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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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JAX-CNV: A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level
Wan-Ping Lee1, Qihui Zhu2, Xiaofei Yang3
1Precision 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.
Genomics, Proteomics & Bioinformatics
|January 27, 2022
Summary
JAX-CNV, a new whole-genome sequencing algorithm, accurately detects copy number variants (CNVs) and identifies significantly more CNVs than current clinical methods. This WGS-based tool shows promise for replacing chromosomal microarray assays in genetic diagnostics.
Area of Science:
- Genomics
- Clinical Diagnostics
- Bioinformatics
Background:
- Chromosomal microarray assay (CMA) is a standard for detecting copy number variants (CNVs) in clinical diagnostics.
- Whole-genome sequencing (WGS) offers a more comprehensive approach to genetic analysis.
- There is a need for robust WGS-based CNV detection algorithms to potentially replace CMA.
Purpose of the Study:
- To develop and evaluate JAX-CNV, a novel algorithm for CNV detection from WGS data.
- To assess JAX-CNV's performance against clinically validated CMA results.
- To determine JAX-CNV's potential as a replacement for CMA in clinical settings.
Main Methods:
- Developed JAX-CNV, a WGS-based CNV calling algorithm.
- Evaluated JAX-CNV performance on 31 blinded samples against 112 known CNVs from CMA.
- Conducted experimental validation on 24 selected CNVs and robustness tests on lower-coverage WGS data.
Main Results:
- JAX-CNV achieved 100% recall of CMA-reported CNVs.
- Identified an average of 30 CNVs per individual, a seven-fold increase over CMA.
- Demonstrated a low false discovery rate (4.17%) and high sensitivity even with lower WGS coverage.
Conclusions:
- JAX-CNV is a highly sensitive WGS-based CNV detection tool with potential to replace CMA.
- The algorithm shows robust performance across various WGS coverages.
- Further multi-institutional studies are recommended to validate JAX-CNV for first-tier genetic testing.
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