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Michael Boehnke

Bio: Michael Boehnke is an academic researcher from University of Michigan. The author has contributed to research in topics: Genome-wide association study & Type 2 diabetes. The author has an hindex of 152, co-authored 511 publications receiving 136681 citations. Previous affiliations of Michael Boehnke include SUNY Downstate Medical Center & Norwegian University of Science and Technology.


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Journal ArticleDOI
TL;DR: Preliminary Whole Genome Sequencing for Psychiatric Disorders Consortium data will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.
Abstract: As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome through pilot WGS projects will be critical to determining which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The Whole Genome Sequencing for Psychiatric Disorders Consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

121 citations

Journal ArticleDOI
Hanieh Yaghootkar1, Claudia Lamina2, Robert A. Scott3, Zari Dastani4, Marie-France Hivert5, Marie-France Hivert6, Liling Warren7, Alena Stančáková8, Sarah Buxbaum9, Leo-Pekka Lyytikäinen10, Peter Henneman11, Ying Wu12, Chloe Y Y Cheung13, James S. Pankow14, Anne U. Jackson15, Stefan Gustafsson16, Jing Hua Zhao3, Christie M. Ballantyne17, Weijia Xie1, Richard N. Bergman18, Michael Boehnke15, Fatiha el Bouazzaoui11, Francis S. Collins19, Sandra H. Dunn20, Josée Dupuis21, Nita G. Forouhi3, Christopher J. Gillson3, Andrew T. Hattersley1, Jaeyoung Hong21, Mika Kähönen10, Johanna Kuusisto8, Lyudmyla Kedenko, Florian Kronenberg2, Alessandro Doria22, Themistocles L. Assimes23, Ele Ferrannini24, Torben Hansen25, Torben Hansen26, Ke Hao27, Hans U. Häring28, Joshua W. Knowles23, Cecilia M. Lindgren29, John J. Nolan30, Jussi Paananen8, Oluf Pedersen, Thomas Quertermous23, Ulf Smith, Terho Lehtimäki10, Ching-Ti Liu21, Ruth J. F. Loos3, Ruth J. F. Loos27, Mark I. McCarthy29, Mark I. McCarthy31, Mark I. McCarthy32, Andrew D. Morris33, Ramachandran S. Vasan21, Tim D. Spector34, Tanya M. Teslovich15, Jaakko Tuomilehto, Ko Willems van Dijk11, Jorma Viikari35, Jorma Viikari36, Na Zhu14, Claudia Langenberg3, Erik Ingelsson29, Erik Ingelsson16, Robert K. Semple32, Robert K. Semple3, Alan R. Sinaiko14, Colin N. A. Palmer33, Mark Walker37, Karen S.L. Lam13, Bernhard Paulweber, Karen L. Mohlke12, Cornelia M. van Duijn38, Olli T. Raitakari36, Olli T. Raitakari35, Aurelian Bidulescu39, Nicholas J. Wareham3, Markku Laakso8, Dawn M. Waterworth40, Debbie A Lawlor41, James B. Meigs5, J. Brent Richards4, J. Brent Richards34, Timothy M. Frayling1 
01 Oct 2013-Diabetes
TL;DR: The results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
Abstract: Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics-based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26-0.35) increase in fasting insulin, a 0.34-SD (0.30-0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47-2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI -0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (-0.20 SD; 95% CI -0.38 to -0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75-1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: -0.03 SD; 95% CI -0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95-1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.

121 citations

Journal ArticleDOI
Ioanna Tachmazidou1, Daniel Suveges1, Josine L. Min2, Graham R. S. Ritchie1, Graham R. S. Ritchie3, Julia Steinberg1, Klaudia Walter1, Valentina Iotchkova1, Valentina Iotchkova4, Jeremy Schwartzentruber1, Jie Huang, Yasin Memari1, Shane A. McCarthy1, Andrew A Crawford, Cristina Bombieri5, Massimiliano Cocca6, Aliki-Eleni Farmaki7, Tom R. Gaunt2, Pekka Jousilahti8, Marjolein N. Kooijman9, Benjamin Lehne10, Giovanni Malerba5, Satu Männistö8, Angela Matchan1, Carolina Medina-Gomez9, Sarah Metrustry11, Abhishek Nag11, Ioanna Ntalla12, Lavinia Paternoster2, Nigel W. Rayner13, Nigel W. Rayner1, Nigel W. Rayner14, Cinzia Sala15, William R. Scott16, William R. Scott10, Hashem A. Shihab2, Lorraine Southam1, Lorraine Southam14, Beate St Pourcain2, Michela Traglia15, Katerina Trajanoska9, Gialuigi Zaza, Weihua Zhang10, Weihua Zhang16, María Soler Artigas17, Narinder Bansal18, Marianne Benn19, Marianne Benn20, Zhongsheng Chen21, Petr Danecek20, Petr Danecek19, Wei-Yu Lin18, Adam E. Locke22, Adam E. Locke21, Jian'an Luan18, Alisa K. Manning23, Alisa K. Manning24, Antonella Mulas25, Carlo Sidore, Anne Tybjærg-Hansen19, Anne Tybjærg-Hansen20, Anette Varbo19, Anette Varbo20, Magdalena Zoledziewska, Chris Finan26, Konstantinos Hatzikotoulas1, Audrey E. Hendricks27, Audrey E. Hendricks1, John P. Kemp2, Alireza Moayyeri11, Alireza Moayyeri26, Kalliope Panoutsopoulou1, Michal Szpak1, Scott Wilson11, Scott Wilson28, Scott Wilson29, Michael Boehnke21, Francesco Cucca25, Emanuele Di Angelantonio18, Emanuele Di Angelantonio30, Claudia Langenberg18, Cecilia M. Lindgren13, Cecilia M. Lindgren14, Mark I. McCarthy31, Mark I. McCarthy14, Mark I. McCarthy13, Andrew P. Morris32, Andrew P. Morris14, Andrew P. Morris33, Børge G. Nordestgaard20, Børge G. Nordestgaard19, Robert A. Scott18, Martin D. Tobin30, Martin D. Tobin17, Nicholas J. Wareham18, Paul Burton2, John C. Chambers16, John C. Chambers34, John C. Chambers10, George Davey Smith2, George Dedoussis7, Janine F. Felix9, Oscar H. Franco9, Giovanni Gambaro35, Paolo Gasparini6, Christopher J Hammond11, Albert Hofman9, Vincent W. V. Jaddoe9, Marcus E. Kleber36, Jaspal S. Kooner8, Jaspal S. Kooner34, Jaspal S. Kooner16, Markus Perola33, Markus Perola37, Markus Perola8, Caroline L Relton2, Susan M. Ring2, Fernando Rivadeneira9, Veikko Salomaa8, Tim D. Spector11, Oliver Stegle4, Daniela Toniolo15, André G. Uitterlinden9, Inês Barroso1, Inês Barroso18, Celia M. T. Greenwood38, Celia M. T. Greenwood39, John R. B. Perry18, John R. B. Perry11, Brian R. Walker3, Adam S. Butterworth30, Adam S. Butterworth18, Yali Xue1, Richard Durbin1, Kerrin S. Small11, Nicole Soranzo2, Nicholas J. Timpson2, Eleftheria Zeggini1 
TL;DR: This work applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals to report 106 genome-wide significant signals that have not been previously identified.
Abstract: Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum

121 citations

Journal ArticleDOI
Louise V. Wain1, Ahmad Vaez2, Rick Jansen2, Roby Joehanes3  +267 moreInstitutions (74)
TL;DR: 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources are identified and these robustly implicated genes may provide new leads for therapeutic innovation.
Abstract: Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project-based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.

121 citations

Journal ArticleDOI
Ying Wu1, Lindsay L. Waite, Anne U. Jackson2, Wayne Huey-Herng Sheu3, Wayne Huey-Herng Sheu4, Steven Buyske5, Devin Absher, Donna K. Arnett6, Eric Boerwinkle7, Lori L. Bonnycastle8, Cara L. Carty9, Iona Cheng10, Barbara Cochran7, Damien C. Croteau-Chonka1, Logan Dumitrescu11, Charles B. Eaton12, Nora Franceschini1, Xiuqing Guo13, Brian E. Henderson14, Lucia A. Hindorff8, Eric Kim13, Leena Kinnunen8, Pirjo Komulainen, Wen-Jane Lee, Loic Le Marchand10, Yi-Chieh Lin9, Jaana Lindström8, Oddgeir Lingaas-Holmen15, Sabrina L. Mitchell11, Narisu Narisu8, Jennifer G. Robinson16, Fred Schumacher14, Alena Stančáková17, Jouko Sundvall8, Yun Ju Sung18, Amy J. Swift8, Wen Chang Wang19, Lynne R. Wilkens10, Tom Wilsgaard20, Alicia M. Young9, Linda S. Adair1, Christie M. Ballantyne21, Petra Bůžková22, Aravinda Chakravarti23, Francis S. Collins8, David Duggan24, Alan B. Feranil25, Low-Tone Ho26, Low-Tone Ho4, Yi-Jen Hung3, Steven C. Hunt27, Kristian Hveem15, Jyh-Ming Jimmy Juang28, Antero Kesäniemi29, Johanna Kuusisto17, Markku Laakso17, Timo A. Lakka17, I-Te Lee4, Mark Leppert27, Tara C. Matise5, Leena Moilanen, Inger Njølstad20, Ulrike Peters22, Ulrike Peters9, Thomas Quertermous30, Rainer Rauramaa, Jerome I. Rotter13, Jouko Saramies, Jaakko Tuomilehto, Matti Uusitupa17, Tzung-Dau Wang28, Michael Boehnke2, Christopher A. Haiman14, Yii-Der Ida Chen13, Charles Kooperberg9, Themistocles L. Assimes30, Dana C. Crawford11, Chao A. Hsiung19, Kari E. North1, Karen L. Mohlke1 
TL;DR: The authors conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density LDL-C, respectively, in individuals of African American, East Asian, and European ancestry.
Abstract: Genome-wide association studies (GWAS) have identified ~100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1 × 10(-4) in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.

119 citations


Cited by
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TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

20,557 citations

Journal ArticleDOI
Giuseppe Mancia1, Robert Fagard, Krzysztof Narkiewicz, Josep Redon, Alberto Zanchetti, Michael Böhm, Thierry Christiaens, Renata Cifkova, Guy De Backer, Anna F. Dominiczak, Maurizio Galderisi, Diederick E. Grobbee, Tiny Jaarsma, Paulus Kirchhof, Sverre E. Kjeldsen, Stéphane Laurent, Athanasios J. Manolis, Peter M. Nilsson, Luis M. Ruilope, Roland E. Schmieder, Per Anton Sirnes, Peter Sleight, Margus Viigimaa, Bernard Waeber, Faiez Zannad, Michel Burnier, Ettore Ambrosioni, Mark Caufield, Antonio Coca, Michael H. Olsen, Costas Tsioufis, Philippe van de Borne, José Luis Zamorano, Stephan Achenbach, Helmut Baumgartner, Jeroen J. Bax, Héctor Bueno, Veronica Dean, Christi Deaton, Çetin Erol, Roberto Ferrari, David Hasdai, Arno W. Hoes, Juhani Knuuti, Philippe Kolh2, Patrizio Lancellotti, Aleš Linhart, Petros Nihoyannopoulos, Massimo F Piepoli, Piotr Ponikowski, Juan Tamargo, Michal Tendera, Adam Torbicki, William Wijns, Stephan Windecker, Denis Clement, Thierry C. Gillebert, Enrico Agabiti Rosei, Stefan D. Anker, Johann Bauersachs, Jana Brguljan Hitij, Mark J. Caulfield, Marc De Buyzere, Sabina De Geest, Geneviève Derumeaux, Serap Erdine, Csaba Farsang, Christian Funck-Brentano, Vjekoslav Gerc, Giuseppe Germanò, Stephan Gielen, Herman Haller, Jens Jordan, Thomas Kahan, Michel Komajda, Dragan Lovic, Heiko Mahrholdt, Jan Östergren, Gianfranco Parati, Joep Perk, Jorge Polónia, Bogdan A. Popescu, Zeljko Reiner, Lars Rydén, Yuriy Sirenko, Alice Stanton, Harry A.J. Struijker-Boudier, Charalambos Vlachopoulos, Massimo Volpe, David A. Wood 
TL;DR: In this article, a randomized controlled trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly people was presented. But the authors did not discuss the effect of the combination therapy in patients living with systolic hypertension.
Abstract: ABCD : Appropriate Blood pressure Control in Diabetes ABI : ankle–brachial index ABPM : ambulatory blood pressure monitoring ACCESS : Acute Candesartan Cilexetil Therapy in Stroke Survival ACCOMPLISH : Avoiding Cardiovascular Events in Combination Therapy in Patients Living with Systolic Hypertension ACCORD : Action to Control Cardiovascular Risk in Diabetes ACE : angiotensin-converting enzyme ACTIVE I : Atrial Fibrillation Clopidogrel Trial with Irbesartan for Prevention of Vascular Events ADVANCE : Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation AHEAD : Action for HEAlth in Diabetes ALLHAT : Antihypertensive and Lipid-Lowering Treatment to Prevent Heart ATtack ALTITUDE : ALiskiren Trial In Type 2 Diabetes Using Cardio-renal Endpoints ANTIPAF : ANgioTensin II Antagonist In Paroxysmal Atrial Fibrillation APOLLO : A Randomized Controlled Trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly People ARB : angiotensin receptor blocker ARIC : Atherosclerosis Risk In Communities ARR : aldosterone renin ratio ASCOT : Anglo-Scandinavian Cardiac Outcomes Trial ASCOT-LLA : Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm ASTRAL : Angioplasty and STenting for Renal Artery Lesions A-V : atrioventricular BB : beta-blocker BMI : body mass index BP : blood pressure BSA : body surface area CA : calcium antagonist CABG : coronary artery bypass graft CAPPP : CAPtopril Prevention Project CAPRAF : CAndesartan in the Prevention of Relapsing Atrial Fibrillation CHD : coronary heart disease CHHIPS : Controlling Hypertension and Hypertension Immediately Post-Stroke CKD : chronic kidney disease CKD-EPI : Chronic Kidney Disease—EPIdemiology collaboration CONVINCE : Controlled ONset Verapamil INvestigation of CV Endpoints CT : computed tomography CV : cardiovascular CVD : cardiovascular disease D : diuretic DASH : Dietary Approaches to Stop Hypertension DBP : diastolic blood pressure DCCT : Diabetes Control and Complications Study DIRECT : DIabetic REtinopathy Candesartan Trials DM : diabetes mellitus DPP-4 : dipeptidyl peptidase 4 EAS : European Atherosclerosis Society EASD : European Association for the Study of Diabetes ECG : electrocardiogram EF : ejection fraction eGFR : estimated glomerular filtration rate ELSA : European Lacidipine Study on Atherosclerosis ESC : European Society of Cardiology ESH : European Society of Hypertension ESRD : end-stage renal disease EXPLOR : Amlodipine–Valsartan Combination Decreases Central Systolic Blood Pressure more Effectively than the Amlodipine–Atenolol Combination FDA : U.S. Food and Drug Administration FEVER : Felodipine EVent Reduction study GISSI-AF : Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico-Atrial Fibrillation HbA1c : glycated haemoglobin HBPM : home blood pressure monitoring HOPE : Heart Outcomes Prevention Evaluation HOT : Hypertension Optimal Treatment HRT : hormone replacement therapy HT : hypertension HYVET : HYpertension in the Very Elderly Trial IMT : intima-media thickness I-PRESERVE : Irbesartan in Heart Failure with Preserved Systolic Function INTERHEART : Effect of Potentially Modifiable Risk Factors associated with Myocardial Infarction in 52 Countries INVEST : INternational VErapamil SR/T Trandolapril ISH : Isolated systolic hypertension JNC : Joint National Committee JUPITER : Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin LAVi : left atrial volume index LIFE : Losartan Intervention For Endpoint Reduction in Hypertensives LV : left ventricle/left ventricular LVH : left ventricular hypertrophy LVM : left ventricular mass MDRD : Modification of Diet in Renal Disease MRFIT : Multiple Risk Factor Intervention Trial MRI : magnetic resonance imaging NORDIL : The Nordic Diltiazem Intervention study OC : oral contraceptive OD : organ damage ONTARGET : ONgoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial PAD : peripheral artery disease PATHS : Prevention And Treatment of Hypertension Study PCI : percutaneous coronary intervention PPAR : peroxisome proliferator-activated receptor PREVEND : Prevention of REnal and Vascular ENdstage Disease PROFESS : Prevention Regimen for Effectively Avoiding Secondary Strokes PROGRESS : Perindopril Protection Against Recurrent Stroke Study PWV : pulse wave velocity QALY : Quality adjusted life years RAA : renin-angiotensin-aldosterone RAS : renin-angiotensin system RCT : randomized controlled trials RF : risk factor ROADMAP : Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention SBP : systolic blood pressure SCAST : Angiotensin-Receptor Blocker Candesartan for Treatment of Acute STroke SCOPE : Study on COgnition and Prognosis in the Elderly SCORE : Systematic COronary Risk Evaluation SHEP : Systolic Hypertension in the Elderly Program STOP : Swedish Trials in Old Patients with Hypertension STOP-2 : The second Swedish Trial in Old Patients with Hypertension SYSTCHINA : SYSTolic Hypertension in the Elderly: Chinese trial SYSTEUR : SYSTolic Hypertension in Europe TIA : transient ischaemic attack TOHP : Trials Of Hypertension Prevention TRANSCEND : Telmisartan Randomised AssessmeNt Study in ACE iNtolerant subjects with cardiovascular Disease UKPDS : United Kingdom Prospective Diabetes Study VADT : Veterans' Affairs Diabetes Trial VALUE : Valsartan Antihypertensive Long-term Use Evaluation WHO : World Health Organization ### 1.1 Principles The 2013 guidelines on hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology …

14,173 citations

Journal ArticleDOI
TL;DR: Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
Abstract: Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface. Availability: http://www.broad.mit.edu/mpg/haploview/ Contact: jcbarret@broad.mit.edu

13,862 citations

Journal ArticleDOI
TL;DR: Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets, including visualizing sliding window results integrated with available genome annotations in the UCSC browser.
Abstract: Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser. Availability: Freely available to academic users from: http://www.ub.edu/dnasp Contact: [email protected]

13,511 citations

Journal ArticleDOI
Adam Auton1, Gonçalo R. Abecasis2, David Altshuler3, Richard Durbin4  +514 moreInstitutions (90)
01 Oct 2015-Nature
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

12,661 citations