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      Variation in the Glucose Transporter gene SLC2A2 is associated with glycaemic response to metformin

      research-article
      1 , 2 , 3 , 4 , 1 , 5 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 19 , 20 , 21 , 22 , 1 , 23 , 23 , 24 , 25 , 2 , 26 , 27 , 28 , 29 , 30 , 31 , 28 , 19 , 15 , 16 , 32 , 33 , 1 , MetGen investigators, DPP investigators, ACCORD investigators, 1 , 11 , 12 , 12 , 37 , 38 , 10 , 10 , 39 , 13 , 40 , 3 , 4 , 8 , 41 , 6 , 5 , 42 , 43 , 44 , 1 , 18 , 45 , 46 , 2 , 29 , 1
      Nature genetics

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear 1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C-allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6x10 -14) greater metformin induced HbA1c reduction in 10,577 participants of European ancestry. rs8192675 is the top cis-eQTL for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. In obese individuals C-allele homozygotes at rs8192675 had a 0.33% (3.6mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes.This is about half the effect seen with the addition of a DPP-4 inhibitor, and equates to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.

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          Most cited references51

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Principal components analysis corrects for stratification in genome-wide association studies.

            Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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              Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)

              (1998)
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                1 July 2016
                08 August 2016
                September 2016
                08 February 2017
                : 48
                : 9
                : 1055-1059
                Affiliations
                [1 ]School of Medicine, University of Dundee, Dundee, UK
                [2 ]Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
                [3 ]Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
                [4 ]Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, The Netherlands
                [5 ]Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
                [6 ]Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
                [7 ]Department of General Practice, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                [8 ]Department of Epidemiology & Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands
                [9 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
                [10 ]department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
                [11 ]Latvian Genome Data Base (LGDB), Latvia
                [12 ]Latvian Biomedical Research and Study centre, Latvia
                [13 ]Bioinformatics Research Center, North Carolina State University, Raleigh NC, USA
                [14 ]Department of Statistics, North Carolina State University, Raleigh NC, USA
                [15 ]Treant Zorggroep, Location Bethesda, Hoogeveen, The Netherlands
                [16 ]Bethesda Diabetes Research Centre, Hoogeveen, The Netherlands
                [17 ]The Biostatistics Center, George Washington University, Rockville, MD, USA
                [18 ]Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital
                [19 ]Faculty of Medicine, Šafárik University, Košice, Slovakia
                [20 ]Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
                [21 ]Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, MI, USA
                [22 ]Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
                [23 ]Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
                [24 ]Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
                [25 ]RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City, Japan
                [26 ]Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
                [27 ]Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
                [28 ]Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
                [29 ]Institute for Human Genetics, University of California, San Francisco, San Francisco, California
                [30 ]Department of Urology, University of California, San Francisco, San Francisco, California
                [31 ]UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California
                [32 ] Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, The Netherlands
                [33 ]Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
                [37 ]Faculty of Medicine, University of Latvia, Riga, Latvia
                [38 ]Department of Endocrinology, Pauls Stradins Clinical University Hospital, Riga, Latvia
                [39 ]Inspectorate of Healthcare, 6401 DA Heerlen, the Netherlands
                [40 ]Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
                [41 ]Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
                [42 ]Wellcome Trust Centre for Human Genetics, University of Oxford
                [43 ]Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
                [44 ]Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
                [45 ]Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA 02142, USA
                [46 ]Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
                Author notes
                Correspondence should be addressed to E.R.P.( e.z.pearson@ 123456dundee.ac.uk ) or K.M.G.( kathy.giacomini@ 123456ucsf.edu )
                [48]

                These authors jointly directed this work.

                34-36: Full lists of members and affiliations appears in the Supplementary Note

                Article
                EMS69042
                10.1038/ng.3632
                5007158
                27500523
                c07155de-a09b-442a-9684-9cd56a00f875

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