Suppr超能文献

确定基因组畸变的起源可提高无创产前检测对22q11.2缺失综合征的阳性预测值。

Determining the origin of genome aberrations improves the positive predictive value of NIPT for 22q11.2 deletion syndrome.

作者信息

Xiang Jiale, Sun Xiangzhong, Peng Jiguang, Zhang Hongfu, Shen Jiankun, Li Jingrou, Li Hongyu, Hu Lanping, Zhang Jingjing, Zhou Shihao, Xu Sihu, Yang Yun, He Jun, Peng Zhiyu

机构信息

BGI Genomics, Shenzhen, 518083, China.

Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, China.

出版信息

Sci Rep. 2025 Jul 9;15(1):24755. doi: 10.1038/s41598-025-10446-8.

Abstract

Non-invasive prenatal testing (NIPT) has been endorsed by the American College of Medical Genetics and Genomics as the preferred method for screening fetal 22q11.2 deletion syndrome (22q11.2 DS). Maternal genomic aberrations represent a significant source of false positives in NIPT, and there are currently no solutions that effectively address this challenge. We have devised an innovative NIPT bioinformatics pipeline designed to discern the origins of copy number variations (CNVs). Then, we recruited a cohort of 39cases of 22q11.2 DS to validate the effectiveness of our methodology. Follow-up tests including amniocentesis and genome sequencing of maternal leukocytes were conducted. Leveraging a dataset of over 900 CNVs, we developed a new pipeline that classifies CNVs into those of fetal, maternal, and maternal-fetal origin based on NIPT data. The use of our pipeline led to a notable increase in the positive predictive value of NIPT for detecting 22q11.2 DS from 87% (34/39) to 94% (34/36). Furthermore, our approach has the potential to reduce the number of invasive tests by 8% (3/39). Our innovative and reliable bioinformatics pipeline has enabled the accurate differentiation of CNV origin into fetal, maternal, and maternal-fetal categories. Incorporating this pipeline into the analytical workflow could reduce false positives in NIPT results and minimize the need for invasive prenatal diagnoses.

摘要

无创产前检测(NIPT)已被美国医学遗传学与基因组学学会认可为筛查胎儿22q11.2缺失综合征(22q11.2 DS)的首选方法。母体基因组畸变是NIPT中假阳性的重要来源,目前尚无有效应对这一挑战的解决方案。我们设计了一种创新的NIPT生物信息学流程,旨在辨别拷贝数变异(CNV)的来源。然后,我们招募了39例22q11.2 DS病例的队列,以验证我们方法的有效性。进行了包括羊水穿刺和母体白细胞基因组测序在内的后续检测。利用一个包含900多个CNV的数据集,我们开发了一种新的流程,可根据NIPT数据将CNV分为胎儿来源、母体来源和母胎来源。使用我们的流程使NIPT检测22q11.2 DS的阳性预测值从87%(34/39)显著提高到94%(34/36)。此外,我们的方法有可能将侵入性检测的数量减少8%(3/39)。我们创新且可靠的生物信息学流程能够准确地将CNV来源区分为胎儿、母体和母胎类别。将此流程纳入分析工作流程可减少NIPT结果中的假阳性,并最大限度地减少侵入性产前诊断的需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ca2/12241613/c52f714de07f/41598_2025_10446_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验