X-CNV: genome-wide prediction of the pathogenicity of copy number variations

• Published in: Genome medicine Vol. 13; no. 1; p. 132
• Main Authors: Zhang, Li, Shi, Jingru, Ouyang, Jian, Zhang, Riquan, Tao, Yiran, Yuan, Dongsheng, Lv, Chengkai, Wang, Ruiyuan, Ning, Baitang, Roberts, Ruth, Tong, Weida, Liu, Zhichao, Shi, Tieliu
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.08.2021; BioMed Central; BMC
• Subjects: Algorithms; Biological diversity; Computational Biology - methods; Computer applications; Copy number; Copy number variation; Databases, Genetic; DNA Copy Number Variations; Gene frequency; Genetic diversity; Genetic Predisposition to Disease; Genome-Wide Association Study - methods; Genomes; Genomics; High-Throughput Nucleotide Sequencing; Humans; Machine Learning; Medical research; Medicine, Experimental; Minority & ethnic groups; Next-generation sequencing; Pathogenicity; Polymorphism; Population genetics; Population studies; Predictions; Proteins; ROC Curve; Software; Web Browser; Workflow; XGBoost; Pathogenicity; Copy number variation; Next-generation sequencing; XGBoost; Machine learning
• ISSN: 1756-994X; 1756-994X
• DOI: 10.1186/s13073-021-00945-4

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. 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. 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.