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      SNP-Based Genetic Risk Score Modeling Suggests No Increased Genetic Susceptibility of the Roma Population to Type 2 Diabetes Mellitus

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          Abstract

          Background: In a previous survey, an elevated fasting glucose level (FG) and/or known type 2 diabetes mellitus (T2DM) were significantly more frequent in the Roma population than in the Hungarian general population. We assessed whether the distribution of 16 single nucleotide polymorphisms (SNPs) with unequivocal effects on the development of T2DM contributes to this higher prevalence. Methods: Genetic risk scores, unweighted (GRS) and weighted (wGRS), were computed and compared between the study populations. Associations between GRSs and FG levels and T2DM status were investigated in separate and combined study populations. Results: The Hungarian general population carried a greater genetic risk for the development of T2DM (GRS General = 15.38 ± 2.70 vs. GRS Roma = 14.80 ± 2.68, p < 0.001; wGRS General = 1.41 ± 0.32 vs. wGRS Roma = 1.36 ± 0.31, p < 0.001). In the combined population models, GRSs and wGRSs showed significant associations with elevated FG ( p < 0.001) and T2DM ( p < 0.001) after adjusting for ethnicity, age, sex, body mass index (BMI), high-density Lipoprotein Cholesterol (HDL-C), and triglyceride (TG). In these models, the effect of ethnicity was relatively strong on both outcomes (FG levels: βethnicity = 0.918, p < 0.001; T2DM status: OR ethnicity = 2.484, p < 0.001). Conclusions: The higher prevalence of elevated FG and/or T2DM among Roma does not seem to be directly linked to their increased genetic load but rather to their environmental/cultural attributes. Interventions targeting T2DM prevention among Roma should focus on harmful environmental exposures related to their unhealthy lifestyle.

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          Most cited references 61

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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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            Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci.

            To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom ∼50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with ∼2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 × 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 × 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 × 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 × 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 × 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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              The genetics of type 2 diabetes: what have we learned from GWAS?

              Type 2 diabetes mellitus has been at the forefront of human diseases and phenotypes studied by new genetic analyses. Thanks to genome-wide association studies, we have made substantial progress in elucidating the genetic basis of type 2 diabetes. This review summarizes the concept, history, and recent discoveries produced by genome-wide association studies for type 2 diabetes and glycemic traits, with a focus on the key notions we have gleaned from these efforts. Genome-wide association findings have illustrated novel pathways, pointed toward fundamental biology, confirmed prior epidemiological observations, drawn attention to the role of β-cell dysfunction in type 2 diabetes, explained ~10% of disease heritability, tempered our expectations with regard to their use in clinical prediction, and provided possible targets for pharmacotherapy and pharmacogenetic clinical trials. We can apply these lessons to future investigation so as to improve our understanding of the genetic basis of type 2 diabetes. © 2010 New York Academy of Sciences.
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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                19 November 2019
                November 2019
                : 10
                : 11
                Affiliations
                [1 ]MTA−DE Public Health Research Group of the Hungarian Academy of Sciences, Public Health Research Institute, University of Debrecen, 4028 Debrecen, Hungary; nardos.abebe@ 123456sph.unideb.hu (N.A.W.); piko.peter@ 123456sph.unideb.hu (P.P.)
                [2 ]Doctorial School of Health Sciences, University of Debrecen, 4028 Debrecen, Hungary
                [3 ]Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, 4028 Debrecen, Hungary; fiatal.szilvia@ 123456sph.unideb.hu (S.F.); sandor.janos@ 123456sph.unideb.hu (J.S.)
                [4 ]WHO Collaborating Centre on Vulnerability and Health, University of Debrecen, 4028 Debrecen, Hungary
                [5 ]Department of Health Visitor Methodology and Public Health, Faculty of Health, University of Debrecen, 4400 Nyíregyháza, Hungary; kosa.zsigmond@ 123456foh.unideb.hu
                Author notes
                [* ]Correspondence: adany.roza@ 123456sph.unideb.hu ; Tel: +36-5251-2764
                Article
                genes-10-00942
                10.3390/genes10110942
                6896051
                31752367
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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