Automated classification of copy number variants based on 2019 ACMG standards

AbstractSince its introduction in the routine diagnostics of patients with mental retardation/developmental delay, array-comparative genomic hybridization (aCGH) has become an indispensable tool for the detection of clinically relevant copy number variants (CNVs). Despite the current tendency for higher resolution arrays, the growing number of public internet databases as well as better calling algorithms allow save reporting and a better classification of CNVs. The application of combined aCGH plus single nucleotide polymorphism (SNP) arrays will increase detection rates by revealing copy number neutral changes, such as uniparental disomy. In the future, next generation sequencing techniques will lead to a further increase in resolution with the simultaneous detection of unbalanced and even balanced chromosomal aberrations.

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Automated prediction of the clinical impact of structural copy number variations

10.1101/2020.07.30.228601 ◽

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}}.

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Utility of copy number variants in the classification of intracranial ependymoma

Cancer Genetics ◽

10.1016/j.cancergen.2019.11.003 ◽

2020 ◽

Vol 240 ◽

pp. 66-72

Author(s):

Michael Evenson ◽

Chunyu Cai ◽

Vishwanathan Hucthagowder ◽

Samantha McNulty ◽

Julie Neidich ◽

...

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Intracranial Ependymoma

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Standardizing the classification of recurrent copy number variants–incorporation of sub-clinical phenotype data for CNVs with reduced penetrance

Molecular Genetics and Metabolism ◽

10.1016/s1096-7192(21)00523-0 ◽

2021 ◽

Vol 132 ◽

pp. S284

Author(s):

John Herriges ◽

Erica Andersen ◽

Bradley Coe ◽

Laura Conlin ◽

Benjamin Hilton ◽

...

Keyword(s):

Copy Number ◽

Clinical Phenotype ◽

Copy Number Variants ◽

Phenotype Data ◽

Reduced Penetrance

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Automated prediction of the clinical impact of structural copy number variations

Scientific Reports ◽

10.1038/s41598-021-04505-z ◽

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.

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P1117 Automated classification of wall motion abnormalities by analysis of left-ventricular endocardial motion patterns

European Heart Journal ◽

10.1016/s0195-668x(03)94409-5 ◽

2003 ◽

Vol 24 (5) ◽

pp. 203

Author(s):

J BOSCH

Keyword(s):

Wall Motion ◽

Left Ventricular ◽

Automated Classification ◽

Motion Patterns ◽

Wall Motion Abnormalities

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Automated classification of colorectal adenoma-dysplasia-carcinoma in tissue biopsies using artificial intelligence based image evaluation

Zeitschrift für Gastroenterologie ◽

10.1055/s-0032-1312443 ◽

2012 ◽

Vol 50 (05) ◽

Author(s):

B Wichmann ◽

P Ittzés ◽

G Valcz ◽

D Szabó ◽

B Barták ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Adenoma ◽

Automated Classification ◽

Image Evaluation

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Understanding Schizophrenia: Genetic Causes and Treatment

Current Neuropsychiatry and Clinical Neuroscience Reports ◽

10.33702/cncnr.2019.1.1.2 ◽

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.

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