High-Resolution Chromosomal Microarray with Diagnostic Potential for Detecting Exon-Level Copy Number Variations Using Targeted and Non-targeted Approaches
- Author:
Yeseul KIM
1
;
Jee-Soo LEE
;
Boram KIM
;
Man Jin KIM
;
Sung Im CHO
;
Seung Won CHAE
;
Ho Seob SHIN
;
Hoyeon LEE
;
Ji Yeon KIM
;
Moon-Woo SEONG
Author Information
- Publication Type:Original Article
- From:Annals of Laboratory Medicine 2026;46(2):190-199
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:Copy number variations (CNVs) play an important role in human genetic disorders. Detection of exon-level CNVs is crucial for accurate clinical diagnosis. The CytoScan XON Array, a high-resolution microarray, was recently developed to detect exonic CNVs of various genes.
Methods:We evaluated the clinical performance of the CytoScan XON Array using 59 patient samples with previously identified CNVs, confirmed via methods including multiple ligation-dependent probe amplification (MLPA), gene-dose PCR, and mRNA assay. Concordance between CytoScan XON and orthogonal methods was evaluated in target regions, and diagnostic utility was compared with that of genome sequencing (GS)-based CNV calling tools through analysis of false-positive CNVs in non-target genomic regions.
Results:For target regions, the CytoScan XON Array achieved concordance rates of 89.8% and 92.5% at the exon and gene levels, respectively, for all CNV calls. Concordance was higher for multi-exon CNVs (100%) than that for single-exon CNVs (82.6%, P = 0.03). For non-target regions, false-positive CNV calls were reduced to fewer than 0.01 per gene per person through filtering strategies. The array exhibited false-positive detection rates within dosage-sensitive genes comparable with those of GS-based tools.
Conclusions:The CytoScan XON Array, a reliable tool for detecting exon-level CNVs in target regions, can serve as a complementary approach to GS-based CNV calling tools for genome-wide CNV screening with high resolution. However, its performance for single-exon CNVs requires further optimization. Cross-validation with GS-based CNV calling tools is recommended to improve diagnostic accuracy.
