scholarly journals AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chunna Fan ◽  
Zhonghua Wang ◽  
Yan Sun ◽  
Jun Sun ◽  
Xi Liu ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Illustrative Case ◽  
Copy Number Loss ◽  
Technical Standards ◽  
Web Based ◽  
Case Examples ◽  
Interpretation System ◽  
Interpretation Process ◽  
Genetics And Genomics

Abstract Background The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively. Results We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV). Conclusions AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation.

scholarly journals Automated prediction of the clinical impact of structural copy number variations

2020 ◽  
Author(s):  
Michaela Gaziova ◽  
Tomas Sladecek ◽  
Ondrej Pos ◽  
Martin Stevko ◽  
Werner Krampl ◽  
...  
Keyword(s):  
Copy Number ◽  
Genetic Diseases ◽  
Copy Number Variants ◽  
Copy Number Gain ◽  
Regulatory Elements ◽  
Copy Number Variations ◽  
Clinical Impact ◽  
Copy Number Loss ◽  
Pathogenicity Prediction

Introduction: Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of structural variants is a challenging problem due to highly variable numbers of gene, regulatory or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. Materials and Methods: We designed a classifier method based on the annotations of CNVs from several publicly available databases. The attributes take into account gene elements, regulatory elements affected by the CNV, as well as other CNVs with known clinical significance that overlap the candidate CNV. We also describe the process of model selection and the construction of training, validation, and test set. Results: The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants and can be improved by imposing probability thresholds to eliminate low confidence predictions. Discussion: Method has shown considerable performance in predicting the clinical impact of CNVs and therefore has a great potential to guide users to more precise conclusions. The CNV annotation and pathogenicity prediction can be fully automated, relieving users of tedious interpretation processes. Availability and Implementation: The results can be reproduced by following instructions at {{https://github.com/tsladecek/isv}}.

Genome-wide association study identified copy number variants associated with sporadic colorectal cancer risk

2017 ◽  
Vol 55 (3) ◽  
pp. 181-188 ◽  
Author(s):  
Lai Fun Thean ◽  
Yee Syuen Low ◽  
Michelle Lo ◽  
Yik-Ying Teo ◽  
Woon-Puay Koh ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Association Study ◽  
Copy Number ◽  
Genome Wide Association Study ◽  
Copy Number Variants ◽  
Genome Wide Association ◽  
Copy Number Loss ◽  
Genome Wide ◽  
A Genome

BackgroundMultiple single nucleotide polymorphisms (SNPs) have been associated with colorectal cancer (CRC) risk. The role of structural or copy number variants (CNV) in CRC, however, remained unclear. We investigated the role of CNVs in patients with sporadic CRC.MethodsA genome-wide association study (GWAS) was performed on 1000 Singapore Chinese patients aged 50 years or more with no family history of CRC and 1000 ethnicity-matched, age-matched and gender-matched healthy controls using the Affymetrix SNP 6 platform. After 16 principal component corrections, univariate and multivariate segmentations followed by association testing were performed on 1830 samples that passed quality assurance tests.ResultsA rare CNV region (CNVR) at chromosome 14q11 (OR=1.92 (95% CI 1.59 to 2.32), p=2.7e-12) encompassing CHD8, and common CNVR at chromosomes 3q13.12 (OR=1.54 (95% CI 1.33 to 1.77), p=2.9e-9) and 12p12.3 (OR=1.69 (95% CI 1.41 to 2.01), p=2.8e-9) encompassing CD47 and RERG/ARHGDIB, respectively, were significantly associated with CRC risk. CNV loci were validated in an independent replication panel using an optimised copy number assay. Whole-genome expression data in matched tumours of a subset of cases demonstrated that copy number loss at CHD8 was significantly associated with dysregulation of several genes that perturb the Wnt, TP53 and inflammatory pathways.ConclusionsA rare CNVR at 14q11 encompassing the chromatin modifier CHD8 was significantly associated with sporadic CRC risk. Copy number loss at CHD8 altered expressions of genes implicated in colorectal tumourigenesis.

scholarly journals Automated prediction of the clinical impact of structural copy number variations

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Gažiová ◽  
T. Sládeček ◽  
O. Pös ◽  
M. Števko ◽  
W. Krampl ◽  
...  
Keyword(s):  
Copy Number ◽  
Genetic Diseases ◽  
Copy Number Variants ◽  
Prediction Method ◽  
Copy Number Gain ◽  
Copy Number Variations ◽  
Copy Number Loss ◽  
Structural Variants ◽  
Uncertain Significance

AbstractCopy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned “uncertain” significance in predictions. We believe that ISV’s prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.

Understanding Schizophrenia: Genetic Causes and Treatment

2019 ◽  
Vol 1 (1) ◽  
pp. 6-12
Author(s):  
Fatima Javeria ◽  
Shazma Altaf ◽  
Alishah Zair ◽  
Rana Khalid Iqbal
Keyword(s):  
Copy Number ◽  
Drug Targets ◽  
Disease Risk ◽  
Mental Disease ◽  
Copy Number Variants ◽  
Aminobutyric Acid ◽  
Epigenetic Mechanisms ◽  
Gamma Aminobutyric Acid ◽  
Wide Range ◽  
Severe Mental Disease

Schizophrenia is a severe mental disease. The word schizophrenia literally means split mind. There are three major categories of symptoms which include positive, negative and cognitive symptoms. The disease is characterized by symptoms of hallucination, delusions, disorganized thinking and speech. Schizophrenia is related to many other mental and psychological problems like suicide, depression, hallucinations. Including these, it is also a problem for the patient’s family and the caregiver. There is no clear reason for the disease, but with the advances in molecular genetics; certain epigenetic mechanisms are involved in the pathophysiology of the disease. Epigenetic mechanisms that are mainly involved are the DNA methylation, copy number variants. With the advent of GWAS, a wide range of SNPs is found linked with the etiology of schizophrenia. These SNPs serve as ‘hubs’; because these all are integrating with each other in causing of schizophrenia risk. Until recently, there is no treatment available to cure the disease; but anti-psychotics can reduce the disease risk by minimizing its symptoms. Dopamine, serotonin, gamma-aminobutyric acid, are the neurotransmitters which serve as drug targets in the treatment of schizophrenia. Due to the involvement of genetic and epigenetic mechanisms, drugs available are already targeting certain genes involved in the etiology of the disease.

Koduojančios ir nekoduojančios genomo sekos variantų bei kopijų skaičiaus pokyčių molekulinis ir funkcinis vertinimas

2020 ◽  
Author(s):  
◽  
Evelina Siavrienė
Keyword(s):  
Genome Sequence ◽  
Copy Number ◽  
Copy Number Variants ◽  
Sequence Variants ◽  
Functional Evaluation

A Molecular and Functional Evaluation of Coding and Non-Coding Genome Sequence Variants and Copy Number Variants

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