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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.4K
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%...
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Genome-wide Association Studies-GWAS01:11

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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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Genome Copying Errors02:46

Genome Copying Errors

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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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,...
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Related Experiment Video

Updated: Dec 9, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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Published on: February 17, 2017

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Bayesian copy number detection and association in large-scale studies.

Stephen Cristiano1, David McKean2, Jacob Carey1

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

BMC Cancer
|September 7, 2020
PubMed
Summary

Detecting germline copy number variants (CNVs) is challenging due to technical noise. CNPBayes identifies batch effects and estimates CNVs, improving disease risk association studies for conditions like pancreatic cancer.

Keywords:
Batch effectsCNPBayesCopy number variantsGenome-wide associationPancreatic cancerSNP array

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Last Updated: Dec 9, 2025

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Germline copy number variants (CNVs) are associated with increased disease risk.
  • Detecting CNVs and their contribution to disease is difficult due to biological and technical heterogeneity in large-scale studies.
  • Existing methods struggle to account for variations within and between samples across the genome.

Purpose of the Study:

  • To develop a novel approach (CNPBayes) for identifying latent batch effects in copy number data.
  • To provide probabilistic estimates of integer copy number.
  • To integrate copy number uncertainty into disease association models.

Main Methods:

  • Developed CNPBayes, a Bayesian framework for copy number analysis.
  • Applied a hidden Markov model (HMM) for CNV identification in the Pancreatic Cancer Case Control (PanC4) study.
  • Hierarchically modeled latent batch effects and incorporated probabilistic copy number estimates into a Bayesian regression model.

Main Results:

  • CNV inference was highly sensitive to technical noise varying among participants.
  • Major technical variation sources were linked to centralized laboratory sample processing, not study sites.
  • Identified candidate associations, including deletions near MYC and TUSC3, for pancreatic cancer risk.

Conclusions:

  • Laboratory effects are significant sources of technical variation in genome-wide association studies.
  • CNPBayes offers a robust Bayesian framework for identifying batch effects and estimating copy number.
  • The approach enhances the evaluation of copy number's role in heritable diseases.