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李卫芹, 黄育北, 刘慧坤, 张爽, 王蕾棽, 李楠, 李薇, 胡刚, 冷俊宏. GWAS发现SNP位点在妊娠期糖尿病女性产后2型糖尿病发病风险预测价值[J]. 中国公共卫生, 2019, 35(6): 698-704. DOI: 10.11847/zgggws1117960
引用本文: 李卫芹, 黄育北, 刘慧坤, 张爽, 王蕾棽, 李楠, 李薇, 胡刚, 冷俊宏. GWAS发现SNP位点在妊娠期糖尿病女性产后2型糖尿病发病风险预测价值[J]. 中国公共卫生, 2019, 35(6): 698-704. DOI: 10.11847/zgggws1117960
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Wei-qin LI, Yu-bei HUANG, Hui-kun LIU, . Significance of SNPs from previous genome-wide association study in prediction of postpartum diabetes among pregnant women with gestational diabetes mellitus[J]. Chinese Journal of Public Health, 2019, 35(6): 698-704. DOI: 10.11847/zgggws1117960
Citation: Wei-qin LI, Yu-bei HUANG, Hui-kun LIU, . Significance of SNPs from previous genome-wide association study in prediction of postpartum diabetes among pregnant women with gestational diabetes mellitus[J]. Chinese Journal of Public Health, 2019, 35(6): 698-704. DOI: 10.11847/zgggws1117960
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GWAS发现SNP位点在妊娠期糖尿病女性产后2型糖尿病发病风险预测价值

Significance of SNPs from previous genome-wide association study in prediction of postpartum diabetes among pregnant women with gestational diabetes mellitus

    摘要
  • 摘要:
    目的 从全基因组关联研究（GWAS）发现的与2型糖尿病（T2DM）发病风险相关的单核苷酸基因多态性（SNP）位点中，筛选出可用于预测妊娠期糖尿病（GDM）女性产后出现T2DM的SNP。
    方法 利用2009 — 2010年天津市妊娠期糖尿病随机干预试验研究基线收集的1 240名GDM女性（80名T2DM和1 160名非T2DM）血样，检测并分析40个GWAS发现的与T2DM发病相关的SNP位点。基于单个SNP的遗传风险解释比例，结合产后T2DM的发生情况，联合采用ROC曲线下面积（AUC）、整合区分指数（IDI）及再分类净优化指数（NRI）进行候选SNP筛选。
    结果 以遗传风险解释比例最大的SNP（rs10906115）为基础，依次纳入其他的SNP，共发现1个SNP可显著改善AUC（P = 0.019），8个SNP可显著改善IDI（P < 0.05），8个SNP可显著改善NRI（P < 0.05）。综合所有40个GWAS发现的SNP位点对AUC、IDI及NRI的改善情况，共有11个SNP（rs10906115，rs2779116，rs7034200，rs7041847，rs780094，rs5015480，rs11212617，rs831571，rs7944584，rs6815464，rs35767）可用于预测GDM女性产后发生T2DM的风险，同时11个SNP可累计解释5.5 %的GDM女性产后发生T2DM的遗传风险。
    结论 为避免资源浪费，建议在众多GWAS发现的与T2DM发病相关的SNP位点中，选择能够显著改善风险预测能力或风险再分类能力的11个SNP用于预测GDM女性产后发生T2DM的风险。

     

    Abstract:
    Objective To screen single-nucleotide polymorphisms (SNPs) loci from previous genome-wide association study (GWAS) related to postpartum type 2 diabetes mellitus (T2DM) among women with previous gestational diabetes mellitus (GDM).
    Methods Using the baseline data of a random intervention study on GDM in Tianjin city, we recruited 80 patients and 1 160 non-patients of postpartum T2DM from 1 240 women with GDM history between 2009 and 2010. The blood samples of the participants were collected for SNPs detection of 40 loci related to postpartum T2DM according to prvious GWAS. Based on the variance of T2DM genetic risk explained by each allele, the area under the curve of receiver operating characteristic (ROC), the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI) were used to select targeted SNPs predicting potential high-risk of postpartum T2DM for the women with GDM.
    Results Totally 39 models were developed by adding the SNPs loci one by one according to their genetically explained proportion of postpartum T2DM risk (PVi), and all the models included the SNP locus (rs10906115) with the highest estimated PVi. Analyses of the models revealed one SNP locus could significantly improve the AUC (P = 0.019), eight SNP loci could significantly improve the IDI, and eight SNP loci could significantly improve the NRI (P < 0.05 for all). Totally 11 SNP loci (rs10906115, rs2779116, rs7034200, rs7041847, rs780094, rs5015480, rs11212617, rs831571, rs7944584, rs6815464, and rs35767) were identified with potential application in predicting postpartum T2DM among the women with GDM history, which together could explained 5.5% of cumulative genetic risk of postpartum T2DM among the women with GDM history.
    Conclusion Among 40 GWAS-identified SNP loci associated with postpartum T2DM among women with GDM history, 11 SNP loci with significant improvement in risk prediction or risk reclassification could be used to predict potential risk of postpartum T2DM after GDM.

     

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