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      GlobAl Distribution of GEnetic Traits (GADGET) web server: polygenic trait scores worldwide

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          Abstract

          Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

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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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            Genomics for the world.

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              Next generation disparities in human genomics: concerns and remedies.

              Studies of human genetics, particularly genome-wide association studies (GWAS), have concentrated heavily on European populations, with individuals of African ancestry rarely represented. Reasons for this include the distribution of biomedical funding and the increased population structure and reduced linkage disequilibrium in African populations. Currently, few GWAS findings have clinical utility and, therefore, the field has not yet contributed to health-care disparities. As human genomics research progresses towards the whole-genome sequencing era, however, more clinically relevant results are likely to be discovered. As we discuss here, to avoid the genetics community contributing to healthcare disparities, it is important to adopt measures to ensure that populations of diverse ancestry are included in genomic studies, and that no major population groups are excluded.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2018
                18 May 2018
                18 May 2018
                : 46
                : Web Server issue
                : W121-W126
                Affiliations
                [1 ]School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, GA 30332, USA
                [2 ]IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA 30332, USA
                [3 ]PanAmerican Bioinformatics Institute, Cali, Valle del Cauca, Colombia
                [4 ]Biomedical Research Institute, Faculty of Health, Universidad Libre-Seccional Cali. Cali, Valle del Cauca, Colombia
                Author notes
                To whom correspondence should be addressed. Tel: +1 404 385 2224; Email: king.jordan@ 123456biology.gatech.edu
                Author information
                http://orcid.org/0000-0002-2055-9392
                Article
                gky415
                10.1093/nar/gky415
                6031022
                29788182
                e3a410ff-0371-4512-b11e-c8aba45230ee
                © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 03 May 2018
                : 01 May 2018
                : 31 January 2018
                Page count
                Pages: 6
                Funding
                Funded by: IHRC-Georgia Tech Applied Bioinformatics Laboratory
                Award ID: RF383
                Categories
                Web Server Issue

                Genetics
                Genetics

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