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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 3, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Analyzing copy number variation using SNP array data: protocols for calling CNV and association tests.

Chiao-Feng Lin¹, Adam C Naj², Li-San Wang¹

¹Department of Pathology and Laboratory Medicine and Institute for Biomedical Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.

Current Protocols in Human Genetics

|February 11, 2014

Summary

This summary is machine-generated.

High-density SNP genotyping aids Genome Wide Association (GWA) studies. This research explores copy number variation (CNV) from SNP array data to address missing heritability in genetic studies.

Keywords:

CNV calling SNP genotyping array association study burden analysis copy number variations (CNV)genome-wide association studies

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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:

Genetics
Bioinformatics
Genomic Research

Background:

Genome Wide Association (GWA) studies using high-density SNP genotyping have identified disease-associated SNPs.
A significant challenge in GWA studies is the 'missing heritability' phenomenon.
Copy Number Variation (CNV) is an emerging area of genetic variation implicated in disease susceptibility.

Purpose of the Study:

To investigate the utility of copy number variation (CNV) in addressing the missing heritability in genetic studies.
To demonstrate a cost-effective method for identifying CNVs from existing SNP array data.
To integrate CNV analysis with established genetic association tools.

Main Methods:

Utilizing high-density SNP array hybridization intensity data.
Employing the PennCNV algorithm for copy number variation (CNV) calling.
Performing association analysis using R and PLINK software.

Main Results:

Demonstrated a methodology for CNV detection from SNP array data.
Showcased the integration of CNV data into genetic association analyses.
Provided a framework for leveraging existing SNP array data for CNV discovery.

Conclusions:

Copy number variation (CNV) analysis from SNP array data offers a valuable approach to supplement GWA studies.
This method provides a low-cost avenue to explore additional genetic factors contributing to heritability.
The integrated approach using PennCNV, R, and PLINK facilitates comprehensive genetic variation analysis.