Author:
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
Abstract
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.
Funder
shanghai municipal science and technology major project
national science foundation of china
the special fund of the pediatric medical coordinated development center of beijing hospitals authority
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Genetics,Molecular Biology,Molecular Medicine
Cited by
29 articles.
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