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Related Concept Videos

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...
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%...
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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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Detection of Copy Number Alterations Using Single Cell Sequencing
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Genome-wide algorithm for detecting CNV associations with diseases.

Yaji Xu1, Bo Peng, Yunxin Fu

  • 1Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 1155 Pressler St,, Houston, Texas 77030, USA.

BMC Bioinformatics
|August 11, 2011
PubMed
Summary
This summary is machine-generated.

A new genome-wide algorithm improves the detection of copy number variations (CNVs) associated with diseases. This method is more sensitive and powerful, especially for small CNVs, enhancing disease risk association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Single-nucleotide polymorphism (SNP) genotyping arrays are used for characterizing SNPs and DNA copy number variations (CNVs).
  • Existing algorithms for CNV detection from SNP data often have high false positive rates, limiting their specificity for small CNVs and reducing the power of association tests.
  • PennCNV offers improved sensitivity by integrating both marker intensities and genotypes.

Purpose of the Study:

  • To develop a novel genome-wide algorithm for detecting CNV-disease associations.
  • To enhance the sensitivity and power of CNV detection, particularly for small CNVs.
  • To compare the performance of the new algorithm against existing methods like PennCNV.

Main Methods:

  • Developed a genome-wide algorithm using a hidden Markov model (HMM) to derive copy number state probabilities, integrated into a logistic regression model.
  • Compared the new algorithm with association tests based on the most probable copy number states from PennCNV (post-Viterbi algorithm).
  • Utilized simulation studies to evaluate performance for both large and small CNVs under various disease models.

Main Results:

  • The new logistic regression-based algorithm demonstrated higher power and sensitivity for detecting small CNVs (fewer than 10 SNPs) compared to PennCNV.
  • For small CNVs, the algorithm yielded significantly smaller p-values (e.g., 7.54e-17 at RR=3.0) and captured signals missed by PennCNV (e.g., p=0.020 at RR=3.0).
  • Simulation studies confirmed the algorithm's superior power in detecting associations with small CNVs (3-5 SNPs) under different penetrance models (e.g., power=0.8030 vs. 0.2879 at RR=3.0).

Conclusions:

  • The new algorithm is more sensitive and powerful for detecting CNV-disease associations than existing HMM-based algorithms.
  • The method is particularly effective when CNV association signals are weak or when limited SNPs are located within the CNV.
  • The developed software, GWCNV, is freely available for research use.