Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association
Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association
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DOI：
10.1038/jhg.2009.17
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发表时间：
2009-04-01
影响因子：
3.5
通讯作者：
Kasuga, Masato
中科院分区：
文献类型：
--
作者：
Miyake, Kazuaki;Yang, Woosung;Kasuga, Masato
疾病状态的预测是遗传学研究的最重要目标之一。为了选择与2型糖尿病相关的强有力证据的基因，我们通过对两个独立样本组中的样本进行基因分型，验证了我们早期研究中提取的7个候选基因座的相关性。然而，除了KCNQ 1，其余7个位点的关联都没有被复制。然后，我们选择了11个基因：KCNQ 1、TCF 7 L2、CDKAL 1、CDKN 2A/B、IGF 2 BP 2、SLC 30 A8、HHEX、GCKR、HNF 1 B、KCNJ 11和PPARG，这些基因与糖尿病的相关性已经在文献或本研究中得到报道并在日本人群中重复。由于11个基因座中没有任何一对基因-基因相互作用的证据，我们通过将11个基因的危险等位基因的数量以及年龄、性别和体重指数作为自变量，使用logistic回归分析构建了疾病的预测模型。累积风险评估显示，增加一个风险等位基因导致该疾病的几率平均增加1.29（95%CI =1.25-1.33，P=5.4 x 10（-53））。受试者工作特征曲线下面积（预测模型功效的估计值）为0.72，从而表明我们的2型糖尿病预测模型可能不那么有用，但具有一定的价值。从额外的风险位点的数据合并是最有可能增加预测能力。Journal of Human Genetics（2009）54，236-241; doi：10.1038/jhg.2009.17; 2009年2月27日在线发表
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. Journal of Human Genetics (2009) 54, 236-241; doi: 10.1038/jhg.2009.17; published online 27 February 2009