Evidence map›Paper›PMID 33632221›Full record

ArticleBMC medical genomics2021

Integrated CNV-seq, karyotyping and SNP-array analyses for effective prenatal diagnosis of chromosomal mosaicism.

Na Ma, Hui Xi, Jing Chen, Ying Peng, Zhengjun Jia, Shuting Yang, Jiancheng Hu, Jialun Pang, Yanan Zhang, Rong Hu and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
21.4field-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

40 citing papers in PubMed, 75 citations in OpenAlex.

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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

12 authors at 2 institutions in 1 country.

Na MaDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Hui XiDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Jing ChenDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Ying PengDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Zhengjun JiaDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Shuting YangDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Jiancheng HuDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Jialun PangDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Yanan ZhangDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Rong HuDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China.
Hua WangDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China. wanghua213@aliyun.com.
Jing LiuDepartment of Medical Genetics, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410008, Hunan, China. kinkilj@sina.com.ORCID 0000-0002-4155-5307
Hunan Provincial Maternal and Child Health Hospital · CNNational Health and Family Planning Commission · CN

Funding

Major Scientific and Technological Projects for Collaborative Prevention and Control of Birth Defects in Hunan Province 2019SK1010Major Scientific and Technological Projects for Collaborative Prevention and Control of Birth Defects in Hunan Province 2019SK1014Natural Science Foundation of Hunan Province 2018JJ3274Natural Science Foundation of Hunan Province 2018JJ3275
6 · The paper itself

Abstract

backgroundEmerging studies suggest that low-coverage massively parallel copy number variation sequencing (CNV-seq) more sensitive than chromosomal microarray analysis (CMA) for detecting low-level mosaicism. However, a retrospective back-to-back comparison evaluating accuracy, efficacy, and incremental yield of CNV-seq compared with CMA is warranted.

methodsA total of 72 mosaicism cases identified by karyotyping or CMA were recruited to the study. There were 67 mosaic samples co-analysed by CMA and CNV-seq, comprising 40 with sex chromosome aneuploidy, 22 with autosomal aneuploidy and 5 with large cryptic genomic rearrangements.

resultsOf the 67 positive mosaic cases, the levels of mosaicism defined by CNV-seq ranged from 6 to 92% compared to the ratio from 3 to 90% by karyotyping and 20% to 72% by CMA. CNV-seq not only identified all 43 chromosomal aneuploidies or large cryptic genomic rearrangements detected by CMA, but also provided a 34.88% (15/43) increased yield compared with CMA. The improved yield of mosaicism detection by CNV-seq was largely due to the ability to detect low level mosaicism below 20%.

conclusionIn the context of prenatal diagnosis, CNV-seq identified additional and clinically significant mosaicism with enhanced resolution and increased sensitivity. This study provides strong evidence for applying CNV-seq as an alternative to CMA for detection of aneuploidy and mosaic variants.

Indexed as

DNA Copy Number VariationsKaryotypingMosaicismPolymorphism, Single NucleotidePrenatal DiagnosisAneuploidyChromosome DisordersFemaleHigh-Throughput Nucleotide SequencingHumansMalePregnancyRetrospective StudiesChromosomal microarray analysis (CMA)Copy number variations (CNVs)Copy number variation sequencing (CNV‐seq)MosaicismPrenatal diagnosis

Identifiers

PMID33632221
PMCPMC7905897
OpenAlexW3114159792

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Registered trials

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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.