Evidence map›Paper›PMID 32451733›Full record

ArticleHuman genetics2020

Low-pass genome sequencing: a validated method in clinical cytogenetics.

Matthew Hoi Kin Chau, Huilin Wang, Yunli Lai, Yanyan Zhang, Fuben Xu, Yanqing Tang, Yanfang Wang, Zihan Chen, Tak Yeung Leung, Jacqueline Pui Wah Chung and 7 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
9.2field-weighted citation impact, top 2% 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

26 citing papers in PubMed, 68 citations in OpenAlex.

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  20. Low-pass genome sequencing-based detection of absence of heterozygosity: validation in clinical cytogenetics.Genetics in medicine : official journal of the American College of Medical Genetics · 2021
    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

17 authors at 4 institutions in 2 countries.

Matthew Hoi Kin ChauDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-0945-1938
Huilin WangDepartment of Center Laboratory, Maternal-Fetal Medicine Institute, Shenzhen Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Baoan Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Shenzhen, 518100, China.
Yunli LaiBirth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530000, Guangxi, China.
Yanyan ZhangDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.
Fuben XuBirth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530000, Guangxi, China.
Yanqing TangBirth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530000, Guangxi, China.
Yanfang WangDepartment of Center Laboratory, Maternal-Fetal Medicine Institute, Shenzhen Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Baoan Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Shenzhen, 518100, China.
Zihan ChenShenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
Tak Yeung LeungDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-9153-0343
Jacqueline Pui Wah ChungDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.
Yvonne K KwokDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.
Shuk Ching ChongHong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China.
Kwong Wai ChoyDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-3616-6200
Yuanfang ZhuDepartment of Center Laboratory, Maternal-Fetal Medicine Institute, Shenzhen Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Baoan Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Shenzhen, 518100, China.
Likuan XiongDepartment of Center Laboratory, Maternal-Fetal Medicine Institute, Shenzhen Key Laboratory of Birth Defects Research, Birth Defects Prevention Research and Transformation Team, Baoan Maternity and Child Health Hospital Affiliated to Jinan University School of Medicine, Shenzhen, 518100, China.
Weihong WeiBirth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530000, Guangxi, China.
Zirui DongDepartment of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China. elvisdong@cuhk.edu.hk.ORCID http://orcid.org/0000-0002-3626-6500
Chinese University of Hong Kong · HKGuangxi Maternal and Child Health Hospital · CNJinan University · CNChinese University of Hong Kong, Shenzhen · CN

Funding

Health and Medical Research Fund 04152666Health Department of Guangxi Province Z20190782National Natural Science Foundation of China 31801042National Natural Science Foundation of China 81741004National Natural Science Foundation of China 81741009
6 · The paper itself

Abstract

Clinically significant copy-number variants (CNVs) known to cause human diseases are routinely detected by chromosomal microarray analysis (CMA). Recently, genome sequencing (GS) has been introduced for CNV analysis; however, sequencing depth (determined by sequencing read-length and read-amount) is a variable parameter across different laboratories. Variating sequencing depths affect the CNV detection resolution and also make it difficult for cross-laboratory referencing or comparison. In this study, by using data from 50 samples with high read-depth GS (30×) and the reported clinically significant CNVs, we first demonstrated the optimal read-amount and the most cost-effective read-length for CNV analysis to be 15 million reads and single-end 50 bp (equivalent to a read-depth of 0.25-fold), respectively. In addition, we showed that CNVs at mosaic levels as low as 30% are readily detected, furthermore, CNVs larger than 2.5 Mb are also detectable at mosaic levels as low as 20%. Herein, by conducting a retrospective back-to-back comparison study of low-pass GS versus routine CMA for 532 prenatal, miscarriage, and postnatal cases, the overall diagnostic yield was 22.4% (119/532) for CMA and 23.1% (123/532) for low-pass GS. Thus, the overall relative improvement of the diagnostic yield by low-pass GS versus CMA was ~ 3.4% (4/119). Identification of cryptic and clinically significant CNVs among prenatal, miscarriage, and postnatal cases demonstrated that CNV detection at higher resolutions is warranted for clinical diagnosis regardless of referral indications. Overall, our study supports low-pass GS as the first-tier genetic test for molecular cytogenetic testing.

Indexed as

Chromosome MappingCytogenetic AnalysisDNA Copy Number VariationsFemaleFetusGenetic TestingGenome, HumanHumansMalePregnancyRetrospective StudiesWhole Genome Sequencing

Identifiers

PMID32451733
OpenAlexW3032717202

What OpenQuestion holds

Textmetadata
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.