| Autor: |
Chul Jun Goh, Hyuk-Jung Kwon, Yoonhee Kim, Seunghee Jung, Jiwoo Park, Isaac Kise Lee, Bo-Ram Park, Myeong-Ji Kim, Min-Jeong Kim, Min-Seob Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Diagnostics, Vol 14, Iss 1, p 84 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4418 |
| DOI: |
10.3390/diagnostics14010084 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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