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A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing.

ABSTRACT: Noninvasive Prenatal Testing (NIPT) has advanced the detection of fetal chromosomal aneuploidy by analyzing cell-free DNA in peripheral maternal blood. The statistic Z-test that it utilizes, which measures the deviation of each chromosome dosage from its negative control, is now widely accepted in clinical practice. However, when a chromosome has loss and gain regions which offset each other in the z-score calculation, merely using the Z-test for the result tends to be erroneous. To improve the performance of NIPT in this aspect, a novel graphic-aided algorithm (gNIPT) that requires no extra experiment procedures is reported in this study. In addition to the Z-test, this method provides a detailed analysis of each chromosome by dividing each chromosome into multiple 2?Mb size windows, calculating the z-score and copy number variation of each window, and visualizing the z-scores for each chromosome in a line chart. Data from 13537 singleton pregnancy women were analyzed and compared using both the normal NIPT (nNIPT) analysis and the gNIPT method. The gNIPT method had significantly improved the overall positive predictive value (PPV) of nNIPT (88.14% vs. 68.00%, p = 0.0041) and the PPV for trisomy 21 (T21) detection (93.02% vs. 71.43%, p = 0.0037). There were no significant differences between gNIPT and nNIPT in PPV for trisomy 18 (T18) detection (88.89% vs. 63.64%, p = 0.1974) and in PPV for trisomy 13 (T13) detection (57.14% vs. 50.00%, p = 0.8004). One false-negative T18 case in nNIPT was detected by gNIPT, which demonstrates the potency of gNIPT in discerning chromosomes that have variation in multiple regions with an offsetting effect in z-score calculation. The gNIPT was also able to detect copy number variation (CNV) in chromosomes, and one case with pathogenic CNV was detected during the study. With no additional test requirement, gNIPT presents a reasonable solution in improving the accuracy of normal NIPT.

SUBMITTER: Zhu Q

PROVIDER: S-EPMC7397410 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing.

Zhu Qingwen Q Wang Jing J Xu Xiaoning X Zhou Shiying S Liao Zhengli Z Zhang Jun J Kong Lingyin L Liang Bo B Cheng Xiaoyan X

BioMed research international 20200725

Noninvasive Prenatal Testing (NIPT) has advanced the detection of fetal chromosomal aneuploidy by analyzing cell-free DNA in peripheral maternal blood. The statistic Z-test that it utilizes, which measures the deviation of each chromosome dosage from its negative control, is now widely accepted in clinical practice. However, when a chromosome has loss and gain regions which offset each other in the z-score calculation, merely using the Z-test for the result tends to be erron ...[more]

PMID: 32775425

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Project description:Introduction: Noninvasive prenatal testing (NIPT) using cell-free DNA (cfDNA) is typically carried out to screen for common fetal chromosomal anomalies, with the option to screen for a wider range of chromosomal changes (expanded NIPT) becoming increasingly available. However, little is known about pregnant patients' attitudes and preferences regarding expanded NIPT. Methods: To address this gap, we surveyed general-risk patients having first-tier cfDNA screening at a private prenatal clinic on their expectations for expanded NIPT. Patients were asked questions regarding their current pregnancy and previous pregnancy history, their opinions on fetal DNA screenings during pregnancy and incidental findings, information and opinions on financial resources for NIPT, as well as socio-cultural questions to determine patient demographics. Results: Of the 200 survey participants, the majority were educated, self-reported as white, had a higher than average income, and reported no aneuploidy risk factors. When asked what information they would like to receive from cfDNA screening, the vast majority of participants wanted all information available that could have an immediate impact on fetal health (88%) or an immediate impact on infant health from birth (82%). Many participants also wanted information that could have a future impact on the child's health or an immediate or future impact on the pregnant woman's own health. Most participants wanted information about the sex of fetus (86%) and common trisomies (71%), with almost half of participants desiring information about rare autosomal aneuploidies and/or all genetic information that may affect the baby. In addition, participants were found to be comfortable screening for conditions that are well-known, influence care during pregnancy, and are treatable. Finally, while most respondents either had insurance coverage for NIPT or were able to afford NIPT out of pocket, the majority of our participants felt that expanded NIPT should be either free for everyone or for those considered high risk. Discussion: Our findings suggest that with appropriate pre-test counseling, pregnant patients may choose NIPT for an expanding list of conditions.

Dataset Information

A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing.

Publications

A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing.

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