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Institution

University of Exeter

EducationExeter, United Kingdom
About: University of Exeter is a education organization based out in Exeter, United Kingdom. It is known for research contribution in the topics: Population & Climate change. The organization has 15820 authors who have published 50650 publications receiving 1793046 citations. The organization is also known as: Exeter University & University of the South West of England.


Papers
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Journal ArticleDOI
Anubha Mahajan1, Daniel Taliun2, Matthias Thurner1, Neil R. Robertson1, Jason M. Torres1, N. William Rayner3, N. William Rayner1, Anthony Payne1, Valgerdur Steinthorsdottir4, Robert A. Scott5, Niels Grarup6, James P. Cook7, Ellen M. Schmidt2, Matthias Wuttke8, Chloé Sarnowski9, Reedik Mägi10, Jana Nano11, Christian Gieger, Stella Trompet12, Cécile Lecoeur13, Michael Preuss14, Bram P. Prins3, Xiuqing Guo15, Lawrence F. Bielak2, Jennifer E. Below16, Donald W. Bowden17, John C. Chambers, Young-Jin Kim, Maggie C.Y. Ng17, Lauren E. Petty16, Xueling Sim18, Weihua Zhang19, Weihua Zhang20, Amanda J. Bennett1, Jette Bork-Jensen6, Chad M. Brummett2, Mickaël Canouil13, Kai-Uwe Ec Kardt21, Krista Fischer10, Sharon L.R. Kardia2, Florian Kronenberg22, Kristi Läll10, Ching-Ti Liu9, Adam E. Locke23, Jian'an Luan5, Ioanna Ntalla24, Vibe Nylander1, Sebastian Schönherr22, Claudia Schurmann14, Loic Yengo13, Erwin P. Bottinger14, Ivan Brandslund25, Cramer Christensen, George Dedoussis26, Jose C. Florez, Ian Ford27, Oscar H. Franco11, Timothy M. Frayling28, Vilmantas Giedraitis29, Sophie Hackinger3, Andrew T. Hattersley28, Christian Herder30, M. Arfan Ikram11, Martin Ingelsson29, Marit E. Jørgensen31, Marit E. Jørgensen25, Torben Jørgensen32, Torben Jørgensen6, Jennifer Kriebel, Johanna Kuusisto33, Symen Ligthart11, Cecilia M. Lindgren34, Cecilia M. Lindgren1, Allan Linneberg6, Allan Linneberg35, Valeriya Lyssenko36, Valeriya Lyssenko37, Vasiliki Mamakou26, Thomas Meitinger38, Karen L. Mohlke39, Andrew D. Morris40, Andrew D. Morris41, Girish N. Nadkarni14, James S. Pankow42, Annette Peters, Naveed Sattar43, Alena Stančáková33, Konstantin Strauch44, Kent D. Taylor15, Barbara Thorand, Gudmar Thorleifsson4, Unnur Thorsteinsdottir45, Unnur Thorsteinsdottir4, Jaakko Tuomilehto, Daniel R. Witte46, Josée Dupuis9, Patricia A. Peyser2, Eleftheria Zeggini3, Ruth J. F. Loos14, Philippe Froguel20, Philippe Froguel13, Erik Ingelsson47, Erik Ingelsson48, Lars Lind29, Leif Groop36, Leif Groop49, Markku Laakso33, Francis S. Collins50, J. Wouter Jukema12, Colin N. A. Palmer51, Harald Grallert, Andres Metspalu10, Abbas Dehghan20, Abbas Dehghan11, Anna Köttgen8, Gonçalo R. Abecasis2, James B. Meigs52, Jerome I. Rotter15, Jonathan Marchini1, Oluf Pedersen6, Torben Hansen25, Torben Hansen6, Claudia Langenberg5, Nicholas J. Wareham5, Kari Stefansson45, Kari Stefansson4, Anna L. Gloyn1, Andrew P. Morris10, Andrew P. Morris1, Andrew P. Morris7, Michael Boehnke2, Mark I. McCarthy1 
TL;DR: Combining 32 genome-wide association studies with high-density imputation provides a comprehensive view of the genetic contribution to type 2 diabetes in individuals of European ancestry with respect to locus discovery, causal-variant resolution, and mechanistic insight.
Abstract: We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency 2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

1,136 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

1,129 citations

Journal ArticleDOI
Paul Kline1
01 Aug 1986-Nature
TL;DR: In this article, a book is one of the greatest friends to accompany while in your lonely time and when you have no friends and activities, reading book can be a great choice.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading models of man as one of the reading material to finish quickly.

1,117 citations

Journal ArticleDOI
TL;DR: The ecotoxicological literature shows that concentrations of Ag NPs below the current and future PECs, as low as just a few ng L(-1), can affect prokaryotes, invertebrates and fish indicating a significant potential, though poorly characterised, risk to the environment.

1,115 citations

Journal ArticleDOI
Helena Furberg1, Yunjung Kim1, Jennifer Dackor1, Eric Boerwinkle2, Nora Franceschini1, Diego Ardissino, Luisa Bernardinelli3, Luisa Bernardinelli4, Pier Mannuccio Mannucci5, Francesco Mauri, Piera Angelica Merlini, Devin Absher, Themistocles L. Assimes6, Stephen P. Fortmann6, Carlos Iribarren7, Joshua W. Knowles6, Thomas Quertermous6, Luigi Ferrucci8, Toshiko Tanaka8, Joshua C. Bis9, Curt D. Furberg10, Talin Haritunians11, Barbara McKnight9, Bruce M. Psaty9, Bruce M. Psaty12, Kent D. Taylor11, Evan L. Thacker9, Peter Almgren13, Leif Groop13, Claes Ladenvall13, Michael Boehnke14, Anne U. Jackson14, Karen L. Mohlke1, Heather M. Stringham14, Jaakko Tuomilehto15, Jaakko Tuomilehto16, Emelia J. Benjamin17, Shih-Jen Hwang8, Daniel Levy17, Sarah R. Preis8, Ramachandran S. Vasan17, Jubao Duan18, Pablo V. Gejman18, Douglas F. Levinson6, Alan R. Sanders18, Jianxin Shi8, Esther H. Lips19, James McKay19, Antonio Agudo, Luigi Barzan, Vladimir Bencko20, Simone Benhamou21, Simone Benhamou22, Xavier Castellsagué, Cristina Canova23, David I. Conway24, Eleonora Fabianova, Lenka Foretova, Vladimir Janout25, Claire M. Healy26, Ivana Holcatova20, Kristina Kjærheim, Pagona Lagiou27, Jolanta Lissowska, Ray Lowry28, Tatiana V. Macfarlane29, Dana Mates, Lorenzo Richiardi30, Peter Rudnai, Neonilia Szeszenia-Dabrowska31, David Zaridze32, Ariana Znaor, Mark Lathrop, Paul Brennan19, Stefania Bandinelli, Timothy M. Frayling33, Jack M. Guralnik8, Yuri Milaneschi, John R. B. Perry33, David Altshuler34, David Altshuler35, Roberto Elosua, S. Kathiresan35, S. Kathiresan34, Gavin Lucas, Olle Melander13, Christopher J. O'Donnell8, Veikko Salomaa15, Stephen M. Schwartz9, Benjamin F. Voight36, Brenda W.J.H. Penninx37, Johannes H. Smit37, Nicole Vogelzangs37, Dorret I. Boomsma37, Eco J. C. de Geus37, Jacqueline M. Vink37, Gonneke Willemsen37, Stephen J. Chanock8, Fangyi Gu34, Susan E. Hankinson34, David J. Hunter34, Albert Hofman38, Henning Tiemeier38, André G. Uitterlinden38, Cornelia M. van Duijn38, Stefan Walter38, Daniel I. Chasman34, Brendan M. Everett34, Guillaume Paré34, Paul M. Ridker34, Ming D. Li39, Hermine H. Maes40, Janet Audrain-McGovern41, Danielle Posthuma37, Laura M. Thornton1, Caryn Lerman41, Jaakko Kaprio16, Jaakko Kaprio15, Jed E. Rose42, John P. A. Ioannidis43, John P. A. Ioannidis44, Peter Kraft34, Danyu Lin1, Patrick F. Sullivan1 
TL;DR: A meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium found the strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3, and three loci associated with number of cigarettes smoked per day were identified.
Abstract: Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], b = 1.03, standard error (s.e.) = 0.053, beta = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], b = 0.367, s. e. = 0.059, beta = 5.7 x 10(-10); and rs1028936[A], b = 0.446, s. e. = 0.074, beta = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], b = 0.333, s. e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.

1,104 citations


Authors

Showing all 16338 results

NameH-indexPapersCitations
Frank B. Hu2501675253464
John C. Morris1831441168413
David W. Johnson1602714140778
Kevin J. Gaston15075085635
Andrew T. Hattersley146768106949
Timothy M. Frayling133500100344
Joel N. Hirschhorn133431101061
Jonathan D. G. Jones12941780908
Graeme I. Bell12753161011
Mark D. Griffiths124123861335
Tao Zhang123277283866
Brinick Simmons12269169350
Edzard Ernst120132655266
Michael Stumvoll11965569891
Peter McGuffin11762462968
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023295
2022782
20214,412
20204,192
20193,721
20183,385