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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

19.1K
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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Single Nucleotide Polymorphisms-SNPs01:05

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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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Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants
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New quality measure for SNP array based CNV detection.

A Macé1, M A Tuke2, J S Beckmann3

  • 1Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland Department of Computational Biology, University of Lausanne, Lausanne, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Bioinformatics (Oxford, England)
|July 13, 2016
PubMed
Summary
This summary is machine-generated.

A new quality score (QS) improves the accuracy of copy number variant (CNV) detection in genome-wide association studies for common diseases. This method enhances statistical power and reliability compared to existing CNV filtering strategies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale genome-wide association studies (GWAS) are crucial for understanding common diseases.
  • Copy Number Variants (CNVs) are significant genetic factors in common diseases, but their accurate identification from array data remains challenging.
  • Existing methods for filtering false positive CNV calls are empirical and lack robust validation.

Purpose of the Study:

  • To develop and validate a novel quality score (QS) for copy number variants (CNVs) identified by PennCNV.
  • To improve the reliability and statistical power of genome-wide CNV association studies.
  • To enhance the accuracy of CNV calling for better understanding of CNV-trait associations.

Main Methods:

  • Developed a new quality score (QS) to estimate the concordance of CNV calls across different software.
  • Evaluated the QS using out-of-sample comparisons and Receiver Operating Characteristic (ROC) curves.
  • Assessed the impact of QS on statistical power through simulations and by re-analyzing known CNV-trait associations.

Main Results:

  • The proposed QS demonstrated a twofold higher correlation with consensus CNV status compared to existing filters.
  • ROC curve analysis yielded an Area Under the Curve (AUC) greater than 0.8, indicating high discriminative ability.
  • Simulations showed up to a 20% increase in statistical power when employing the QS, with improved performance in identifying CNV-trait associations.

Conclusions:

  • The developed quality score significantly enhances the accuracy and reliability of CNV detection in GWAS.
  • This approach offers a robust method for filtering false positive CNV calls, leading to increased statistical power.
  • The QS facilitates more accurate identification of disease-associated CNVs, advancing the study of genetic contributions to common diseases.