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Goh CJ, Kwon HJ, Kim Y, Jung S, Park J, Lee IK, Park BR, Kim MJ, Kim MJ, Lee MS. Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach. Diagnostics (Basel) 2023; 14:84. [PMID: 38201393 PMCID: PMC10871075 DOI: 10.3390/diagnostics14010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.
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Affiliation(s)
- Chul Jun Goh
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Hyuk-Jung Kwon
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
| | - Yoonhee Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Seunghee Jung
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Jiwoo Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Isaac Kise Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
- NGENI Foundation, San Diego, CA 92127, USA
| | - Bo-Ram Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Myeong-Ji Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Min-Jeong Kim
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
| | - Min-Seob Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
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