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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 references 56

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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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              Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

              Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
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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 .
                Article
                201900088
                10.1210/er.2019-00088
                6760294
                31322649

                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).

                Page count
                Pages: 21
                Product
                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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