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      Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

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          Abstract

          During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.

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          Most cited references56

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          Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

          By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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            FCM: The fuzzy c-means clustering algorithm

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              The personal and clinical utility of polygenic risk scores

              Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
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                Author and article information

                Journal
                Endocr Rev
                Endocr. Rev
                edrv
                Endocrine Reviews
                Endocrine Society (Washington, DC )
                0163-769X
                1945-7189
                December 2019
                19 July 2019
                19 July 2019
                : 40
                : 6
                : 1500-1520
                Affiliations
                [1 ] Diabetes Unit, Massachusetts General Hospital , Boston, Massachusetts
                [2 ] Center for Genomic Medicine, Massachusetts General Hospital , Boston, Massachusetts
                [3 ] Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard , Cambridge, Massachusetts
                [4 ] Department of Medicine, Harvard Medical School , Boston, Massachusetts
                [5 ] Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford , Headington, Oxford, United Kingdom
                [6 ] Wellcome Centre for Human Genetics, University of Oxford , Oxford, United Kingdom
                [7 ] Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital , Oxford, United Kingdom
                Author notes
                Correspondence and Reprint Requests:  Mark I. McCarthy, MD, Genentech, 1 DNA Way, South San Francisco, California 94080. E-mail: mccarthy.mark@ 123456gene.com .
                Author information
                http://orcid.org/0000-0003-3824-9162
                http://orcid.org/0000-0002-4393-0510
                http://orcid.org/0000-0002-1730-9325
                http://orcid.org/0000-0001-5585-3420
                Article
                201900088
                10.1210/er.2019-00088
                6760294
                31322649
                c01465e7-c644-43df-a353-c502eed9f966

                This article has been published under the terms of the Creative Commons Attribution License (CC BY; https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright for this article is retained by the author(s).

                History
                : 26 April 2019
                : 08 July 2019
                Page count
                Pages: 21
                Funding
                Funded by: Wellcome Trust 10.13039/100010269
                Award ID: 090532
                Award ID: 106130
                Award ID: 098381
                Award ID: 203141
                Award ID: 212259
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062
                Award ID: u01-DK105535
                Award ID: R01 DK105154
                Award ID: U01 DK105554
                Award ID: K24 DK110550
                Award ID: U54 DK118612
                Award ID: K23 1K23DK114551
                Funded by: National Institute for Health Research 10.13039/501100000272
                Award ID: NF-SI-0617-10090
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01 GM117163
                Categories
                Reviews
                Diabetes, Pancreatic and Gastrointestinal Hormones

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