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Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

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Nature genetics 2018年第5期50卷 766-767页

作者： Valérie Turcot Yingchang Lu Heather M Highland Claudia Schurmann Anne E Justice Rebecca S Fine Jonathan P Bradfield Tõnu Esko Ayush Giri Mariaelisa Graff Xiuqing Guo Audrey E Hendricks Tugce Karaderi Adelheid Lempradl Adam E Locke Anubha Mahajan Eirini Marouli Suthesh Sivapalaratnam Kristin L Young Tamuno Alfred Mary F Feitosa Nicholas G D Masca Alisa K Manning Carolina Medina-Gomez Poorva Mudgal Maggie C Y Ng Alex P Reiner Sailaja Vedantam Sara M Willems Thomas W Winkler Gonçalo Abecasis Katja K Aben Dewan S Alam Sameer E Alharthi Matthew Allison Philippe Amouyel Folkert W Asselbergs Paul L Auer Beverley Balkau Lia E Bang Inês Barroso Lisa Bastarache Marianne Benn Sven Bergmann Lawrence F Bielak Matthias Blüher Michael Boehnke Heiner Boeing Eric Boerwinkle Carsten A Böger Jette Bork-Jensen Michiel L Bots Erwin P Bottinger Donald W Bowden Ivan Brandslund Gerome Breen Murray H Brilliant Linda Broer Marco Brumat Amber A Burt Adam S Butterworth Peter T Campbell Stefania Cappellani David J Carey Eulalia Catamo Mark J Caulfield John C Chambers Daniel I Chasman Yii-Der I Chen Rajiv Chowdhury Cramer Christensen Audrey Y Chu Massimiliano Cocca Francis S Collins James P Cook Janie Corley Jordi Corominas Galbany Amanda J Cox David S Crosslin Gabriel Cuellar-Partida Angela D'Eustacchio John Danesh Gail Davies Paul I W Bakker Mark C H Groot Renée Mutsert Ian J Deary George Dedoussis Ellen W Demerath Martin Heijer Anneke I Hollander Hester M Ruijter Joe G Dennis Josh C Denny Emanuele Di Angelantonio Fotios Drenos Mengmeng Du Marie-Pierre Dubé Alison M Dunning Douglas F Easton Todd L Edwards David Ellinghaus Patrick T Ellinor Paul Elliott Evangelos Evangelou Aliki-Eleni Farmaki I Sadaf Farooqi Jessica D Faul Sascha Fauser Shuang Feng Ele Ferrannini Jean Ferrieres Jose C Florez Ian Ford Myriam Fornage Oscar H Franco Andre Franke Paul W Franks Nele Friedrich Ruth Frikke-Schmidt Tessel E Galesloot Wei Gan Ilaria Gandin Paolo Gasparini Jane Gibson Vilmantas Giedraitis Anette P Gjesing Penny Gordon-Larsen Mathias Gorski Hans-Jörgen Montreal Heart Institute Université de Montréal Montreal Quebec Canada. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA. Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA. Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA. Department of Epidemiology University of North Carolina Chapel Hill NC USA. Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genetics Harvard Medical School Boston MA USA. Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children's Hospital Boston MA USA. Center for Applied Genomics Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA USA. Quantinuum Research LLC San Diego CA USA. Estonian Genome Center University of Tartu Tartu Estonia. Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA. Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA. Wellcome Trust Sanger Institute Hinxton UK. Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA. Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK. Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus. Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany. Department of Biostatistics and Center for Statistical Genetics University of M

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (department of biostatistics and epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

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Corrigendum re "Genetic Variants Related to Longer Telomere Length are Associated with Increased Risk of Renal Cell Carcinoma" [Eur Urol 2017;72:747-54]

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European urology 2018年第3期74卷 e85-e86页

作者： Mitchell J Machiela Jonathan N Hofmann Robert Carreras-Torres Kevin M Brown Mattias Johansson Zhaoming Wang Matthieu Foll Peng Li Nathaniel Rothman Sharon A Savage Valerie Gaborieau James D McKay Yuanqing Ye Marc Henrion Fiona Bruinsma Susan Jordan Gianluca Severi Kristian Hveem Lars J Vatten Tony Fletcher Kvetoslava Koppova Susanna C Larsson Alicja Wolk Rosamonde E Banks Peter J Selby Douglas F Easton Paul Pharoah Gabriella Andreotti Laura E Beane Freeman Stella Koutros Demetrius Albanes Satu Mannisto Stephanie Weinstein Peter E Clark Todd E Edwards Loren Lipworth Susan M Gapstur Victoria L Stevens Hallie Carol Matthew L Freedman Mark M Pomerantz Eunyoung Cho Peter Kraft Mark A Preston Kathryn M Wilson J Michael Gaziano Howard S Sesso Amanda Black Neal D Freedman Wen-Yi Huang John G Anema Richard J Kahnoski Brian R Lane Sabrina L Noyes David Petillo Leandro M Colli Joshua N Sampson Celine Besse Helene Blanche Anne Boland Laurie Burdette Egor Prokhortchouk Konstantin G Skryabin Meredith Yeager Mirjana Mijuskovic Miodrag Ognjanovic Lenka Foretova Ivana Holcatova Vladimir Janout Dana Mates Anush Mukeriya Stefan Rascu David Zaridze Vladimir Bencko Cezary Cybulski Eleonora Fabianova Viorel Jinga Jolanta Lissowska Jan Lubinski Marie Navratilova Peter Rudnai Neonila Szeszenia-Dabrowska Simone Benhamou Geraldine Cancel-Tassin Olivier Cussenot H B As Bueno-de-Mesquita Federico Canzian Eric J Duell Börje Ljungberg Raviprakash T Sitaram Ulrike Peters Emily White Garnet L Anderson Lisa Johnson Juhua Luo Julie Buring I-Min Lee Wong-Ho Chow Lee E Moore Christopher Wood Timothy Eisen James Larkin Toni K Choueiri G Mark Lathrop Bin Tean Teh Jean-Francois Deleuze Xifeng Wu Richard S Houlston Paul Brennan Stephen J Chanock Ghislaine Scelo Mark P Purdue Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department Health and Human Services Bethesda MS USA. International Agency for Research on Cancer (IARC) Lyon France. St. Jude Children's Research Hospital Memphis TN USA. Department of Epidemiology Division of Cancer Prevention and Population Sciences The University of Texas MD Anderson Cancer Center Houston TX USA. Icahn School of Medicine New York NY USA. Cancer Epidemiology and Intelligence Division Cancer Council Victoria Melbourne Australia. QIMR Berghofer Medical Research Institute Herston Queensland Australia School of Public Health The University of Queensland Brisbane Australia. Cancer Epidemiology and Intelligence Division Cancer Council Victoria Melbourne Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Carlton Australia Human Genetics Foundation (HuGeF) Torino Italy Centre de Recherche en Épidémiologie et Santé des Populations Université Paris-Saclay UPS USQ Gustave Roussy Villejuif France. HUNT Research Centre Department of Public Health and General Practice Norwegian University of Science and Technology Levanger Sweden. Department of Public Health and General Practice Faculty of Medicine Norwegian University of Science and Technology Trondheim Norway. London School of Hygiene and Tropical Medicine University of London London UK. Regional Authority of Public Health in Banska Bystrica Banska Bystrica Slovakia. Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden. Leeds Institute of Cancer and Pathology University of Leeds Cancer Research Building St James's University Hospital Leeds UK. Department of Oncology and Department of Public Health and Primary Care University of Cambridge Cambridge UK. National Institute for Health and Welfare Helsinki Finland. Vanderbilt-Ingram Cancer Center Nashville TN USA. American Cancer Society At

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Risk assessment and evaluation of predictions

Risk assessment and evaluation of predictions

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International conference on Risk Assessment and Evaluation of Predictions, 2011

作者： Lee, Mei-Ling Ting Gail, Mitchell Pfeiffer, Ruth Satten, Glen Cai, Tianxi Gandy, Axel Department of Epidemiology and Biostatistics University of Maryland College ParkMD United States Division of Cancer Epidemiology and Genetics National Cancer Institute BethesdaMD United States National Cancer Institute NCI Shady Grove BethesdaMD United States Centers for Disease Control and Prevention AtlantaGA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Department of Mathematics Imperial College London London United Kingdom

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Daily patterns of physical activity by type 2 diabetes definition: Comparing diabetes, prediabetes, and participants with normal glucose levels in NHANES 2003-2006

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Preventive Medicine Reports 2015年 2卷 152-157页

作者： Steeves, Jeremy A. Murphy, Rachel A. Crainiceanu, Ciprian M. Zipunnikov, Vadim Van Domelen, Dane R. Harris, Tamara B. Cancer Prevention Fellowship Program Division of Cancer Control and Population Sciences National Cancer Institute Rockville MD United States Department of Kinesiology and Center for Aging and Translational Research College of Health Sciences University of Wisconsin-Milwaukee 2400 E Hartford Ave Milwaukee 53211 WI United States Laboratory of Epidemiology and Population Sciences Intramural Research Program National Institute on Aging Bethesda MD United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD United States Department of Biostatistics and Bioinformatics Emory University Atlanta GA United States

Objective: Diabetes is associated with low levels of physical activity (PA), but detailed objective information about how PA patterns vary by diabetes definition is lacking. Methods: PA was measured with ActiGraph accelerometers in older (60. +) adults from the 2003-2006 National Health and Nutrition Examination Survey (. n=. 1,043) and analyzed in 2014. Diabetes definition (normal glucose levels, prediabetes, and diabetes) was assessed (fasting glucose, hemoglobin A1C, and self-report). Accelerometer data were used to characterize total activity counts (TAC) per day and hour-by-hour activity counts by diabetes definition. Multiple linear regression models explored the relationship between diabetes definition and TAC. Results: Despite similar patterns of PA, diabetes participants had significantly lower TAC compared to participants with normal glucose levels and prediabetes. Diabetes participants' activity counts per hour declined more rapidly after 12. p.m., with the biggest differences between the groups occurring at 4:00. p.m. Participants with normal glucose levels and prediabetes had similar TAC and daily PA profiles. Conclusion: Our novel methodology provides information about PA patterns by diabetes definition. Significantly lower TAC in the diabetes group, their significant drop in afternoon PA, and the similarity of PA between participants with normal glucose levels and prediabetes provide insight into potential targets for intervention. © 2015 Published by Elsevier Inc.

关键词： Cross sectional Diabetes PA Patterns Prediabetes

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Particulate Matter from Electronic Cigarettes and Conventional Cigarettes: a Systematic Review and Observational Study

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Current environmental health reports 2015年第4期2卷 423-429页

作者： Fernández, Esteve Ballbè, Montse Sureda, Xisca Fu, Marcela Saltó, Esteve Martínez-Sánchez, Jose M. Tobacco Control Unit Cancer Prevention and Control Program Institut Català d’Oncologia WHO Collaborating Center for Tobacco Control L’Hospitalet de Llobregat Barcelona Spain Cancer Prevention and Control Group Institut d’Investigació Biomèdica de Bellvitge (IDIBELL) L’Hospitalet de Llobregat Barcelona Spain School of Medicine Universitat de Barcelona Barcelona Spain Addictions Unit Institute of Neurosciences Hospital Clínic de Barcelona Barcelona Spain Department of Preventive Medicine Universidad de Alcalá Alcalá de Henares Spain Health Plan Directorate Ministry of Health Generalitat de Catalunya Spain Biostatistics Unit Department of Basic Sciences Universitat Internacional de Catalunya Sant Cugat del Vallès Spain

Objectives: The aim of this study is to review the literature on the composition of aerosols from electronic cigarettes (e-cigarettes) originated by human vaping and to describe the emission of particulate matter ≤2.5 μm in diameter (PM_2.5) from conventional and e-cigarettes at home in real-use conditions. Methods: We conducted a systematic literature search in PubMed and Web of Science. We measured PM_2.5 in four different homes: one from a conventional cigarette smoker, one from an e-cigarette user, and two from non-smokers. Results: The review identified eight previous investigations on the composition of aerosols from e-cigarettes originated by human vaping and indicated that emissions from e-cigarettes can contain potential toxic compounds such as nicotine, carbonyls, metals, and organic volatile compounds, besides particulate matter. In the observational study, the PM_2.5 median concentration was 9.88 μg/m³ in the e-cigarette user home and 9.53 and 9.36 μg/m³ in the smoke-free homes, with PM_2.5 peaks concurrent with the e-cigarette puffs. Conclusion: Both the literature review and the observational study indicate that e-cigarettes used under real-conditions emit toxicants, including PM_2.5. Further research is needed to characterize the chemicals emitted by different types of e-cigarettes and to assess secondhand exposure to e-cigarette aerosol using biological markers. © 2015, Springer International Publishing AG.

关键词： E-cigarette Electronic cigarette Electronic nicotine delivery system Particulate matter Tobacco smoke pollution

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American journal of obstetrics and gynecology 2016年第5期214卷 667-8页

作者： Robert A Bednarczyk Daniella Figueroa-Downing Kevin Ault Hubert Department of Global Health and Department of Epidemiology Rollins School of Public Health Emory University Cancer Prevention and Control Program Winship Cancer Institute and Emory Vaccine Center Atlanta GA. Electronic address: rbednar@emory.edu. Hubert Department of Global Health and Department of Epidemiology Rollins School of Public Health Emory University Atlanta GA. Department of Obstetrics and Gynecology University of Kansas Medical Center Kansas City KS.

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Acceptable and Preferred Cervical cancer Screening Intervals among U.S. Women

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American Journal of Preventive Medicine 2015年第6期49卷 e99-e107页

作者： Cooper, Crystale Purvis Saraiya, Mona Sawaya, George F. Soltera Center for Cancer Prevention and Control Tucson AZ United States Division of Cancer Prevention and Control National Center for Chronic Disease Prevention and Health Promotion CDC MS K76 4770 Buford Hwy Atlanta 30341 GA United States Department of Obstetrics Gynecology and Reproductive Sciences Epidemiology and Biostatistics University of California San Francisco CA United States

Introduction Current U.S. cervical cancer screening guidelines recommend a 3- or 5-year screening interval depending on age and screening modality. However, many women continue to be screened annually. The purpose of this study is to investigate U.S. women's self-reported frequency of cervical cancer screening, acceptance of an extended screening interval (once every 3-5 years), and preferred screening options. Methods Data from a 2012 web-based survey of U.S. women aged ≥18 years who had not undergone a hysterectomy or been diagnosed with cervical cancer (N=1,380) were analyzed in 2014. Logistic regression models of extended screening interval use, acceptance, and preference were developed. Results Annual Pap testing was the most widely used (48.5%), accepted (61.0%), and preferred (51.1%) screening option. More than one third of respondents (34.4%) indicated that an extended screening interval would be acceptable, but only 6.3% reported that they were currently screened on an extended interval. Women who preferred an extended screening interval (32.9% of those willing to accept regular screening) were more likely to report no primary care visits during the last 12 months (AOR=2.05, p<0.003), no history of abnormal Pap test results (AOR=1.71, p=0.013), and that their last Pap test was performed by an internist/family practitioner rather than an obstetrician-gynecologist (AOR=2.03, p<0.001). Conclusions U.S. women's acceptance of and preference for an extended cervical cancer screening interval appears to be more widespread than utilization. Strategies to educate women about the reasoning behind recommendations for less-than-annual testing and to foster informed preferences should be devised and evaluated.

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Correction to: Vitamin D and pancreatic cancer: a pooled analysis from the Pancreatic cancer Case–control Consortium

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Annals of Oncology 2016年第1期27卷 208-208页

作者： M. Waterhouse H.A. Risch C. Bosetti K.E. Anderson G.M. Petersen W.R. Bamlet M. Cotterchio S.P. Cleary T.I. Ibiebele C. La Vecchia H.G. Skinner L. Strayer P.M. Bracci P. Maisonneuve H.B. Bueno-de-Mesquita W. Zatoński L. Lu H. Yu R.E. Neale Division of Population Health QIMR Berghofer Medical Research Institute Herston Centre for Research Excellence in Sun and Health Queensland University of Technology Kelvin Grove Australia Department of Chronic Disease Epidemiology Yale School of Public Health New Haven USA Department of Epidemiology IRCCS–Istituto di Ricerche Farmacologiche ‘Mario Negri’ Milan Italy Division of Epidemiology and Community Health University of Minnesota Minneapolis Department of Health Sciences Research Mayo Clinic Rochester USA Prevention and Cancer Control Cancer Care Ontario Toronto Dalla Lana School of Public Health University of Toronto Toronto Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital Toronto Department of Surgery University of Toronto Toronto Canada Department of Clinical Sciences and Community Health Università degli Studi di Milano Milan Italy Truven Health Analytics Durham Department of Epidemiology and Biostatistics University of California San Francisco San Francisco USA Division of Epidemiology and Biostatistics European Institute of Oncology Milan Italy National Institute for Public Health and the Environment Bilthoven Department of Gastroenterology and Hepatology University Medical Centre Utrecht Utrecht The Netherlands Department of Epidemiology and Biostatistics The School of Public Health Imperial College London London UK Department of Epidemiology The Maria Skłodowska-Curie Memorial Cancer Center and Institute of OncologyWarsaw Poland Epidemiology Program University of Hawaii Cancer Center Honolulu USA

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A genome-wide association study meta-analysis of clinical fracture in 10,012 African American women

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Bone Reports 2016年 5卷 233-242页

作者： Taylor, Kira C. Evans, Daniel S. Edwards, Digna R. Velez Edwards, Todd L. Sofer, Tamar Li, Guo Liu, Yongmei Franceschini, Nora Jackson, Rebecca D. Giri, Ayush Donneyong, Macarius Psaty, Bruce Rotter, Jerome I. LaCroix, Andrea Z. Jordan, Joanne M. Robbins, John A. Lewis, Beth Stefanick, Marcia L. Garcia, Melissa Harris, Tamara Cauley, Jane A. North, Kari E. School of Public Health and Information Sciences University of Louisville 485 E Gray St. Louisville 40202 KY United States Gillings School of Global Public Health University of North Carolina at Chapel Hill 137 E. Franklin St. Chapel Hill 27514 NC United States University of North Carolina at Chapel Hill 137 E. Franklin St. Chapel Hill 27514 NC United States California Pacific Medical Center Research Institute 550 16th Street Box 0560 San Francisco 94158-2549 CA United States Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt Genetics Institute Vanderbilt University 2525 West End Avenue Nashville 37203 TN United States Vanderbilt Genetics Institute Division of Epidemiology Department of Medicine Vanderbilt University 2525 West End Avenue Nashville 37203 TN United States Department of Biostatistics University of Washington UW Tower 15th floor 4333 Brooklyn Ave NE Seattle 98105 United States Cardiovascular Health Research Unit Department of Medicine University of Washington Metropolitan Park East Tower 1730 Minor Ave Suite 1360 Seattle 98101 WA United States Thurston Arthritis Research Center University of North Carolina at Chapel Hill 3300 Thurston Bldg. CB# 7280 Chapel Hill 27599-7280 NC United States The Ohio State University 376 W 10th Avenue Suite 260 Columbus 43210 OH United States Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital 1620 Tremont St St 3030 Boston 02120 MA United States Cardiovascular Health Research Unit Departments of Medicine Epidemiology and Health Services University of Washington Institute of Translational Genomics and Population Sciences Los Angeles Biomedical Research Institute and Department of Pediatrics Harbor-UCLA Medical Center 1124 W. Carson Street Bldg. E-5 Torrance 90502 CA United States Fred Hutchinson Cancer Research Center 1100 Fairview Ave N Seattle 98109 WA United States Department of Medicine Uni

Background Osteoporosis is a major public health problem associated with excess disability and mortality. It is estimated that 50–70% of the variation in osteoporotic fracture risk is attributable to genetic factors. The purpose of this hypothesis-generating study was to identify possible genetic determinants of fracture among African American (AA) women in a GWAS meta-analysis. Methods Data on clinical fractures (all fractures except fingers, toes, face, skull or sternum) were analyzed among AA female participants in the Women's Health Initiative (WHI) (N = 8155), Cardiovascular Health Study (CHS) (N = 504), BioVU (N = 704), Health ABC (N = 651), and the Johnston County Osteoarthritis Project (JoCoOA) (N = 291). Affymetrix (WHI) and Illumina (Health ABC, JoCoOA, BioVU, CHS) GWAS panels were used for genotyping, and a 1:1 ratio of YRI:CEU HapMap haplotypes was used as an imputation reference panel. We used Cox proportional hazard models or logistic regression to evaluate the association of ~ 2.5 million SNPs with fracture risk, adjusting for ancestry, age, and geographic region where applicable. We conducted a fixed-effects, inverse variance-weighted meta-analysis. Genome-wide significance was set at P < 5 × 10^− 8. Results One SNP, rs12775980 in an intron of SVIL on chromosome 10p11.2, reached genome-wide significance (P = 4.0 × 10^− 8). Although this SNP has a low minor allele frequency (0.03), there was no evidence for heterogeneity of effects across the studies (I² = 0). This locus was not reported in any previous osteoporosis-related GWA studies. We also interrogated previously reported GWA-significant loci associated with fracture or bone mineral density in our data. One locus (SMOC1) generalized, but overall there was not substantial evidence of generalization. Possible reasons for the lack of generalization are discussed. Conclusion This GWAS meta-analysis of fractures in African American women identified a potentially novel lo

关键词： African American Fracture Genetic association study Genome-wide association study (GWAS) Meta-analysis Osteoporosis

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