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09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
The effect of algorithms on copy number variant detection.
Debby W Tsuang1, Steven P Millard, Benjamin Ely
1Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, United States of America. dwt1@uw.edu
Plos One
|January 7, 2011
Summary
Copy number variant (CNV) detection methods significantly impact study results. Different algorithms and overlap definitions yield varied CNV calls, highlighting the need for standardized validation in genetic disorder research.
Area of Science:
- Genetics
- Bioinformatics
- Genomic Variation
Background:
- Array-based technologies infer copy number variants (CNVs), leading to variability in detection.
- CNV-calling algorithms exhibit substantial false positive and false negative rates.
- Inconsistent CNV definitions affect disease association study outcomes.
Purpose of the Study:
- To illustrate the effects of varying CNV detection algorithms and overlap definitions on CNV discovery.
- To assess the impact of different CNV calling methods on results.
- To evaluate CNV detection variability in schizophrenia cases and controls.
Main Methods:
- Utilized four common CNV detection algorithms: PennCNV, QuantiSNP, HMMSeg, and cnvPartition.
- Applied two overlap definitions: any overlap and at least 40% overlap of the smaller CNV.
- Analyzed data from a 56K Illumina genotyping array for 48 schizophrenia cases and 48 controls.
Main Results:
- No algorithm identified a difference in CNV burden between schizophrenia cases and controls.
- The total number of CNVs called varied widely, from 102 to 3,765 across algorithms.
- Mean CNV size ranged from 46 kb to 787 kb, with an average of 1 to 39 CNVs per subject.
Conclusions:
- The number and validity of CNVs identified in array-based studies depend heavily on the algorithms used.
- Varied methods lead to numerous false positives and false negatives in CNV detection.
- Guidelines for CNV identification and a gold standard for validation are essential for complex genetic disorder research.
Related Concept Videos
02:26Comparing 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%...
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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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.
01:05Single 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,...