Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach
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 influen...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/14/1/84 |
| _version_ | 1797358914749071360 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Chul Jun Goh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:09:06Z |
| format | Article |
| id | doaj.art-1d169bfa2e214cc4b0bd34619280351a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-08T15:09:06Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-1d169bfa2e214cc4b0bd34619280351a2024-01-10T14:53:55ZengMDPI AGDiagnostics2075-44182023-12-011418410.3390/diagnostics14010084Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based ApproachChul Jun Goh0Hyuk-Jung Kwon1Yoonhee Kim2Seunghee Jung3Jiwoo Park4Isaac Kise Lee5Bo-Ram Park6Myeong-Ji Kim7Min-Jeong Kim8Min-Seob Lee9Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaDiagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USAEone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of KoreaCopy 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.https://www.mdpi.com/2075-4418/14/1/84CNVgenome-wide SNP arrayKorean newbornmachine learninggenomic wave |
| spellingShingle | 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 Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach Diagnostics CNV genome-wide SNP array Korean newborn machine learning genomic wave |
| title | Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach |
| title_full | Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach |
| title_fullStr | Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach |
| title_full_unstemmed | Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach |
| title_short | Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach |
| title_sort | improving cnv detection performance in microarray data using a machine learning based approach |
| topic | CNV genome-wide SNP array Korean newborn machine learning genomic wave |
| url | https://www.mdpi.com/2075-4418/14/1/84 |
| work_keys_str_mv | AT chuljungoh improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT hyukjungkwon improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT yoonheekim improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT seungheejung improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT jiwoopark improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT isaackiselee improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT borampark improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT myeongjikim improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT minjeongkim improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach AT minseoblee improvingcnvdetectionperformanceinmicroarraydatausingamachinelearningbasedapproach |