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. 2022 Dec;20(6):1197-1206.
doi: 10.1016/j.gpb.2021.06.003. Epub 2022 Jan 25.

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

Affiliations

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

Wan-Ping Lee et al. Genomics Proteomics Bioinformatics. 2022 Dec.

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.

Keywords: Chromosomal microarray assay; Copy number variant; Genetic testing; JAX-CNV; Whole-genome sequencing.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
WGS analysis workflow A. An overview of the CNV calling pipeline consisting of three major steps: pre-processing, alignment, and CNV calling. B.–J. Details of CNV calling approach. WGS, whole-genome sequencing; CNV, copy number variant; DB, database; HMM, hidden Markov model.
Figure 2
Figure 2
JAX-CNV accurately detected all CNVs in the 31 testing samples A. Deletion. B. Duplication. The 31 testing samples were examined by three different methods, including the Corriell Institute CMA (red inner circle), the JAX-GM CMA (green middle circle), and the JAX-CNV algorithm (blue outer circle).
Figure 3
Figure 3
JAX-CNV detected a number of unique CNV calls compared to the array-based method A. Bar chart summarizing the unique JAX-CNV calls (in red) and CNV calls overlapping with the truth set (in green) as a function of CNV sizes. B. Concordance of CNV calls per sample.
Figure 4
Figure 4
JAX-CNV maintained a high sensitivity for CNV detection at 10× WGS coverage The top two panels are the truth set using 300 kb as the cutoff. Red-colored bars indicate deletions, while blue-colored bars indicate duplications.

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