Evidence map›Paper›PMID 35085778›Full record

ArticleGenomics, proteomics & bioinformatics2022

JAX-CNV: A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level.

Wan-Ping Lee, Qihui Zhu, Xiaofei Yang, Silvia Liu, Eliza Cerveira, Mallory Ryan, Adam Mil-Homens, Lauren Bellfy, Kai Ye, Charles Lee and 1 more

Abstract read
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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  6. InEJIFCC · 2023
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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

11 authors.

Wan-Ping LeePrecision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: wan-ping.lee@pennmedicine.upenn.edu.
Qihui ZhuThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Xiaofei YangThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Silvia LiuThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Eliza CerveiraThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Mallory RyanThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Adam Mil-HomensThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Lauren BellfyThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Kai YePrecision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Charles LeePrecision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Life Sciences, Ewha Womans University, Seoul 03760, South Korea.
Chengsheng ZhangPrecision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA. Electronic address: cszhang99@xjtu.edu.cn.

Funding

Copy Number Variation Identification and Association Study on Alzheimer's Disease Whole Genome Sequencing DataRF1AG074328 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LEE, WAN-PING, TZENG, JUNG-YING · 2021 to 2021
$2.3M
NIA NIH HHS RF1 AG074328
6 · The paper itself

Abstract

We aimed to develop a whole-genome sequencing (WGS)-based copy number variant (CNV) calling algorithm with the potential of replacing chromosomal microarray assay (CMA) for clinical diagnosis. JAX-CNV is thus developed for CNV detection from WGS data. The performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 samples. The result showed that JAX-CNV recalled 100% of these CNVs. Besides, JAX-CNV identified an average of 30 CNVs per individual, respresenting an approximately seven-fold increase compared to calls of clinically validated CMAs. Experimental validation of 24 randomly selected CNVs showed one false positive, i.e., a false discovery rate (FDR) of 4.17%. A robustness test on lower-coverage data revealed a 100% sensitivity for CNVs larger than 300 kb (the current threshold for College of American Pathologists) down to 10× coverage. For CNVs larger than 50 kb, sensitivities were 100% for coverages deeper than 20×, 97% for 15×, and 95% for 10×. We developed a WGS-based CNV pipeline, including this newly developed CNV caller JAX-CNV, and found it capable of detecting CMA-reported CNVs at a sensitivity of 100% with about a FDR of 4%. We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS. JAX-CNV is available at https://github.com/TheJacksonLaboratory/JAX-CNV.

Indexed as

AlgorithmsDNA Copy Number VariationsHumansWhole Genome SequencingChromosomal microarray assayCopy number variantGenetic testingJAX-CNVWhole-genome sequencing

Identifiers

PMID35085778
PMCPMC10225484

What OpenQuestion holds

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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.