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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: May 27, 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

PlatinumCNV: a Bayesian Gaussian mixture model for genotyping copy number polymorphisms using SNP array signal

Natsuhiko Kumasaka1, Hironori Fujisawa, Naoya Hosono

  • 1Research Group for Medical Informatics, Center for Genomic Medicine, RIKEN, Shirokane-dai, Minato-ku,Tokyo, Japan. kumasaka@src.riken.jp

Genetic Epidemiology
|November 30, 2011
PubMed
Summary

We developed a statistical model to identify allele-specific copy number polymorphisms (CNPs) from SNP array data. This method accurately detects CNPs, improving genetic association studies for diseases and traits.

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Last Updated: May 27, 2026

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Copy number polymorphisms (CNPs) are significant sources of human genetic variation.
  • Accurate identification of allele-specific CNPs (ASCNs) is crucial for genetic association studies.
  • Existing methods struggle with instrumental errors and unobserved genotypes in SNP array data.

Purpose of the Study:

  • To present a robust statistical model for detecting ASCNs in SNP array data.
  • To address challenges posed by instrumental errors and scale imbalances in signal intensities.
  • To enable more effective genome-wide association studies using CNPs.

Main Methods:

  • Developed a Bayesian Gaussian mixture model (GMM) to analyze allele-specific fluorescent signal intensities.
  • The GMM accounts for overlapping cluster memberships to handle instrumental errors.
  • Applied the model to Illumina HumanHap 610K array data.

Main Results:

  • Identified over 4,000 allele-specific CNPs with high accuracy (1% genotyping error rate).
  • Validated genotyping accuracy using quantitative PCR and replicated subject data.
  • Demonstrated the model's utility in a preliminary genome-wide association study for hematological traits.

Conclusions:

  • The developed statistical model provides a robust and accurate method for ASCN detection.
  • This approach enhances the capability for mapping disease genes and quantitative trait loci.
  • The associated software package, PlatinumCNV, facilitates the application of these methods.