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      PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations

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          Abstract

          Summary

          PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.

          Availability and implementation

          PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.

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          Most cited references2

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          Modeling sample variables with an Experimental Factor Ontology

          Motivation: Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions; however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users. Results: We present the Experimental Factor Ontology, designed to meet cross-domain, application focused use cases for gene expression data. We describe our methodology and open source tools used to create the ontology. These include tools for creating ontology mappings, ontology views, detecting ontology changes and using ontologies in interfaces to enhance querying. The application of reference ontologies to data is a key problem, and this work presents guidelines on how community ontologies can be presented in an application ontology in a data-driven way. Availability: http://www.ebi.ac.uk/efo Contact: malone@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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            Unraveling the polygenic architecture of complex traits using blood eQTL meta analysis

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              Author and article information

              Contributors
              Role: Associate Editor
              Journal
              Bioinformatics
              Bioinformatics
              bioinformatics
              Bioinformatics
              Oxford University Press
              1367-4803
              1367-4811
              15 November 2019
              24 June 2019
              24 June 2019
              : 35
              : 22
              : 4851-4853
              Affiliations
              [1 ] MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge CB1 8RN, UK
              [2 ] MRC Biostatistics Unit, University of Cambridge , Cambridge CB2 0SR, UK
              [3 ] Wellcome Trust Sanger Institute , Hinxton CB10 1SA, UK
              [4 ] NIHR Blood and Transplant Research Unit, Department of Public Health and Primary Care, University of Cambridge , Cambridge CB1 8RN, UK
              [5 ] MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol , Bristol BS8 2BN, UK
              Author notes
              To whom correspondence should be addressed. E-mail: asb38@ 123456medschl.cam.ac.uk
              Article
              btz469
              10.1093/bioinformatics/btz469
              6853652
              31233103
              15c0a78d-027f-4f3c-8c3d-74416b040dcf
              © The Author(s) 2019. Published by Oxford University Press.

              This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

              History
              : 20 December 2018
              : 01 May 2019
              : 19 June 2019
              Page count
              Pages: 3
              Funding
              Funded by: UK Medical Research Council
              Award ID: G0800270
              Award ID: MR/L003120/1
              Funded by: British Heart Foundation 10.13039/501100000274
              Award ID: SP/09/002
              Award ID: RG/13/13/30194
              Award ID: RG/18/13/33946
              Funded by: Pfizer 10.13039/100004319
              Award ID: G73632
              Funded by: European Research Council 10.13039/100010663
              Award ID: 268834
              Funded by: European Commission Framework Programme 7
              Award ID: HEALTH-F2-2012-279233
              Funded by: National Institute for Health Research; and Health Data Research UK
              Funded by: NHS
              Funded by: NIHR 10.13039/100006662
              Categories
              Applications Notes
              Databases and Ontologies

              Bioinformatics & Computational biology
              Bioinformatics & Computational biology

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