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
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University Medical Center Groningen1, University of Tartu2, University of Exeter3, Greifswald University Hospital4, National Institute for Health and Welfare5, University of Washington6, Churchill Hospital7, Technische Universität München8, University of Queensland9, University Medical Center Utrecht10, Erasmus University Rotterdam11, Leiden University Medical Center12, National Institutes of Health13, University College London14, University of Auckland15, QIMR Berghofer Medical Research Institute16, Wellcome Trust Centre for Human Genetics17, University of Düsseldorf18, Ludwig Maximilian University of Munich19, University of Groningen20, Group Health Cooperative21, Wellcome Trust Sanger Institute22, University of Helsinki23
TL;DR: Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
Abstract: Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3' UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
1,627 citations
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Maastricht University Medical Centre1, deCODE genetics2, Utrecht University3, University of California, Los Angeles4, University of Oslo5, University of Bonn6, Ludwig Maximilian University of Munich7, Copenhagen University Hospital8, Wellcome Trust Sanger Institute9, Aarhus University Hospital10, Aarhus University11, University of Iceland12, University of Helsinki13, Bispebjerg Hospital14, Glostrup Hospital15, Heidelberg University16, Semmelweis University17, University of Verona18, Radboud University Nijmegen Medical Centre19, Russian Academy20, University of Valencia21, King's College London22, Royal Cornhill Hospital23, Duke University24, University of Santiago de Compostela25, Hospital General Universitario Gregorio Marañón26, Karolinska Institutet27, Hammersmith Hospital28, GlaxoSmithKline29, Sichuan University30
TL;DR: Findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.
Abstract: Schizophrenia is a complex disorder, caused by both genetic and environmental factors and their interactions. Research on pathogenesis has traditionally focused on neurotransmitter systems in the brain, particularly those involving dopamine. Schizophrenia has been considered a separate disease for over a century, but in the absence of clear biological markers, diagnosis has historically been based on signs and symptoms. A fundamental message emerging from genome-wide association studies of copy number variations (CNVs) associated with the disease is that its genetic basis does not necessarily conform to classical nosological disease boundaries. Certain CNVs confer not only high relative risk of schizophrenia but also of other psychiatric disorders. The structural variations associated with schizophrenia can involve several genes and the phenotypic syndromes, or the 'genomic disorders', have not yet been characterized. Single nucleotide polymorphism (SNP)-based genome-wide association studies with the potential to implicate individual genes in complex diseases may reveal underlying biological pathways. Here we combined SNP data from several large genome-wide scans and followed up the most significant association signals. We found significant association with several markers spanning the major histocompatibility complex (MHC) region on chromosome 6p21.3-22.1, a marker located upstream of the neurogranin gene (NRGN) on 11q24.2 and a marker in intron four of transcription factor 4 (TCF4) on 18q21.2. Our findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.
1,625 citations
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TL;DR: A tool to predict the effect that newly discovered genomic variants have on known transcripts is indispensible in prioritizing and categorizing such variants in Ensembl, and a web-based tool (the SNP Effect Predictor) and API interface can now functionally annotate variants in all EnsembL and Ensemble Genomes supported species.
Abstract: Summary: A tool to predict the effect that newly discovered genomic variants have on known transcripts is indispensible in prioritizing and categorizing such variants. In Ensembl, a web-based tool (the SNP Effect Predictor) and API interface can now functionally annotate variants in all Ensembl and Ensembl Genomes supported species.
Availability: The Ensembl SNP Effect Predictor can be accessed via the Ensembl website at http://www.ensembl.org/. The Ensembl API (http://www.ensembl.org/info/docs/api/api_installation.html for installation instructions) is open source software.
Contact:wm2@ebi.ac.uk; fiona@ebi.ac.uk
Supplementary information:Supplementary data are available at Bioinformatics online.
1,617 citations
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TL;DR: Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity.
Abstract: The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates' recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins, implemented in Python and C and supported on Linux and Mac OS X.
1,608 citations
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Wellcome Trust Sanger Institute1, Wellcome Trust2, Katholieke Universiteit Leuven3, University of East Anglia4, Guy's and St Thomas' NHS Foundation Trust5, Academia Sinica6, Singapore General Hospital7, Netherlands Cancer Institute8, University of Queensland9, University of British Columbia10, Erasmus University Rotterdam11, Harvard University12, University of Cambridge13, Claude Bernard University Lyon 114, Curie Institute15, Royal Brisbane and Women's Hospital16, University of Amsterdam17, Université libre de Bruxelles18, The Breast Cancer Research Foundation19, Cambridge University Hospitals NHS Foundation Trust20, Oslo University Hospital21
TL;DR: Strong correlations between mutation number, age at which cancer was diagnosed and cancer histological grade are found, and multiple mutational signatures are observed, including one present in about ten per cent of tumours characterized by numerous mutations of cytosine at TpC dinucleotides.
Abstract: All cancers carry somatic mutations in their genomes. A subset, known as driver mutations, confer clonal selective advantage on cancer cells and are causally implicated in oncogenesis, and the remainder are passenger mutations. The driver mutations and mutational processes operative in breast cancer have not yet been comprehensively explored. Here we examine the genomes of 100 tumours for somatic copy number changes and mutations in the coding exons of protein-coding genes. The number of somatic mutations varied markedly between individual tumours. We found strong correlations between mutation number, age at which cancer was diagnosed and cancer histological grade, and observed multiple mutational signatures, including one present in about ten per cent of tumours characterized by numerous mutations of cytosine at TpC dinucleotides. Driver mutations were identified in several new cancer genes including AKT2, ARID1B, CASP8, CDKN1B, MAP3K1, MAP3K13, NCOR1, SMARCD1 and TBX3. Among the 100 tumours, we found driver mutations in at least 40 cancer genes and 73 different combinations of mutated cancer genes. The results highlight the substantial genetic diversity underlying this common disease.
1,606 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 |