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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Genome Copying Errors

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their survival. Therefore, the copying errors are checked and repaired at three levels.

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: May 9, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Analysis of Copy Number Variants Is an Important Consideration in Exome Sequencing.

Asmaa K Amin¹, Sara H El-Dessouky², Marwa Abd Elmaksoud³

¹Human Genetics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.

Clinical Genetics

|April 30, 2025

Summary

Integrating copy number variant (CNV) analysis into exome sequencing (ES) significantly improves diagnostic yield for rare genetic diseases. This approach identified additional causative genetic variants in patients with undiagnosed conditions, particularly those with neurodevelopmental delays.

Keywords:

CNVs ES ExomeDepth copy number variations deletions duplications exome sequencing

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Published on: October 18, 2013

Area of Science:

Genetics
Genomic Medicine
Bioinformatics

Background:

Copy number variants (CNVs) are key drivers of rare genetic disorders, often causing more severe phenotypes than single nucleotide variants (SNVs).
Exome sequencing (ES) is a crucial tool for identifying genetic causes of disease.
Standard ES analysis may miss pathogenic CNVs.

Purpose of the Study:

To evaluate the impact of integrating CNV analysis tools into standard ES pipelines.
To determine the effect on the diagnostic yield for rare genetic diseases.

Main Methods:

Analysis of exome sequencing data from 840 patients with rare genetic diseases.
Application of the ExomeDepth algorithm for CNV detection in previously unsolved cases.
Classification of identified CNVs based on pathogenicity.

Main Results:

Initial SNV/indel analysis yielded a diagnosis in 45.6% of patients.
Integrating CNV analysis identified causative variants in 55 additional patients (out of 457 unsolved cases), increasing the overall diagnostic yield to 52.1%.
Deletions were more common (74.1%) than duplications (25.9%), with identified CNVs frequently linked to neurodevelopmental delay.

Conclusions:

Incorporating CNV detection tools into ES workflows enhances diagnostic capabilities for rare genetic disorders.
CNVs play a significant role in the etiology of genetic diseases, underscoring the need for comprehensive variant analysis.