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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-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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 19, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array.

Yali Hou¹, Derek M Bickhart, Miranda L Hvinden

¹Bovine Functional Genomics Laboratory, ANRI, USDA-ARS, BARC-East, Beltsville, MD 20705, USA.

|August 8, 2012

Summary

This study identified over 3,000 cattle copy number variation (CNV) regions using high-density arrays on two genome assemblies. Results show increased CNV detection sensitivity and highlight the impact of genome assembly choice on CNV analysis.

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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

Genomics
Comparative Genomics
Animal Genetics

Background:

Two cattle reference genome assemblies, Btau_4.0 and UMD3.1, exist.
Previous low-density array analysis identified 682 CNV regions on Btau_4.0.
A need for higher resolution and sensitivity in cattle CNV analysis exists.

Purpose of the Study:

To perform high-resolution copy number variation (CNV) analyses on cattle using high-density SNP arrays.
To compare CNV detection between two cattle genome assemblies (Btau_4.0 and UMD3.1).
To evaluate the impact of genome assembly choice on CNV calling and compare with previous findings.

Main Methods:

High-density BovineHD SNP array analysis was performed on 674 cattle from 27 breeds.
CNV analyses were conducted on both Btau_4.0 and UMD3.1 genome assemblies.
CNV results were compared between the two assemblies and with a previous low-density array study; selected CNVs were validated via PCR.

Main Results:

On Btau_4.0, 3,346 candidate CNV regions (142.7 Mb) were identified, a 5-fold increase in event count with a shorter average length compared to previous study.
UMD3.1 analysis yielded comparable results (3,438 regions, 146.9 Mb) but with approximately 50% more and 20% longer CNVs than Btau_4.0.
A 73% PCR validation rate was achieved; 20-45% of CNV regions overlapped with annotated cattle genes involved in immune response and development.

Conclusions:

This study presents comprehensive cattle CNV data at higher resolution and sensitivity.
Over 3,000 candidate CNV regions were identified on both Btau_4.0 and UMD3.1.
Genome assembly significantly impacts CNV calling, with UMD3.1 showing increased detection compared to Btau_4.0.