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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TL;DR: VBQTL is presented, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors and is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternatives.
Abstract: Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.
468 citations
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TL;DR: This work reports the first single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling and validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.
Abstract: Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.
467 citations
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McGill University1, University of Bristol2, University of Queensland3, Garvan Institute of Medical Research4, Imperial College London5, New York University6, McGill University Health Centre7, Erasmus University Rotterdam8, University of Rochester9, Anschutz Medical Campus10, University of Cambridge11, University of Oxford12, University of Southampton13, University Hospital Southampton NHS Foundation Trust14, University of Ioannina15, Brown University16, University of Gothenburg17, Children's Mercy Hospital18, Wellcome Trust Sanger Institute19
TL;DR: This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.
Abstract: Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.
466 citations
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TL;DR: An evolutionary tree is proposed for these populations, rooted on Yersinia pseudotuberculosis, which invokes microevolution over millennia, during which enzootic pestoides isolates evolved and led to populations that are more frequently associated with human disease.
Abstract: The association of historical plague pandemics with Yersinia pestis remains controversial, partly because the evolutionary history of this largely monomorphic bacterium was unknown. The microevolution of Y. pestis was therefore investigated by three different multilocus molecular methods, targeting genomewide synonymous SNPs, variation in number of tandem repeats, and insertion of IS100 insertion elements. Eight populations were recognized by the three methods, and we propose an evolutionary tree for these populations, rooted on Yersinia pseudotuberculosis. The tree invokes microevolution over millennia, during which enzootic pestoides isolates evolved. This initial phase was followed by a binary split 6,500 years ago, which led to populations that are more frequently associated with human disease. These populations do not correspond directly to classical biovars that are based on phenotypic properties. Thus, we recommend that henceforth groupings should be based on molecular signatures. The age of Y. pestis inferred here is compatible with the dates of historical pandemic plague. However, it is premature to infer an association between any modern molecular grouping and a particular pandemic wave that occurred before the 20th century.
465 citations
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TL;DR: OpenMS 2.0 is presented, a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data.
Abstract: High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.
465 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 |