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首页> 外文期刊>Scientific reports. >Statistical Approach to Decreasing the Error Rate of Noninvasive Prenatal Aneuploid Detection caused by Maternal Copy Number Variation
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Statistical Approach to Decreasing the Error Rate of Noninvasive Prenatal Aneuploid Detection caused by Maternal Copy Number Variation

机译:降低由孕产妇拷贝数变异引起的非侵入性产前非整倍体检测错误率的统计方法

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Analyses of cell-free fetal DNA (cff-DNA) from maternal plasma using massively parallel sequencing enable the noninvasive detection of feto-placental chromosome aneuploidy; this technique has been widely used in clinics worldwide. Noninvasive prenatal tests (NIPT) based on cff-DNA have achieved very high accuracy; however, they suffer from maternal copy-number variations (CNV) that may cause false positives and false negatives. In this study, we developed an algorithm to exclude the effect of maternal CNV and refined the Z-score that is used to determine fetal aneuploidy. The simulation results showed that the algorithm is robust against variations of fetal concentration and maternal CNV size. We also introduced a method based on the discrepancy between feto-placental concentrations to help reduce the false-positive ratio. A total of 6615 pregnant women were enrolled in a prospective study to validate the accuracy of our method. All 106 fetuses with T21, 20 with T18, and three with T13 were tested using our method, with sensitivity of 100% and specificity of 99.97%. In the results, two cases with maternal duplications in chromosome 21, which were falsely predicted as T21 by the previous NIPT method, were correctly classified as normal by our algorithm, which demonstrated the effectiveness of our approach.
机译:使用大规模平行测序分析母体血浆中的无细胞胎儿DNA(cff-DNA),可以无创检测胎儿-胎盘染色体的非整倍性;该技术已在全世界的诊所中广泛使用。基于cff-DNA的非侵入性产前检查(NIPT)取得了很高的准确性;但是,它们患有母体拷贝数变异(CNV),可能导致假阳性和假阴性。在这项研究中,我们开发了一种算法来排除母体CNV的影响,并完善了用于确定胎儿非整倍性的Z评分。仿真结果表明,该算法对胎儿浓度和孕妇CNV大小的变化具有鲁棒性。我们还介绍了一种基于胎盘胎盘浓度差异的方法,以帮助降低假阳性率。总共6615名孕妇参加了一项前瞻性研究,以验证我们方法的准确性。使用我们的方法测试了106例T21胎儿,20例T18胎儿和3例T13胎儿,其敏感性为100%,特异性为99.97%。结果,通过我们的算法正确地将2例在21号染色体上具有母亲重复的孕妇错误地预测为T21,这些错误被我们的算法正确分类为正常,证明了我们方法的有效性。

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