Evidence map›Paper›PMID 40634435›Full record

ArticleScientific reports2025

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

Jiale Xiang, Xiangzhong Sun, Jiguang Peng, Hongfu Zhang, Jiankun Shen, Jingrou Li, Hongyu Li, Lanping Hu, Jingjing Zhang, Shihao Zhou and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Jiale Xiang *BGI Genomics, Shenzhen, 518083, China.
Xiangzhong Sun *BGI Genomics, Shenzhen, 518083, China.
Jiguang Peng *BGI Genomics, Shenzhen, 518083, China.
Hongfu ZhangBGI Genomics, Shenzhen, 518083, China.
Jiankun ShenBGI Genomics, Shenzhen, 518083, China.
Jingrou LiBGI Genomics, Shenzhen, 518083, China.
Hongyu LiHunan 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.
Lanping HuHunan 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.
Jingjing ZhangHunan 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.
Shihao ZhouHunan 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.
Sihu XuBGI Genomics, Shenzhen, 518083, China.
Yun YangBGI Genomics, Shenzhen, 518083, China.
Jun HeHunan 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.
Zhiyu PengBGI Genomics, Shenzhen, 518083, China. pengzhiyu@bgi.com.

Funding

National Key R&D Project of China 2023YFC3402504
6 · The paper itself

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.

Indexed as

DiGeorge SyndromeNoninvasive Prenatal TestingPrenatal DiagnosisAdultComputational BiologyDNA Copy Number VariationsFemaleHumansMalePredictive Value of TestsPregnancy22q11.2 deletion syndromeFalse positiveMaternal CNVNIPTPositive predictive value

Identifiers

PMID40634435
PMCPMC12241613

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.