RT Journal Article
SR Electronic
T1 Automated prediction of the clinical impact of structural copy number variations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.30.228601
DO 10.1101/2020.07.30.228601
A1 Michaela Gaziova
A1 Ondrej Pös
A1 Werner Krampl
A1 Zuzana Kubiritova
A1 Marcel Kucharik
A1 Jan Radvanszky
A1 Jaroslav Budis
A1 Tomas Szemes
YR 2020
UL http://biorxiv.org/content/early/2020/07/31/2020.07.30.228601.abstract
AB Copy number variants (CNVs) play important roles in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analysis. This led to the demand for interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors and clinical geneticists from the laborious process of annotation and classification of CNVs. Here we demonstrate that the prediction of the clinical impact of CNVs can be automated using modern machine learning methods applied to publicly available genomic annotations, requiring only basic input information about the genomic location and structural type (duplication/deletion) of the analyzed CNV. The presented approach achieved 0.95 prediction accuracy on deletions and 0.96 on duplications from the ClinVar dataset and therefore have a great potential to guide users to more precise conclusions.Competing Interest StatementAll authors are employees of Geneton Ltd., where they also participate in development of a commercial application for the annotation and interpretation of CNV. The presented method was filed as a patent application under the number PCT / EP2020 / 025292. Apart from the above mentioned all authors have declared no conflicts of interest. The presented work was supported by the the Slovak Research and Development Agency (grant ID APVV-18-0319) (20% of charges) and the 'REVOGENE - Research centre for molecular genetics' project (ITMS 26240220067) supported by the Operational Programme Research and Development funded by the ERDF (80% of charges).