dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations

Abstract

Background Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features. Results From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs. Conclusion The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

CNVoyant: A Highly Performant and Explainable Multi-Classifier Machine Learning Approach for Determining the Clinical Significance of Copy Number Variants

“…CNVoyant predictions were compared to five published ML-based CNV pathogenicity classifiers, X-CNV ( Zhang et al 2021 ), TADA ( Hertzberg et al 2022 ), dbCNV ( Lv et al 2023 ), StrVCTVRE ( Sharo et al 2022 ), and ISV ( Gažiová et al 2022 ), as well as the algorithmic implementation of the ACMG technical standards for CNV interpretation, ClassifyCNV ( Gurbich and Ilinsky 2020 ). The test set was passed to CNVoyant and all comparator algorithms to test for generalizability and generate accuracy metrics for benchmarking.…”

“…To address this problem, several machine learning ( ML ) approaches have been proposed to enhance the precision of classifying the clinical significance of CNVs. These algorithms statistically learn from data elements related to dosage sensitivity, overlapping genes, population frequencies, regulatory elements, topologically associated domains, and genomic position to predict pathogenic potential ( Zhang et al 2021 ; Gažiová et al 2022 ; Sharo et al 2022 ; Hertzberg et al 2022 ; Lv et al 2023 ). None of these algorithms combine all informative features in a single model.…”

Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis

“…These variations, especially when they alter gene coding segments, can significantly affect gene dosage and expression, leading to various syndromes. 2,3 Their association with Mendelian disorders such as Potocki-Lupski syndrome and Williams-Beuren syndrome, neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia, and various cancers, 4 highlights the necessity to understand CNVs for comprehensive genomic analysis. Although technologies are evolving very rapidly to detect the full spectrum of structural variations (SVs), the annotation and classification of CNVs to determine their pathogenic potential remain critical for their practical application in clinical settings.…”

Genome-wide CNV analysis uncovers novel pathogenic regions in cohort of five multiplex families with neurodevelopmental disorders

“…Genomic duplications, deletions or inversions range in size from several kb to Mb and even smaller [ 4 ]. CNVs occupy 4.8–9.5% of the human DNA according to publicly accessible CNV databases [ 5 ]. CNVs play key role in human genomic structure, diversity as well as in pathogenicity of various neurodevelopmental disorders, neuropsychiatric conditions, autoimmune diseases, and cancer [ 6 ].…”