无创基因检测在唐氏综合征筛查中的应用
Application of non-invasive gene detection in screening of Down's syndrome
相元翠 1李红娟 1刘文枝 1王雅莉1
作者信息
- 1. 郑州大学附属郑州中心医院妇产科 河南郑州 450000
- 折叠
摘要
目的:比较无创基因检测和血清学筛查两种方法对唐氏综合征的筛查阳性率,确定无创基因检测在唐氏综合征产前筛查中的应用价值.方法:对1293例单胎孕妇进行血清三联指标检测,应用配套软件计算唐氏综合征风险;1 056例孕妇均于孕11～ 36周时抽取5 ml外周血,于深圳华大基因行血浆胎儿游离DNA筛查,得出胎儿患唐氏综合征的风险,并追踪胎儿的情况,对两种检测方法均出现高风险者行染色体核型分析进行确诊.结果:唐氏综合征血清检测组高危孕妇51例,阳性率为3.94％,假阳性率3.80％;无创基因检测组检测阳性孕妇14例,阳性率为1.33％,唐氏综合征检出率为100.00％,假阳性率0.00％.两种方法用于唐氏综合征产前筛查的差异有统计学意义(P＜0.05).结论:无创基因检测与血清学相比较准确率高,适用于唐氏综合征的产前筛查.
Abstract
Objective:To compare the positive rates of non-invasive gene detection and serological detection in screening of Down's syndrome,explore the application value of non-invasive gene detection in prenatal screening of Down's syndrome.Methods:Serological triple-index detection was conducted among 1 293 single fetal pregnant women,bundled software was used to calculate the risk of Down's syndrome.Five milliliters of peripheral blood were obtained from 1 056 pregnant women during 11-36 gestational weeks,fetal fasting DNA screening was performed in Shenzhen Huada Gene Research Institute,then the risk of Down's syndrome in fetuses was obtained.The fetuses were tracked,the high risk cases found by the two methods were diagnosed definitely by chromosomal karyotyping.Results:In serological detection group,51 high risk pregnant women were found,the positive rate was 3.94％ and the false positive rate was 3.80％ ; in non-invasive gene detection group,14 high risk pregnant women were found,the positive rate was 1.33％,the detection rate of Down's syndrome was 100.00％,the false positive rate was 0.00％.There was statistically significant difference between the two methods (P ＜ 0.05).Conclusion:Compared with serological detection,the accurate rate of non-invasive gene detection is high,which is suitable for prenatal screening of Down's syndrome.
关键词
唐氏综合征/胎儿游离DNA/无创基因检测/产前诊断Key words
Down's syndrome/Fetal free DNA/Non-invasive gene detection/Prenatal diagnosis引用本文复制引用
基金项目
郑州市科技攻关项目(121PPTGG504-6)
出版年
2014
NETL
NSTL
维普
万方数据