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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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-SNPs01:05

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-GWAS01:11

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...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Related Experiment Video

Updated: Jun 4, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Copy Number Variation Detection via High-Density SNP Genotyping.

Kai Wang1, Maja Bucan

  • 1Department of Genetics, University of Pennsylvania, Philadelphia, PA 19446, USA.

CSH Protocols
|March 2, 2011
PubMed
Summary

PennCNV is a new computational method for detecting copy number variations (CNVs) using high-density single nucleotide polymorphism (SNP) genotyping arrays. This approach offers higher precision for genome variation analysis and disease association studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-density single nucleotide polymorphism (SNP) genotyping arrays are increasingly utilized for copy number variation (CNV) detection.
  • These arrays offer a dual role for both SNP and CNV association studies, providing higher precision and resolution than traditional methods.

Purpose of the Study:

  • To introduce PennCNV, a novel computational protocol for detecting CNVs from high-density SNP genotyping data.
  • To enhance the comprehensive analysis of genome variation and complement genome-wide association studies.

Main Methods:

  • PennCNV extracts allele-specific signal intensities from genotyping arrays.
  • It integrates SNP spacing and allele frequency information.
  • A hidden Markov model (HMM) algorithm is employed for CNV calling.

Main Results:

  • The PennCNV protocol enables accurate CNV detection from high-density SNP genotyping data.
  • It provides a more detailed view of genomic variations.

Conclusions:

  • PennCNV offers a precise and high-resolution method for CNV analysis using SNP arrays.
  • This approach complements existing genome-wide association studies in identifying disease susceptibility loci.