Evidence map›Paper›PMID 33546747›Full record

ArticleGenome medicine2021

Noninvasive prenatal testing of α-thalassemia and β-thalassemia through population-based parental haplotyping.

Chao Chen, Ru Li, Jun Sun, Yaping Zhu, Lu Jiang, Jian Li, Fang Fu, Junhui Wan, Fengyu Guo, Xiaoying An and 13 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
11.0field-weighted citation impact, top 1% of its field
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

14 citing papers in PubMed, 45 citations in OpenAlex.

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  6. Identification of maternalAnnals of medicine · 2025
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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

23 authors at 3 institutions in 2 countries.

Chao Chen *BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Ru Li *Department of Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Jun Sun *BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Yaping ZhuBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Lu JiangBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Jian LiDepartment of Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Fang FuDepartment of Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Junhui WanDepartment of Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China.
Fengyu GuoBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Xiaoying AnBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Yaoshen WangBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Linlin FanBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Yan SunBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Xiaosen GuoBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Sumin ZhaoBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Wanyang WangBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Fanwei ZengBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Yun YangBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Peixiang NiBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Yi DingBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Bixia XiangBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China.
Zhiyu PengBGI Genomics, BGI-Shenzhen, Shenzhen, 518083, China. pengzhiyu@bgi.com.ORCID 0000-0003-2349-750X
Can LiaoDepartment of Prenatal Diagnostic Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, China. canliao6008@163.com.
BGI Group (China) · CNGuangzhou Medical University · CNGuangzhou Women and Children Medical Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNoninvasive prenatal testing (NIPT) of recessive monogenic diseases depends heavily on knowing the correct parental haplotypes. However, the currently used family-based haplotyping method requires pedigrees, and molecular haplotyping is highly challenging due to its high cost, long turnaround time, and complexity. Here, we proposed a new two-step approach, population-based haplotyping-NIPT (PBH-NIPT), using α-thalassemia and β-thalassemia as prototypes.

methodsFirst, we deduced parental haplotypes with Beagle 4.0 with training on a large retrospective carrier screening dataset (4356 thalassemia carrier screening-positive cases). Second, we inferred fetal haplotypes using a parental haplotype-assisted hidden Markov model (HMM) and the Viterbi algorithm.

resultsWith this approach, we enrolled 59 couples at risk of having a fetus with thalassemia and successfully inferred 94.1% (111/118) of fetal alleles. We confirmed these alleles by invasive prenatal diagnosis, with 99.1% (110/111) accuracy (95% CI, 95.1-100%).

conclusionsThese results demonstrate that PBH-NIPT is a sensitive, fast, and inexpensive strategy for NIPT of thalassemia.

Indexed as

Noninvasive Prenatal TestingParentsalpha-Thalassemiabeta-ThalassemiaGenetics, PopulationHaplotypesHumansSample SizeHaplotypesNIPTPopulation-based haplotypingRecessive monogenic diseasesα-Thalassemiaβ-Thalassemia

Identifiers

PMID33546747
PMCPMC7866698
OpenAlexW3128723078

What OpenQuestion holds

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LicenceCC BY
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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.