Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying Genes Associated with Obstructive Congenital Heart Defects Using a Family-Based Genetic Random Field Method: Results from the National Birth Defects Prevention Study.

HGG advances·2026
Same author

Allele Frequencies at Recessive Disease Genes are Mainly Determined by Pleiotropic Effects in Heterozygotes.

Genetics·2026
Same author

Contextualizing the Utility of Polygenic Risk Scores using Absolute Risk Models in Diverse Ancestry Populations.

medRxiv : the preprint server for health sciences·2026
Same author

Germline polygenic score for prostate cancer aggressiveness.

medRxiv : the preprint server for health sciences·2026
Same author

Allele Frequencies at Recessive Disease Genes are Mainly Determined by Pleiotropic Effects in Heterozygotes.

bioRxiv : the preprint server for biology·2026
Same author

Evaluation of Polygenic Risk Scores for a Possible Genetic Basis of the Inverse Association Between Cancer and Cognitive Decline.

Alzheimer disease and associated disorders·2025

Related Experiment Video

Updated: Jun 20, 2026

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

Segmentation and estimation for SNP microarrays: a Bayesian multiple change-point approach.

Yu Chuan Tai1, Mark N Kvale, John S Witte

  • 1Institute for Human Genetics, Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94143-0794, USA. taiy@humgen.ucsf.edu

Biometrics
|September 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian multiple change-point model (BMCP) for analyzing single-nucleotide polymorphism (SNP) microarray data to detect copy number variants (CNVs). The BMCP method accurately segments chromosomes and estimates copy numbers, providing confidence measures and handling multiple samples.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Related Experiment Videos

Last Updated: Jun 20, 2026

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

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • High-density single-nucleotide polymorphism (SNP) microarrays are crucial for detecting copy number variants (CNVs).
  • Analyzing large SNP microarray datasets for accurate CNV detection and precise copy number estimation presents significant computational challenges.
  • Identifying the exact locations of copy number changes and their corresponding values is complex.

Purpose of the Study:

  • To develop a robust statistical model for segmenting and estimating copy number variations from SNP microarray data.
  • To provide accurate detection of copy number difference locations and reliable copy number estimates.
  • To offer confidence measures for the estimated parameters.

Main Methods:

  • A Bayesian multiple change-point (BMCP) model is proposed for the segmentation and estimation of SNP microarray data.
  • The model segments chromosomes into regions of equal copy number differences and detects change-point locations.
  • It estimates true copy numbers for each segment and provides posterior estimates with confidence intervals.

Main Results:

  • The BMCP model successfully segments SNP microarray data, identifying copy number difference locations and estimating true copy numbers.
  • The approach provides valuable confidence measures for the inferred parameters.
  • The algorithm can simultaneously segment multiple samples, enabling the inference of common and rare CNVs across individuals.
  • An adjustment factor for signal attenuation in tumor samples improves copy number estimates, accounting for heterogeneity or normal contamination.

Conclusions:

  • The proposed Bayesian multiple change-point model offers an effective solution for analyzing SNP microarray data to detect and estimate copy number variants.
  • This method enhances the accuracy of CNV detection, provides reliable copy number estimates, and offers confidence measures.
  • The capability to analyze multiple samples simultaneously and adjust for tumor-specific factors makes it a versatile tool for genomic studies, particularly in cancer research.