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基于 11 个具有强烈关联证据的基因构建日本人群 2 型糖尿病预测模型。

Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.

机构信息

Division of Diabetes, Metabolism and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.

出版信息

J Hum Genet. 2009 Apr;54(4):236-41. doi: 10.1038/jhg.2009.17. Epub 2009 Feb 27.

Abstract

Prediction of the disease status is one of the most important objectives of genetic studies. To select the genes with strong evidence of the association with type 2 diabetes mellitus, we validated the associations of the seven candidate loci extracted in our earlier study by genotyping the samples in two independent sample panels. However, except for KCNQ1, the association of none of the remaining seven loci was replicated. We then selected 11 genes, KCNQ1, TCF7L2, CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, HHEX, GCKR, HNF1B, KCNJ11 and PPARG, whose associations with diabetes have already been reported and replicated either in the literature or in this study in the Japanese population. As no evidence of the gene-gene interaction for any pair of the 11 loci was shown, we constructed a prediction model for the disease using the logistic regression analysis by incorporating the number of the risk alleles for the 11 genes, as well as age, sex and body mass index as independent variables. Cumulative risk assessment showed that the addition of one risk allele resulted in an average increase in the odds for the disease of 1.29 (95% CI=1.25-1.33, P=5.4 x 10(-53)). The area under the receiver operating characteristic curve, an estimate of the power of the prediction model, was 0.72, thereby indicating that our prediction model for type 2 diabetes may not be so useful but has some value. Incorporation of data from additional risk loci is most likely to increase the predictive power.

摘要

疾病状态预测是遗传研究的最重要目标之一。为了选择与 2 型糖尿病具有强关联证据的基因,我们通过对两个独立样本组中的样本进行基因分型,验证了我们之前研究中提取的七个候选基因座的关联。然而,除了 KCNQ1 之外,其余七个基因座的关联均未得到复制。然后,我们选择了 11 个基因,包括 KCNQ1、TCF7L2、CDKAL1、CDKN2A/B、IGF2BP2、SLC30A8、HHEX、GCKR、HNF1B、KCNJ11 和 PPARG,这些基因与糖尿病的关联已经在文献或本研究的日本人群中得到了报道和复制。由于没有证据表明这 11 个基因中的任何两个基因之间存在基因-基因相互作用,我们使用逻辑回归分析构建了一个疾病预测模型,将 11 个基因的风险等位基因数量以及年龄、性别和体重指数作为独立变量纳入其中。累积风险评估显示,增加一个风险等位基因会使疾病的几率平均增加 1.29 倍(95%CI=1.25-1.33,P=5.4x10(-53))。预测模型的接收者操作特征曲线下面积,即预测模型的效能估计值为 0.72,这表明我们的 2 型糖尿病预测模型可能不是那么有用,但具有一定的价值。纳入更多风险基因座的数据可能会提高预测能力。

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