URLs - University of Manchester
Trans-ancestry meta-analyses identify novel rare and common variants associated with blood pressure and hypertensionPraveen Surendran 1,167, Fotios Drenos 2,3,167, Robin Young 1,167, Helen Warren 4,5,167, James P Cook 6,7,167, Alisa K Manning 8,9,10,167, Niels Grarup 11,167, Xueling Sim 12,13,14,167, Daniel R Barnes 1, Kate Witkowska 4,5, James R Staley 1, Vinicius Tragante 15, Taru Tukiainen 8,9,16, Hanieh Yaghootkar 17, Nicholas Masca 18,19, Daniel F Freitag 1, Teresa Ferreira 20, Olga Giannakopoulou 21, Andrew Tinker 21,5, Magdalena Harakalova 15, Evelin Mihailov 22, Chunyu Liu 23, Aldi T Kraja 24,25, Sune Fallgaard Nielsen 26, Asif Rasheed 27, Maria Samuel 27, Wei Zhao 28, Lori L Bonnycastle 29, Anne U Jackson 13,12, Narisu Narisu 29, Amy J Swift 29, Lorraine Southam 30,20, Jonathan Marten 31, Jeroen R Huyghe 13,12, Alena Stan?áková 32, Cristiano Fava 33,34, Therese Ohlsson 33, Angela Matchan 30, Kathleen E Stirrups 21,35, Jette Bork-Jensen 11, Anette P Gjesing 11, Jukka Kontto 36, Markus Perola 36,37,22, Susan Shaw-Hawkins 4, Aki S Havulinna 36, He Zhang 38, Louise A Donnelly 39, Christopher J Groves 40, N William Rayner 40,20,30, Matt J Neville 40,41, Neil R Robertson 20,40, Andrianos M Yiorkas 42,43, Karl-Heinz Herzig 44,45, Eero Kajantie 36,46,47, Weihua Zhang 48,49, Sara M Willems 50, Lars Lannfelt 51, Giovanni Malerba 52, Nicole Soranzo 53,35,54, Elisabetta Trabetti 52, Niek Verweij 55,9,56, Evangelos Evangelou 48,57, Alireza Moayyeri 48,58, Anne-Claire Vergnaud 48, Christopher P Nelson 18,19, Alaitz Poveda 59,60, Tibor V Varga 59, Muriel Caslake 61, Anton JM de Craen 62,63, Stella Trompet 62,64, Jian’an Luan 50, Robert A Scott 50, Sarah E Harris 65,66, David CM Liewald 65,67, Riccardo Marioni 65,66,68, Cristina Menni 69, Aliki-Eleni Farmaki 70, G?ran Hallmans 71, Frida Renstr?m 59,71, Jennifer E Huffman 31,23, Maija Hassinen 72, Stephen Burgess 1, Ramachandran S Vasan 23,73,74, Janine F Felix 75, CHARGE-Heart Failure Consortium 76, Maria Uria-Nickelsen 77, Anders Malarstig 78, Dermot F Reily 79, Maarten Hoek 80, Thomas Vogt 80,81, Honghuang Lin 23,82, Wolfgang Lieb 83, EchoGen Consortium 76, Matthew Traylor 84, Hugh F Markus 84, METASTROKE Consortium 76, Heather M Highland 85, Anne E Justice 85, Eirini Marouli 21, GIANT Consortium 76, Jaana Lindstr?m 36, Matti Uusitupa 86,87, Pirjo Komulainen 72, Timo A Lakka 72,88,89, Rainer Rauramaa 72,89, Ozren Polasek 90,91, Igor Rudan 90, Olov Rolandsson 92, Paul W Franks 59,92,93, George Dedoussis 70, Timothy D Spector 69, EPIC-InterAct Consortium 76, Pekka Jousilahti 36, Satu M?nnist? 36, Ian J Deary 65,67, John M Starr 65,94, Claudia Langenberg 50, Nick J Wareham 50, Morris J Brown 4, Anna F Dominiczak 95, John M Connell 39, J Wouter Jukema 64,96, Naveed Sattar 95, Ian Ford 61, Chris J Packard 61, T?nu Esko 22,97,8,9, Reedik M?gi 22, Andres Metspalu 22,98, Rudolf A de Boer 99, Peter van der Meer 99, Pim van der Harst 99,100,101, Lifelines Cohort Study 76, Giovanni Gambaro 102, Erik Ingelsson 103,104, Lars Lind 103, Paul IW de Bakker 105,106, Mattijs E Numans 107,106, Ivan Brandslund 108,109, Cramer Christensen 110, Eva RB Petersen 111, Eeva Korpi-Hy?v?lti 112, Heikki Oksa 113, John C Chambers 48,49,114, Jaspal S Kooner 49,115,114, Alexandra IF Blakemore 42,43, Steve Franks 116, Marjo-Riitta Jarvelin 117,118,119,120, Lise L Husemoen 121, Allan Linneberg 121,122,123, Tea Skaaby 121, Betina Thuesen 121, Fredrik Karpe 40,41, Jaakko Tuomilehto 36,124,125,126, Alex SF Doney 39, Andrew D Morris 127, Colin NA Palmer 39, Oddgeir Lingaas Holmen 128,129, Kristian Hveem 128,130, Cristen J Willer 38,131,132, Tiinamaija Tuomi 133,134, Leif Groop 135,134, AnneMari K?r?j?m?ki 136,137, Aarno Palotie 16,9,134,138, Samuli Ripatti 134,139,30, Veikko Salomaa 36, Dewan S Alam 140, Abdulla al Shafi Majumder 141, Emanuele Di Angelantonio 1,54, Rajiv Chowdhury 1, Mark I McCarthy 40,41,20, Neil Poulter 142, Alice V Stanton 143, Peter Sever 142, Philippe Amouyel 144,145,146,147, Dominique Arveiler 148, Stefan Blankenberg 149,150, Jean Ferrières 151, Frank Kee 152, Kari Kuulasmaa 36, Martina Müller-Nurasyid 153,154,155, Giovanni Veronesi 156, Jarmo Virtamo 36, Panos Deloukas 21,157, Wellcome Trust Case Control Consortium 76, Paul Elliott 117, Understanding Society Scientific Group 76, Eleftheria Zeggini 30, Sekar Kathiresan 56,158,159,9, Olle Melander 33, Johanna Kuusisto 32, Markku Laakso 32, Sandosh Padmanabhan 95, David Porteous 66, Caroline Hayward 31, Generation Scotland 160, Francis S Collins 29, Karen L Mohlke 161, Torben Hansen 11, Oluf Pedersen 11, Michael Boehnke 13,12, Heather M Stringham 13,12, EPIC-CVD Consortium 76, Philippe Frossard 27, Christopher Newton-Cheh 56,158, CHARGE+ Exome Chip Blood Pressure Consortium 76, Martin D Tobin 6, B?rge Gr?nne Nordestgaard 26, T2D-GENES Consortium 76, GoT2DGenes Consortium 76, ExomeBP Consortium 76, CHD Exome+ Consortium 76, Mark J Caulfield 4,5, Anubha Mahajan 20, Andrew P Morris 20,7, Maciej Tomaszewski 18,19,162, Nilesh J Samani 18,19, Danish Saleheen 28,27,1,167, Folkert W Asselbergs 15,101,163,167, Cecilia M Lindgren 164,9,20,167, John Danesh 1,165,54,167, Louise V Wain 6,167, Adam S Butterworth 1,166,167, Joanna MM Howson 1,167,168, Patricia B Munroe 4,5,167,168 1. Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK2. Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK3. Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, Rayne Building University College London, London, UK4. Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, UK5. National Institute for Health Research Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, UK6. Department of Health Sciences, University of Leicester, Leicester, UK7. Department of Biostatistics, University of Liverpool, Liverpool, UK8. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA9. Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts, USA10. Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA11. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark12. Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA13. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA14. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore15. Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands16. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA17. Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK18. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK19. National Institute for Health Research Leicester Biomedical Research Unit in Cardiovascular Disease, Leicester, UK20. Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK21. Heart Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK22. Estonian Genome Center, University of Tartu, Tartu, Estonia23. National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Massachusetts, USA24. Division of Statistical Genomics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, USA25. Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA26. Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark27. Centre for Non-Communicable Diseases, Karachi, Pakistan28. Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA29. Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland, USA30. Wellcome Trust Sanger Institute, Genome Campus, Hinxton, UK31. Medical Research Council Human Genetics Unit, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK32. Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland33. University of Lund, Department of Clinical Sciences, Malm?, Sweden34. University of Verona, Department of Medicine, Verona, Italy35. Department of Haematology, University of Cambridge, Cambridge, UK36. Department of Health, National Institute for Health and Welfare, Helsinki, Finland37. Institute of Molecular Medicine FIMM, University of Helsinki, Finland38. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, USA39. Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK40. Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK41. National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital Trusts, Oxford, UK42. Section of Investigative Medicine, Imperial College London, London, UK43. Department of Life Sciences, Brunel University London, London, UK44. Institute of Biomedicine, Biocenter Oulu, University of Oulu, Oulu, Finland 45. Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland46. Hospital for Children and Adolescents, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland47. Department of Obstetrics and Gynaecology, Oulu University Hospital and University of Oulu, Oulu, Finland48. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK49. Department of Cardiology, Ealing Hospital, Middlesex, UK50. Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK51. Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden52. Section of Biology and Genetics, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy53. Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK54. The National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK55. University Medical Center Groningen, University of Groningen, Department of Cardiology, The Netherlands56. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA57. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece58. Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, UK59. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malm?, Sweden60. Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain61. University of Glasgow, Glasgow, UK62. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands63. Mr. De Craen suddenly passed away January 201664. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands65. Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK66. Centre for Genomic and Experimental Medicine, Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK67. Department of Psychology, University of Edinburgh, Edinburgh, UK68. Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia69. Department of Twin Research and Genetic Epidemiology, King’s College London, UK70. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece 71. Department of Biobank Research, Ume? University, Ume?, Sweden72. Kuopio Research Institute of Exercise Medicine, Kuopio, Finland73. Section of Cardiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA74. Sections of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA75. Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands76. A full list of members and affiliations appears in the Supplementary Note77. Development Management and Planning, Pfizer Worldwide Research and Development78. Pfizer Worldwide Research and Development, Stockholm, Sweden79. Merck Research Laboratories, Genetics and Pharmacogenomics, Boston, Massachusetts, USA.80. Merck Research Laboratories, Cardiometabolic Disease, Kenilworth, New Jersey, USA81. CHDI Management/CHDI Foundation, Princeton, New Jersey, USA82. Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA83. Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany84. Neurology Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK85. University of North Carolina at Chapel Hill, Department of Epidemiology, Chapel Hill, North Carolina, USA86. Department of Public Health and Clinical Nutrition, University of Eastern Finland, Finland87. Research Unit, Kuopio University Hospital, Kuopio, Finland88. Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Finland89. Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland90. Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK91. Faculty of Medicine, University of Split, Croatia92. Department of Public Health and Clinical Medicine, Ume? University, Ume?, Sweden93. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA94. Alzheimer Scotland Research Centre, University of Edinburgh, Edinburgh, UK95. Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK96. The Interuniversity Cardiology Institute of the Netherlands, Utrecht, The Netherlands97. Division of Endocrinology, Boston Children’s Hospital, Boston, Massachusetts, USA98. Institute of Molecular and Cell Biology, Tartu, Estonia99. Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands100. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands101. Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands102. Division of Nephrology, Department of Internal Medicine and Medical Specialties, Columbus - Gemelli University Hospital, Catholic University, Rome, Italy103. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden104. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA105. Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands106. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands107. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands108. Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark109. Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark110. Medical Department, Lillebaelt Hospital, Vejle, Denmark111. Department of Clinical Immunology and Biochemistry, Lillebaelt Hospital, Vejle, Denmark112. South Ostrobothnia Central Hospital, Sein?joki, Finland?113. Tampere University Hospital, Tampere, Finland114. Imperial College Healthcare NHS Trust, London, UK115. National Heart and Lung Institute, Imperial College London, London, UK116. Institute of Reproductive and Developmental Biology, Imperial College London, London, UK117. Department of Epidemiology and Biostatistics, Medical Research Council Public Health England Centre for Environment and Health, School of Public Health, Faculty of Medicine, Imperial College London, St. Mary’s Campus, London, UK118. Centre for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland119. Biocenter Oulu, University of Oulu, Oulu, Finland120. Unit of Primary Care, Oulu University Hospital, Oulu, Finland121. Research Centre for Prevention and Health, Capital Region of Denmark, Copenhagen, Denmark122. Department of Clinical Experimental Research, Glostrup University Hospital, Glostrup, Denmark123. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark124. Dasman Diabetes Institute, Dasman, Kuwait125. Centre for Vascular Prevention, Danube-University Krems, Krems, Austria126. King Abdulaziz University, Jeddah, Saudi Arabia127. School of Molecular, Genetic and Population Health Sciences, University of Edinburgh, Medical School, Teviot Place, Edinburgh, UK128. HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway129. St. Olav Hospital, Trondheim University Hospital, Trondheim, Norway130. Department of Medicine, Levanger Hospital, Nord- Tr?ndelag Health Trust, Levanger, Norway131. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA132. Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA133. Folkh?lsan Research Centre, Helsinki, Finland; Department of Endocrinology, Helsinki University Central Hospital, Helsinki, Finland134. Institute for Molecular Medicine Finland University of Helsinki, Helsinki, Finland135. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malm?, Sweden.136. Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland137. Diabetes Center, Vaasa Health Care Center, Vaasa, Finland138. Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA139. Department of Public Health, University of Helsinki, Finland140. ICDDR, B; Mohakhali, Dhaka, Bangladesh141. National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka, Bangladesh142. International Centre for Circulatory Health, Imperial College London, UK143. Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland144. University of Lille, UMR1167, Risk Factors and Molecular Determinants of aging-related diseases, Lille, France145. Inserm, Lille, France146. Centre Hospitalier Universitaire Lille, Public Health, Lille, France147. Institut Pasteur de Lille, Lille, France148. Department of Epidemiology and Public Health, EA 3430, University of Strasbourg, Strasbourg, France149. Department of General and Interventional Cardiology, University Heart Center Hamburg, Germany150. University Medical Center Hamburg-Eppendorf, Hamburg, Germany151. Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse, Toulouse, France152. Director, UKCRC Centre of Excellence for Public Health, Queens University, Belfast, Northern Ireland153. Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany154. Department of Medicine I, University Hospital Grosshadern,?Ludwig-Maximilians-Universit?t, Munich, Germany155. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany156. Research Center in Epidemiology and Preventive Medicine, Department of Clinical and Experimental Medicine, University of Insubria, Varese, Italy157. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia158. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA159. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA160. A Collaboration between the University Medical Schools and NHS, Aberdeen, Dundee, Edinburgh and Glasgow, UK161. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA162. Institute of Cardiovascular Sciences, University of Manchester, Manchester, UK163. Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London, UK 164. The Big Data Institute?at the Li Ka Shing Centre for Health Information and Discovery,?University of Oxford, Oxford, UK165. Wellcome Trust Sanger Institute, Hinxton, UK166. The National Institute for Health Research Blood and Transplant Research167. Contributed equally to this work168. Jointly supervised the workCorresponding authors: Joanna M M Howson, jmmh2@medschl.cam.ac.uk and Patricia B Munroe, p.b.munroe@qmul.ac.ukAbstractHigh blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to ~192,000 individuals, and used ~155,063 samples for independent replication. We identified 31 novel blood pressure or hypertension associated genetic regions in the general population, including three rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5mmHg/allele) than common variants. Multiple rare, nonsense and missense variant associations were found in A2ML1 and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention. IntroductionHigh blood pressure (BP) or hypertension is a highly prevalent chronic disorder. It is estimated to be responsible for a larger proportion of global disease burden and premature mortality than any other disease risk factorPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaW08L0F1dGhvcj48WWVhcj4yMDEyPC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA 1. Elevated systolic and/or diastolic BP increases the risk of several cardiovascular disorders including stroke, coronary heart disease (CHD), heart failure, peripheral arterial disease and abdominal aortic aneurysmsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYXBzb21hbmlraTwvQXV0aG9yPjxZZWFyPjIwMTQ8L1ll
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ADDIN EN.CITE.DATA 2. BP is a complex, heritable, polygenic phenotype for which genome-wide association studies (GWAS) have identified over 67 genetic regions associated with BP and/or hypertension to datedHkgb2YgU2luZ2Fwb3JlLCBTaW5nYXBvcmUgMTE3NTk3LCBTaW5nYXBvcmU7IFNhdyBTd2VlIEhv
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ADDIN EN.CITE.DATA 3-11. These variants are common (minor allele frequency, MAF≥0.05), mostly map to intronic or intergenic regions, with the causal alleles and genes not readily identified due to linkage disequilibrium (LD)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5FaHJldDwvQXV0aG9yPjxZZWFyPjIwMTE8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 4,5, and explain only ~2% of trait variance ADDIN EN.CITE <EndNote><Cite><Author>Salfati</Author><Year>2015</Year><RecNum>8358</RecNum><DisplayText><style face="superscript">12</style></DisplayText><record><rec-number>8358</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437204242">8358</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Salfati, E.</author><author>Morrison, A. C.</author><author>Boerwinkle, E.</author><author>Chakravarti, A.</author></authors></contributors><auth-address>Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, United States of America; Universite Paris Descartes, Sorbonne Paris Cite, 75005, Paris, France.
Human Genetics Center, University of Texas Health Science Center, Houston, TX, 77030, United States of America.
Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, United States of America.</auth-address><titles><title>Direct Estimates of the Genomic Contributions to Blood Pressure Heritability within a Population-Based Cohort (ARIC)</title><secondary-title>PLoS One</secondary-title><alt-title>PloS one</alt-title></titles><periodical><full-title>PLoS One</full-title></periodical><alt-periodical><full-title>PLoS One</full-title></alt-periodical><pages>e0133031</pages><volume>10</volume><number>7</number><dates><year>2015</year></dates><isbn>1932-6203 (Electronic)
1932-6203 (Linking)</isbn><accession-num>26162070</accession-num><urls><related-urls><url>. Low-frequency (0.01<MAF<0.05) and rare (MAF≤0.01) single nucleotide variants (SNVs), predominantly unexplored by GWAS may have larger phenotypic effects than common SNVs ADDIN EN.CITE <EndNote><Cite><Author>Schork</Author><Year>2009</Year><RecNum>8189</RecNum><DisplayText><style face="superscript">13</style></DisplayText><record><rec-number>8189</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1422817917">8189</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Schork, N. J.</author><author>Murray, S. S.</author><author>Frazer, K. A.</author><author>Topol, E. J.</author></authors></contributors><auth-address>Scripps Genomic Medicine, and Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, United States. nschork@scripps.edu</auth-address><titles><title>Common vs. rare allele hypotheses for complex diseases</title><secondary-title>Curr Opin Genet Dev</secondary-title><alt-title>Current opinion in genetics & development</alt-title></titles><periodical><full-title>Curr Opin Genet Dev</full-title><abbr-1>Current opinion in genetics & development</abbr-1></periodical><alt-periodical><full-title>Curr Opin Genet Dev</full-title><abbr-1>Current opinion in genetics & development</abbr-1></alt-periodical><pages>212-9</pages><volume>19</volume><number>3</number><keywords><keyword>Alleles</keyword><keyword>Base Sequence</keyword><keyword>Gene Frequency</keyword><keyword>Genetic Predisposition to Disease/*genetics</keyword><keyword>Genetic Variation/*genetics</keyword><keyword>Genome-Wide Association Study</keyword><keyword>Humans</keyword><keyword>Molecular Sequence Data</keyword><keyword>Mutation</keyword><keyword>Phenotype</keyword></keywords><dates><year>2009</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>1879-0380 (Electronic)
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ADDIN EN.CITE.DATA 14. To identify novel coding variants and loci influencing BP traits and hypertension we performed the largest meta-analysis to date that included a total of ~350,000 individuals, directly genotyped with the Exome chip. The Exome chip contains ~240,000 mostly rare and low-frequency variants (Methods). A single-variant discovery analysis was performed, and candidate SNVs were taken forward for validation using independent replication samples. Gene-based tests were used to identify BP associated genes harboring multiple rare variant associations. We next assessed whether the newly identified BP associated SNVs were associated with expression levels of nearby genes, and tested these variants in aggregate for a causal association of BP with other cardiovascular traits and risk factors. Our findings highlight the contribution of rare variants in the aetiology of blood pressure in the general population, and provide new insights into the pathophysiology of hypertension. ResultsDiscovery of single variant BP associations We genotyped 192,763 individuals from 51 studies, and assessed association of 242,296 SNVs with diastolic BP (DBP), systolic BP (SBP), pulse pressure (PP) and hypertension (HTN; Supplementary Tables 1, 2 and 3; Methods). An overview of the SNV discovery study design is given in Figure 1. A fixed effects meta-analysis for each trait was performed using study-level association summary statistics from i) samples of European (EUR) ancestry (up to 165,276 individuals), and ii) a trans-ethnic meta-analysis of the EUR and additional South Asian (SAS) ancestry samples (EUR_SAS; up to 192,763 individuals). Two analyses of DBP, SBP and PP were performed, one in which the trait was inverse normal transformed and a second in which the raw phenotype was analysed. Both sets of results were consistent (Methods), therefore to minimise sensitivity to deviations from normality in the analysis of rare variants, the results from the analyses of the transformed traits were used for discovery. Strong correlations between the BP traits were observed across studies (Methods), hence no adjustment of significance thresholds for independent trait testing was applied. The discovery meta-analyses identified 50 genomic regions with genome-wide significant (GWS) evidence of association with at least one of the four BP traits tested (P<5x10-8; Supplementary Table 4). There were 45 regions associated in the EUR_SAS samples, of which 13 were novel (Figure 2). An additional five regions were GWS in the EUR only meta-analyses of which two were novel (Supplementary Figure 1). In total, 16 genomic regions were identified that were GWS for at least one BP trait that have not been previously reported.Replication of single variant BP associations Next we sought support for our findings, in an independent replication dataset comprising of 18 studies, 16 of which were from the Cohorts for Heart and Aging Research in Genomic Epidemiology+ (CHARGE+) exome chip blood pressure consortium (Figure 1; Liu et al. Nature Genetics, submitted). Variants were selected for replication first using the larger (transformed) EUR_SAS data, with additional variants from the (transformed) EUR data also selected. SNVs were selected if they mapped outside of known BP genomic regions and had MAF≥0.05 and P<1x10-5 or MAF<0.05 and P<1x10-4 with at least one BP trait, i.e. choosing a lower significance threshold for the selection of rare variants (full details of the selection criteria are provided in the Methods). In total 81 candidate SNVs were selected for replication (Supplementary Table 5). Eighty variants were selected from EUR_SAS (transformed) results and one SNV at the ZNF101 locus from the EUR (transformed) analyses. The results for EUR_SAS and EUR were consistent (association statistics were correlated, ?=0.9 across ancestries for each of the traits). Of the 81 variants, 30 SNVs were selected for association with DBP as the primary trait, 26 for SBP, 19 for PP and 6 for HTN, with the primary trait defined as the BP trait with the smallest association P-value in the EUR-SAS discovery analyses.Meta-analyses were performed on results from analyses of untransformed DBP, SBP, PP and HTN (as only results of untransformed traits were available from CHARGE+) in (i) up to 125,713 individuals of EUR descent, and (ii) up to 155,063 individuals of multiple ethnicities (4,632 of Hispanic descent, 22,077 of African American descent, 2,641 SAS samples with the remainder EUR; Figure 1). Given that a large proportion of the ancestries in the trans-ethnic meta-analyses were not included in our discovery samples, we used the EUR meta-analyses as the main data set for replication, but we also report any additional associations identified within the larger trans-ethnic dataset.Novel BP-SNV associations were identified based on two criteria (Figure 1; Methods). Firstly, replication of the primary BP trait-SNV association was sought at a Bonferroni adjusted P-value threshold in the replication data (P≤6.17x10-4, assuming α=0.05 for 81 SNVs tested and same direction of effect; Methods) without the need for GWS. Secondly, meta-analyses of discovery and replication results across all four (untransformed) BP traits were performed to assess the overall level of support across all samples for the 81 candidate SNVs; those BP-SNV associations that were GWS (with statistical support in the replication studies; P<0.05 and the same direction of effect) were also declared as novel.Seventeen SNV-BP associations formally replicated with concordant direction of effect at a Bonferroni adjusted significance level for the primary trait. Fourteen were in the EUR meta-analyses, and amongst these was a rare non-synonymous (ns) SNV mapping to COL21A1 (Table 1, Supplementary Table 6). Three associations were in the trans-ethnic meta-analyses, these included two rare nsSNVs in RBM47 and RRAS (Table 1, Supplementary Table 7; Methods).??In addition to the 17 SNV-BP trait associations that formally replicated, we identified 13 further SNV-associations that were GWS in the combined (discovery and replication) meta-analyses. Ten of these were GWS in the combined EUR analyses, (Table 2; Supplementary Tables 6 and 8a), and three were GWS in the combined trans-ethnic meta-analyses (Table 2; Supplementary Tables 7 and 8b).This gives a total of 30 novel SNV-BP associations (15 SNV-DBP, 9 SNV-SBP and 6 SNV-PP; Tables 1 and 2; Supplementary Figures 2 and 3). Five of the SNVs were GWS with more than one BP trait (Figure 3: Tables 1 and 2; Supplementary Table 8). Four loci (CERS5, TBX2, RGL3 and OBFC1) had GWS associations with HTN in addition to GWS associations with DBP and SBP. 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ADDIN EN.CITE.DATA 15 allows conditional analyses to be performed using summary level data. Hence, analyses of the transformed primary traits and HTN were re-run in RMW across the discovery studies (Figure 4). The results of the RMW single variant tests were consistent with the initial discovery analyses (Supplementary Information). Given the RMW analyses were based on our discovery samples, the larger EUR-SAS data was used as the main analysis to increase power, but we also report any additional associations with evidence in EUR. We identified secondary independent signals of association in four loci, PREX1, PRKAG1 and RRP1B within the EUR_SAS analyses and COL21A1 in the EUR analyses (Pconditional<1x10-4, Bonferroni adjusted for ~500 variants within each region; Methods; Supplementary Tables 9 and 10). Three independent association signals were identified in the MYH6 locus in the EUR_SAS analyses (Supplementary Table 11). Gene-based BP associations To improve statistical power to detect associations in genes harbouring rare variants, analytical methods that combine effects of variants across a gene into a single test have been devised and are implemented in the RMW packagePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaXU8L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA 15. We applied the gene-based sequence kernel association test (SKAT) ADDIN EN.CITE <EndNote><Cite><Author>Wu</Author><Year>2011</Year><RecNum>8196</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>8196</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1423415258">8196</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wu, M. C.</author><author>Lee, S.</author><author>Cai, T.</author><author>Li, Y.</author><author>Boehnke, M.</author><author>Lin, X.</author></authors></contributors><auth-address>Department of Biostatistics, The University of North Carolina at Chapel Hill, 27599, USA.</auth-address><titles><title>Rare-variant association testing for sequencing data with the sequence kernel association test</title><secondary-title>Am J Hum Genet</secondary-title><alt-title>American journal of human genetics</alt-title></titles><periodical><full-title>Am J Hum Genet</full-title></periodical><alt-periodical><full-title>Am J Hum Genet</full-title><abbr-1>American journal of human genetics</abbr-1></alt-periodical><pages>82-93</pages><volume>89</volume><number>1</number><keywords><keyword>Computer Simulation</keyword><keyword>*Databases, Genetic</keyword><keyword>Gene Frequency</keyword><keyword>Genetic Association Studies/*methods</keyword><keyword>Genetic Loci</keyword><keyword>*Genetic Variation</keyword><keyword>Humans</keyword><keyword>Models, Genetic</keyword><keyword>Sequence Analysis/*methods</keyword><keyword>*Software</keyword></keywords><dates><year>2011</year><pub-dates><date>Jul 15</date></pub-dates></dates><isbn>1537-6605 (Electronic)
0002-9297 (Linking)</isbn><accession-num>21737059</accession-num><urls><related-urls><url> and Burden tests ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2008</Year><RecNum>41</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>41</rec-number><foreign-keys><key app="EN" db-id="s0vdw2a0u9fawce0rpbxv9ve00tt05xtzdtz" timestamp="1456319336">41</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, B.</author><author>Leal, S. M.</author></authors></contributors><auth-address>Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.</auth-address><titles><title>Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data</title><secondary-title>Am J Hum Genet</secondary-title><alt-title>American journal of human genetics</alt-title></titles><periodical><full-title>Am J Hum Genet</full-title><abbr-1>American journal of human genetics</abbr-1></periodical><alt-periodical><full-title>Am J Hum Genet</full-title><abbr-1>American journal of human genetics</abbr-1></alt-periodical><pages>311-21</pages><volume>83</volume><number>3</number><keywords><keyword>Genetic Markers</keyword><keyword>*Genetic Predisposition to Disease</keyword><keyword>*Genetic Variation</keyword><keyword>*Genome, Human</keyword><keyword>Haplotypes</keyword><keyword>Humans</keyword><keyword>Linkage Disequilibrium</keyword><keyword>*Models, Genetic</keyword><keyword>*Polymorphism, Single Nucleotide</keyword></keywords><dates><year>2008</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1537-6605 (Electronic)
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ADDIN EN.CITE.DATA 18. We sought replication in the CHARGE+ studies for this gene, however there was no evidence of association with HTN (P= 0.45). Given the very low frequencies of the variants involved, however, studies in which the variants are polymorphic will be required to replicate the association with HTN. The DBH gene was found to be associated with DBP using the SKAT test (P=2.88x10-6). However, this was not due to multiple rare variants as the association was driven by rs77273740 (Supplementary Table 5) and the SNV was not validated in the replication samples.Rare and common variant associations in established BP lociOf the 67 established BP loci, 35 loci were on the Exome chip (N=43 SNVs or close proxies r2>0.7). All 43 SNVs had at least nominal evidence of association with BP in our discovery samples (P<0.01; Supplementary Table 13). We also assessed if any of the established BP loci contained coding variants that are associated with BP traits and in LD (r2>0.2) with the known BP variants on the Exome chip (Supplementary Table 13), using the 1000G phase 3 release for LD annotation. Focusing on SNVs that were GWS for any BP trait from our transformed discovery data for either ancestry, there were 25 coding variants, of which 6 were predicted to be damaging at loci labelled CDC25A, SLC39A8, HFE, ULK4, ST7L-CAPZA1-MOV10 and CYP1A1-ULK3. Three of these are published variants at loci labelled SLC39A8, HFE and ST7-CAPZA1-MOV10. At CYP1A1-ULK3, the coding variant was in moderate LD with the reported variant, but was less significantly associated with DBP in our EUR_SAS dataset (P=2.24x10-8 compared to P=1.68x10-15 for the published variant). At the ULK4 locus the predicted damaging coding variant had similar association as the published coding variant (predicted to be benign), and prior work has already indicated several associated nsSNVs in strong LD in ULK4 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5JbnRlcm5hdGlvbmFsIENvbnNvcnRpdW0gZm9yIEJsb29k
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ADDIN EN.CITE.DATA 19. The nsSNV within the CDC25A locus (rs11718350 in SPINK8) had similar association with DBP as the intergenic published SNV in our EUR_SAS dataset (P=2.00x10-8 compared to P=2.27x10-8 for the published variant). Overall at least 5 of the known loci are consistent with having a coding causal variant.Gene-based SKAT tests of all genes that map within 1 Mb of a previously reported SNV association (Supplementary Table 14), indicated no genes with multiple rare or low-frequency variant associations. Single variant conditional analyses showed that rs33966350, a rare nonsense variant in ENPEP (MAF=0.01) was associated with SBP (Pconditional=1.61x10-5) in the EUR_SAS samples (Supplementary Tables 14 and 15; Methods) independently of the known SNV (rs6825911). ENPEP encodes aminopeptidase A (APA) an enzyme of the renin-angiotensin-aldosterone system (RAAS) that converts angiotensin II (AngII) to AngIII. There were no other established loci with convincing low-frequency or rare SNV associations in the EUR_SAS samples. However, HOXC4, had evidence of a second independent signal with a rare missense SNV in EUR samples (rs78731604; MAF=0.005, Pconditional= 5.76x10-5; Supplementary Table 15). The secondary signal in the HOXC4 region, mapped to CALCOCO1, ~300kb from the known SNV. The gene association (MAF≤0.01, P=2.37x10-5) was below the required significance threshold and attributable to rs78731604, which is not predicted to have detrimental effects on protein structure. Therefore, replication of this association is required. Three loci (ST7L-CAPZA1-MOV10, FIGN-GRB14, and TBX5-TBX3) had evidence of a second independent signal in the region in EUR_SAS samples with a common variant (Pconditional<1x10-4; Supplementary Table 15) that has not been previously reported. Having identified 30 novel loci associated with BP traits, as well as additional new independent SNVs at four novel loci and five known loci, we calculated the percent of the trait variance explained (Methods). This was 2.08%/2.11%/1.15% for SBP/DBP/PP for the 43 previously reported BP-SNVs covered in our dataset, increasing to 3.38%/3.41%/2.08% respectively with the inclusion of the 30 lead SNVs from novel loci, plus new independent SNV-BP associations identified from novel and known loci.Effect of BP SNVs on cardiovascular traits & risk factorsAmongst our novel BP-SNV associations, some have previously been reported to be associated with other cardiovascular traits and risk factors (Supplementary Table 16); these include coronary heart disease (CHD: PHACTR1, ABO)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3JvbmFyeSBBcnRlcnkgRGlzZWFzZSBHZW5ldGljczwv
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ADDIN EN.CITE.DATA 24.To test the impact of BP variants on cardiovascular endpoints and risk factors we created three weighted genetic risk scores (GRS) according to SBP/DBP/PP based on the newly identified and previously published BP variants (up to N=125; Methods). The GRS models were used to test the causal effect of BP on the following traits: ischemic stroke (including the subtypes, cardiometabolic, large and small vessel PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UcmF5bG9yPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48
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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UcmF5bG9yPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48
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ADDIN EN.CITE.DATA 27, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein (LDL-c), triglycerides, total cholesterol, body mass index (BMI), waist-hip ratio adjusted BMI, height and estimated glomerular filtration rate (eGFR) (Methods). As expected, BP was positively associated with increased CHD risk (OR [95% CI]=1.39[1.22-1.59] per 10mmHg increase in SBP, P=6.07×10-7; 1.62[1.28-2.05] per 10mmHg increase in DBP, P=5.99x10-5; 1.70[1.34-2.16] per 10mmHg increase in PP, P=1.20x10-5; Table 3), and increased risk of ischemic stroke (OR [95% CI]=1.93[1.47-2.55] per 10mmHg increase in DBP, P=2.81×10-6; 1.57[1.35-1.84] per 10mmHg increase in SBP, P=1.16×10-8; 2.12[1.58-2.84] per 10mmHg increase in PP, P=5.35x10-7). The positive association with ischemic stroke was primarily due to large vessel stroke (Table 3). DBP and SBP were also positively associated with left ventricular mass (9.57 [3.98-15.17] gram increase per 10mmHg increase in DBP, P=8.02x10-4 and 5.13 [1.77-8.48] gram increase per 10mmHg increase in SBP, P=0.0027) and left ventricular wall thickness (0.10 [0.06-0.13] cm increase per 10mmHg increase in DBP, P=1.88x10-8 and 0.05 [0.03-0.07] cm increase per 10mmHg increase in SBP, P=5.52x10-6, Table 3). There was no convincing evidence to support the BP associated variants having an effect on lipid levels (P>0.1), BMI (P>0.005), waist hip ratio adjusted BMI (P>0.1), height (P>0.06), eGFR (P>0.02) or heart failure (P>0.04). The causal associations with CHD, stroke, and left ventricular measures augment the results from a previous association analysis using 29 BP variantsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5FaHJldDwvQXV0aG9yPjxZZWFyPjIwMTE8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 28. Our data strongly support the previous observations of no causal relationship between BP and eGFR. Lack of evidence of a BP effect with heart failure may only be due to lack of power, as the association was in the expected direction. Possible functional variants at BP loci and candidate genesTwenty-six of our newly discovered BP associated SNVs had MAF≥0.05 and therefore due to extensive LD with other SNVs not genotyped on the Exome array, identifying the causal genes requires additional information. If a SNV is associated with increased or decreased expression of a particular gene, i.e. it is an expression quantitative trait locus (eQTL) this suggests the gene on which the SNV acts could be in the causal pathway. To help identify potential candidate causal genes in the novel BP loci (Supplementary Table 9), information from publicly available eQTL databases was investigated (MuTHER for LCL, adipose and skin and GTEx for nine tissues including the heart and tibial artery; Methods).The DBP increasing allele of the nsSNV, rs7302981-A, was associated with increased expression of CERS5 in: LCLs (PMuTHER=3.13x10-72) skin (PMuTHER=2.40x10-58) adipose (PMuTHER=2.87x10-54) and nerve (PGTEx=4.5x10-12) (Supplementary Figure 4). Additional testing (Methods) provided no evidence against colocalisation of the eQTL and DBP association signals, implicating CERS5 as a candidate causal gene for this DBP locus. CERS5 (LAG1 homolog, ceramide synthase 5) is involved in the synthesis of ceramide, a lipid molecule involved in several cellular signaling pathways. Cers5 knockdown has been shown to reduce cardiomyocyte hypertrophy in mouse modelsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SdXNzbzwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 29. However, it is unclear whether the blood pressure raising effects at this locus are the cause or result of any potential effects on cardiac hypertrophy. Future studies investigating this locus in relation to parameters of cardiac hypertrophy and function (e.g. ventricular wall thickness) should help address this question.The DBP raising allele of the nsSNV (rs867186-A) was associated with increased expression of PROCR in adipose tissue (PMuTHER=3.24x10-15) and skin (PMuTHER=1.01x10-11) (Supplementary Figure 4). There was no evidence against colocalisation of the eQTL and DBP association thus supporting PROCR as a candidate causal gene. PROCR encodes the Endothelial Protein C receptor, a serine protease involved in the blood coagulation pathway, and rs867186 has previously been associated with coagulation and haematological factors.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5PdWRvdC1NZWxsYWtoPC9BdXRob3I+PFllYXI+MjAxMjwv
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ADDIN EN.CITE.DATA 30,31 The PP decreasing allele of, rs10407022-T, which is predicted to have detrimental effects on protein structure (Methods) was associated with increased expression of AMH in muscle (PGTEx=9.95x10-15), thyroid (PGTEx=8.54x10-7), nerve (PGTEx=7.15x10-8), tibial artery (PGTEx=6.46x10-9), adipose (PGTEx=4.69x10-7), and skin (PGTEx=5.88x10-8) (Supplementary Figure 4). There was no evidence against colocalisation of the eQTL and PP association, which supports AMH as a candidate causal gene for PP. Low AMH levels have been previously associated with hypertensive status in women with the protein acting as a marker of ovarian reservePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CbGVpbDwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 32. The intergenic SBP raising allele of rs4728142-A was associated with reduced expression of IRF5 in skin (PMuTHER=5.24x10-31) and LCLs (PMuTHER=1.39x10-34), whole blood (PGTEx=3.12x10-7) and tibial artery (PGTEx=1.71x10-7). Three novel rare nsSNVs were identified that map to RBM47, RRAS (both associated with SBP) and COL21A1 (associated with PP). They had larger effect sizes than common variant associations (>1.5mmHg per allele; Supplementary Figure 5) and were predicted to have detrimental effects on protein structure (Supplementary Table 16; Methods). In RBM47, rs35529250 (p.Gly538Arg) is located in a highly conserved region of the gene and was most strongly associated with SBP (MAF=0.008; +1.59 mmHg per T allele; P=5.90x10-9). RBM47 encodes the RNA binding motif protein 47 and is responsible for post-transcriptional regulation of RNA, through its direct and selective binding with the molecule.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HdWFuPC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 34. The nsSNV in COL21A1 (rs200999181, p.Gly665Val) was most strongly associated with PP (MAF=0.001; +3.14 mmHg per A allele; P=1.93x10-9). COL21A1 encodes the collagen alpha-1 chain precursor of type XXI collagen, a member of the FACIT (fibril-associated collagens with an interrupted triple helix) family of proteins ADDIN EN.CITE <EndNote><Cite><Author>Tuckwell</Author><Year>2002</Year><RecNum>8210</RecNum><DisplayText><style face="superscript">35</style></DisplayText><record><rec-number>8210</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1425130507">8210</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tuckwell, D.</author></authors></contributors><auth-address>2.205 Stopford Building, School of Biological Sciences, University of Manchester, Manchester M13 9PT, UK. danny.tuckwell@man.ac.uk</auth-address><titles><title>Identification and analysis of collagen alpha 1(XXI), a novel member of the FACIT collagen family</title><secondary-title>Matrix Biol</secondary-title><alt-title>Matrix biology : journal of the International Society for Matrix Biology</alt-title></titles><periodical><full-title>Matrix Biol</full-title><abbr-1>Matrix biology : journal of the International Society for Matrix Biology</abbr-1></periodical><alt-periodical><full-title>Matrix Biol</full-title><abbr-1>Matrix biology : journal of the International Society for Matrix Biology</abbr-1></alt-periodical><pages>63-6</pages><volume>21</volume><number>1</number><keywords><keyword>Animals</keyword><keyword>Computational Biology/*methods/trends</keyword><keyword>Databases, Genetic</keyword><keyword>*Evolution, Molecular</keyword><keyword>Fibril-Associated Collagens/*analysis/chemistry/genetics</keyword><keyword>Humans</keyword><keyword>Phylogeny</keyword><keyword>Protein Structure, Tertiary</keyword></keywords><dates><year>2002</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0945-053X (Print)
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ADDIN EN.CITE.DATA 36 to ascertain if there are other candidate SNVs at these loci (Supplementary Table 17). There were no SNVs within 1Mb of the RBM47 locus in LD with the BP associated SNV. At the COL21A1 locus there were only SNVs in moderate LD, and these were annotated as intronic, intergenic or in the 5’UTR. At the RRAS locus, there were two SNVs in strong LD with the BP associated SNV, which both mapped to introns of SCAF1 and are not predicted to be damaging. All SNVs in LD at both loci were rare as expected (Supplementary Table 17) supporting a role for rare variants. Hence, the rare BP associated nsSNVs at RBM47, COL21A1 and RRAS remain the best causal candidates.Pathway and network analysesTo identify connected gene sets and pathways implicated by the BP associated genes we used Meta-Analysis Gene-set Enrichment of variant Associations (MAGENTA) ADDIN EN.CITE <EndNote><Cite><Author>Segre</Author><Year>2010</Year><RecNum>8357</RecNum><DisplayText><style face="superscript">37</style></DisplayText><record><rec-number>8357</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437157827">8357</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Segre, A. V.</author><author>Diagram Consortium</author><author>Magic investigators</author><author>Groop, L.</author><author>Mootha, V. K.</author><author>Daly, M. J.</author><author>Altshuler, D.</author></authors></contributors><auth-address>Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</auth-address><titles><title>Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits</title><secondary-title>PLoS Genet</secondary-title><alt-title>PLoS genetics</alt-title></titles><periodical><full-title>PLoS Genet</full-title></periodical><alt-periodical><full-title>PLoS Genet</full-title><abbr-1>PLoS genetics</abbr-1></alt-periodical><volume>6</volume><number>8</number><keywords><keyword>Case-Control Studies</keyword><keyword>Cohort Studies</keyword><keyword>Diabetes Mellitus, Type 2/*genetics/metabolism</keyword><keyword>*Genetic Variation</keyword><keyword>Genome-Wide Association Study</keyword><keyword>Glucose/metabolism</keyword><keyword>Mitochondrial Proteins/*genetics</keyword><keyword>Polymorphism, Single Nucleotide</keyword><keyword>*Quantitative Trait, Heritable</keyword></keywords><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1553-7404 (Electronic)
1553-7390 (Linking)</isbn><accession-num>20714348</accession-num><urls><related-urls><url> and GeneGo MetaCore (Thomson Reuters, UK). MAGENTA tests for over-representation of BP associated genes in pre-annotated pathways (gene sets) (Methods and Supplementary Table 18a). GeneGo Metacore identifies potential gene networks. The MAGENTA analysis was used for hypothesis generation and results were compared with the GeneGo Metacore outputs to cross-validate findings.Using MAGENTA there was an enrichment (P<0.01 and FDR<5% in either the EUR_SAS or the EUR participants) of six gene sets with DBP, three gene sets with HTN and two gene sets for SBP (Supplementary Table 18b). The RNA polymerase I promoter clearance (chromatin modification) pathway showed the most evidence of enrichment with genes associated with DBP (PReactome=8.4x10-5, FDR=2.48%). NOTCH signalling was the most associated pathway with SBP (PReactome = 3.00x10-4, FDR = 5%) driven by associations at the FURIN gene. The inorganic cation anion solute carrier (SLC) transporter pathway had the most evidence of enrichment by HTN associated genes (PReactome=8.00x10-6, FDR=2.13%). Using GeneGo MetaCore, five network processes were enriched (FDR<5%; Methods; Supplementary Tables 19 and 20). These included several networks with genes known to influence vascular tone and BP: inflammation signalling, P=1.14x10-4 and blood vessel development P=2.34x10-4. The transcription and chromatin modification network (P=2.85x10-4) was also enriched, a pathway that was also highlighted in the MAGENTA analysis, with overlap of the same histone genes (HIST1H4C, HIST1H2AC, HIST1H2BC, HIST1H1T) and has also been recently reported in an integrative network analysis of published BP loci and whole blood expression profiling ADDIN EN.CITE <EndNote><Cite><Author>Huan</Author><Year>2015</Year><RecNum>8349</RecNum><DisplayText><style face="superscript">38</style></DisplayText><record><rec-number>8349</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437128021">8349</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Huan, T.</author><author>Meng, Q.</author><author>Saleh, M. A.</author><author>Norlander, A. E.</author><author>Joehanes, R.</author><author>Zhu, J.</author><author>Chen, B. H.</author><author>Zhang, B.</author><author>Johnson, A. D.</author><author>Ying, S.</author><author>Courchesne, P.</author><author>Raghavachari, N.</author><author>Wang, R.</author><author>Liu, P.</author><author>O'Donnell, C. J.</author><author>Vasan, R.</author><author>Munson, P. J.</author><author>Madhur, M. S.</author><author>Harrison, D. G.</author><author>Yang, X.</author><author>Levy, D.</author></authors></contributors><titles><title>Integrative network analysis reveals molecular mechanisms of blood pressure regulation</title><secondary-title>Mol Syst Biol</secondary-title><alt-title>Molecular systems biology</alt-title></titles><periodical><full-title>Mol Syst Biol</full-title></periodical><pages>799</pages><volume>11</volume><number>4</number><dates><year>2015</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1744-4292 (Electronic)
1744-4292 (Linking)</isbn><accession-num>26043448</accession-num><urls><related-urls><url>. Two cardiac development pathways were enriched: the oxidative stress-driven (ROS/NADPH) (P=4.12x10-4) and the Wnt/β-catenin/integrin-driven (P=0.0010). Both these cardiac development pathways include the MYH6, MYH7, and TBX2 genes, revealing a potential overlap with cardiomyopathies and hypertension, and suggesting some similarity in the underlying biological mechanisms.DiscussionBy conducting the largest ever genetic study of BP, we identified further novel common variants with small effects on BP traits, similar to what has been observed for obesity and heightZS4mI3hEOzFdIEluc3RpdHV0ZSBvZiBDYXJkaW92YXNjdWxhciBTY2llbmNlLCBVbml2ZXJzaXR5
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ADDIN EN.CITE.DATA 40. More importantly, our study identified some of the first rare coding variants of strong effect (>1.5mmHg) that are robustly associated with BP traits in the general population, complementing and extending the previous discovery and characterisation of variants underlying rare Mendelian disorders of blood pressure regulation PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXJrPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 41. Using SNV associations in 17 genes reported to be associated with monogenic disorders of blood pressure (Methods) we found no convincing evidence of enrichment (Penrichment=0.044). This suggests that BP control in the general population may occur through different pathways to monogenic disorders of BP re-enforcing the importance of our study findings. The identification of 30 novel BP loci plus further new independent secondary signals within four novel and five known loci (Methods) has augmented the trait variance explained by 1.3%, 1.2% and 0.93% for SBP, DBP and PP respectively within our data-set. This suggests that with substantially larger sample sizes, for example through UK BioBankPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdWRsb3c8L0F1dGhvcj48WWVhcj4yMDE1PC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 42, we expect to identify 1000s more loci associated with BP traits, and replicate more of our discovery SNV associations that are not yet validated in the current report. The discovery of rare missense variants has implicated several interesting candidate genes, which are often difficult to identify from common variant GWAS, and should therefore lead to more rapidly actionable biology. A2ML1, COL21A1, RRAS and RBM47 all warrant further follow-up studies to define the role of these genes in regulation of BP traits, as well as functional studies to understand their mechanisms of action. COL21A1 and RRAS warrant particular interest since both are involved in blood vessel remodelling, a pathway of known aetiological relevance to hypertension. We observed a rare nonsense SBP associated variant in ENPEP (rs33966350; p.Trp317* ): this overlaps a highly conserved region of both the gene and protein and is predicted to result in either a truncated protein with reduced catalytic function or is subject to nonsense mediated RNA decay. ENPEP converts angiotensin II (AngII) to Ang-III. AngII activates the angiotensin 1 (AT1) receptor resulting in vasoconstriction, while AngIII activates the angiotensin 2 (AT2) receptor that promotes vasodilation and protects against hypertension. ADDIN EN.CITE <EndNote><Cite><Author>Te Riet</Author><Year>2015</Year><RecNum>297</RecNum><DisplayText><style face="superscript">43</style></DisplayText><record><rec-number>297</rec-number><foreign-keys><key app="EN" db-id="5d0t5t5a1fv920e2dxl5de9e9zaasvaef2df">297</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Te Riet, L.</author><author>van Esch, J. H.</author><author>Roks, A. J.</author><author>van den Meiracker, A. H.</author><author>Danser, A. H.</author></authors></contributors><auth-address>From the Division of Pharmacology and Vascular Medicine, Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
From the Division of Pharmacology and Vascular Medicine, Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. a.danser@erasmusmc.nl.</auth-address><titles><title>Hypertension: Renin-Angiotensin-aldosterone system alterations</title><secondary-title>Circ Res</secondary-title><alt-title>Circulation research</alt-title></titles><periodical><full-title>Circ Res</full-title><abbr-1>Circulation research</abbr-1></periodical><alt-periodical><full-title>Circ Res</full-title><abbr-1>Circulation research</abbr-1></alt-periodical><pages>960-75</pages><volume>116</volume><number>6</number><dates><year>2015</year><pub-dates><date>Mar 13</date></pub-dates></dates><isbn>1524-4571 (Electronic)
0009-7330 (Linking)</isbn><accession-num>25767283</accession-num><urls><related-urls><url> The predicted truncated protein may lead to predominant AngII signaling in the body, and increases in BP. This new observation could potentially inform therapeutic strategies. Of note, angiotensin-converting-enzyme (ACE) inhibitors are commonly used in the treatment of hypertension. However, patients who suffer from adverse reactions to ACE inhibitors, such as dry cough and skin rash, would benefit from alternative drugs that target RAAS. Murine studies have shown that in the brain, AngIII is the preferred AT1 agonist that promotes vasoconstriction and increases blood pressure, as opposed to AngII in the peripheral system. These results have motivated the development of brain specific APA inhibitors to treat hypertensionPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HYW88L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA 44. Our results confirm APAs, such as ENPEP, as a valid target to modify blood pressure, but suggest that long-term systemic reduction in APA activity may lead to an increase in blood pressure. Future studies are needed to examine the effects of the p.Trp317* variant on the RAAS system, specifically in the brain and peripheral vasculature, in order to test the benefits of the proposed therapeutic strategy in humans. In addition to highlighting new genes in pathways of established relevance to BP and hypertension, and identifying new pathways, we have also identified multiple signals at new loci. For example, there are three distinct signals at the locus containing the MYH6/MYH7 genes, and we note that TBX2 maps to one of the novel regions. These genes are related to cardiac development and/or cardiomyopathies, and provide an insight into the shared inheritance of multiple complex traits. Unravelling the causal networks within these polygenic pathways may provide opportunities for novel therapies to treat or prevent both hypertension and cardiomyopathies.URLsExome chip design information: information: SNV association results: used for variant annotation: and reference file used for annotation of variants with gene and exon information: used for pathway analysis: MAGENTA () and THOMSON REUTERS MetaCoreTM Single Experiment Analysis workflow tool ( HYPERLINK "" ). Acknowledgements1. CHD Exome+ ConsortiumCCHS, CGPS, CIHDS: We thank participants and staff of the Copenhagen City Heart Study, Copenhagen Ischemic Heart Disease Study, and the Copenhagen General Population Study for their important contributions.EPIC-InterAct: Funding for the InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197). We thank all EPIC participants and staff for their contribution to the study. We thank the lab team at the MRC Epidemiology Unit for sample management and Nicola Kerrison for data management. EPIC-CVD: CHD case ascertainment and validation, genotyping, and clinical chemistry assays in EPIC-CVD were principally supported by grants awarded to the University of Cambridge from the EU Framework Programme 7 (HEALTH-F2-2012-279233), the UK Medical Research Council (G0800270) and British Heart Foundation (SP/09/002), and the European Research Council (268834). We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management, and the team at the EPIC-CVD Coordinating Centre for study coordination and administration.MORGAM: This work has been sustained by the MORGAM Project's recent funding:? European Union FP 7 projects ENGAGE (HEALTH-F4-2007-201413), CHANCES (HEALTH-F3-2010-242244) and BiomarCaRE (278913). This has supported central coordination, workshops and part of the activities of the The MORGAM Data Centre, at THL in Helsinki, Finland. The MORGAM Participating Centres are funded by regional and national governments, research councils, charities, and other local sources.WOSCOPS/PROSPER: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° HEALTH-F2-2009-223004BRAVE: The BRAVE study genetic epidemiology working group is a collaboration between the Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, UK, the Centre for Control of Chronic Diseases, icddr,b, Dhaka, Bangladesh and the National Institute of Cardiovascular Diseases, Dhaka, Bangladesh.PROMIS: We are thankful to all the study participants in Pakistan. Recruitment in PROMIS was funded through grants available to investigators at the Center for Non-Communicable Diseases, Pakistan (Danish Saleheen and Philippe Frossard) and investigators at the University of Cambridge, UK (Danish Saleheen and John Danesh). Field-work, genotyping, and standard clinical chemistry assays in PROMIS were principally supported by grants awarded to the University of Cambridge from the British Heart Foundation, UK Medical Research Council, Wellcome Trust, EU Framework 6-funded Bloodomics Integrated Project, Pfizer, Novartis, and Merck. We would like to acknowledge the contributions made by the following individuals who were involved in the field work and other administrative aspects of the study: Mohammad Zeeshan Ozair, Usman Ahmed, Abdul Hakeem, Hamza Khalid, Kamran Shahid, Fahad Shuja, Ali Kazmi, Mustafa Qadir Hameed, Naeem Khan, Sadiq Khan, Ayaz Ali, Madad Ali, Saeed Ahmed, Muhammad Waqar Khan, Muhammad Razaq Khan, Abdul Ghafoor, Mir Alam, Riazuddin, Muhammad Irshad Javed, Abdul Ghaffar, Tanveer Baig Mirza, Muhammad Shahid, Jabir Furqan, Muhammad Iqbal Abbasi, Tanveer Abbas, Rana Zulfiqar, Muhammad Wajid, Irfan Ali, Muhammad Ikhlaq, Danish Sheikh and Muhammad Imran.CHD Exome+ Consortium: This work was funded by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002), UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834), European Commission Framework Programme 7 (HEALTH-F2-2012-279233) and Merck and Pfizer.2. ExomeBP ConsortiumAirwave: We thank all participants of the Airwave Health Monitoring Study. The study is funded by the UK Home Office, (Grant number 780-TETRA) with additional support from the National Institute for Health Research (NIHR) Imperial College Health Care NHS Trust (ICHNT) and Imperial College Biomedical Research Centre (BRC) (Grant number BRC-P38084). We thank Andy Heard and the Airwave Health Monitoring Study team for invaluable support. SNP Genotyping was performed at the Wellcome Trust Centre for Human Genetics, University of Oxford.ASCOT: The ASCOT study was supported by Pfizer, New York, NY, USA for the ASCOT study and the collection of the ASCOT DNA repository; by Servier Research Group, Paris, France; and by Leo Laboratories, Copenhagen, Denmark. We thank all ASCOT trial participants, physicians, nurses, and practices in the participating countries for their important contribution to the study. In particular we thank Clare Muckian and David Toomey for their help in DNA extraction, storage, and handling. Genotyping of the Exome chip in ASCOT-SC and ASCOT-UK was funded by the National Institutes of Health Research (NIHR). We would also like to acknowledge the Barts and The London Genome Centre staff for genotyping the Exome chip array. This work forms part of the research programme of the NIHR Cardiovascular Biomedical Research Unit at Barts.1958BC: We are grateful for using the British 1958 Birth Cohort DNA collection. Sample collection funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. Genotyping was funded by the Wellcome Trust.BRIGHT: This work was supported by the Medical Research Council of Great Britain (grant number G9521010D), and the British Heart Foundation (grant number PG/02/128). A.F.D. was supported by the British Heart Foundation (grant numbers RG/07/005/23633, SP/08/005/25115); the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant number LSHM-C7-2006-037093). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. The Exome chip was funded by the Wellcome Trust Strategic Awards (083948 and 085475). We would also like to thank the Barts Genome Centre staff for their assistance with this project. This work forms part of the research programme of the NIHR Cardiovascular Biomedical Research Unit at Barts.CROATIA-Korcula: We would like to acknowledge the invaluable contributions of the recruitment team in Korcula, the administrative teams in Croatia and Edinburgh and the people of Korcula. The CROATIA-Korcula study was funded by grants from the Medical Research Council (UK), European Commission Framework 6 project EUROSPAN (Contract No. LSHG-CT-2006-018947), Ministry of Science, Education and Sports of the Republic of Croatia (grant 216-1080315-0302) and the Croatian Science Foundation (grant 8875). DIABNORD: We are indebted to the study participants who dedicated their time and samples to these studies. We thank John Hutiainen and ?sa ?gren (Ume? Medical Biobank) for data organization and Kerstin Enquist and Thore Johansson (V?sterbottens County Council) for technical assistance with DNA extraction. We also thank M Sterner, M Juhas and P Storm for their expert technical assistance with genotyping and genotype data preparation.EGCUT: EGCUT received financing from European Regional Development Fund, road-map grant no.3.2.0304.11-0312 and grant "Center of Excellence in Genomics" (EXCEGEN). EGCUT studies were covered also by targeted financing from Estonian Government (IUT24-6, IUT20-60)and CTG grant (SP1GVARENG) from Development Fund of the University of Tartu. Fenland study: The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment.? We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams.FINRISK 97/02: Veikko Salomaa-Dr. Salomaa was supported by the Academy of Finland, grant number 139635, and the Finnish Foundation for Cardiovascular Research.GS:SFHS: We would like to acknowledge the invaluable contributions of the families who took part in the Generation Scotland: Scottish Family Health Study, the general practitioners and Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team, which includes academic researchers, IT staff, laboratory technicians, statisticians and research managers. SNP genotyping was performed at the Wellcome Trust Clinical Research Facility in Edinburgh. GS:SFHS is funded by the Scottish Executive Health Department, Chief Scientist Office, grant number CZD/16/6. SNP genotyping was funded by the Medical Research Council UK GLACIER: We are indebted to the study participants who dedicated their time and samples to these studies. We thank John Hutiainen and ?sa ?gren (Ume? Medical Biobank) for data organization and Kerstin Enquist and Thore Johansson (V?sterbottens County Council) for technical assistance with DNA extraction. We also thank M Sterner, M Juhas and P Storm for their expert technical assistance with genotyping and genotype data preparation.GoDARTS: We acknowledge the support of the Health Informatics Centre, University of Dundee for managing and supplying the anonymised data and NHS Tayside, the original data owner. We are grateful to all the participants who took part in the Go-DARTS study, to the general practitioners, to the Scottish School of Primary Care for their help in recruiting the participants, and to the whole team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. GRAPHIC: Recruitment and genotyping of the Genetic Regulation of Arterial Pressure of Humans in the Community cohort was funded by the British Heart Foundation. N.J.S. holds a British Heart Foundation Chair of Cardiology and is a senior National Institute for Health Research Investigator . This study is part of the research portfolio supported by the Leicester National Institute for Health Research Biomedical Research Unit in Cardiovascular Disease.HELIC-MANOLIS: This work was funded by the Wellcome Trust (098051) and the European Research Council (ERC-2011-StG 280559-SEPI). The MANOLIS study is dedicated to the memory of Manolis Giannakakis, 1978–2010. We thank the residents of the Mylopotamos villages for taking part. We thank the Sample Management and Genotyping Facilities staff at the Wellcome Trust Sanger Institute for sample preparation, quality control and genotyping.The Nord-Tr?ndelag Health Study (The HUNT Study): This is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology NTNU), Nord-Tr?ndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. CJW is supported by HL094535 and HL109946.INCIPE: The INCIPE study was supported by Foundation CARIVR, Verona, Italy and by the University of Verona. LBC21 and LBC36: We thank the cohort participants and team members who contributed to these studies. Phenotype collection in the Lothian Birth Cohort 1921 was supported by the UK’s Biotechnology and Biological Sciences Research Council (BBSRC), The Royal Society and The Chief Scientist Office of the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was supported by Age UK (The Disconnected Mind project).? Genotyping was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, and the Royal Society of Edinburgh. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the BBSRC and Medical Research Council (MRC) is gratefully acknowledged.LIFELINES: The Lifelines Cohort Study, and generation and management of GWAS genotype data for the Lifelines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. N. Verweij is supported by the Netherlands Heart Foundation (grant NHS2010B280). Lifelines is a multi-disciplinary prospective population-based cohort study examining in a unique three-generation design the health and health-related behaviours of 167,729 persons living in the North of The Netherlands. It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioural, physical and psychological factors which contribute to the health and disease of the general population, with a special focus on multi-morbidity and complex genetics"LOLIPOP: The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0601966, G0700931), the Wellcome Trust (084723/Z/08/Z), the NIHR (RP-PG-0407-10371), European Union FP7 (EpiMigrant, 279143) and Action on Hearing Loss (G51). The work was carried out in part at the NIHR/Wellcome Trust Imperial Clinical Research Facility. We thank the participants and research staff who made the study possible.MDC: This study was funded by the European Research Council (StG-282255), the Swedish Heart and Lung Foundation and the Swedish Research Council. Exome chip genotyping in the MDC Cohort was supported in part by NIH R01HL107816 to Kathiresan. Dr. Kathiresan's work was also funded by Fondation Leducq.NFBC1966 and NFBC1986: The NFBC1966 and NFBC1986 studies received financial support from the Academy of Finland (project grants 104781, 120315, 129269, 1114194, 24300796, Center of Excellence in Complex Disease Genetics and SALVE), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01), NIH/NIMH (5R01MH63706:02), ENGAGE project and grant agreement HEALTH-F4-2007-201413, EU FP7 EurHEALTHAgeing -277849, the Medical Research Council, UK (G0500539, G0600705, G1002319, G0802782, PrevMetSyn/SALVE) and the MRC, Centenary Early Career Award. The DNA extractions, sample quality controls, biobank up-keeping and aliquotting was performed in the National Public Health Institute, Biomedicum Helsinki, Finland and supported financially by the Academy of Finland and Biocentrum Helsinki. The Section of Investigative Medicine, Imperial College is funded by grants from the MRC, BBSRC, NIHR, and an Integrative Mammalian Biology (IMB) Capacity Building Award, an FP7- HEALTH- 2009- 241592 EuroCHIP grant and is supported by the NIHR Imperial Biomedical Research Centre Funding Scheme. AIFB is also supported by the MRC, Diabetes UK, EPSRC and an EU FP7 NutriTech grant. We thank the late Professor Paula Rantakallio (launch of NFBCs), and Ms Outi Tornwall and Ms Minttu Jussila (DNA biobanking). The authors would like to acknowledge the contribution of the late Academian of Science Leena Peltonen. OBB: National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital Trust, Oxford, UKPIVUS/ULSAM: - ULSAM and PIVUS are supported by the Swedish Research Council, Swedish Heart-Lung Foundation, Swedish Diabetes Foundation and Uppsala University. The investigators want to express their deepest gratitude towards the study participants.Twins UK: -The study was funded by the Wellcome Trust: European Community’s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR)- funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. SNP Genotyping was performed at performed by the Genomics Core at the Wellcome Trust Centre for Human Genetics, University of Oxford.UHP: The Utrecht Health Project received grants from the Ministry of Health, Welfare and Sports (VWS), the University of Utrecht, the Province of Utrecht, the Dutch Organisation of GPs and inhabitants of the “Leidsche Rijn” district, Care Research, the University Medical Centre of Utrecht, and the Dutch College of Healthcare Insurance Companies. We thank Utrecht, for their cooperation in this project.UKHLS: These data are from Understanding Society: The UK Household Longitudinal Study, which is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The data were collected by NatCen and the genome wide scan data were analysed by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website . The 'Understanding Society Scientific Group' include the following: Understanding Society Scientific Group: Michaela Benzeval, Jonathan Burton, Nicholas Buck, Annette J?ckle, Meena Kumari, Heather Laurie, Peter Lynn, Stephen Pudney, Birgitta Rabe, Shamit Saggar, Noah Uhrig, Dieter WolkeF. D. wishes to acknowledge the MRC Unit at the University of Bristol (MC_UU_12013/1-9)A.P.M. is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science (grant number WT098017).M.I.M. is a Wellcome Trust Senior Investigator (WT098381); and a National Institute of Health Research Senior Investigator.M. H. is supported by NIH RO1 grant LM010098M.D.T. has been supported by MRC fellowship G0902313. F.W.A is supported by the UCL Hospitals NIHR Biomedical Research Centre and by a Dekker scholarship (Junior Staff Member 2014T001) from the Dutch Heart FoundationP.B.M, M.J.C, H.W.W, A. T, K.W. wish to acknowledge the NIHR Cardiovascular Biomedical Research Unit at Barts and The London, Queen Mary University of London, UK for support.H. Y. is funded by the European Research Council (ERC) award (323195 and SZ-50371).S.R. was supported by the Academy of Finland (251217 and 255847), Center of Excellence in Complex Disease Genetics, EU FP7 projects ENGAGE (201413) and BioSHaRE (261433), the Finnish Foundation for Cardiovascular Research, Biocentrum Helsinki, and the Sigrid Juselius Foundation’Peter Sever is an NIHR Senior Investigator and acknowledges support from the Biomedical Research Centre award to Imperial College Healthcare NHS Trust.Nicole Soranzo's research is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510) and the?National Institute for?Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics at the University of Cambridge in partnership with NHS Blood and Transplant (NHSBT). ?The views expressed are those of the author(s) and not?necessarily those of the NHS, the NIHR, the Department of Health or NHSBT.3. GoT2D and T2D-GENES consortiaADDITION: We would like to thank Torsten Lauritzen and Annelli Sandb?k for the use of the ADDITION cohort. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (metabol.ku.dk).DPS: has been financially supported by grants from the Academy of Finland (117844 and 40758, 211497, and 118590 (MU); The EVO funding of the Kuopio University Hospital from Ministry of Health and Social Affairs (5254), Finnish Funding Agency for Technology and Innovation (40058/07), Nordic Centre of Excellence on ‘Systems biology in controlled dietary interventions and cohort studies, SYSDIET (070014), The Finnish Diabetes Research Foundation, Yrj? Jahnsson Foundation (56358), Sigrid Juselius Foundation and TEKES grants 70103/06 and 40058/07. DR's EXTRA: This Study was supported by grants to Rainer Rauramaa by the Ministry of Education and Culture of Finland (627; 2004-2011), Academy of Finland (102318; 123885), Kuopio University Hospital, Finnish Diabetes Association, Finnish Heart Association, P?ivikki and Sakari Sohlberg Foundation and by grants from European Commission FP6 Integrated Project (EXGENESIS); LSHM-CT-2004-005272, City of Kuopio and Social Insurance Institution of Finland (4/26/ 2010).FIN-D2D 2007: This study was supported by funds from the hospital districts of Pirkanmaa; Southern Ostrobothnia; North Ostrobothnia; Central Finland and Northern Savo; the Finnish National Public Health Institute; the Finnish Diabetes Association; the Ministry of Social Affairs and Health in Finland; Finland’s Slottery Machine Association; the Academy of Finland [grant number 129293] and Commission of the European Communities, Directorate C-Public Health [grant agreement no. 2004310]. FUSION: The FUSION study was supported by DK093757, DK072193, DK062370, and 1Z01 HG000024.Health 2006/2008: This work was supported by the Timber Merchant Vilhelm Bang’s Foundation, the Danish Heart Foundation (Grant number 07-10-R61-A1754-B838-22392F), and the Health Insurance Foundation (Helsefonden; Grant number 2012B233). The Health2006 was financially supported by grants from the Velux Foundation; The Danish Medical Research Council, Danish Agency for Science, Technology and Innovation; The Aase and Ejner Danielsens Foundation; ALK-Abello A/S, H?rsholm, Denmark, and Research Centre for Prevention and Health, the Capital Region of Denmark.Inter99: The Inter99 was initiated by Torben J?rgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises Torben J?rgensen and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association.METSIM: The METSIM study was supported by the Academy of Finland (contract 124243), the Finnish Heart Foundation, the Finnish Diabetes Foundation, Tekes (contract 1510/31/06), and the Commission of the European Community (HEALTH-F2-2007 201681), and the US National Institutes of Health grants DK093757, DK072193, DK062370, and 1Z01 HG000024. Genotyping of the METSIM and DPS studies, and part of the FUSION study, was conducted at the Genetic Resources Core Facility (GRCF) at the Johns Hopkins Institute of Genetic Medicine. The Broad Genomics Platform for genotyping of the FIN-D2D 2007, FINRISK 2007, DR'sEXTRA, and FUSION studies.Funding for the GoT2D and T2D-GENES studies was provided by grants NIH U01s DK085526, DK085501, DK085524, DK085545, and DK085584 (Multiethnic Study of Type 2 Diabetes Genes) and DK088389 (Low-Pass Sequencing and High-Density SNP Genotyping for Type 2 Diabetes).C.M.L. is funded by the Wellcome Trust (086596/Z/08/Z) and the?Li Ka Shing FoundationT.T. is funded by personal grants from the Finnish Cultural Foundation, the Emil Aaltonen Foundation and the Orion-Farmos Research Foundation.4. GTEx Project: The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute 10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplements to University of Miami grants DA006227, Washington University St Louis (MH101810), and the University of Pennsylvania (MH101822). The data used for the analyses described in this manuscript were obtained from the GTEx Portal and dbGaP accession number phs000424.v3.p1.5. MuTHER consortium (TwinsUK): The study was funded by the Wellcome Trust; European Community’s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at?Guy's and St Thomas' NHS Foundation Trust and King's College London. Tim Spector is holder of an ERC Advanced Principal Investigator award. SNP Genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR.6. METASTROKE: provided in PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UcmF5bG9yPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48
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ADDIN EN.CITE.DATA 278. CHARGE-Heart Failure consortiumARIC Study: The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute (NHLBI) contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. CHS: This CHS research was supported by NHLBI contracts HHSN268201200036C, HSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, and R01HL120393 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. FHS: The FHS was supported by NHLBI (Contract No. N01-HC-25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). This work was also supported in part by grants from the NHLBI 2K24HL04334, R01HL077477, and R01HL093328 (all to RSV). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. The analyses reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project. The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters for their help in creating the GWAS database, and Karol Estrada and Maksim V. Struchalin for their support in creation and analysis of imputed data. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff of the Rotterdam Study and the participating general practitioners and pharmacists. CL is funded by the NHLBI/NIH Contract #N01-HC-25195, NIH NIDDK R01 DK078616 and K24 DK080140, and by the Boston University School of MedicineATK is supported in part by NHLBI grant R01HL117078?(MAP)?and NIDDK grant R01DK89256 (IBB).Author contributionsSupervision and management of the project: JMHH and PBM. The following authors contributed to the drafting of the manuscript: JMMH, PBM, PSu, HW, ASB, FD, JPC, DRB, KW, MT, FWA, LVW, NJS, JD AKM, HY, CMM, NG, XS, TaT, DFF, MHs, OG, TF, VT. All authors critically reviewed and approved the final version of the manuscript. Statistical analysis review: JMMH, PSu, FD, HW, JPC, RY, NM, PBM, LVW, HY, TF, EMi, ADM, AM, AM, EE, ASB, FWA, MJC, CF, TF, SEH, ASH, JEH, JL, GM, JM, NM, APM, APo, NJS, RAS, LS, KE, MT, VT, TVV, NV, KW, AMY, WZg, NG, CML, AKM, XS, TT. Central Data QC: JMMH, ASB, PSu, RY, FD, HW, JPC, TF, LVW, PBM, EMi, NM, CML, NG, XS, AKM. Central Data analysis: JMMH, PSu, FD, HW, JPC, NG, CML, AKM, XS. Pathway analysis and literature review: JMMH, DRB, PBM, MT, KW, VT, OG, AT, FWA. GWAS lookups, eQTL analysis, GRS, variant annotation and enrichment analyses: JMMH, ASB, DRB, JRS, DFF, FD, MHr, PBM, FWA, TT, CML, AKM, SBu. Study Investigators in alphabetical order by consortium (CHD Exome+, ExomeBP and GoT2D): DSA, PA, EA, DA, ASB, RC, JD, JF, IF, PF, JWJ, FKe, ASM, SFN, BGN, DS, NSa, JV, FWA, PIWB, MJB, MJC, JCC, JMC, IJD, GD, AFD, PE, TE, PWF, GG, PH, CH, KH, EI, MJ, FKa, SK, JSK, LLi, MIM, OM, AMe, ADM, APM, PBM, MEN, SP, CP, OPo, DP, SR, OR, IR, VS, NJS, PSe, TDS, JMS, NJW, CJW, EZ, MB, IB, FSC, LG, TH, EKH, PJ, JKu, ML, TAL, AL, KLM, HO, OPe, RR, JT, MU. Study Phenotyping in alphabetical order by consortium (CHD Exome+, ExomeBP and GoT2D): PA, DA, SBl, MC, JF, JWJ, FKe, KK, SFN, BGN, CJP, AR, MS, NSa, JV, WZo, RAB, MJB, MJC, JCC, JMC, AFD, ASFD, LAD, TE, AF, GG, GH, PH, AS H, OLH, EI, MJ, FK, JSK, LLi, LLa, GM, AMc, PM, AMe, RMg, MJN, MEN, OPo, NP, FR, VS, NJS, TDS, AVS, JMS, MT, AV, NV, NJW, TiT, CC, LLH, MEJ, AK, PK, JL DPS, SM, ERBP, AS, TS, HMS, BT. Study Data QC and analysis in alphabetical order by consortium (CHD Exome+, ExomeBP and GoT2D): ASB, AJMC, JMMH, JK, SFN, BGN, MMN, SP, MP, PSu, ST, GV, SMW, RY, FWA, JPC, FD, AF, TF, CH, AMc, AMj, APM, PBM, CP, WR, FR, NJS, MT, VT, HW, HY, NG, AKM, XS. Exome chip data QC in alphabetical order by consortium (CHD Exome+, ExomeBP and GoT2D): ASB, JMMH, SFN, BGN, PSu, RY, FWA, PIWB, AIFB, JCC, JPC, PD, LAD, FD, EE, CF, TF, SEH, PH, SSH, KH, JEH, EK, AMj, GM, JM, NM, EMi, AMo, APM, PBM, CPN, MJN, CP, AP, WR, NRR, RAS, NS, LS, KES, MDT, VT, TVV, TVV, NV, HW, HY, AMY, EZ, WZg, NG, CML, AKM, XS. Exome chip Data analysis in alphabetical order by consortium (CHD Exome+, ExomeBP and GoT2D): JMMH, PSu, RY, FWA, PIWB, AIFB, RAB, MJC, JCC, JPC, PD, LAD, PE, EE, CF, TF, PWF, SF, CG, SEH, PH, ASH, CH, OLH, JEH, EI, MJ, FKa, JSK, DCML, LLi, JL, GM, RMr, JM, NM, MIM, PM, OM, CM, EMi, AMo, APM, RMg, PBM, CPN, MJN, TO, APo, APa, WR, NRR, NJS, RAS, NS, LS, TDS, KES, MDT, ET, VT, TVV, NV, LVW, NJW, HW, HY, AMY, EZ, HZ, WZg, LLB, APG, NG, MHs, JRH, AUJ, JBJ, CML, AKM, NN, XS, AS, AJS. GRS lookups: AEJ, EMa, HFM, HL, HMH, JFF, MTr, RSV, WL. Conflict of interestsN. P. has received financial support from several pharmaceutical companies that manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees.S. K. has received Research Grant-Merck, Bayer, Aegerion; SAB-Catabasis, Regeneron Genetics Center, Merck, Celera; Equity-San Therapeutics, Catabasis; Consulting-Novartis, Aegerion, Bristol Myers-Squibb, Sanofi, AstraZeneca, Alnylam. P. Sever has received research awards from Pfizer Inc. A. Malarstig and M. Uria-Nickelsen are full time employees of Pfizer.D. Reily and M. Hoek are full time employees of Merck and co Inc. M.J. Caulfield is Chief Scientist for Genomics England a UK Government company.The authors declare no competing financial interest.References ADDIN EN.REFLIST 1.Lim, S.S. et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224-60 (2012).2.Rapsomaniki, E. et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people. Lancet 383, 1899-911 (2014).3.Munroe, P.B., Barnes, M.R. & Caulfield, M.J. Advances in blood pressure genomics. Circ Res 112, 1365-79 (2013).4.Ehret, G.B. et al. 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UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e1001779 (2015).43.Te Riet, L., van Esch, J.H., Roks, A.J., van den Meiracker, A.H. & Danser, A.H. Hypertension: Renin-Angiotensin-aldosterone system alterations. Circ Res 116, 960-75 (2015).44.Gao, J. et al. A new strategy for treating hypertension by blocking the activity of the brain renin-angiotensin system with aminopeptidase A inhibitors. Clin Sci (Lond) 127, 135-48 (2014). ADDIN EN.REFLIST Figure LegendsFigure 1 Study design and work flow diagram of single variant discovery analyses. EUR=European, SAS=South Asian, HIS=Hispanic, AA=African American, HTN=hypertension, BP=blood pressure, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure, N=sample size, MAF=minor allele frequency, P=P-value significance threshold, SNV=single-nucleotide variant, GWS=genome-wide significance *Further details of the selection criteria are provided in the methods.Figure 2 Discovery SNV-BP associations. Results are provided for (a) transformed SBP (b) transformed DBP (c) transformed PP and (d) HTN in the European and South Asian (EUR_SAS) discovery samples. The y-axis represents –log10P for association. Red triangles represent variants that map to one of the 81 regions selected for replication, blue triangles represent SNVs that map to previously published BP regions, and grey triangles represent all remaining SNVs. SNVs are ordered according to chromosome (black lines on the outside of the plot) and physical position. Genes that SNVs map to are given in the outer blocks.Figure 3 Overlap of the 30 novel loci associations across SBP, DBP, PP and HTN. The Venn diagram shows which of the 30 newly identified BP loci are associated with multiple BP traits. Only SNV-BP trait associations that were genome-wide significant (P < 5x10-8) in the combined discovery and replication meta-analyses are listed for any given BP trait, within the corresponding ancestry dataset that the given locus was validated for (see Tables 1 and 2). The association of RRAS variant with SBP was replicated in the independent samples, but did not achieve GWS in the combined discovery and replication meta-analysis and is therefore only included for SBP. HTN=hypertension, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure.Figure 4 Study design for conditional analyses and rare variant gene-based discovery analyses. RMW=RareMetalWorker, EUR=European, SAS = South Asian, HTN=hypertension, BP=blood pressure, SBP=systolic blood pressure, DBP= diastolic blood pressure, PP=pulse pressure. N=sample size, MAF=minor allele frequency, P=P-value significance threshold, Pcond=conditional P-value significance thresholdFigure 5 Locus plot for A2ML1 and secondary amino acid structure of the gene product. (a) Locus plot for A2ML1 associated with HTN identified through gene based tests. The variants’ positions along the gene (x axis, based on human genome build 37) and the –log10(P-value of association) (y axis) are indicated. The variants are colour coded: nonsense (black), missense, predicted damaging (blue), and missense (orange). The schematic above the x-axis represents the intron / exon (black vertical bars) structure, the untranslated regions are shown as grey vertical bars.(b) The white box denotes the full-length amino acid sequence for each of the two gene products. Black numbers denote amino acid residue positions of note. Coloured boxes depict putative functional domains (see below). Coloured vertical lines indicate the amino acid substitutions corresponding to the variants depicted in the locus plots above using the same colour coding. Bold, italic indicates the SNV association with smallest P-value.Dark grey – signal peptide sequence. Brown – regions of intramolecular disulfide bonds. For simplicity only those regions coinciding with variants described were indicated. Black – bait region described to interact with proteases. Purple – thiol ester sequence region aiding in interaction with proteases. Light grey – alpha helical regions thought to mediate A2ML1 interaction with LRP1, facilitating receptor-mediated endocytosis.Table 1 Novel blood pressure trait associated loci. Variants with formal replicationVariant informationDiscoveryReplicationCombinedLocusrsIDChr:Pos (EA:EAF)TraitPtPUNβPNβPEURRNF207rs7092091:6.28 (A:0.655)PP4.57 x 10-61.60 x 10-6122,7800.175.83 x 10-4284,6830.209.62 x 10-9C5orf56rs125218685:131.78 (T:0.373)DBP1.59 x 10-63.03 x 10-7122,795-0.182.29 x 10-5282,023-0.196.12 x 10-11PHACTR1rs93493796:12.90 (A:0.566)SBP2.11 x 10-81.78 x 10-7122,8090.244.06 x 10-4284,6730.298.84 x 10-10COL21A1rs200999181?6:55.94 (A:0.002)PP3.08 x 10-82.46 x 10-7121,4872.701.90 x 10-4242,4863.256.27 x 10-10ABOrs6876219:136.14 (A:0.615)DBP8.80 x 10-82.55 x 10-7122,7980.161.96 x 10-4276,0140.195.45 x 10-10ADOrs1099531110:64.56 (C:0.567)DBP1.86 x 10-61.14 x 10-6122,7980.238.47 x 10-8266,4560.211.12 x 10-12LMO1rs11041911:8.25 (A:0.48)DBP9.41 x 10-62.22 x 10-5122,7980.161.81 x 10-4279,9350.163.04 x 10-8OR5B12rs1122945711:58.21 (T:0.236)SBP1.58 x 10-64.62 x 10-5122,809-0.327.53 x 10-5284,680-0.312.70 x 10-8CERS5rs730298112:50.54 (A:0.361)DBP1.35 x 10-134.60 x 10-11122,7980.242.64 x 10-8284,7180.251.38 x 10-17MYH6rs45203614:23.87 (A:0.327)PP4.59 x 10-112.80 x 10-13122,780-0.211.81 x 10-5284,672-0.282.96 x 10-16DPEP1rs112646416:89.70 (C:0.256)DBP1.19 x 10-94.35 x 10-11118,6770.241.68 x 10-6261,5640.281.02 x 10-15TBX2rs8068318?17:59.48 (T:0.698)DBP7.46 x 10-135.71 x 10-10122,7980.263.23 x 10-8281,9780.261.95 x 10-16RGL3rs16747919:11.53 (T:0.486)DBP2.22 x 10-231.97 x 10-22122,797-0.293.01 x 10-11283,332-0.331.99 x 10-31PREX1rs609524120:47.31 (A:0.452)DBP5.65 x 10-62.29 x 10-5122,798-0.182.56 x 10-5281,322-0.174.75 x 10-9ALL ancestryRBM47rs35529250?4:40.43 (T:0.01)SBP6.56 x 10-76.15 x 10-6148,878-1.435.02 x 10-4306,352-1.552.42 x 10-8OBFC1rs438728710:105.68 (A:0.157)SBP2.23 x 10-81.32 x 10-7147,7910.283.37 x 10-4320,4940.369.12 x 10-10RRASrs61760904?19:50.14 (T:0.008)SBP1.96 x 10-61.90 x 10-5148,8781.385.70 x 10-4322,6641.508.45 x 10-8SNV-BP associations are reported for the newly identified BP loci that replicated at P < 6.2 x 10-4 (Bonferroni correction for the 81 variants selected for replication for a primary blood pressure trait; Methods). Loci are categorised into EUR and ALL ancestry based on the meta-analysis used to replicate the variants for the primary BP trait shown in columns labelled ‘Trait’. In the columns that contains the discovery meta-analyses results, Pt represents the P-value for association of the variant with the transformed primary BP trait in the EUR_SAS discovery meta-analyses (which was also used to select the variant for replication) and Pu represents the P-value for association with the untransformed primary BP trait in the ancestry in which the variant replicated. N, β and P, which denote the number of samples, estimated allelic effect and P-value for association with the primary BP trait, are provided for the untransformed primary BP trait in the replication data and also from the combined (discovery and replication) meta-analyses. NB: ALL ancestry corresponds to all ancestries in the combined (discovery + replication) meta-analysesLocus – Gene or region containing the SNV, rsID - dbSNP rsID. Chr:Pos (EA:EAF) – Chromosome:NCBI Build 37 position in Mb (effect allele:effect allele frequency), Trait – primary blood pressure trait for which the variant was and also replicated, β - effect estimate, N:sample size, EUR - European. ? indicates it is a non-synonymous SNV (nsSNV) or is in linkage disequilibrium with a nsSNV (r2 > 0.8) that is predicted to be damaging Table 2 Novel blood pressure trait associated loci. Variants with GWS evidence of association in combined meta-analysesVariant informationDiscoveryReplicationCombinedLocusrsIDChr:Pos (EA:EAF)TraitPtPUNβPNβPEUR2q36.3rs29721462:227.10 (T:0.652)DBP§ (HTN)1.51 x 10-92.47 x 10-7122,7980.132.20 x 10-3275,6100.178.40 x 10-9ZBTB38rs168513973:141.13 (A:0.953)DBP§ (SBP)6.87 x 10-63.20 x 10-5122,798-0.381.20 x 10-4284,717-0.383.01 x 10-8PRDM6rs10080585:122.44 (A:0.135)SBP5.09 x 10-71.01 x 10-843,1090.463.61 x 10-3176,3620.552.99 x 10-10GPR20rs345915168:142.37 (T:0.055)SBP§(DBP)1.54 x 10-61.01 x 10-7122,8070.514.20 x 10-4282,0090.646.10 x 10-10HOXB7rs740691017:46.69 (T:0.118)SBP6.07 x 10-102.74 x 10-9122,809-0.204.89 x 10-2284,690-0.463.80 x 10-8AMHrs10407022?19:2.25 (T:0.82)PP1.63 x 10-71.73 x 10-7118,656-0.191.62 x 10-3252,525-0.265.94 x 10-9ZNF101rs230413019:19.79 (A:0.914)DBP1.66 x 10-81.92 x 10-8122,798-0.171.71 x 10-2284,705-0.291.53 x 10-8PROCRrs86718620:33.76 (A:0.873)DBP1.44 x 10-64.15 x 10-7122,7980.212.48 x 10-3284,7220.261.19 x 10-8RRP1Brs930616021:45.11 (T:0.374)DBP§ (SBP)1.04 x 10-81.90 x 10-6100,489-0.164.30 x 10-4249,817-0.186.80 x 10-9TNRC6Brs47011322:40.73 (A:0.804)PP1.48 x 10-101.31 x 10-9122,780-0.141.37 x 10-2284,683-0.251.67 x 10-9ALL ancestry7q32.1rs47281427:128.57 (A:0.433)SBP8.10 x 10-64.21 x 10-6150,542-0.218.62 x 10-4338,338-0.243.45 x 10-8PRKAG1rs1126930?12:49.40 (C:0.036)PP2.12 x 10-64.62 x 10-7151,4810.363.74 x 10-3314,8940.503.34 x 10-8SBNO1rs106010512:123.81 (T:0.209)DBP6.66 x 10-71.09 x 10-6150,532-0.152.67 x 10-3336,413-0.183.07 x 10-8SNV-BP associations are reported for the newly identified BP loci that showed genome-wide significant association (P < 5 x 10-8) in the combined discovery and replication meta-analyses. In the columns that contain results from the discovery meta-analyses, Pt represents the P-value for association of the variant with the transformed primary BP trait in the EUR_SAS discovery meta-analyses (used to select the variant for replication) and Pu represents the P-value for association with the untransformed BP trait in the ancestry in which the variant was validated. Loci are categorised into EUR and ALL ancestry based on the ancestry in which the variant showed association with a blood pressure trait at P < 5 x 10-8. N, β and P, which denote the number of samples, estimated allelic effect and P-value for association with the validated BP trait, are provided for the untransformed BP trait in the replication data and also from the combined (discovery and replication) meta-analyses. NB: ALL ancestry corresponds to all ancestries in the combined (discovery + replication) meta-analyses. Locus – Gene or region containing the SNV, rsID - dbSNP rsID. Chr:Pos (EA:EAF) – Chromosome:NCBI Build 37 position in Mb (effect allele:effect allele frequency), Trait - blood pressure trait for which association is reported, EUR - European. § At four loci (2q36.3, ZBTB38, GPR20 and RRP1B) the primary trait used to select the variants for replication is given in parentheses because the variant associations were validated in the combined meta-analysis for the listed secondary trait. For these variants, Pt denotes the P-value for association with the primary trait, the other P-values provided are for the secondary trait. ? indicates it is a non-synonymous SNV (nsSNV) or is linkage disequilibrium with a nsSNV (r2>0.8) that is predicted to be damagingTable 3 Results of the genetic risk score analyses across CVD traits and risk factors.OutcomeUnitsNDBP (per 10mmHg increase)SBP (per 10mmHg increase)PP (per 10mmHg increase)Effect [95% CI]PEffect [95% CI]PEffect [95% CI]PCHDOR82,056 1.62 [ 1.28, 2.05]5.99 x 10-5 1.39 [ 1.22, 1.59]6.07 x 10-7 1.70 [ 1.34, 2.16]1.20 x 10-5Ischemic strokeOR25,799 1.93 [ 1.47, 2.55]2.81 x 10-6 1.57 [ 1.35, 1.84]1.16 x 10-8 2.12 [ 1.58, 2.84]5.35 x 10-7Cardioembolic strokeOR16,113 1.43 [ 0.86, 2.39]0.1683 1.33 [ 0.99, 1.80]0.0584 1.73 [ 1.00, 3.02]0.0518Large vessel strokeOR13,903 2.26 [ 1.25, 4.08]0.0068 1.85 [ 1.32, 2.59]3.61 x 10-4 3.05 [ 1.64, 5.68]4.37 x 10-4Small vessel strokeOR15,617 1.96 [ 1.13, 3.41]0.0168 1.56 [ 1.13, 2.16]0.0064 1.98 [ 1.09, 3.61]0.0248Heart failureOR13,282 1.48 [ 1.02, 2.17]0.0409 1.25 [ 1.00, 1.57]0.0512 1.33 [ 0.88, 2.02]0.1757Left ventricular massg11,273 9.57 [ 3.98,15.17]8.02 x 10-4 5.13 [ 1.77, 8.48]0.0027 5.97 [-0.38,12.31]0.0653Left ventricular wall thicknesscm11,311 0.10 [ 0.06, 0.13]1.88 x 10-8 0.05 [ 0.03, 0.07]5.52 x 10-6 0.05 [ 0.01, 0.09]0.0187HDLmg/dl80,395 0.25 [-1.00, 1.51]0.6930 0.21 [-0.50, 0.92]0.5622 0.47 [-0.79, 1.73]0.4668LDLmg/dl77,021-1.57 [-5.20, 2.06]0.3972 0.07 [-2.03, 2.16]0.9498 1.87 [-1.86, 5.59]0.3255Total cholesterolmg/dl80,455-1.34 [-5.90, 3.22]0.5639 0.70 [-1.93, 3.32]0.6029 3.68 [-0.97, 8.33]0.1209Triglyceridesmg/dl77,779 0.02 [-0.03, 0.08]0.3859 0.02 [-0.01, 0.05]0.2697 0.03 [-0.03, 0.08]0.3025BMIINVT526,508-0.10 [-0.18,-0.01]0.0342-0.07 [-0.13,-0.02]0.0058-0.12 [-0.23,-0.02]0.0165WHRadjBMIINVT344,369 0.03 [-0.04, 0.11]0.4025 0.03 [-0.02, 0.08]0.2170 0.06 [-0.03, 0.15]0.1885HeightINVT458,927 0.02 [-0.15, 0.18]0.8592-0.04 [-0.15, 0.06]0.4170-0.18 [-0.37, 0.01]0.0683eGFRINVT51,039-0.02 [-0.15, 0.11]0.7810-0.03 [-0.10, 0.04]0.4080-0.07 [-0.20, 0.06]0.2741CHD, coronary heart disease; HDL, high density lipoprotein; LDL, low density lipoprotein; eGFR, estimated glomerular filtration rate; DBP, diastolic blood pressure; SBP systolic blood pressure; PP, pulse pressure; OR, odds ratio; g, grams; INVT, inverse normally transformed (hence no units); N, sample size; P, P-value of association of BP with the trait listed; CI, confidence interval. Results are considered significant if P < 0.0038, which corresponds to a Bonferroni correction for 13 phenotypes tested.Online MethodsOverview of discovery studiesThe cohorts contributing to the discovery meta-analyses comprise studies from three consortia (CHD Exome+, ExomeBP, and GoT2D/T2D-GENES) with a total number of 192,763 unique samples. All participants provided written informed consent and the studies were approved by their local Research Ethics Committees and/or Institutional Review Boards. The CHD Exome+ consortium comprised 77,385 samples: eight studies (49,898 samples) of European (EUR) ancestry, two studies (27,487 samples) of South Asian (SAS) ancestry (Supplementary Table 1). The ExomeBP consortium included 25 studies (75,620 samples) of EUR ancestry (Supplementary Table 1). The GoT2D consortium comprised 14 studies (39,758 samples) of Northern EUR ancestry from Denmark, Finland, and Sweden (Supplementary Table 1). The participating studies and their characteristics including BP phenotypes are detailed in Supplementary Tables 1 and 2. Note, any studies contributing to multiple consortia were only included once in all meta-analyses. PhenotypesFour blood pressure (BP) traits were analysed: systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP) and hypertension (HTN). For individuals known to be taking BP lowering medication, 15/10 mmHg was added to the raw SBP/DBP values, respectively, to obtain medication-adjusted SBP/DBP values ADDIN EN.CITE <EndNote><Cite><Author>Tobin</Author><Year>2005</Year><RecNum>42</RecNum><DisplayText><style face="superscript">45</style></DisplayText><record><rec-number>42</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="0">42</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tobin, M. D.</author><author>Sheehan, N. A.</author><author>Scurrah, K. J.</author><author>Burton, P. R.</author></authors></contributors><auth-address>Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, 22-28 Princess Road West, Leicester LE1 6TP, U.K. mt47@leicester.ac.uk</auth-address><titles><title>Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure</title><secondary-title>Stat Med</secondary-title></titles><periodical><full-title>Stat Med</full-title></periodical><pages>2911-35</pages><volume>24</volume><number>19</number><edition>2005/09/10</edition><keywords><keyword>Aged</keyword><keyword>Antihypertensive Agents/*pharmacology</keyword><keyword>Bias (Epidemiology)</keyword><keyword>Blood Pressure/drug effects/*genetics</keyword><keyword>Computer Simulation</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>*Models, Genetic</keyword><keyword>*Models, Statistical</keyword><keyword>*Quantitative Trait, Heritable</keyword></keywords><dates><year>2005</year><pub-dates><date>Oct 15</date></pub-dates></dates><isbn>0277-6715 (Print)</isbn><accession-num>16152135</accession-num><urls><related-urls><url>. PP was defined as SBP minus DBP, post-adjustment. For HTN, individuals were classified as hypertensive cases if they satisfied at least one of: (i) SBP≥140 mmHg, (ii) DBP≥90 mmHg, (iii) taking anti-hypertensive or BP lowering medication. All other individuals were included as controls. The four BP traits were correlated (SBP:DBP correlations were between 0.6 and 0.8, and SBP:PP correlations were ~0.8). However, they measure partly distinct physiological features including, cardiac output, vascular resistance, and arterial stiffness, all measures for determining a cardiovascular risk profile. Therefore the genetic architecture of the individual phenotypes are of interest, and a multi-phenotype mapping approach was not adopted.GenotypingAll samples were genotyped using one of the Illumina HumanExome Beadchip arrays (Supplementary Table 3). An Exome chip quality control Standard Operating Procedure (SOP) developed by Anubha Mahajan, Neil Robertson and Will Rayner at the Wellcome Trust Centre for Human Genetics, University of Oxford was used by most studies for genotype calling and QCPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYWhhamFuPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48
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ADDIN EN.CITE.DATA 46 (Supplementary Table 3). All genotypes were aligned to the plus strand of the human genome reference sequence (Build37) prior to any analyses and any unresolved mappings were removed. Genotype cluster plots were reviewed for all the novel rare variants (both lead and secondary signals) and for rare variants that contributed to the gene-based testing.Meta-analysesMeta-analyses were performed using METAL ADDIN EN.CITE <EndNote><Cite><Author>Willer</Author><Year>2010</Year><RecNum>48</RecNum><DisplayText><style face="superscript">47</style></DisplayText><record><rec-number>48</rec-number><foreign-keys><key app="EN" db-id="vavfdvf2hpvxz2epv5ex9wfnzwwt2z05925f" timestamp="1411151411">48</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Willer, C. J.</author><author>Li, Y.</author><author>Abecasis, G. R.</author></authors></contributors><auth-address>Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109, USA.</auth-address><titles><title>METAL: fast and efficient meta-analysis of genomewide association scans</title><secondary-title>Bioinformatics</secondary-title><alt-title>Bioinformatics</alt-title></titles><periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></periodical><alt-periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></alt-periodical><pages>2190-1</pages><volume>26</volume><number>17</number><edition>2010/07/10</edition><keywords><keyword>Chromosome Mapping</keyword><keyword>Computational Biology/*methods</keyword><keyword>*Genome-Wide Association Study</keyword><keyword>*Software</keyword></keywords><dates><year>2010</year><pub-dates><date>Sep 1</date></pub-dates></dates><isbn>1367-4811 (Electronic)
1367-4803 (Linking)</isbn><accession-num>20616382</accession-num><work-type>Meta-Analysis
Research Support, N.I.H., Extramural</work-type><urls><related-urls><url>, for both discovery and replication analyses, using inverse variance weighted fixed effect meta-analysis for the continuous traits (SBP, DBP and PP) and sample size weighted meta-analysis for the binary trait (HTN).Discovery SNV analysesAnalyses of both untransformed and inverse normal transformed SBP, DBP and PP were conducted within each contributing study. The analyses of the transformed traits were performed in order to minimise sensitivity to deviations from normality in the analysis of rare variants and for discovery of new SNV-BP associations. The residuals from the null model obtained after regressing the medication-adjusted trait on the covariates (age, age2, sex, BMI, and disease status for CHD) within a linear regression model, were ranked and inverse normalised. These normalised residuals were used to test trait-SNV associations. All SNVs that passed QC were analysed for association, without any further filtering by MAF, but a minor allele count of 10 was used for the analysis of HTN. An additive allelic effects model was assumed. Two meta-analyses were performed for each trait, one with EUR and SAS ancestries combined (EUR_SAS) and another for EUR ancestry alone. Contributing studies used principal components (PCs) to adjust for population stratification. Consequently minimal inflation in the association test statistics, λ, was observed (λ=1.07 for SBP, 1.10 for DBP, 1.04 for PP and <1 for HTN in the transformed discovery meta-analysis in EUR_SAS; λ= 1.06 for SBP, 1.09 for DBP, 1.05 for PP and <1 for HTN in the transformed discovery meta-analysis in EUR; Supplementary Figure 6). The meta-analyses were performed independently in two centres and results were found to be concordant between centres. Given the studies contributing to the discovery analyses were ascertained on CHD or T2D, we tested potential systematic bias in calculated effect estimates amongst these studies. No evidence of bias in the overall effect estimates was obtained.The results for the transformed traits were taken forward and used to select candidate SNVs for replication. Results (P-values) from the transformed and untransformed analyses were strongly correlated (r2>0.9). Replication SNV analysesSNVs associated with any of the transformed traits (SBP, DBP, PP) or HTN were annotated using the Illumina SNV annotation file, humanexome-12v1_a_gene_annotation.txt, independently across two centres. Given the difference in power to detect common versus low frequency and rare variant associations, two different significance thresholds were chosen for SNV selection. For SNVs with MAF≥0.05, P≤1x10-5 was selected, while, P≤ 1x10-4 was used for SNVs with MAF < 0.05. By choosing a significance threshold of P<1x10-4 we maximized the opportunity to follow-up rare variants (making the assumption that any true signals at this threshold could replicate at Bonferroni adjusted significance, P≤6.17x10-4, assuming α=0.05 for 81 SNVs). All previously published BP associated SNVs and any variants in LD with them (r2>0.2), were removed from the list of associated SNVs as we aimed to replicate new findings only. SNVs for which only one study contributed to the association result or showed evidence of heterogeneity (Phet<0.0001) were removed from the list as they were likely to be an artefact. Where SNVs were associated with multiple traits, to minimise the number of tests performed, only the trait with the smallest P-value was selected as the primary trait in which replication was sought. Where multiple SNVs fitted these selection criteria for a single region, only the SNV with the smallest P-value was selected. In total, 81 SNVs were selected for validation in independent samples. These 81 SNVs had concordant association results for both transformed and non-transformed traits. Eighty SNVs were selected from EUR_SAS results (with consistent support in EUR), and one SNV from EUR results only. In the next step, we looked up the 81 SNV-BP associations using data from a separate consortium, the CHARGE+ exome chip blood pressure consortium (who had analysed untransformed SBP, DBP, PP and HTN), and UHP and Lolipop (ExomeBP consortium; Supplementary Tables 2 and 3). The analysed residuals from CHARGE+ were approximately normally distributed in their largest studies (Supplementary Figure 7).Two meta-analyses of the replication datasets were performed: one of EUR samples, and a second of EUR, African American, Hispanics and SAS ancestries (“ALL”). Replication was confirmed if P (1-tailed) < 0.05/81=6.17x10-4 and the effect (beta) was in the direction observed in discovery meta-analyses for the selected trait. A combined meta-analysis was performed of discovery (untransformed results as only untransformed data was available from CHARGE+ exome chip blood pressure consortium) and replication results across the four traits to assess the overall support for each locus. For the combined meta-analyses, a GWS threshold of, P≤5x10-8, was used to declare a SNV as novel rather than a less stringent experiment wide threshold, as GWS is used to declare significance in GWAS and we wish to minimise the possibility of false positive associations. (Note that GWS is equivalent to an exome-wide threshold of P≤2x10-7 adjusted for four traits).Note: all validated BP-associated variants were associated at P<10-5 in the discovery dataset (for the primary trait). Hence, we could have used the same inclusion criteria for both common and rare SNVs. 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ADDIN EN.CITE.DATA 15 (version 4.13.3) that does not require individual level data to perform conditional analyses and gene-based tests was used for conditional analyses. All studies that contributed to the SNV discovery analyses were re-contacted and asked to run RMW. Only FENLAND, GoDARTS, HELIC-MANOLIS, UKHLS and EPIC-InterAct were unable to run RMW, while two new studies were included, INCIPE and NFBC1966 (Supplementary Table 1 and 2). In total, 43 studies (147,402 samples) were included in the EUR analyses and 45 studies (173,329 samples) in the EUR_SAS analyses (Supplementary Tables 2 and 3). Comparison of discovery and RMW study level results were made (Supplementary Information). For each novel locus, the genomic coordinates and size of the region were defined according to recombination rates (Supplementary Table 9) around the lead variant. For known loci, a 1 Mb window was used (Supplementary Table 14). Conditional analyses were performed across each region, in both EUR and EUR_SAS samples, for the transformed phenotype corresponding to the validated BP trait for novel loci and the published BP trait for known loci.Gene based tests were performed in both the EUR and EUR_SAS datasets using the Sequence Kernel Association Test (SKAT) ADDIN EN.CITE <EndNote><Cite><Author>Wu</Author><Year>2011</Year><RecNum>8196</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>8196</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1423415258">8196</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wu, M. C.</author><author>Lee, S.</author><author>Cai, T.</author><author>Li, Y.</author><author>Boehnke, M.</author><author>Lin, X.</author></authors></contributors><auth-address>Department of Biostatistics, The University of North Carolina at Chapel Hill, 27599, USA.</auth-address><titles><title>Rare-variant association testing for sequencing data with the sequence kernel association test</title><secondary-title>Am J Hum Genet</secondary-title><alt-title>American journal of human genetics</alt-title></titles><periodical><full-title>Am J Hum Genet</full-title></periodical><alt-periodical><full-title>Am J Hum Genet</full-title><abbr-1>American journal of human genetics</abbr-1></alt-periodical><pages>82-93</pages><volume>89</volume><number>1</number><keywords><keyword>Computer Simulation</keyword><keyword>*Databases, Genetic</keyword><keyword>Gene Frequency</keyword><keyword>Genetic Association Studies/*methods</keyword><keyword>Genetic Loci</keyword><keyword>*Genetic Variation</keyword><keyword>Humans</keyword><keyword>Models, Genetic</keyword><keyword>Sequence Analysis/*methods</keyword><keyword>*Software</keyword></keywords><dates><year>2011</year><pub-dates><date>Jul 15</date></pub-dates></dates><isbn>1537-6605 (Electronic)
0002-9297 (Linking)</isbn><accession-num>21737059</accession-num><urls><related-urls><url> method implemented in RMW as it allows for the SNVs to have different directions and magnitudes of effect. Burden tests were also performed but are not presented as only SKAT provided significant results. The variants in the gene-based tests using SKAT were weighted using the default settings, i.e. a beta distribution density function to up-weight rare variants, Beta(MAFj,1,25) where MAFj represents the pooled MAF for variant j across all studies. Analyses were restricted to coding SNVs with MAF<5% and <1%. Genes were deemed to be associated if P <2.8x10-6 (Bonferroni adjusted for 17,996 genes). To confirm the gene associations were not attributable to a solitary SNV, a gene-based test conditional on the most associated SNV was performed (Pconditional< 0.001). The QC of all SNVs contributing to the gene based tests including the number of samples and studies were checked prior to claiming association. We sought replication of associated genes in the CHARGE+ exome chip blood pressure consortium. Pathway analyses with MAGENTAWe tested seven databases in MAGENTA ADDIN EN.CITE <EndNote><Cite><Author>Segre</Author><Year>2010</Year><RecNum>8357</RecNum><DisplayText><style face="superscript">37</style></DisplayText><record><rec-number>8357</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437157827">8357</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Segre, A. V.</author><author>Diagram Consortium</author><author>Magic investigators</author><author>Groop, L.</author><author>Mootha, V. K.</author><author>Daly, M. J.</author><author>Altshuler, D.</author></authors></contributors><auth-address>Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.</auth-address><titles><title>Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits</title><secondary-title>PLoS Genet</secondary-title><alt-title>PLoS genetics</alt-title></titles><periodical><full-title>PLoS Genet</full-title></periodical><alt-periodical><full-title>PLoS Genet</full-title><abbr-1>PLoS genetics</abbr-1></alt-periodical><volume>6</volume><number>8</number><keywords><keyword>Case-Control Studies</keyword><keyword>Cohort Studies</keyword><keyword>Diabetes Mellitus, Type 2/*genetics/metabolism</keyword><keyword>*Genetic Variation</keyword><keyword>Genome-Wide Association Study</keyword><keyword>Glucose/metabolism</keyword><keyword>Mitochondrial Proteins/*genetics</keyword><keyword>Polymorphism, Single Nucleotide</keyword><keyword>*Quantitative Trait, Heritable</keyword></keywords><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1553-7404 (Electronic)
1553-7390 (Linking)</isbn><accession-num>20714348</accession-num><urls><related-urls><url> (BioCarta, Kyoto Encyclopedia of Genes and Genomes, Ingenuity, Panther, Panther Biological Processes, Panther Molecular Functions and Reactome) for overrepresentation of the SNV discovery results from both EUR and EUR_SAS ancestries. Each of the four BP phenotypes were tested. Pathways exhibiting P<0.01 and FDR<5% were considered statistically significant.GeneGo MetaCore Network analysesA set of BP genes based on previously published studies and our current results (locus defined as r2>0.4 and 500kb on either side of the lead SNV; Supplementary Table 19) were tested for enrichment using the THOMSON REUTERS MetaCoreTM Single Experiment Analysis workflow tool. The data were mapped onto selected MetaCore ontology databases: pathway maps, process networks, GO processes and diseases / biomarkers, for which functional information is derived from experimental literature. Outputs were sorted based on P- and FDR-values. A gene set was considered enriched for a particular process if P<0.05 and FDR<5%.Genetic Risk ScoreTo assess the effect of BP on CHD, ischemic stroke (and subtypes: large vessel, small vessel and cardioembolic stroke) left ventricular mass, left ventricular wall thickness, heart failure, HDL-c, LDL-c, total cholesterol, triglycerides and eGFR, we performed a weighted generalized linear regression of the genetic associations with each outcome variable on the genetic associations with BP. When genetic variants are uncorrelated, the estimates from such a weighted linear regression analysis using summarized data, and a genetic risk score analysis using individual-level data, are equal ADDIN EN.CITE <EndNote><Cite><Author>Burgess</Author><Year>2015</Year><RecNum>8351</RecNum><DisplayText><style face="superscript">48</style></DisplayText><record><rec-number>8351</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437128388">8351</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Burgess, S.</author><author>Thompson, S. G.</author></authors></contributors><titles><title>Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects</title><secondary-title>Am J Epidemiol</secondary-title><alt-title>American journal of epidemiology</alt-title></titles><periodical><full-title>Am J Epidemiol</full-title></periodical><pages>251-60</pages><volume>181</volume><number>4</number><keywords><keyword>Biological Markers/blood</keyword><keyword>Causality</keyword><keyword>Cholesterol, HDL/blood</keyword><keyword>Cholesterol, LDL/blood</keyword><keyword>Coronary Disease/blood/*diagnosis/epidemiology/*genetics</keyword><keyword>Genetic Pleiotropy/*genetics</keyword><keyword>Genetic Variation/genetics</keyword><keyword>Great Britain/epidemiology</keyword><keyword>Humans</keyword><keyword>Mathematical Computing</keyword><keyword>*Mendelian Randomization Analysis/methods</keyword><keyword>Models, Genetic</keyword><keyword>Predictive Value of Tests</keyword><keyword>Risk Factors</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Triglycerides/*blood</keyword></keywords><dates><year>2015</year><pub-dates><date>Feb 15</date></pub-dates></dates><isbn>1476-6256 (Electronic)
0002-9262 (Linking)</isbn><accession-num>25632051</accession-num><urls><related-urls><url>. We refer to the analysis as a genetic risk score (also known as a polygenic risk score) analysis as this is likely to be more familiar to applied readers. As some of the genetic variants in our analysis are correlated, a generalized weighted linear regression model is fitted that accounts for the correlations between variants, as follows: If βX are the genetic associations (beta-coefficients) with the risk factor (here, BP) and βY are the genetic associations with the outcome, then the causal estimate from a weighted generalized linear regression is (βXT ?-1βX)-1 βXT?-1βY, with standard error, σ(βXTΩ-1βX)-1,where T is a matrix transpose, σ is the estimate of the residual standard error from the regression model, and the weighting matrix ? has termsΩj1j2=σYj1σYj2ρj1j2,where σYj is the standard error of the genetic association with the outcome for the jth SNV, and ρj1j2 is the correlation between the j1th and j2 th SNVs. The presence of the estimated residual standard error allows for heterogeneity between the causal estimates from the individual SNVs as overdispersion in the regression model (in the case of underdispersion, the residual standard error estimate is set to unity). This is equivalent to combining the causal estimates from each SNV using a multiplicative random-effects modelPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UaG9tcHNvbjwvQXV0aG9yPjxZZWFyPjE5OTk8L1llYXI+
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ADDIN EN.CITE.DATA 49.For each of SBP, DBP and PP, the score was created using both the novel and known BP SNVs or a close proxy (r2>0.8). Both the sentinel SNV association and any secondary SNV associations that remained after adjusting for the sentinel SNV were included in the genetic risk score. For the 30 validated novel SNV-BP associations, βs were taken from the independent replication analyses (Table 1 and 2) to weight the SNV in the genetic risk score. For the secondary SNVs from the seven novel loci and five known loci, βs were taken from the discovery analyses (Supplementary Tables 10 and 15). For the 82 known SNVs, 43 were either genotyped or had proxies on the Exome chip and the βs were taken from discovery results (Supplementary Table 13), the remaining βs were taken from published effect estimates. This strategy for selecting betas for use in the GRS was taken to minimize the influence of winner’s curse. The associations between the BP variants with CHD, HDL-c, LDL-c, total cholesterol, log(triglycerides) and log(eGFR) were obtained using the CHD Exome+ Consortium studies, the associations with BMI, waist-hip ratio adjusted BMI and height from the GIANT consortium (unpublished data), ischemic stroke from METASTROKEPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UcmF5bG9yPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48
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ADDIN EN.CITE.DATA 26. A causal interpretation of the association of GRS with the outcome as the effect of BP on the outcome assumes that the effects of genetic variants on the outcome are mediated via blood pressure and not via alternate causal pathways, for example via LV thickness. There are also limitations of the Mendelian randomization approach in distinguishing between the causal effects of different measures of blood pressure, due to the paucity of genetic variants associated with only one measure of blood pressure.eQTL analysesThe MuTHER dataset contains gene expression data from 850 UK twins for 23,596 probes and 2,029,988 (HapMap 2 imputed) SNVs. All cis–associated SNVs with FDR<1%, within each of the 30 novel regions (IMPUTE info score >0.8) were extracted from the MuTHER project dataset for, LCL (n=777), adipose (n=776) and skin (n=667) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5OaWNhPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 50. The pilot phase of the GTEx Project (dbGaP Accession?phs000424.v3.p1) provides expression data from up to 156 individuals for 52,576 genes and 6,820,472 genotyped SNVs (imputed to 1000 Genomes project, MAF≥5%) ADDIN EN.CITE <EndNote><Cite><Author>Consortium</Author><Year>2015</Year><RecNum>8353</RecNum><DisplayText><style face="superscript">51</style></DisplayText><record><rec-number>8353</rec-number><foreign-keys><key app="EN" db-id="55zaap5p8920d6er9pc5aw2izpta2trx2wxt" timestamp="1437155897">8353</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>GTEx, ,Consortium,</author></authors></contributors><titles><title>Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans</title><secondary-title>Science</secondary-title><alt-title>Science</alt-title></titles><periodical><full-title>Science</full-title></periodical><alt-periodical><full-title>Science</full-title></alt-periodical><pages>648-60</pages><volume>348</volume><number>6235</number><keywords><keyword>Alleles</keyword><keyword>Blood Pressure/genetics</keyword><keyword>Disease/*genetics</keyword><keyword>GTPase-Activating Proteins/genetics</keyword><keyword>*Gene Expression Regulation</keyword><keyword>Gene Regulatory Networks</keyword><keyword>*Genetic Variation</keyword><keyword>Genome, Human/*genetics</keyword><keyword>Genome-Wide Association Study</keyword><keyword>Genotype</keyword><keyword>Humans</keyword><keyword>Multigene Family</keyword><keyword>Organ Specificity/genetics</keyword><keyword>Pilot Projects</keyword><keyword>*Quantitative Trait Loci</keyword><keyword>RNA Splicing</keyword><keyword>RNA, Untranslated/genetics</keyword><keyword>Sequence Analysis, RNA</keyword><keyword>Tibial Arteries/metabolism</keyword><keyword>*Transcriptome</keyword></keywords><dates><year>2015</year><pub-dates><date>May 8</date></pub-dates></dates><isbn>1095-9203 (Electronic)
0036-8075 (Linking)</isbn><accession-num>25954001</accession-num><urls><related-urls><url>. The eQTL analysis was focused on subcutaneous adipose tissue (n=94), tibial artery (n=112), heart (left ventricle) (n=83), lung (n=119), skeletal muscle (n=138), tibial nerve (n=88), skin (sun exposed, lower leg) (n=96), thyroid (n=105) and whole blood (n=156) which have >80 samples and genes expressed at least 0.1 RPKM in 10 or more individuals in a given tissue. All transcripts with a transcription start site (TSS) within one of the 30 new BP loci and for which there was a cis-associated SNV (IMPUTE info score >0.4) within 1Mb of the TSS at FDR<5%, were identified. Kidney was not evaluated because the sample size was too small (n=8). From each resource, we report eQTL signals, which reach the resource-specific thresholds for significance described above, for SNVs that are in LD (r2>0.8) with our sentinel SNV.For identified eQTLs, we tested whether they colocalised with the BP associated SNVPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HaWFtYmFydG9sb21laTwvQXV0aG9yPjxZZWFyPjIwMTQ8
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ADDIN EN.CITE.DATA 52. Colocalisation analyses were considered to be significant if the posterior probability of colocalisation was greater than 0.95.Annotation of variantsIn silico prediction of the functional effect of associated variants was based on the annotation from dbSNP, the Ensembl Variant Effect Predictor tool and the Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP), Seattle, WA.Trait variance explainedThe percentage trait variance explained for SBP, DBP, PP was assessed with?5,861 individuals with complete information for all phenotypes and covariates from the population-based cohort, 1958BC.? Two genetic models were investigated: one containing the 43 previously known BP associated SNVs covered on the Exome chip; the other additionally including the 30 novel lead SNVs and 9 conditionally independent SNVs from both novel and known loci. These nine conditionally independent SNVs were taken from the EUR results, as 1958BC is EUR. They included four from novel loci (PREX1, COL21A1, PRKAG1 and MYH6 (there was only 1 in EUR); Supplementary Table 10) and five from known loci (ST7L-CAPZA1-MOV10, FIGN-GRB14, ENPEP, TBX5-TBX3 and HOXC4; Supplementary Table 15).The residual trait was obtained by adjusting each of the BP traits in a regression model with sex and BMI variables (not age or age2 as all 1958BC individuals were aged 44 years).? The residual trait was regressed on all SNVs within the corresponding model and adjusted for the first ten PCs.? The R2 calculated from this regression model was used as the percentage trait variance explained.Monogenic Enrichment analysesTo determine if sub-significant signals of association were present in a set of genes associated with monogenic forms of disease, we performed an enrichment analysis of the discovery single variant meta-analyses association results for all four traits, both for EUR and EUR_SAS datasets.The monogenic gene set included: WNK1, WNK4, KLHL3, CUL3, PPARG, NR3C2, CYP11B1, CYP11B2, CYP17A1, HSD11B2, SCNN1A, SCNN1B, SCNN1G, CLCNKB, KCNJ1, SLC12A1, SLC12A3PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NdW5yb2U8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 3. The association results of coding SNVs in these genes were extracted and the number of tests with P<0.001 observed. In order to determine how often such an observation would be observed by chance, we constructed 1,000 matched gene sets. The matching criteria for each monogenic gene was the intersection of all genes in the same exon length quintile and all genes in the same coding variant count decile. Within the matched sets, the number of variants with P<0.001 was observed. The empirical P-value was calculated as the fraction of matched sets with an equal or larger number of variants less than 0.001.References ADDIN EN.REFLIST 45.Tobin, M.D., Sheehan, N.A., Scurrah, K.J. & Burton, P.R. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med 24, 2911-35 (2005).46.Mahajan, A. et al. Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus. PLoS Genet 11, e1004876 (2015).47.Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190-1 (2010).48.Burgess, S. & Thompson, S.G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 181, 251-60 (2015).49.Thompson, S.G. & Sharp, S.J. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med 18, 2693-708 (1999).50.Nica, A.C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet 7, e1002003 (2011).51.GTEx, Consortium,. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-60 (2015).52.Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10, e1004383 (2014). ................
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