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      Polymorphisms in Four Genes ( KCNQ1 rs151290, KLF14 rs972283, GCKR rs780094 and MTNR1B rs10830963) and Their Correlation with Type 2 Diabetes Mellitus in Han Chinese in Henan Province, China

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          Abstract

          Genetic variants at KCNQ1 rs151290, KLF14 rs972283, GCKR rs780094 and MTNR1B rs10830963 have been associated with type 2 diabetes mellitus (T2DM), but the results are contradictory in Chinese populations. The aim of the present study was to investigate the association of these four SNPs with T2DM in a large population of Han Chinese at Henan province, China. Seven-hundred-thirty-six patients with T2DM (cases) and Seven-hundred-sixty-eight healthy glucose-tolerant controls were genotyped for KCNQ1 rs151290, KLF14 rs972283, GCKR rs780094 and MTNR1B rs10830963. The association of genetic variants in these four genes with T2DM was analyzed using multivariate logistic regression. Genotypes and allele distributions of KCNQ1 rs151290 were significantly different between the cases and controls ( p < 0.05). The AC and CC genotypes and the combined AC + CC genotype of rs151290 in KCNQ1 were associated with increases risk of T2DM before (OR = 1.482, 95% CI = 1.062–2.069; p = 0.021; OR = 1.544, 95% CI = 1.097–2.172, p = 0.013; and OR = 1.509, 95% CI = 1.097–2.077, p = 0.011, respectively) and after (OR = 1.539, 95% CI = 1.015–2.332, p = 0.042; OR = 1.641, 95% CI = 1.070–2.516, p = 0.023; and OR = 1.582, 95% CI = 1.061–2.358, p = 0.024; respectively) adjustment for sex, age, anthropometric measurements, biochemical indexes, smoking and alcohol consumption. Consistent with results of genotype analysis, the C allele of rs151290 in KCNQ1 was also associated with increased risk of T2DM (OR = 1.166, 95% CI = 1.004–1.355, p = 0.045). No associations between genetic variants of KLF14 rs972283, GCKR rs780094 or MTNR1B rs10830963 and T2DM were detected. The AC and CC genotypes and the C allele of rs151290 in KCNQ1 may be risk factors for T2DM in Han Chinese in Henan province.

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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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            Diagnosis and classification of diabetes mellitus.

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              Genetic structure of the Han Chinese population revealed by genome-wide SNP variation.

              Population stratification is a potential problem for genome-wide association studies (GWAS), confounding results and causing spurious associations. Hence, understanding how allele frequencies vary across geographic regions or among subpopulations is an important prelude to analyzing GWAS data. Using over 350,000 genome-wide autosomal SNPs in over 6000 Han Chinese samples from ten provinces of China, our study revealed a one-dimensional "north-south" population structure and a close correlation between geography and the genetic structure of the Han Chinese. The north-south population structure is consistent with the historical migration pattern of the Han Chinese population. Metropolitan cities in China were, however, more diffused "outliers," probably because of the impact of modern migration of peoples. At a very local scale within the Guangdong province, we observed evidence of population structure among dialect groups, probably on account of endogamy within these dialects. Via simulation, we show that empirical levels of population structure observed across modern China can cause spurious associations in GWAS if not properly handled. In the Han Chinese, geographic matching is a good proxy for genetic matching, particularly in validation and candidate-gene studies in which population stratification cannot be directly accessed and accounted for because of the lack of genome-wide data, with the exception of the metropolitan cities, where geographical location is no longer a good indicator of ancestral origin. Our findings are important for designing GWAS in the Chinese population, an activity that is expected to intensify greatly in the near future.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                26 February 2016
                March 2016
                : 13
                : 3
                : 260
                Affiliations
                [1 ]Department of Preventive Medicine, School of Medicine, Shenzhen University, Shenzhen 518060, China; wxl@ 123456szu.edu.cn (X.W.); lijianna@ 123456szu.edu.cn (J.L.); daisysg@ 123456szu.edu.cn (Z.L.); lixiong1@ 123456email.szu.edu.cn (X.L.); hyx413459261@ 123456sina.com (Y.H.); lxp2005@ 123456szu.edu.cn (X.-P.L.); hud@ 123456szu.edu.cn (D.H.)
                [2 ]Department of Traditional Chinese Medicine Prevention, Preventive Medicine Research Evaluation Center, Henan University of Traditional Chinese Medicine, Zhengzhou 450001, China; wangjinjin510@ 123456163.com
                [3 ]Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou 450001, China; lilinlin@ 123456zzu.edu.cn (L.L.); zhaiyujiamodi@ 123456163.com (Y.Z.); ryc12@ 123456sina.com (Y.R.); youhaifei1987@ 123456163.com (H.Y.); wangby95@ 123456163.com (B.W.)
                [4 ]Division of Neurogenetics, Center for Neurological Diseases and Cancer, University Graduate School of Medicine, Nagoya 4668550, Japan; ohnok@ 123456med.nagoya-u.ac.jp
                Author notes
                [* ]Correspondence: gao_kp@ 123456szu.edu.cn (K.G.); tjwcj2005@ 123456126.com (C.W.); Tel.: +86-755-86671951 (K.G. & C.W.); Fax: +86-755-86671906 (K.G. & C.W.)
                [†]

                The authors contributed equally to this work

                Article
                ijerph-13-00260
                10.3390/ijerph13030260
                4808923
                26927145
                3f7fc81d-d4f5-4661-bfd1-f9ba804afe80
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

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

                History
                : 15 September 2015
                : 16 February 2016
                Categories
                Article

                Public health
                single nucleotide polymorphism,kcnq1,type 2 diabetes,risk factors
                Public health
                single nucleotide polymorphism, kcnq1, type 2 diabetes, risk factors

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