Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.
Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.
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DOI：
10.1371/journal.pone.0083093
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Ma RC
中科院分区：
文献类型：
--
作者：
Tam CH;Ho JS;Wang Y;Lam VK;Lee HM;Jiang G;Lau ES;Kong AP;Fan X;Woo JL;Tsui SK;Ng MC;So WY;Chan JC;Ma RC
近期全基因组关联研究（GWAS）确定了70多个2型糖尿病（T2D）的新基因位点，其中一些在亚洲人群中得到了广泛重复验证。在这项研究中，我们在中国人群中调查了它们对T2D的个体效应和联合效应。
我们在中国人群中选取了在亚洲人群中已验证的与β细胞功能相关的2型糖尿病基因中的14个单核苷酸多态性（SNPs），并对5882例中国2型糖尿病患者和2569例健康对照进行了基因分型。在加性遗传模型下，通过累加风险等位基因的数量或根据每个SNP的效应大小加权来计算综合遗传评分（CGS）。我们分别通过逻辑回归分析和线性回归分析来检验与2型糖尿病和数量性状的关联。通过受试者工作特征（ROC）分析和净重分类改善（NRI）评估CGS对预测2型糖尿病风险的贡献。
我们观察到IGF2BP2、WFS1、CDKAL1、SLC30A8、CDKN2A/B、HHEX、TCF7L2和KCNQ1与2型糖尿病风险存在一致且显著的关联（8.5×10⁻¹⁸ < P < 8.5×10⁻³），以及NOTCH2、JAZF1、KCNJ11和HNF1B与2型糖尿病风险存在名义上的关联（0.05 < P < 0.1），其优势比范围从1.07到2.09。这8个显著的SNPs在增加2型糖尿病风险、空腹血糖和胰岛素治疗的使用以及降低HOMA - β、体重指数（BMI）、腰围和2型糖尿病诊断年龄方面表现出联合效应。添加CGS略微增加了曲线下面积（AUC）（2%），但使用NRI方法时，未加权和加权的CGS分别显著提高了对2型糖尿病风险的预测能力11.2%和11.3%（P < 0.001）。
在中国人群中，使用8个SNPs的综合遗传评分适度但显著地提高了其预测2型糖尿病的判别能力，超出了临床风险因素（性别、年龄和BMI）所具有的预测能力。
Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population. We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI). We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10−18<P<8.5×10−3), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001). In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).
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评估18种常见遗传变异的综合遗传变异对2型糖尿病风险的综合影响。
影响因子：
7.7
作者：
Lango H;UK Type 2 Diabetes Genetics Consortium;Palmer CN;Morris AD;Zeggini E;Hattersley AT;McCarthy MI;Frayling TM;Weedon MN
通讯作者：
Weedon MN
影响因子：
7.7
作者：
McCarthy, Mark I.;Hattersley, Andrew T.
通讯作者：
Hattersley, Andrew T.
全基因组关联研究的荟萃分析确定了东亚人 2 型糖尿病的 8 个新位点
影响因子：
30.8
作者：
Cho, Yoon Shin;Chen, Chien-Hsiun;Hu, Cheng;Long, Jirong;Ong, Rick Twee Hee;Sim, Xueling;Takeuchi, Fumihiko;Wu, Ying;Go, Min Jin;Yamauchi, Toshimasa;Chang, Yi-Cheng;Kwak, Soo Heon;Ma, Ronald C. W.;Yamamoto, Ken;Adair, Linda S.;Aung, Tin;Cai, Qiuyin;Chang, Li-Ching;Chen, Yuan-Tsong;Gao, Yutang;Hu, Frank B.;Kim, Hyung-Lae;Kim, Sangsoo;Kim, Young Jin;Lee, Jeannette Jen-Mai;Lee, Nanette R.;Li, Yun;Liu, Jian Jun;Lu, Wei;Nakamura, Jiro;Nakashima, Eitaro;Ng, Daniel Peng-Keat;Tay, Wan Ting;Tsai, Fuu-Jen;Wong, Tien Yin;Yokota, Mitsuhiro;Zheng, Wei;Zhang, Rong;Wang, Congrong;So, Wing Yee;Ohnaka, Keizo;Ikegami, Hiroshi;Hara, Kazuo;Cho, Young Min;Cho, Nam H.;Chang, Tien-Jyun;Bao, Yuqian;Hedman, Asa K.;Morris, Andrew P.;McCarthy, Mark I.;Takayanagi, Ryoichi;Park, Kyong Soo;Jia, Weiping;Chuang, Lee-Ming;Chan, Juliana C. N.;Maeda, Shiro;Kadowaki, Takashi;Lee, Jong-Young;Wu, Jer-Yuarn;Teo, Yik Ying;Tai, E. Shyong;Shu, Xiao Ou;Mohlke, Karen L.;Kato, Norihiro;Han, Bok-Ghee;Seielstad, Mark
通讯作者：
Seielstad, Mark
影响因子：
8.2
作者：
Gupta, V.;Vinay, D. G.;Rafiq, S.;Kranthikumar, M. V.;Janipalli, C. S.;Giambartolomei, C.;Evans, D. M.;Mani, K. R.;Sandeep, M. N.;Taylor, A. E.;Kinra, S.;Sullivan, R. M.;Bowen, L.;Timpson, N. J.;Smith, G. D.;Dudbridge, F.;Prabhakaran, D.;Ben-Shlomo, Y.;Reddy, K. S.;Ebrahim, S.;Chandak, G. R.
通讯作者：
Chandak, G. R.
影响因子：
--
作者：
Cauchi S;Nead KT;Choquet H;Horber F;Potoczna N;Balkau B;Marre M;Charpentier G;Froguel P;Meyre D
通讯作者：
Meyre D