Adaptive parameter of standard deviation enhances the power of noninvasive prenatal screens

Traditional <i>Z-</i>test methods during noninvasive prenatal screens (NIPS) use the fixed parameter of standard deviation (SD), which ignores the influence of actual sequencing read counts of a sample on the results. The aim of this study is to eliminate the influence of the sequencing depth of individual samples on the results and enhance the power of NIPS. In this study, we propose an improved NIPS method, which calculates the SD in the <i>Z-</i>score process adaptively according to the actual read count of the test sample. Our approach obtained the SD linear fitting function along with the read count with a large number of reference samples, in which SD and read count fit well. The effectiveness of our enhanced NIPS method was evaluated on three common trisomy syndromes and five recurrent CNV syndromes with 3219 and 6592 samples based on whole genome sequencing of maternal peripheral blood. A total of 3,219 pregnant samples have been used for validating the proposed method on detecting fetal trisomy syndromes (T13, T18, and T21), in which eight false negative (FN) samples have been corrected as true positive (TP) and eight false positive (FP) samples have been fixed as true negative (TN) with our proposed adaptive-SD method. Another 6592 samples were used to compare the two methods on detecting five recurrent fetal copy number variation (CNV) syndromes, in which the FP samples have decreased from 99 to 39. Our adaptive-SD NIPS method shows more power on detecting both trisomy syndromes and five recurrent CNVs in the pregnant samples with diverse read counts. Besides, our proposed method contributes to lower FP and FN samples than the traditional <i>Z-</i>test method in NIPS. Our results show that our enhanced NIPS methods are effective in detecting both abnormal fetal trisomy syndromes and recurrent CNV syndromes in pregnant women.

传统无创产前筛查（noninvasive prenatal screens, NIPS）中的Z检验方法均采用固定标准差（standard deviation, SD）参数，忽略了样本实际测序读段计数对结果的影响。
本研究旨在消除单个样本测序深度对结果的干扰，提升NIPS的检测效能。本研究提出一种改进型NIPS方法，在Z评分（Z-score）流程中根据待测样本的实际读段计数自适应计算标准差。我们通过大量参考样本构建了标准差与读段计数的线性拟合函数，二者拟合度极佳。
本研究基于母体外周血全基因组测序数据，分别使用3219例与6592例样本，针对三种常见三体综合征及五种复发性染色体拷贝数变异（copy number variation, CNV）综合征，评估了改进后NIPS方法的有效性。其中3219例妊娠样本用于验证所提方法在胎儿三体综合征（T13、T18、T21）检测中的性能：采用自适应标准差方法后，8例假阴性（false negative, FN）样本被修正为真阳性（true positive, TP），8例假阳性（false positive, FP）样本被修正为真阴性（true negative, TN）。另有6592例样本用于对比两种方法在五种复发性胎儿CNV综合征检测中的表现，假阳性样本数量从99例降至39例。
相较于传统NIPS的Z检验方法，我们提出的自适应标准差NIPS方法在不同读段计数的妊娠样本中，对三体综合征与五种复发性CNV均展现出更优异的检测效能，且能有效降低假阳性与假阴性样本数量。
本研究结果证实，改进后的NIPS方法可有效检出妊娠女性胎儿的三体综合征与复发性CNV异常。