Institution
Wellcome Trust Sanger Institute
Nonprofit•Cambridge, United Kingdom•
About: Wellcome Trust Sanger Institute is a nonprofit organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Genome. The organization has 4009 authors who have published 9671 publications receiving 1224479 citations.
Topics: Population, Genome, Gene, Genome-wide association study, Genomics
Papers published on a yearly basis
Papers
More filters
••
Thomas W. Winkler1, Anne E. Justice2, Mariaelisa Graff2, Llilda Barata3 +435 more•Institutions (106)
TL;DR: In this paper, the authors performed meta-analyses of 114 studies with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium.
Abstract: Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.
584 citations
••
TL;DR: A system wherein the inhibitor units of the peptidase inhibitors are assigned to 48 families on the basis of similarities detectable at the level of amino acid sequence, and a simple system of nomenclature is introduced for reference to each clan, family and inhibitor.
Abstract: The proteins that inhibit peptidases are of great importance in medicine and biotechnology, but there has never been a comprehensive system of classification for them. Some of the terminology currently in use is potentially confusing. In the hope of facilitating the exchange, storage and retrieval of information about this important group of proteins, we now describe a system wherein the inhibitor units of the peptidase inhibitors are assigned to 48 families on the basis of similarities detectable at the level of amino acid sequence. Then, on the basis of three-dimensional structures, 31 of the families are assigned to 26 clans. A simple system of nomenclature is introduced for reference to each clan, family and inhibitor. We briefly discuss the specificities and mechanisms of the interactions of the inhibitors in the various families with their target enzymes. The system of families and clans of inhibitors described has been implemented in the MEROPS peptidase database (http://merops.sanger.ac.uk/), and this will provide a mechanism for updating it as new information becomes available.
582 citations
••
Wellcome Trust Sanger Institute1, National Autonomous University of Mexico2, University of Buenos Aires3, Iowa State University4, University of Zurich5, University of the Republic6, University of Oxford7, Beijing Institute of Genomics8, University of Toronto9, University of Würzburg10, Natural History Museum11, National University of Ireland, Galway12, University of Calgary13, McGill University14
TL;DR: An analysis of tapeworm genome sequences using the human-infective species Echinococcus multilocularis, E. granulosus, Taenia solium and the laboratory model Hymenolepis microstoma offers insights into the evolution of parasitism and identifies new potential drug targets.
Abstract: Tapeworms (Cestoda) cause neglected diseases that can be fatal and are difficult to treat, owing to inefficient drugs. Here we present an analysis of tapeworm genome sequences using the human-infective species Echinococcus multilocularis, E. granulosus, Taenia solium and the laboratory model Hymenolepis microstoma as examples. The 115- to 141-megabase genomes offer insights into the evolution of parasitism. Synteny is maintained with distantly related blood flukes but we find extreme losses of genes and pathways that are ubiquitous in other animals, including 34 homeobox families and several determinants of stem cell fate. Tapeworms have specialized detoxification pathways, metabolism that is finely tuned to rely on nutrients scavenged from their hosts, and species-specific expansions of non-canonical heat shock proteins and families of known antigens. We identify new potential drug targets, including some on which existing pharmaceuticals may act. The genomes provide a rich resource to underpin the development of urgently needed treatments and control.
581 citations
••
University Hospital of Lausanne1, University of Lausanne2, Swiss Institute of Bioinformatics3, University of Greifswald4, Western General Hospital5, Prevention Institute6, King's College London7, University of Cambridge8, Wellcome Trust Centre for Human Genetics9, University of Oxford10, Uppsala University11, QIMR Berghofer Medical Research Institute12, Erasmus University Rotterdam13, University of Edinburgh14, University of Glasgow15, Innsbruck Medical University16, National Institute for Health Research17, GlaxoSmithKline18, Queen Mary University of London19, University of Leicester20, National Institutes of Health21, Ludwig Maximilian University of Munich22, Wellcome Trust Sanger Institute23
TL;DR: In this article, a meta-analysis of genome-wide association scans from 14 studies with 28,141 participants of European descent was conducted, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genomewide significance, five of which are novel.
Abstract: Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-9)). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-54), respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.
581 citations
••
Broad Institute1, North Carolina State University2, University of Oxford3, Stanford University4, University of Rochester5, Mississippi State University6, City University of New York7, College of Charleston8, Harvard University9, University of Colorado Denver10, Indiana University11, Children's Hospital Oakland Research Institute12, University of California, Santa Cruz13, University of New Mexico14, Smithsonian Institution15, Wellcome Trust Sanger Institute16, Michigan State University17, University of Georgia18, Boston University19, University of North Carolina at Chapel Hill20, Uppsala University21
TL;DR: Comparative gene analysis shows that amniote egg proteins have evolved significantly more rapidly than other proteins, and an anole phylogeny resolves basal branches to illuminate the history of their repeated adaptive radiations.
Abstract: The evolution of the amniotic egg was one of the great evolutionary innovations in the history of life, freeing vertebrates from an obligatory connection to water and thus permitting the conquest of terrestrial environments 1 . Among amniotes, genome sequences are available for mammals and birds 2–4 , but not for non-avian
580 citations
Authors
Showing all 4058 results
Name | H-index | Papers | Citations |
---|---|---|---|
Nicholas J. Wareham | 212 | 1657 | 204896 |
Gonçalo R. Abecasis | 179 | 595 | 230323 |
Panos Deloukas | 162 | 410 | 154018 |
Michael R. Stratton | 161 | 443 | 142586 |
David W. Johnson | 160 | 2714 | 140778 |
Michael John Owen | 160 | 1110 | 135795 |
Naveed Sattar | 155 | 1326 | 116368 |
Robert E. W. Hancock | 152 | 775 | 88481 |
Julian Parkhill | 149 | 759 | 104736 |
Nilesh J. Samani | 149 | 779 | 113545 |
Michael Conlon O'Donovan | 142 | 736 | 118857 |
Jian Yang | 142 | 1818 | 111166 |
Christof Koch | 141 | 712 | 105221 |
Andrew G. Clark | 140 | 823 | 123333 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |