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Nilesh J. Samani

Bio: Nilesh J. Samani is an academic researcher from University of Leicester. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 149, co-authored 779 publications receiving 113545 citations. Previous affiliations of Nilesh J. Samani include University Hospitals of Leicester NHS Trust & Glenfield Hospital.


Papers
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Journal ArticleDOI
Aysu Okbay1, Jonathan P. Beauchamp2, Mark Alan Fontana3, James J. Lee4  +293 moreInstitutions (81)
26 May 2016-Nature
TL;DR: In this article, the results of a genome-wide association study (GWAS) for educational attainment were reported, showing that single-nucleotide polymorphisms associated with educational attainment disproportionately occur in genomic regions regulating gene expression in the fetal brain.
Abstract: Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

1,102 citations

Journal ArticleDOI
Sekar Kathiresan1, Benjamin F. Voight1, Shaun Purcell2, Kiran Musunuru1, Diego Ardissino, Pier Mannuccio Mannucci3, Sonia S. Anand4, James C. Engert5, Nilesh J. Samani6, Heribert Schunkert7, Jeanette Erdmann7, Muredach P. Reilly8, Daniel J. Rader8, Thomas M. Morgan9, John A. Spertus10, Monika Stoll11, Domenico Girelli12, Pascal P. McKeown13, Christopher Patterson13, David S. Siscovick14, Christopher J. O'Donnell15, Roberto Elosua, Leena Peltonen16, Veikko Salomaa17, Stephen M. Schwartz14, Olle Melander18, David Altshuler1, Pier Angelica Merlini, Carlo Berzuini19, Luisa Bernardinelli19, Flora Peyvandi3, Marco Tubaro, Patrizia Celli, Maurizio Ferrario, Raffaela Fetiveau, Nicola Marziliano, Giorgio Casari20, Michele Galli, Flavio Ribichini12, Marco Rossi, Francesco Bernardi21, Pietro Zonzin, Alberto Piazza22, Jean Yee14, Yechiel Friedlander23, Jaume Marrugat, Gavin Lucas, Isaac Subirana, Joan Sala24, Rafael Ramos, James B. Meigs1, Gordon H. Williams1, David M. Nathan1, Calum A. MacRae1, Aki S. Havulinna17, Göran Berglund18, Joel N. Hirschhorn1, Rosanna Asselta, Stefano Duga, Marta Spreafico25, Mark J. Daly1, James Nemesh2, Joshua M. Korn1, Steven A. McCarroll1, Aarti Surti2, Candace Guiducci2, Lauren Gianniny2, Daniel B. Mirel2, Melissa Parkin2, Noël P. Burtt2, Stacey Gabriel2, John R. Thompson6, Peter S. Braund6, Benjamin J. Wright6, Anthony J. Balmforth26, Stephen G. Ball26, Alistair S. Hall26, Patrick Linsel-Nitschke7, Wolfgang Lieb7, Andreas Ziegler7, Inke R. König7, Christian Hengstenberg27, Marcus Fischer27, Klaus Stark27, Anika Grosshennig7, Michael Preuss7, H-Erich Wichmann28, Stefan Schreiber29, Willem H. Ouwehand19, Panos Deloukas30, Michael Scholz, François Cambien31, Mingyao Li8, Zhen Chen8, Robert L. Wilensky8, William H. Matthai8, Atif Qasim8, Hakon Hakonarson8, Joe Devaney32, Mary-Susan Burnett32, Augusto D. Pichard32, Kenneth M. Kent32, Lowell F. Satler32, Joseph M. Lindsay32, Ron Waksman32, Stephen E. Epstein32, Thomas Scheffold, Klaus Berger11, Andreas Huge11, Nicola Martinelli12, Oliviero Olivieri12, Roberto Corrocher12, Hilma Holm33, Gudmar Thorleifsson33, Unnur Thorsteinsdottir34, Kari Stefansson34, Ron Do5, Changchun Xie4, David S. Siscovick14 
TL;DR: SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with my Cardiovascular Infarction risk.
Abstract: We conducted a genome-wide association study testing single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) for association with early-onset myocardial infarction in 2,967 cases and 3,075 controls We carried out replication in an independent sample with an effective sample size of up to 19,492 SNPs at nine loci reached genome-wide significance: three are newly identified (21q22 near MRPS6-SLC5A3-KCNE2, 6p24 in PHACTR1 and 2q33 in WDR12) and six replicated prior observations1, 2, 3, 4 (9p21, 1p13 near CELSR2-PSRC1-SORT1, 10q11 near CXCL12, 1q41 in MIA3, 19p13 near LDLR and 1p32 near PCSK9) We tested 554 common copy number polymorphisms (>1% allele frequency) and none met the pre-specified threshold for replication (P < 10-3) We identified 8,065 rare CNVs but did not detect a greater CNV burden in cases compared to controls, in genes compared to the genome as a whole, or at any individual locus SNPs at nine loci were reproducibly associated with myocardial infarction, but tests of common and rare CNVs failed to identify additional associations with myocardial infarction risk

1,092 citations

Journal ArticleDOI
TL;DR: It is concluded that variants of ALOX5AP are involved in the pathogenesis of both myocardial infarction and stroke by increasing leukotriene production and inflammation in the arterial wall.
Abstract: We mapped a gene predisposing to myocardial infarction to a locus on chromosome 13q12-13. A four-marker single-nucleotide polymorphism (SNP) haplotype in this locus spanning the gene ALOX5AP encoding 5-lipoxygenase activating protein (FLAP) is associated with a two times greater risk of myocardial infarction in Iceland. This haplotype also confers almost two times greater risk of stroke. Another ALOX5AP haplotype is associated with myocardial infarction in individuals from the UK. Stimulated neutrophils from individuals with myocardial infarction produce more leukotriene B4, a key product in the 5-lipoxygenase pathway, than do neutrophils from controls, and this difference is largely attributed to cells from males who carry the at-risk haplotype. We conclude that variants of ALOX5AP are involved in the pathogenesis of both myocardial infarction and stroke by increasing leukotriene production and inflammation in the arterial wall.

948 citations

Journal ArticleDOI
01 Sep 2011-Nature
TL;DR: A comprehensive analysis of genotype-dependent metabolic phenotypes using a genome-wide association study with non-targeted metabolomics to identify genetic loci associated with blood metabolite concentrations and generates many new hypotheses for biomedical and pharmaceutical research.
Abstract: Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

937 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 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
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

Journal ArticleDOI
Paul Burton1, David Clayton2, Lon R. Cardon, Nicholas John Craddock3  +192 moreInstitutions (4)
07 Jun 2007-Nature
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

9,244 citations

Journal ArticleDOI
Monkol Lek, Konrad J. Karczewski1, Konrad J. Karczewski2, Eric Vallabh Minikel2, Eric Vallabh Minikel1, Kaitlin E. Samocha, Eric Banks1, Timothy Fennell1, Anne H. O’Donnell-Luria2, Anne H. O’Donnell-Luria3, Anne H. O’Donnell-Luria1, James S. Ware, Andrew J. Hill4, Andrew J. Hill2, Andrew J. Hill1, Beryl B. Cummings1, Beryl B. Cummings2, Taru Tukiainen2, Taru Tukiainen1, Daniel P. Birnbaum1, Jack A. Kosmicki, Laramie E. Duncan1, Laramie E. Duncan2, Karol Estrada2, Karol Estrada1, Fengmei Zhao2, Fengmei Zhao1, James Zou1, Emma Pierce-Hoffman2, Emma Pierce-Hoffman1, Joanne Berghout5, David Neil Cooper6, Nicole A. Deflaux7, Mark A. DePristo1, Ron Do, Jason Flannick2, Jason Flannick1, Menachem Fromer, Laura D. Gauthier1, Jackie Goldstein2, Jackie Goldstein1, Namrata Gupta1, Daniel P. Howrigan2, Daniel P. Howrigan1, Adam Kiezun1, Mitja I. Kurki1, Mitja I. Kurki2, Ami Levy Moonshine1, Pradeep Natarajan, Lorena Orozco, Gina M. Peloso1, Gina M. Peloso2, Ryan Poplin1, Manuel A. Rivas1, Valentin Ruano-Rubio1, Samuel A. Rose1, Douglas M. Ruderfer8, Khalid Shakir1, Peter D. Stenson6, Christine Stevens1, Brett Thomas1, Brett Thomas2, Grace Tiao1, María Teresa Tusié-Luna, Ben Weisburd1, Hong-Hee Won9, Dongmei Yu, David Altshuler1, David Altshuler10, Diego Ardissino, Michael Boehnke11, John Danesh12, Stacey Donnelly1, Roberto Elosua, Jose C. Florez2, Jose C. Florez1, Stacey Gabriel1, Gad Getz1, Gad Getz2, Stephen J. Glatt13, Christina M. Hultman14, Sekar Kathiresan, Markku Laakso15, Steven A. McCarroll1, Steven A. McCarroll2, Mark I. McCarthy16, Mark I. McCarthy17, Dermot P.B. McGovern18, Ruth McPherson19, Benjamin M. Neale1, Benjamin M. Neale2, Aarno Palotie, Shaun Purcell8, Danish Saleheen20, Jeremiah M. Scharf, Pamela Sklar, Patrick F. Sullivan21, Patrick F. Sullivan14, Jaakko Tuomilehto22, Ming T. Tsuang23, Hugh Watkins16, Hugh Watkins17, James G. Wilson24, Mark J. Daly1, Mark J. Daly2, Daniel G. MacArthur2, Daniel G. MacArthur1 
18 Aug 2016-Nature
TL;DR: The aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC) provides direct evidence for the presence of widespread mutational recurrence.
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

8,758 citations