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On the frequency of copy number variants
- Source :
- Bioinformatics. 24:2350-2355
- Publication Year :
- 2008
- Publisher :
- Oxford University Press (OUP), 2008.
-
Abstract
- Motivation: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission. Results: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates. Availability: Software implementing the methods described in this article is available for download at the following address: http://www.isites.harvard.edu/icb/icb.do?keyword=k36162 Contact: iionita@hsph.harvard.edu Supplementary informantion: Supplementary data are available at Bioinformatics online.
- Subjects :
- Statistics and Probability
congenital, hereditary, and neonatal diseases and abnormalities
endocrine system diseases
Gene Dosage
Biology
computer.software_genre
Polymorphism, Single Nucleotide
Biochemistry
mental disorders
False positive paradox
Humans
Genetic Predisposition to Disease
Copy-number variation
Molecular Biology
Genetics
Supplementary data
High rate
Models, Genetic
Genome, Human
Skew
Genetic Variation
Small sample
Original Papers
Computer Science Applications
Computational Mathematics
Validation methods
Computational Theory and Mathematics
Data mining
Relevant information
computer
Algorithms
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 24
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....7bd322148dd88d8f9e0a4b9983aa7694
- Full Text :
- https://doi.org/10.1093/bioinformatics/btn421