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Author Correction: c-Met activation leads to the establishment of a TGFβ-receptor regulatory network in bladder cancer progression

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Nature communications 2019年第1期10卷 5148页

作者： Wen Jing Sim Prasanna Vasudevan Iyengar Dilraj Lama Sarah Kit Leng Lui Hsien Chun Ng Lior Haviv-Shapira Eytan Domany Dennis Kappei Tuan Zea Tan Azad Saei Patrick William Jaynes Chandra Shekhar Verma Alan Prem Kumar Mathieu Rouanne Hong Koo Ha Camelia Radulescu Peter Ten Dijke Pieter Johan Adam Eichhorn Jean Paul Thiery Institute of Molecular and Cell Biology A*STAR Singapore 138672 Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore. Department of Cell and Chemical Biology and Oncode Institute Leiden University Medical Center 2333 ZC Leiden The Netherlands. Bioinformatics Institute (A*STAR) 30 Biopolis Street 07-01 Matrix Singapore 138671 Singapore. Department of Physics of Complex Systems The Weizmann Institute of Science Rehovot Israel. Department of Biochemistry Yong Loo Lin School of Medicine National University of Singapore Singapore 117596 Singapore. Genome Institute of Singapore A*STAR Singapore 138672 Singapore. Department of Biological Sciences National University of Singapore 14 Science Drive Singapore 117543 Singapore. School of Biological Sciences Nanyang Technological University 60 Nanyang Drive Singapore 637551 Singapore. Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore. Cancer Program Medical Science Cluster Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Curtin Medical School Faculty of Health Sciences Curtin University Perth WA Australia. Department of Urology Hôpital Foch Université Versailles-Saint-Quentin-en-Yvelines Université Paris-Saclay Suresnes France. INSERM Unit 1015 Laboratoire de Recherche Translationnelle en Immunologie (LRTI) Gustave Roussy Université Paris-Saclay Villejuif France. Department of Urology Pusan National University Hospital Pusan National University School of Medicine Busan 602-739 Korea. Department of Pathology Hôpital Foch Université Versailles-Saint-Quentin-en-Yvelines Université Paris-Saclay Suresnes France. Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore. Pieter.Eichhorn@curtin.edu.au. Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 1

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

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Effects of Maternal and Lactational Exposure to 2-Hydroxy-4 Methoxybenzone on Development and Reproductive Organs in Male and Female Rat Offspring (vol 104, pg 35, 2015)

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BIRTH DEFECTS RESEARCH PART B-DEVELOPMENTAL AND REPRODUCTIVE TOXICOLOGY 2015年第3期104卷 140-140页

作者： Nakamura, Noriko Inselman, Amy L. White, Gene A. Chang, Ching-Wei Trbojevich, Raul A. Sephr, Estatira Voris, Kristie L. Patton, Ralph E. Bryant, Matthew S. Harrouk, Wafa McIntyre, Barry S. Foster, Paul M. D. Hansen, Deborah K. Division of Systems Biology National Center for Toxicological Research Food and Drug Administration Jefferson Arkansas Division of Bioinformatics and Biostatistics National Center for Toxicological Research Food and Drug Administration Jefferson Arkansas Division of Biochemical Toxicology National Center for Toxicological Research Food and Drug Administration Jefferson Arkansas Toxicologic Pathology Associates Jefferson Arkansas Center for Drug Evaluation and Research Food and Drug Administration Silver Spring Maryland National Toxicology Program National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park North Carolina

2-Hydroxy-4-methoxybenzophenone (HMB) is an ultraviolet (UV) absorbing compound used in many cosmetic products as a UV-protecting agent and in plastics for preventing UV-induced photodecomposition. HMB has been detected in over 95% of randomly collected human urine samples from adults and from premature infants, and it may have estrogenic *** determine the effects of maternal and lactational exposure to HMB on development and reproductive organs of offspring, time-mated female Harlan Sprague-Dawley rats were dosed with 0, 1000, 3000, 10,000, 25,000, or 50,000 ppm HMB (seven to eight per group) added to chow from gestation day 6 until weaning on postnatal day (PND) *** AND CONCLUSIONExposure to HMB was associated with reduced body and organ weights in female and male offspring. No significant differences were observed in the number of implantation sites/litter, mean resorptions/litter, % litters with resorptions, number and weights of live fetuses, or sex ratios between the control and HMB dose groups. Normalized anogenital distance in male pups at PND 23 was decreased in the highest dose group. Spermatocyte development was impaired in testes of male offspring in the highest dose group. In females, follicular development was delayed in the highest dose group. However, by evaluating levels of the compound in rat serum, the doses at which adverse events occurred are much higher than usual human exposure levels. Thus, exposure to less than 10,000 ppm HMB does not appear to be associated with adverse effects on the reproductive system in rats

关键词： 2-hydroxy-4-methoxybenzophenone body weight rats offspring testes ovary fetal exposure

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Learning Cellular Objectives from Fluxes by Inverse Optimization

Learning Cellular Objectives from Fluxes by Inverse Optimiza...

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IEEE Annual Conference on Decision and Control

作者： Qi Zhao Arion Stettner Ed Reznik Daniel Segre Ioannis Ch. Paschalidis Dept. of Electrical and Computer Eng. and Division of Systems Engineering Boston University Department of Biomedical Engineering Boston University Bioinformatics Program Department of Biology and Department of Biomedical Engineering Boston University

ISBN: (纸本)9781479978878

Flux Balance Analysis (FBA) is a widely used approach for studying biochemical networks, and in particular the genome-scale metabolic network reconstructions. It formulates the problem of predicting a cell's chemical reaction fluxes as the linear optimization problem of maximizing a cellular objective (e.g., growth) subject to constraints capturing stoichiometry mass balances of the metabolic network and bounds that reflect the composition of the growth medium. In practice, however, reaction fluxes of the cells under specific growth conditions are available to be measured, but the primal FBA objective function is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, we have developed an Inverse Flux Balance Analysis (InvFBA) method which is a novel inverse optimization-based framework for inferring metabolic objective functions. Within this framework, we present three different forms of objective functions: linear, quadratic, and non-parametric. We show that in all cases, the inverse problem is tractable and can be solved efficiently. We provide several numerical examples to show that the inference of the objective function is consistent with simulated flux data and actual measurements.

关键词： Linear programming Kernel Optimization Biochemistry Biomass Biomedical measurement

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Proceedings of the 16th Annual UT-KBRIN bioinformatics Summit 2016: bioinformatics

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BMC bioinformatics 2017年第9期18卷 1-9页

作者： Eric C. Rouchka Juw Won Park Julia H. Chariker David A. Tieri Shreedharkumar Rajurkar Vikas Singh Nishchal K. Verma Yan Cui Mark Farman Bradford Condon Jolanta Jaromczyk Neil Moore Jerzy Jaromczyk Daniel Harris Patrick Calie Eun Kyong Shin Robert L. Davis Arash Shaban-Nejad Joshua M. Mitchell Hunter N. B. Moseley Robert M. Flight Qing Jun Wang Richard M. Higashi Teresa W-M Fan Andrew N. Lane Liangqun Lu Bernie J Daigle Jr. Xi Chen Andrey Smelter Jerzy W. Jaromczyk Bailey K. Phan Nathaniel J. Serpico Ethan G. Toney Li Chen Caroline E. Melton Jennifer R. Mandel Bernie J. Daigle Jr. Hao Chen Kazi I. Zaman Ramin Homayouni Patrick J. Trainor Andrew P. DeFilippis Samantha M. Carlisle Shesh N. Rai Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville KY 40292 USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core 522 East Gray Street Louisville KY 40292 USA Department of Psychological and Brain Sciences University of Louisville Louisville KY 40292 USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville KY 40292 USA Department of Electrical Engineering IIT Kanpur India Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN 38103 USA Center for Integrative and Translational Genomics University of Tennessee Health Science Center Memphis TN 38103 USA Department of Plant Pathology University of Kentucky Lexington KY 40546 USA UK Healthcare Genomics University of Kentucky Lexington KY 40536 USA Department of Entomology & Plant Pathology University of Tennessee Knoxville TN 37996 USA Department of Forestry University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40506 USA Department of Computer Science University of Kentucky Lexington KY 40508 USA Department of Biological Sciences Eastern Kentucky University Richmond KY 40475 USA University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics Department of Pediatrics Memphis TN 38103 USA Department of Molecular & Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Markey Cancer Center University of Kentucky Lexington KY 40536 USA Center for Environmental and Systems Biochemistry University of Kentucky Lexington KY 40536 USA Institute for Biomedical Informatics University of Kentucky Lexington KY 40536 USA Department of Molecular and Cellular Biochemistry University of Kentucky Lexington KY 40536 USA Department of Statistics University of Kentucky Louisville KY

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Addendum: The FAIR Guiding Principles for scientific data management and stewardship

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Scientific data 2019年第1期6卷 6页

作者： Mark D Wilkinson Michel Dumontier Ijsbrand Jan Aalbersberg Gabrielle Appleton Myles Axton Arie Baak Niklas Blomberg Jan-Willem Boiten Luiz Bonino da Silva Santos Philip E Bourne Jildau Bouwman Anthony J Brookes Tim Clark Mercè Crosas Ingrid Dillo Olivier Dumon Scott Edmunds Chris T Evelo Richard Finkers Alejandra Gonzalez-Beltran Alasdair J G Gray Paul Groth Carole Goble Jeffrey S Grethe Jaap Heringa Peter A C 't Hoen Rob Hooft Tobias Kuhn Ruben Kok Joost Kok Scott J Lusher Maryann E Martone Albert Mons Abel L Packer Bengt Persson Philippe Rocca-Serra Marco Roos Rene van Schaik Susanna-Assunta Sansone Erik Schultes Thierry Sengstag Ted Slater George Strawn Morris A Swertz Mark Thompson Johan van der Lei Erik van Mulligen Andra Waagmeester Peter Wittenburg Katherine Wolstencroft Jun Zhao Barend Mons Center for Plant Biotechnology and Genomics Universidad Politécnica de Madrid Madrid 28223 Spain. Stanford University Stanford 94305-5411 USA. Elsevier Amsterdam 1043 NX The Netherlands. Nature Genetics New York 10004-1562 USA. Euretos and Phortos Consultants Rotterdam 2741 CA The Netherlands. ELIXIR Wellcome Genome Campus Hinxton CB10 1SA UK. Lygature Eindhoven 5656 AG The Netherlands. Vrije Universiteit Amsterdam Dutch Techcenter for Life Sciences Amsterdam 1081 HV The Netherlands. Office of the Director National Institutes of Health Rockville 20892 USA. TNO Zeist 3700 AJ The Netherlands. Department of Genetics University of Leicester Leicester LE1 7RH UK. Harvard Medical School Boston Massachusetts MA 02115 USA. Harvard University Cambridge Massachusetts MA 02138 USA. Data Archiving and Networked Services (DANS) The Hague 2593 HW The Netherlands. GigaScience Beijing Genomics Institute Shenzhen 518083 China. Department of Bioinformatics Maastricht University Maastricht 6200 MD The Netherlands. Wageningen UR Plant Breeding Wageningen 6708 PB The Netherlands. Oxford e-Research Center University of Oxford Oxford OX1 3QG UK. Heriot-Watt University Edinburgh EH14 4AS UK. School of Computer Science University of Manchester Manchester M13 9PL UK. Center for Research in Biological Systems School of Medicine University of California San Diego La Jolla California 92093-0446 USA. Dutch Techcenter for the Life Sciences Utrecht 3501 DE The Netherlands. Department of Human Genetics Leiden University Medical Center Dutch Techcenter for the Life Sciences Leiden 2300 RC The Netherlands. Dutch TechCenter for Life Sciences and ELIXIR-NL Utrecht 3501 DE The Netherlands. VU University Amsterdam Amsterdam 1081 HV The Netherlands. Leiden Center of Data Science Leiden University Leiden 2300 RA The Netherlands. Netherlands eScience Center Amsterdam 1098 XG The Netherlands. National Center for Microscopy and Imaging Research UCSD San Di

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Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

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Frontiers of Electrical and Electronic Engineering in China 2014年第4期9卷 127-141页

作者： Congmin Zhu Rui Jiang Ting Chen MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic & Systems Biology TNLIST/ Department of Automation Tsinghua University Beijing 100084 China Computational and Molecular Biology Program University of Southern California Loa Angles CA 90089 USA

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

关键词： Boolean implication metagenome marine OTUs environmental factors

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

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NATURE GENETICS 2021年第5期53卷 762-762页

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

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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

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

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

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

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