstream processing stages within the existing MetaPathways pipeline, and is available for download as a stand alone application at https://*** under the MIT license.

关键词： bioinformatics Pipelines Genomics Databases Soil Proteins Assembly

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Correction to: FLAGS, frequently mutated genes in public exomes

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BMC medical genomics 2017年第1期10卷 69页

作者： Casper Shyr Maja Tarailo-Graovac Michael Gottlieb Jessica Jy Lee Clara van Karnebeek Wyeth W Wasserman Centre for Molecular Medicine and Therapeutics Child and Family Research Institute Vancouver BC Canada. Treatable Intellectual Disability Endeavour in British Columbia Vancouver Canada. Bioinformatics Graduate Program University of British Columbia Vancouver BC Canada. Department of Medical Genetics University of British Columbia Vancouver BC Canada. Genome Science and Technology Graduate Program University of British Columbia Vancouver BC Canada. Division of Biochemical Diseases BC Children's Hospital Vancouver BC Canada. Department of Pediatrics University of British Columbia Vancouver BC Canada. Centre for Molecular Medicine and Therapeutics Child and Family Research Institute Vancouver BC Canada. wyeth@cmmt.ubc.ca. Department of Medical Genetics University of British Columbia Vancouver BC Canada. wyeth@cmmt.ubc.ca. Treatable Intellectual Disability Endeavour in British Columbia Vancouver Canada. wyeth@cmmt.ubc.ca.

Unfortunately, the original article [1] contained an error. The additional files were included incorrectly. The correct additional files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14 are published in this correction.

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