X-CNV: genome-wide prediction of the pathogenicity of copy number variations
Abstract Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational fr...
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-021-00945-4 |
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| author | Li Zhang Jingru Shi Jian Ouyang Riquan Zhang Yiran Tao Dongsheng Yuan Chengkai Lv Ruiyuan Wang Baitang Ning Ruth Roberts Weida Tong Zhichao Liu Tieliu Shi |
| author_facet | Li Zhang Jingru Shi Jian Ouyang Riquan Zhang Yiran Tao Dongsheng Yuan Chengkai Lv Ruiyuan Wang Baitang Ning Ruth Roberts Weida Tong Zhichao Liu Tieliu Shi |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | Abstract Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. Conclusions The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening. |
| first_indexed | 2024-12-22T07:32:18Z |
| format | Article |
| id | doaj.art-59773080cff24d2787f4d094c6cf1ccc |
| institution | Directory Open Access Journal |
| issn | 1756-994X |
| language | English |
| last_indexed | 2024-12-22T07:32:18Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj.art-59773080cff24d2787f4d094c6cf1ccc2022-12-21T18:33:59ZengBMCGenome Medicine1756-994X2021-08-0113111510.1186/s13073-021-00945-4X-CNV: genome-wide prediction of the pathogenicity of copy number variationsLi Zhang0Jingru Shi1Jian Ouyang2Riquan Zhang3Yiran Tao4Dongsheng Yuan5Chengkai Lv6Ruiyuan Wang7Baitang Ning8Ruth Roberts9Weida Tong10Zhichao Liu11Tieliu Shi12Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversitySchool of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityNational Center for Toxicological Research, Food and Drug AdministrationApconiX Ltd, Alderley ParkNational Center for Toxicological Research, Food and Drug AdministrationNational Center for Toxicological Research, Food and Drug AdministrationCenter for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal UniversityAbstract Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. Conclusions The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.https://doi.org/10.1186/s13073-021-00945-4XGBoostCopy number variationPathogenicityNext-generation sequencingMachine learning |
| spellingShingle | Li Zhang Jingru Shi Jian Ouyang Riquan Zhang Yiran Tao Dongsheng Yuan Chengkai Lv Ruiyuan Wang Baitang Ning Ruth Roberts Weida Tong Zhichao Liu Tieliu Shi X-CNV: genome-wide prediction of the pathogenicity of copy number variations Genome Medicine XGBoost Copy number variation Pathogenicity Next-generation sequencing Machine learning |
| title | X-CNV: genome-wide prediction of the pathogenicity of copy number variations |
| title_full | X-CNV: genome-wide prediction of the pathogenicity of copy number variations |
| title_fullStr | X-CNV: genome-wide prediction of the pathogenicity of copy number variations |
| title_full_unstemmed | X-CNV: genome-wide prediction of the pathogenicity of copy number variations |
| title_short | X-CNV: genome-wide prediction of the pathogenicity of copy number variations |
| title_sort | x cnv genome wide prediction of the pathogenicity of copy number variations |
| topic | XGBoost Copy number variation Pathogenicity Next-generation sequencing Machine learning |
| url | https://doi.org/10.1186/s13073-021-00945-4 |
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